rafmacalaba/datause-encoder-data huggingface dataset:
Unlike tenure insecurity due to village land reallocations, land requisition by higher levels of government for non-agricultural uses has a different impact on migration decisions. As it relies on the state’s eminent domain power and its timing and scope are beyond farmers’ control, the prospect of land requisition is a threat to land rights that farmers cannot guard against by focusing on agricultural production and staying in the village. As a result, the risk of losing land through requisition significantly increases individual farmers’ migration rates (Ma and Mu, 2020).

# **3. Migration Trends**

Reduction in the barriers to population mobility created by the _Hukou_ and land tenure systems, and the shift of labor out of agriculture and into higher productivity employment opportunities in urban and coastal areas, is recognized for its important contribution to China’s economic growth since 1990. From the village-level survey data of the Research Center for Rural Economy (RCRE) in China’s Ministry of Agriculture, it is evident that fewer than 10% of the registered rural population worked as migrants in 1993. The migrant share of the registered population increased steadily to 38% by 2017, with 22% working outside of their home counties and 11% outside of home provinces (Figure 1). Expanding the reach of telecommunications and investments in transportation infrastructure were enabling factors that facilitated increases in both rural-to-urban migration and the duration of migrant employment. China’s internal migrants, like economic migrants around the world, move to places with higher wages and more economic opportunity.

Below we discuss the strengths and weaknesses of using census versus individual and household survey data, respectively, for the study of internal migration in China. China-specific issues related to sampling and definition of household membership are highlighted. Long-term patterns in rural-to-urban migration trends are summarized using village, household and individual data from surveys conducted by the Research Center for Rural Economy (RCRE) at China’s Ministry of Agriculture. These data have the benefit of using a consistent standard for defining a migrant over three decades. The discussion on trends is then supplemented with data from a more recent survey, the China Family Panel Survey (CFPS). Attention also focusses on how the individual characteristics of migrants and their jobs have evolved over time.

# **3.1 Measurement Issues and Data**

One important challenge for the study of migration in China involves fundamental problems of measurement. Although there is a general understanding in China that a “rural migrant” has a 8
strata or pairs to equation 2.

# **2.2 Ancova**

While the difference-in-means estimator is unbiased, efficiency can be improved through the inclusion of control variables that help explain the outcome of interest _y_ . One such variable that takes a special place in applied work is the baseline value of the outcome of interest, _y_ 0. Approximating _g_ ( _zi_ ) with _y_ 0 gives the Ancova estimator: Where the _Si_ are a set of dummy variables for any randomization strata used. McKenzie (2012) shows this estimator improves power over the difference-in-means estimator, with the gain in power larger the more autocorrelated the outcome of interest is. This basic Ancova specification has become the default specification for many randomized field experiments. In addition to improving power, it adjusts the estimate for any baseline differences in the key outcome of interest that may have arisen from chance imbalances in the random draw, or from attrition, with the amount of adjustment data-driven and depending on how much this baseline difference predicts the future outcome.

In many applications we expect the baseline value of the outcome to have the strongest predictive power for future outcome values out of any baseline covariates. However, baseline measurement of the outcome may not always be available, or in some cases may be the same for everyone in the sample. For example, an experiment on young job-seekers may have employment and income as the main outcomes of interest, but at baseline everyone may be unemployed and earning zero income.

# **2.3 PDS Lasso in Theory**

The baseline outcome is only one of many potential control variables that researchers could use. Belloni et al. (2014b) consider the problem of selecting controls from a set of _p_ potential regressors _xi_ = _P_ ( _zi_ ), which can consist of _zi_ and different transformations and interactions of _zi_ that aim to approximate the function _g_ ( _zi_ ). They note that it is possible that _p > n_ , that is, that the number of potential controls is high-dimensional and may even exceed the number of observations in the dataset. There are three reasons this could occur in a randomized field experiment. First, it is common for baseline surveys or application forms for programs to collect data on many characteristics of individuals or firms applying for a program. This may be augmented further by community or geographical characteristics. Second, since the functional form _g_ ( _._ ) is unknown, one may wish to consider interactions, polynomials, or other non-linear transformations of the baseline variables.

