The effect of land fragmentation on household income in vietnam

Khảo sát về phân mảnh đất đai ảnh hưởng đến thu nhập hộ gia đình ở khu vực nông thôn trên quy mô toàn quốc dựa trên bộ dữ liệu quốc gia cập nhật đến năm 2018. Tình trạng đất đai manh mún ảnh hưởng tiêu cực đến thu nhập, mặt khác, yếu tố giáo dục chưa đóng góp đáng kể vào việc nâng cao thu nhập bình quân

Table of Contents ABSTRACT ACKNOWLEDGEMENT LIST OF TABLES LIST OF FIGURES CHAPTER INTRODUCTION 1.1 Research Background 1.2 Research Objectives 1.3 Scope of study 1.4 Research Structure CHAPTER LITERATURE REVIEW 2.1 Definitions 2.2 Land fragmentation indices 2.3 Effects of land fragmentation 2.3.1 The effect of land fragmentation on agricultural production 2.3.2 The effect of land fragmentation on household income 2.4 Summary and conceptual framework CHAPTER RESEARCH METHODOLOGY 3.1 The situation of land fragmentation in Vietnam 3.2 Econometric models 3.2.1 Measuring land fragmentation 3.2.2 Estimating the effect of land fragmentation on household income 3.3 Data 3.3.1 Vietnam Access to Resources Household Survey (VARHS) 3.3.2 Data for the study 3.3.3 Research sample 3.3.4 Data analysis 3.4 Methodology CHAPTER RESULTS 4.1 Descriptive Statisitcs 4.2 Regression Results CHAPTER CONCLUSION 5.1 Conclusion 5.2 Limitation of study REFERENCES APPENDIX iii LIST OF TABLES Table Page Table 1: Cost and Benefit of land fragmentation 15 Table 2: Descriptive Statistics 25 Table 3: Mean of variables follow regions 26 Table 4: Autocorrelation test 34 Table 5: Result when regression with all observation 35 Table 6: First-stage regression summary statistics 36 iv LIST OF FIGURES Figure Page Figure 1: Plots per using purpose 22 Figure 2: Households in Regions 26 Figure 3: Labors in Household 27 Figure 4: Literacy of Household Head 28 Figure 5: Household size in regions 29 Figure 6: Scatter of Land Fragmentation and Region 30 Figure 7: Scatter of Land fragmentation index and log of agriculture income 30 per capita Figure 8: Livestock status and Marital status in regions 31 Figure 9: Scatter of labors in household and log of agriculture income per 32 capita Figure 10: Scatter of age of household head and log of agriculture income per capita v 32 CHAPTER INTRODUCTION 1.1 Research Background The fragmentation of property, the accumulation of land, and productivity are all subjects that have been the focus of discussion in several different research papers Many of them were concerned with the relationship between the variables, and this was partly due to the different performances In relation to land characteristics like fragmentation, soil fertility, and yield, there have been discovered some inconsistencies There have been some previous investigations carried out in Vietnam about land fragmentation and land consolidation (Pham et al., 2007; Tran and Vu, 2019; Markussen et al., 2016; Nguyen et al., 2020) In investigations carried out by the World Bank (2016), more than a few authors have investigated the impact of two factors: land fragmentation and the effects of the process of land consolidation It is interesting to note that they discover a direct connection between the splintering of land and decreased production The authors have conducted research on how work is divided up They reasoned that if they switched to more profitable crops and increased their use of machinery, they would be able to reduce their labor requirements and free up more time to devote to activities that were not related to farming They saw that a correlation exists between the amount of labor put into farming and the fragmentation of land However, there has been no success in establishing a connection between land fragmentation and employment in non-agricultural sectors; this may suggest that labor markets in rural areas are not well developed, and that the labor market is not well developed The primary source of income for rural residents in rural areas is agricultural produce (cultivation, livestock, aquaculture), which leads to the idea that fragmented land may influence agricultural income and then lead to affect total household income In this investigation, the index for fragmented land was computed, and the income from agriculture was measured to determine the connection between the two factors The assumption underlying this test was that the fragmentation of land would have a negative impact on the outcome of production (the final income) It was recognized by the outcome, which also demonstrated that there is a relationship that is statistically significant between these two variables In addition, the discussion portion of this research referred to other aspects, such as technological considerations or the planning involved in government involvement 1.2 Research Objectives In rural areas in Vietnam, the main economic activity that generates household income comes from farming and animal husbandry There have been several studies on the relationship between fragmented land and agricultural productivity in Vietnam or the impact of fragmented land on many aspects of Vietnam However, most of the above studies have not considered the index directly related to people's quality of life, which is per capita income from