5
TABLE 1: GDP GROWTH AND CRIME AGAINST FIRMS

||Logit:<br>Experienced Crime|OLS: % of Losses<br>Due to Crime Over<br>Sales|Logit:<br>Experienced<br>Crime|OLS: % of<br>Losses Due to<br>Crime Over<br>Sales|
|---|---|---|---|---|
||Full S|ample|Small and Medi|um Firms sample|
||1|2|3|4|
|Real GDP per Capita Growth|-0.0941***|-0.0808***|-0.0914***|-0.0833***|
||[0.0226]|[0.0208]|[0.0224]|[0.0201]|
|Police per 100,000 persons lagged|-0.0008|-0.0011*|-0.0006|-0.0010*|
||[0.0009]|[0.0005]|[0.0008]|[0.0006]|
|i ih l|0.1357**|0.1181*|0.092|0.1815**|
|Frm wt Femae Owner|[0.0621]|[0.0632]|[0.0579]|[0.0834]|
|Real GNI per capita in 100s|0.0040**|-0.0058***|0.0035**|-0.0063***|
||[0.0018]|[0.0012]|[0.0016]|[0.0014]|
|GINI|-0.0023|-0.0065|-0.0011|-0.0019|
||[0.0121]|[0.0113]|[0.0115]|[0.0113]|
|Population in Millions, Total|-0.004|0.0034*|-0.0012|0.0038*|
||[0.0035]|[0.0017]|[0.0032]|[0.0020]|
|Small firm dummy variable|-0.8113***|0.4023***|-0.4262***|0.2446***|
||[0.1138]|[0.0940]|[0.0557]|[0.0846]|
|Medium firm dummy variable|-0.3551***|0.1664**|||
||[0.0820]|[0.0658]|||
|Large City or Capital dummy variable|0.0157|0.0331|0.0012|0.0826|
||[0.0657]|[0.0953]|[0.0728]|[0.0974]|
|Manufacturing firm dummy variable|-0.6394***|-0.2238**|-0.4717***|-0.1835*|
||[0.0741]|[0.0813]|[0.0667]|[0.1032]|
|Number of Countries|27|27|27|27|
|Number of Observations|12801|12274|9482|9160|

* significant at 10%; ** significant at 5%; *** significant at 1%, Standard errors in brackets clustered at the country level
A different way of examining the results presented in Table 6 is to consider two particularly important configurations of spousal responses. First, we look at the case where both spouses _agree_ with each other that the wife has sole or joint decision-making power. This is the response scenario the reproductive health literature has most consistently analyzed, and where the literature has found positive associations with outcomes (e.g., Allendorf, 2007; Story and Burgard, 2012). We also examine a second scenario, namely all (pooled) disagreement configurations in which the wife attributes _more_ power to herself than the husband does. Table 7 presents OLS regressions similar to Table 5 above, where for column (1), the outcome = 1 if both the husband and wife agree that she is the main decision-maker/decisionmaking is joint, and = 0 otherwise, and for column (2), the outcome = 1 if the wife attributes more power to herself than the husband attributes to her.

45
jobs, commercial farms fail to generate much employment. Finally, for most crops, commercial farms’ yields are roughly double those by smallholders though the highest yields are normally obtained by those in the 10-20 ha category which often also managed to expand area cultivated quite rapidly. While lack of data on prices and inputs makes it impossible to assess how much of this difference is due to higher efficiency rather than just more intensive use of inputs, in particular improved seeds, or locations, commercial farms could create spillovers for small farms through a number of channels that would merit further study.

Beyond providing analytical insights that are of interest in their own right, having reliable data on outcomes by commercial farms can generate feedback loops for policy formulation. In our case, these include a continued impetus to improve the survey instruments used, efforts to better link to existing data, such as investment licenses and survey plans, that are issued and maintained by other agencies, and exploration of ways, such as yield monitoring based on remotely-sensed data, that could help to complement, cross-check, and eventually even partially replace survey information.

The paper is structured as follows. Section two provides background information on the ‘global land rush’ and discusses the Ethiopian context with efforts to improve the quality of data collected in the sector. Section three and four use the case of Ethiopia to illustrate how a relatively modest investment can significantly improve availability of information on key parameters in the debate, including (i) the share of national vs. foreign investors; (ii) the amount of large farm land that is not utilized; (iii) key constraints used by large farmers; (iv) input use and yields for main crops in different farm size groups; and (v) comparing yields between large and small farmers. Finally, in addition to describing outcomes, we highlight potential alternatives for situations where no regular farm surveys are conducted and draw out how these data can inform the research agenda.

# **2. Background and literature**

Although the volume of new land deals has declined, large-scale land acquisition will not disappear quickly. To regulate it in a way that allows to attract capable investors and to address the legacy of failed land acquisition, good information on the overall level of land transfers and the efficiency of land use is indispensable. Yet, while a review of the literature points towards a number of stylized facts regarding such investment, it is often based on one-off case studies the representativeness of which is not clear. The case of Ethiopia provides some lessons in the area.