the household The aim of this study is to assess not only the link between farm household income and land fragmentation in some regions of Vietnam but also to assess other socioeconomic characteristics of households This study updates the effect of land fragmentation on a more recent data set and its direct effect on income as this is the variable that best represents the outcome of production agricultural activities Besides, the study also examined the influence of demographic and educational characteristics that have a significant effect on household income 1.3 Scope of study Numerous studies have been conducted to investigate the effects of land fragmentation on a variety of factors, including levels of poverty, total income, and levels of production The goal of this study is to make use of a specialized dataset on rural Vietnam in order to produce some results that can be directly used for the process of rural development in Vietnam since land use and apply policies that affect demographics and educational attainment The purpose of this study is to produce some results that can be directly used for the process of rural development in Vietnam 1.4 Research Structure The following is a breakdown of the paper's structure The research background and scope of study are provided in Chapter – Introduction The theories and discoveries regarding land fragmentation and its effects are presented in Chapter – Literature Review The study methods and data are covered in Chapter The analyses' findings are given in Chapter Chapter closes with findings, study limitations, and recommendations for further research CHAPTER LITERATURE REVIEW This chapter focuses on introducing the definition of land, fragmentation, the theory of land fragmentation, and some studies on the impact of land fragmentation on household income The definition of land and fragmented land is introduced in section 2.1, section 2.2 discusses the impact of land fragmentation on agricultural and economic output, section 2.3 deals with theories on the impact of land fragmentation and finally, 2.4 introduces methods for calculating the land fragmentation index 2.1.Definitions Land is a finite, usable resource that provides a source of livelihood and financial security that is passed down through generations and transformed into wealth (Ellis, 1992) According to Hartvigsen (2014), there are two fundamentally different aspects of agricultural land allocation, namely the dispersion of ownership and the dispersion of land use In both developed and developing nations, agricultural production, and the ability of many people to make a living are intrinsically linked to land Because of this, academics and those who make policy are always interested in the efficient distribution and utilization of land resources Because of this, access to land fragmentation measurement and analysis is afforded a substantial and materially rich foundation The term, "fragmentation", refers to a splitting up of a previously integrated production process into two or more components, or "fragments" (Jones R W.,2000) Land fragmentation is defined as a situation in which a farm consists of several parcels of land that are spatially separated (Binns, 1950; King and Burton, 1982; Blarel et al., 1992, Pham et al., 2007) McPherson (1982) argued that land fragmentation is the practice of farming a number of spatially separated plots of owned or rented land by the same farmer (cited in Veljanoska, 2018) According to Van Dijk (2003), The fragmentation of land typically involves the parceling (a physical characteristic) and or legal claims on land (invisible), two theoretically distinct layers The fragmentation can be evaluated on numerous scales The scale establishes what constitutes "the whole." This research employs four types of land fragmentation: (1) fragmentation of land ownership, (2) fragmentation of land use, (3) fragmentation within a farm, and (4) separation of ownership and use There are several positive effects of fragmentation In addition to ecological and aesthetic benefits, farming fragmentation can be valued for its ability to reduce the likelihood that all crops will be destroyed by disease or extreme weather Some types of farming also need spatially separated parcels so they can be used for various agricultural purposes Land fragmentation, according to Sundqvist and Andersson (2007), is the situation in which a farmer owns a huge amount of unconnected land that is spread out across a vast area This is a common occurrence in many countries around the world, and it is frequently viewed as a roadblock to productivity and corporate modernization Fragile land is a farm with numerous meanings of land, a typical aspect of agriculture in many countries, especially in developing countries, according to Pham et al (2007) The fragmented land reflects the state light and contains several plots of varying quality, allowing them to diversify crops, allocate labor demand, and reduce output and pricing hazards (Ciaian et al., 2018; Tran and Vu, 2019) In general, land fragmentation is defined as a single farm including numerous spatially scattered pieces of land To put it another way, fragmentation happens when a family