# **2.1 The global land rush (nearly) a decade on**

After decades of stagnant or declining commodity prices when agriculture was considered a “sunset industry”, increases in the level and volatility of commodity prices and a concomitant rise in global demand 3
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|---|---|---|
|EW|EconomicSectorWork|1045|
|IE|Impact Evaluation|116|
|PA<br>|ProgmaticApproach<br>|31g<br>|
|TA<br>|TechnicalAssistance<br>|2333<br>|
|TE<br>|ClientTraining<br>|144<br>|
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|RF|ResearchServices|a|
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|WD|WorldDevelopmentReport|3|

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|CP|Country Partnership|253|
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|PE|IDAGuarantee|76|
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||<br>.||

<!-- Start of picture text --> Country engagements<br>(9.3 percent)<br>Journal articles<br>(6.8 percent)<br>Doing Business inputs<br>(11.6 percent)<br>Knowledge management<br>(18.9 percent)<br>= Economic updates and monitors<br>= Journal articles<br>= Briefs, newsletters and digests<br>= Other working papers<br><!-- End of picture text -->
19 is larger when the contribution rate and the size of the original pension system is larger, we expect a sufficiently large redistributive PAYG system to be inferior to a neutral funded pension system, from the point of view of the poor alive in the new steady state.

The quantitative question is how large is sufficiently large.

# 4.1 Results in **the** Base **Case**

Table 2 presents the effects the on individual welfare, measured by the level of indiiLect utility function (see Arrau and Schmidt-Hebbel, 1993 for the definitions) and for the base case.

c* is defined as the critical contribution rate for which the PAYG pension system is sufficiently large so that its negative effect on capital accumulation is so substantial that the poor are indifferent when the redistributive PAYG pension system is replaced by a fully-funded and neutral system. The implication of c* being zero is that the poor alive in the steady state are always better off after the redistributive PAYG-financed pensions are replaced by non-solidary fully funded pensions.

Table 2 shows that c is small, at least smaller han what we expected before starting tis research. In fact _c_ is zero for values of z above 0.27. It may seem surprising that a poor person that eams 10% of the middle income wage can be compensated for the loss of redistribution in his favor by a rise in wages. However, it must be recalled that our parametrzation incorporates a restriction on the design of redistribution, which is that the basic pension can atmost be 100 _O_ of the last wage of the poorest significant social group. This implies that the total pension received by the poor iss at most (1+0 ) times their last wage, regardless of the degree of income inequality. As this is appropriate for cross-country comparisons, our result that c* is very low stands up as applicable to a wide range of countries.

Note also that for a given (c,z) the steady-state welfare gains of all tiree income classes of switching to a neutral and funded pension system are similar in size, although they are always higher for the rich, as expectedl<sup>1</sup> . This is because all three incomne classes are benefited by a common factor: the changes in real wages and real interest rates brought by capital accumulationWhen the PAYG system is replaced by the neutral fully-funded scheme, the capital-output ratio inaeases from 2.46 to 2.62 and the real interest rate falls from 10.7% to 9.9% before tax.

Previous discussion showed that a redistributive PAYG system should have a contribution rate of at least 5% to offer acceptable replacement rates for the middle income groups. The implication is that for PAYG systems that yield politically acceptable pension benefits for the middle class, all groups alive in the new steady state gain from substituting the PAYG pension system, including the poor.

11 As mentioned before, this effect would be smaller if the income tax were progressive.
**DRAFT – Not for citation**

We find the largest welfare gains for most people when abolishing the Hukou restrictions, because this would enable each individual to move to where he/she is most productive. Continuing to restrict migration with the objective of more balanced regional development therefore has high aggregate costs. Relaxing the Hukou system, which Chinese policy makers are considering, will moreover result in a much more evenly urbanized China, with urbanization levels approaching those observed in most developed countries. On the other hand this rise in income and urbanization will most likely come with a strong further concentration of people and economic activity, very often in China’s coastal regions that today already constitute the wealthiest parts of the country.

This is not to say that further investment in infrastructure is not important, but we do find that the construction of the NEN has resulted in a (much) more modest increase in overall Chinese income per worker of about 6.5 percent. So far, these large-scale infrastructure investments tended to favor the already large, urbanized places. And, with the Hukou system in place, many people in lagging regions are prevented from benefitting from the resulting increase in real incomes in these now better-connected places. As a result, the welfare effect of the construction of the NEN has been small, or even negative, in many of the currently already lagging Chinese prefectures. This is quite the opposite of the spread of economic activity that the large scale investment in the NEN meant to achieve.