owns numerous non-contiguous parcels of land that are dispersed over a vast area Land fragmentation is a phenomena that has both costs and benefits for agricultural output, with favorable and unfavorable effects depending on the circumstances 2.2 Land fragmentation indices Land fragmentation is a geographical phenomenon that is influenced by a variety of factors Organizational size, number of parcels of land held, size of each parcel, shape of each parcel, spatial distribution of parcels of land, and size distribution of parcels of land were all presented by King and Burton (1982) According to the theory, the average number of parcels of land held by farmers by region or country can be used to evaluate fragmented land, with the two measured characteristics being the average size of the holdings As well as average parcel size Edwards (1961) advocated measuring land fragmentation as a percentage of a property owner's land area that is not adjacent to the farm Simmons (1964) presented a land fragmentation index based on the number of parcels of land held and their relative size According to Simmons, the land fragmentation index is calculated as follows: 𝐹𝐼 = ∑ (2.1) Where “FI” is the fragmentation index, “n” is the number of land parcels owned, “a” is the size of a land parcel, and “A” is the total number of holdings An FI value of indicates that the holding consists of a single parcel of land, while values close to indicate greater fragmentation Dovring (1965) assessed land fragmentation by estimating the distance a farmer would have to go to each of their plots, returning to their farm after each visit (cited in Kadigi et al., 2017) This figure, however, ignores the actual number of trips per year as well as the option of visiting any area of property without returning to the farm Januszewski (1968) proposed the K-index to quantify land fragmentation, with the K-index being the ratio between the number of parcels of land held and the parcel size distribution The following is the formula: 𝐾=∑ ∑ (2.2) Where “n” is the number of land parcels and “a” is the size of each land parcel K has a value ranging from to K shows a significant degree of fragmentation when the values tend to be zero The K-index has three main characteristics: (1) fragmentation increases proportionally to the number of parcels of land; (2) fragmentation increases as the size range of small parcels of land increases; and (3) fragmentation decreases as the area of large parcels increases and the area of small parcels decreases Igozurike (1974) advocated using a relative index of land fragmentation to measure land fragmentation This computation is based on the average size of the plots and the distance traveled by the farmer to visit each plot sequentially (i.e., in a round trip) This index is calculated using the following formula: 𝑃 = / 𝐷𝑡 (2.3) Where Pi is the fragmentation index of holding i, Si is the size of each parcel of land, and Dt is the sum of all parcels' round-trip lengths The P-index, on the other hand, has the limitation that the distance is not clearly defined and does not account for the number of parcels of land (King and Burton, 1982) Schmook (1976) proposed the P0 land fragmentation metric, which measures the ratio of the area of a polygon enclosing all plots held by farmers to the area held by the farmer (main farm) This index's value is always greater than one; a high P value indicates high fragmentation Furthermore, the Simpson index is commonly utilized in the land fragmentation literature (Blarel et al., 1992, Pham et al., 2007; Latruffe and Piet, 2014; Ciaian et al., 2018; Tran and Vu, 2019) since it is sensitive to both the size of the parcel and the number of portions of land The Simpson index is calculated using the following formula: 𝑆𝐼 = − ∑ 𝑎 /𝐴 (2.4) The SI stands for the Simpson index; Ai is the area of the ith land; and A is the total area of the farm The zero value implies concentrated land (just one parcel), implying that farmers have only one parcel or piece of land, whereas the value is nearly equal to one, implying that the family has several "highly fragmented" plots of land and farms (Pham et al., 2007) 2.3 Effects of land fragmentation The effects of land fragmentation are primarily explained using classical economic theory (Hartvigsen et al., 2014) Because land is a fixed factor of production, Ricardio (1817) demonstrated the law of diminishing returns in agriculture Soil fertility varies, and labor is applied in fixed proportions to less fertile soils As a result, labor productivity declines on average and marginally as the margin of cultivation widens due to capital accumulation and increased land employment As a result, to raise production scale, it is required to use increasingly poor terrain, resulting in higher production costs To maximize earnings, farmers must use a combination of commercial and natural inputs According to Ricardo, production was based on land and labor, with technological considerations being overlooked According to the Economies of scale view, the cost advantage that a producer obtains through the expansion of production scale, such as in agriculture, with a larger land area, farmers can easily apply mechanization and irrigation as well as organize commodity production than with land is fragmented (e.g., Simons, 1985; Niroula and Thapa, 2005; Tran and Vu, Nguyen et al., 2020; Wang et al., 2021) Similarly, the view of Production Theory also holds that producers choose combinations based on available production resources (capital, labor, technology, and natural conditions) to achieve goals of profit maximization and production efficiency This demonstrates that agricultural production resources include natural resources, the natural environment, and biodiversity, of which land is a critical component; land is both a labor object and an investment whether labor in agricultural production cannot be substituted (Mundlak, 2000) The Cobb-Douglas production function or Stochastic frontier Approach, according to the production theory-based explanation, is frequently used to estimate the influence of land fragmentation on output and production efficiency (e.g., Pham et al., 2007; DiFalco et al., 2010; Manjunatha et al., 2013; Deininger et al., 2017; Tran and Vu, 2019) In a broader sense, Tornado (1985) contends that agricultural development must proceed in three stages, from low to high Subsistence agriculture is the first stage: simple production methods rely on the fertility of the soil, labor, and capital investment is low, and the items produced are eaten internally Profits fall as production spreads to non-fertile land The second stage is agricultural restructuring in the direction of diversification: the structure of plants and animals evolves in the direction of a mixture and diversity, employing new varieties in conjunction with chemical fertilizers and irrigation to increase agricultural productivity and market-oriented production Agriculture is progressing towards modernity in Stage 3: farms are specialized, capital and technology are critical factors in production efficiency, and economies of scale are significantly created with industry support The output of technical technology is completely supplied to the consumer market The impact of crop failures brought on by drought, hail, epidemics, and other natural disasters is mitigated by soil fragmentation, which contributes to the reduction of hunger This is especially true in the context of diverse farming conditions, both in terms of the types of soil present and the conditions under which crops must grow (Bentley, 1987; King and Burton, 1982; Blarel et al., 1992; Hartvigsen, 2014) Even though fragmented soils have been shown to be ineffective in developed regions such as China, Vietnam, Bangladesh, and Europe, these places are still capable of producing modern agriculture that is focused on the market (Latruffe and Piet, 2014; Tran and Vu, 2019) According to Mwebaza R (2002), the views of Ugandan farmers were gathered through interviews; all of them agreed that fragmentation had both positive and negative aspects The ability to grow a variety of crops based on the varying fertility of the soil was one of the benefits that was mentioned most frequently in the survey In addition, the difficulties of managing dispersed land holdings and the time wasted traveling from plot to plot were cited as negative aspects of land fragmentation It is demonstrated here that economic theories and empirical documents have laid a solid foundation for research into the effects of land fragmentation on agricultural production under specific local conditions The household economy and the farm economy are two of the most common types of production organizations in the world 2.3.1 The effect of land fragmentation on agricultural production Following an analysis of all the data acquired using the approach described in the prior chapter, the table of findings consists of the items listed below: Table 5: Regression Result Variables Coef Std.Err Land fragmentation index -3.1424** 0.3933 Ethnic of household head -0.3545** 0.0783 Age of household head 0.0114** 0.0027 Marital status of household head 0.3634** 0.0862 Livestock status -0.9073** 0.0897 Number of labors -0.1260** 0.0317 Size of household 0.0045 0.0230 Not trained - household head -0.1865 0.2784 Trained – household head -0.3592 0.2913 4.1948** 0.3758 _Cons Number of observations: 2,530 Prob > chi2 = Wald chi2(9) = 218.98 Notes: ** p < 0.05 The table of findings that can be seen above is the result of computing the results for each observation contained in the dataset The findings of the calculations indicate that land fragmentation has a detrimental effect on the agricultural income per capita There is a negative correlation between the dependent variable and the following factors: the number of employees, the presence of livestock activities, and the ethnicity of the person who heads the household To provide a description of the distribution, instrumental variables such as the slope of the land reduced to the level of the household, as well as region (northern lowlands) and region (northern highlands) If everything else stays the same, a decrease of 0.031% in the income per capita of agricultural households can be attributed to an increase of the land fragmentation index by one percentage point This result is in line with both the predictions