Our analysis extends only to 2007. After this year it becomes more difficult to isolate the effect of the NEN. Since then, further highway construction and the development of an extensive high speed rail network has linked major cities even more closely together. Future analysis could extend our framework to assess the impact of these investments, also in light of the recent gradual relaxation of the Hukou regulations in several parts of China.

# **References**

- Banerjee, A., Duflo, E., & Qian, N. (2012). _On the road: Access to transportation infrastructure and economic growth in China_ (No. w17897). National Bureau of Economic Research.

Baum-Snow, N., & Turner, M. (2012). Transportation and the decentralization of Chinese cities.

- Baum-Snow, N., Brandt, L., Henderson, J. V., Turner, M. A., & Zhang, Q. (2012). Roads, railways and decentralization of Chinese cities. Mimeo.

- Behrens, K., Mion, G., Murata, Y., & Südekum, J. (2013). Spatial frictions (No. 7175). Discussion Paper Series, Forschungsinstitut zur Zukunft der Arbeit.

- Bosker, M., Brakman, S., Garretsen, H., & Schramm, M. (2012). Relaxing Hukou: Increased labor mobility and China’s economic geography. _Journal of Urban Economics_ , _72_ (2), 252-266.

- Chan, K. W. (2013). China: internal migration, in _The Encyclopedia of Global Human Migration_ . Immanuel Ness and Peter Bellwood (eds.), Blackwell Publishing, London.

33
disparity in marital status reflects far higher male remarriage rates following widowhood and divorce, the higher life expectancy of women on average and, for some countries, the ravages of HIV and conflict. To provide a different perspective on marital status, one can examine the share of all adults of a given marital status who are heads by gender. Overall, 62% of adult men are heads compared to 18% of women. With the exception of single women, married women have the lowest probability of being heads at 10% while widows have the highest (71%), followed by divorcees (56%).

As noted, there is heterogeneity among FHHs. One aspect that may be expected to matter is whether there is an adult male resident in the household. The 26% of households that have female heads can be disaggregated into16% that contains no adult male and the10% that does. In all regions, a preponderance of FHHs contains no adult male. Among the latter, 38% of heads are widowed, 31% married, 19% divorced and the rest single (Table 3). We suspect that FHHs with no adult male and an unmarried head (and hence no remitting migrant husband) are the worst off among them. When male adults are present, they are most often sons (67%).

FHHs without a male adult tend to have younger, more educated heads, smaller household size but higher dependency ratios at 1.5 versus 0.9 (Table 3). Of these, those with an unmarried head appear to be similar to FHHs with a resident male –older, and less educated, although they have far fewer total members. They are composed of widows, divorced and single heads.

The best proxy for economic status available in the DHSs is a wealth index based on the household’s assets.<sup>18</sup> It is important to note that the index is not comparable across countries as it is computed separately for each country. However, one can compare what share of FHHs and of MHHs are classified in their country’s bottom quintile. Figure 4 shows this controlling for the age of the head and by region. We see that there is regional heterogeneity, but that with the exception of Western Africa, there is a tendency for a larger share of FHHs to be ranked in the bottom quintile with the difference rising with age.

A general pattern emerges in which female-headed households are a quite heterogeneous group, some of whom are clearly disadvantaged in a number of ways.

> 18 The DHS Asset (Wealth) Index is a composite measure of a household's living standard calculated using data on the ownership of selected assets, such as televisions and bicycles; materials used for housing construction; and types of water access and sanitation facilities. Generated using principal components analysis, the wealth index places households on a continuous scale of relative wealth, and then places them into five wealth quintiles.

10
Table D.7. Effects of Guerrilla Territorial Control on Main Outcomes Using Ordinary Least Squares

||Night Light Arcsine|Wealth Index|Years of Education|
|---|---|---|---|
||(2013)|(2007)|(2007)|
||(1)|(2)|(3)|
|Guerrilla control|-0.477***|-0.471***|-1.100***|
||(0.0257)|(0.0221)|(0.0607)|
|Observations|12,411|12,370|12,384|
|Bandwidth (Km)|67.01|67.01|67.01|
|Dependent mean|3.457|-0.0310|6.505|

_Note:_ The table presents the results of equation 1 via Ordinary Least Squares using the whole sample. The unit of observation in all Columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. We use triangular kernel weights. Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

82