and the findings of previous research on the fragmented land in Vietnam The difference in agricultural household per capita income between households headed by a Kinh and households headed by someone else is expected to be 42.55 percent if all other factors remain the same This can be explained by the nature of the data set when it was collected in provinces that had a 35 significantly higher proportion of ethnic minorities compared to most other provinces The projections for farm household income per capita indicate an increase of 1.14 percentage points for each year that the household head is older This is the case even if all other factors remain unchanged The difference in predicted agricultural household per capita income between households with a married head and higher per capita income of the predicted agricultural household when compared to households with a single head is 43.83 percent, assuming that all other factors remain unchanged If everything else remains the same, the per capita income that can be anticipated for agricultural households that engage in livestock activities is 147% higher than the per capita income that can be anticipated for agricultural households that not engage in livestock activities This difference is significant, possibly because of the fact that the selling price of products derived from livestock activities (food sources derived from animals) is always higher than the selling price of products derived from farming activities Assuming that there is no change in any of the other relevant factors, one can anticipate a decrease of 12.6% in the per capita income of agricultural households for every additional person of working age living in the household The variables of household size and the dummy factors that determine the education level of the household head are not statistically significant in the calculation of per capita income based on the number of family members who are employed This is because the variables of household size and the dummy factors are both used to determine the education level of the household head After the regresssion, the result of the Hansen's J Test was used to test the appropriateness of the instrumental variables that were used in this test, was Hansen's J chi2 (2) = 5.71027 with p = 0.0575, were not found to be statistica It means that the instrumental variables (Region 1, Region 2, and the variable measuring the slope of the land) should be considered reliable Besides, the author performs endogenous tests to determine whether the endogenous regressors in the model are indeed exogenous It is hypothesis that the variables are exogenous, and the result is: GMM statistic chi2(1) = 106.618 (p = 0.0000) Therefore, the land fragmentation, the target independent variable to find the relationship between it and dependent variable is endogenous variables The first - stage regression summary statistics the results as shown in the table below 36 Table First-stage regression summary statistics Variable R-squared Adjusted Rsquared Partial Rsquared Robust F (3,2518) Prob > F Land fragmentation index 0.1268 0.123 0.0826 64.1552 For providing a condensed summary, the following are some of the factors that are consistent across all regions when testing the impact of the land fragmentation index, as well as the factors associated to households and household heads When land is divided up into smaller parcels, it has a negative effect on the average revenue that households earn from agricultural operations per worker This is because each new parcel of land requires more labor to cultivate Both the fact that the person in charge of the household is married and the individual's age have a positive influence on the variable that is the subject of the current research when they are considered as household independent variables (the one that is being studied is the dependent variable) It has been demonstrated that the presence of activities related to livestock has a positive impact, both directly and indirectly, on the average income On the other hand, the level of education is not relevant to the statistical analysis of the dependent variable This is because educational aspects are rated on a scale of degrees To quantify the extent of an educational factor's influence, a degree scale is typically utilized This may be because many observations were made by household heads who had only completed general education, or it may be because the application of technical qualifications to agricultural activities is based primarily on experience, rather than training Both explanations are possible This difference is significant when considered in comparison to that of household heads who have received specialized training in general education 37 CHAPTER CONCLUSION 5.1 Conclusion The findings of the studies indicate that the fragmentation of land has an adverse impact on the money generated from agricultural activities Based on data collected and processed from VARHS 2018 data, land fragmentation has a negative impact on the agricultural income of surveyed households, although at rather a low level This can be explained by the increasing annual agricultural productivity of crops thanks to technological advances and other inputs such as seed quality, fertilizers, or the value of outputs Previous studies have only focused on annual cropland or regional characteristics to a more limited extent As a result, they have concluded that land fragmentation has a negative effect, generally or specifically, on agricultural income or output This result has not changed even after additional research and data update in 2018, after a few years the agricultural industry has increased the application of science and technology This proves that land consolidation and the policies implemented by countries related to this topic are still essential to improve the value of agriculture's contribution to the economy or raising incomes or improving the quality of life of households At the same time, the contribution of livestock activities to the average income of a household continues to have a significant impact In addition, social characteristics of the household, such as the marital status of the household head and the number of workers in the household, continue to have an impact on income generated from agricultural activities This is consistent with the fact that Vietnam's agricultural sector has not fully utilized modern technologies and mainly relies on human labor on a small scale by households or groups of a few households Historically and culturally, this is understandable because in Vietnam, especially in rural areas, the head of the household still plays an important role in family decisions, such as decisions about production and exchange of agricultural products Within the limitations of this study, one of the factors predicted to have a favorable effect on per capita income which has not yet shown any evidence of such an effect is education This is a point to note because it proves that the education factor has not really affected agricultural production, although general education activities have really been universalized to residents in rural areas However, other educational activities, especially education related to orientation, vocational training, and professional training in agriculture have not yet promoted the contribution to the value of economic activities from agriculture This shows that rural education policies that encourage continuous vocational training 38 related to agriculture or short-term educational activities that apply practically to production activities have not yet created an impact on agricultural activities agriculture, bringing higher income to farmers There are a few policy implications that can be discussed to further increase incomes derived from agriculture, in particular for households First and foremost, it is necessary for policies to be implemented not only at the government level but also in each province and each district through guidelines for the purpose of problem-solving These policies should promote land concentration and reduce the amount of land area that is fragmented method that is more specific Building large fields, planning planting areas according to the geographical characteristics of each region and providing financial assistance to farmers are some of the ways that the government has been encouraging businesses to invest in the agricultural sector over the course of the past few years Expanding policies that help businesses invest in agriculture is a worthy goal, and one way to this would be to make it possible for businesses to set aside tracts of land large enough to be worked by agricultural machinery Mechanization of farming in rural areas has already had and will continue to have the effect of increasing the total land area while simultaneously decreasing the number of plots owned by individual households On the other hand, many existing policies not have much of an impact on the income of the average household The findings of the clinical tests that were carried out as part of this research could have a few policy repercussions that should be given some thought Training and enhancing the professional capacity of household farmers to provide farmers with access to more contemporary farming technology is a measure that is both straightforward and desirable In addition, encouraging households to link up in a cooperative production chain can help people cooperate better in improving soil quality and dealing with pollution, slope, and fertility issues This can be accomplished by encouraging households to link up in a cooperative production chain land to have an impact on the surrounding environment, which will have a beneficial effect on the outcomes of agricultural production The shift away from crops with low economic value and toward crops with high economic value, as well as the standardization of growing processes to achieve high-quality products, will bring about greater economic value than an increase in the quantity of products produced 5.2 Limitation of study In conclusion, much like any other piece of research, this thesis contains a few deficiencies, which is to say that it does not fulfill all its potential The VARHS dataset can 39 satisfy the requirements for a comprehensive understanding of rural homes; however, the number of households that participated in the survey was relatively small, which results in a limitation in the data The limitation of the data is that the number of households that participated in the survey was relatively small The figures only took into account 12 of Vietnam's 63 provinces Second, the study did not reveal any shifts in the variables that influence household income, even though it was conducted just some years ago (2018) This finding is although the research was carried out In addition, the approach that is used in the thesis on the topic of household income is primarily focused on annual cropland (especially the land fragmentation index), as well as variables connected to households There is no mention of the other use of land, productivity, policy considerations, or weather, all of which are the limits of this research The fact that this study was only carried out in a single nation is another restriction of this investigation When the constraints of this study are taken into consideration, future research will probably require a data strategy that is more comprehensive in scope (number of observations and time variation) Not only the social variables of families and elements linked to land need to be enlarged, but the variables themselves also need to be expanded This is because the expansion of these social variables and aspects will help better understand how land is used Because of this, researchers can obtain a better understanding of the elements that influence household income from agriculture in rural Vietnam As a result of this, they can propose policy suggestions 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ttest defrag, by(hheth) Two-sample t test with equal variances -Group | Obs Mean Std Err Std Dev [95% Conf Interval] -+ -Other | 1,335 5132369 007111 2598209 4992869 527187 Kinh | 1,196 4362628 0082857 2865457 4200067 4525189 -+ -combined | 2,531 4768635 0054745 2754167 4661286 4875985 -+ -diff | 0769741 0108604 055678 0982703 -diff = mean(Other) - mean(Kinh) t = 7.0876 Ho: diff = degrees of freedom = 2529 Ha: diff < Pr(T < t) = 1.0000 Ha: diff != Pr(|T| > |t|) = 0.0000 Ha: diff > Pr(T > t) = 0.0000 ttest defrag, by(hhmar) Two-sample t test with equal variances -Group | Obs Mean Std Err Std Dev [95% Conf Interval] -+ -0 | 444 442966 0138184 2911709 4158083 4701237 | 2,087 4840751 0059425 2714747 4724212 4957289 -+ -combined | 2,531 4768635 0054745 2754167 4661286 4875985 -+ -diff | -.041109 0143737 -.0692944 -.0129236 -diff = mean(0) - mean(1) t = -2.8600 Ho: diff = degrees of freedom = 2529 Ha: diff < Pr(T < t) = 0.0021 Ha: diff != Pr(|T| > |t|) = 0.0043 47 Ha: diff > Pr(T > t) = 0.9979 anova defrag region Number of obs = Root MSE = 2,531 260222 R-squared = Adj R-squared = 0.1084 0.1073 Source | Partial SS df MS F Prob>F -+ -Model | 20.794783 6.9315944 102.36 0.0000 | region | 20.794783 6.9315944 102.36 0.0000 | Residual | 171.11677 2,527 06771538 -+ -Total | 191.91155 2,530 07585437 global IV pslope psoil pirri global X1 hheth hhage hhmar hhlabor hhsize none diploma degree global X2 hhbreed corr LnAgrinc $X1 $X2 (obs=2,531) | LnAgrinc hheth hhage hhmar hhlabor hhsize none diploma degree hhbreed -+ LnAgrinc | 1.0000 hheth | -0.1194 1.0000 hhage | -0.0197 0.2694 1.0000 hhmar | 0.0522 -0.1024 -0.3119 1.0000 hhlabor | -0.1112 -0.2086 -0.0294 0.2386 1.0000 hhsize | -0.0457 -0.2961 0.0069 0.1677 0.7063 1.0000 none | 0.0627 -0.1371 0.0836 -0.1028 0.0687 0.0569 1.0000 diploma | -0.0681 0.1264 -0.0705 0.0958 -0.0544 -0.0431 -0.9375 1.0000 degree | 0.0058 0.0487 -0.0477 0.0336 -0.0488 -0.0458 -0.3115 0.0385 1.0000 hhbreed | -0.2503 0.3889 0.0749 -0.1276 -0.1186 -0.1494 -0.1183 0.1059 0.0506 1.0000 order hhid2018 LnAgrinc $IV $X1 $X2 label data "Land fragmentation and Income, VARHS 2018" save varhs2018final, replace file varhs2018final.dta saved 48 ivregress gmm LnAgrinc hheth hhage hhmar hhbreed hhlabor hhsize none diploma (defrag = pslope reg1 reg2), robust Instrumental variables (GMM) regression Number of obs Wald chi2(9) Prob > chi2 R-squared Root MSE GMM weight matrix: Robust = = = = = 2,530 218.98 0.0000 1.4684 -| Robust LnAgrinc | Coef Std Err z P>|z| [95% Conf Interval] -+ -defrag | -3.142444 3933232 -7.99 0.000 -3.913344 -2.371545 hheth | -.3545116 0783018 -4.53 0.000 -.5079802 -.201043 hhage | 0113742 0026954 4.22 0.000 0060912 0166571 hhmar | 3634445 0861749 4.22 0.000 1945448 5323441 hhbreed | -.9072934 0896679 -10.12 0.000 -1.083039 -.7315475 hhlabor | -.1259636 0317236 -3.97 0.000 -.1881408 -.0637864 hhsize | 0044828 0229917 0.19 0.845 -.04058 0495456 none | -.1865234 2784291 -0.67 0.503 -.7322344 3591876 diploma | -.3591951 2912724 -1.23 0.218 -.9300784 2116883 _cons | 4.194833 3757909 11.16 0.000 3.458296 4.931369 -Instrumented: defrag Instruments: hheth hhage hhmar hhbreed hhlabor hhsize none diploma pslope reg1 reg2 estat overid Test of overidentifying restriction: Hansen's J chi2(2) = 5.71027 (p = 0.0575) estat endogenous Test of endogeneity (orthogonality conditions) Ho: variables are exogenous GMM C statistic chi2(1) = 106.618 (p = 0.0000) estat firststage First-stage regression summary statistics -| Adjusted Partial Robust Variable | R-sq R-sq R-sq F (3,2518) Prob > F -+ -defrag | 0.1268 0.1230 0.0826 64.1552 0.0000 49