rafmacalaba/datause-encoder-data huggingface dataset:
finds a negative and robust association between inequality, inversely related to the share of the middle class (third and fourth quantiles of the income distribution), and growth. He finds that social political instability and fertility rates could be driving the relationship between inequality and growth.

The impact of inequality on growth can also depend on the initial level of development. Barro (2000) estimates the impact of inequality on growth by splitting a sample of 100 countries into high- and low-income samples. In that specification, there is a negative relationship between inequality and growth for poor countries, similar to previous studies, while the relationship is positive for richer countries. The empirical results suggest that in the presence of credit constraints, inequality prevents low-income households from accumulating human and physical capital, resulting in lower growth in poor countries. On the other hand, the positive relationship observed in richer economies is consistent with the traditional growth-enhancing effects of inequality emphasized by Kaldor (1957).

The effects of inequality on output might also differ across economic sectors. For instance, Erman and te Kaat (2019) identify the effect of inequality on industry-level value added growth. The authors use a data set that includes 22 industries in 86 countries for the period between 1980 and 2012. They find that that higher income inequality increases the growth rates of industries that use physical capital intensively, while it decreases the growth rates of industries that use skilled labor intensively. Thus, the lower human capital stock associated with inequality drives its negative effect on growth. At the country level, these results are consistent with the theoretical predictions by Galor and Moav (2004).

Studies based on panel data techniques find conflicting results regarding the impact of inequality on economic growth. Forbes (2000) estimates the impact of inequality on growth in a panel of 45 advanced economies and emerging markets for the period between 1966 and1995. Contrary to the cross-country results, she finds that higher inequality leads to higher economic growth in the short and medium term. These results are robust to alternative samples and model specifications. Forbes mentions several theoretical models that are consistent with a positive relationship between inequality and growth. For example, Galor and Tsiddon (1997) find that a concentration of high-skilled workers in technologically advanced sectors allows a higher rate of technological innovation, promoting higher growth rates but also increasing inequality. More recently, using fixed effects panel data techniques, Cingano (2014) finds a negative effect of inequality on growth for a sample of 30 OECD countries for the period between 1970 and 2010. Berg et al. (2018) find that net inequality has a negative effect on growth in a sample of advanced and developing countries, and moderate redistribution through taxes and transfers does not have statistically significant effects on growth.

26
|**Table 4: attrition and business closure at fi**|**rst follow-up**|**survey**<br>Differ|ence betw|een [...]|||||P-values of joint|
|---|---|---|---|---|---|---|---|---|---|
||Mean [SD]|and|Controlg|roup||P-valu|e for diffe|rence...|test|
||in Control|||||Group 1|Group 1|<br>Group 2|group1=group2=|
||Group|Group1|Group2|Group3|N|and 2|and 3|and 3|group3=0|
|Data collected with success|0.881|0.023|-0.011|0.007|3600|0.184|0.477|0.219|0.512|
||[0.324]|(0.023)|(0.014)|(0.013)||||||
|_Including:_||||||||||
|Survey completed|0.81|0.009|-0.022|-0.012|3600|0.333|0.473|0.567|0.61|
||[0.392]|(0.029)|(0.018)|(0.016)||||||
|Business shut down or owner deceased|0.061<br>[0.239]|-0.001<br>(0.018)|0.009<br>(0.011)|0.004<br>(0.01)|3600|0.596|0.763|0.644|0.864|
|Small survey on phone for businesses|0.01|0.015|0.002|0.014***|3600|0.184|0.897|0.03**|0.025**|
|which moved to another location|[0.1]|(0.009)|(0.006)|(0.005)||||||
|_Reasons for non-response:_||||||||||
|Refused the survey or not available|0.066<br>[0.248]|-0.007<br>(0.018)|0.007<br>(0.011)|0.009<br>(0.01)|3600|0.495|0.376|0.836|0.729|
|Moved to another location and not<br>reached on the phone|0.022<br>[0.146]|0.001<br>(0.011)|0.004<br>(0.007)|-0.006<br>(0.006)|3541|0.749|0.555|0.129|0.473|
|Not found|0.022|-0.012|-0.003|-0.005|3600|0.363|0.45|0.684|0.586|
||[0.146]|(0.01)|(0.006)|(0.005)||||||
|Other reason (sickness, traveling during||||||||||
|the survey, maternity leave...)<br>Notes: Sources: Survey Data. Column 1: Sta<br>parentheses) from an OLS regression of the<br>triplet). ***, **, * indicate statistical signifi|0.01<br>[0.1]<br>ndard deviat<br>outcome va<br>cance at 1, 5|-0.005<br>(0.007)<br>ions prese<br>riable on t<br>and 10%.|0.004<br>(0.004)<br>nted in br<br>reatment|-0.006<br>(0.004)<br>ackets. Col<br>dummies, c|3600<br>umns 2-<br>ontroll|0.262<br>4: coeffic<br>ing for str|0.851<br>ients and<br>ata dumm|0.021**<br>standard e<br>ies (dumm|0.126<br>rrors (in<br>ies for each|
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 21: Individual Average Months Formally Employed - Nontradable Sector Worker Sample - 1990-2009 <!-- Start of picture text --> 5.5<br>4.5<br>3.5<br>2.5<br>1.5<br>0.5<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>-0.5<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is the average number of months formally employed per year from 1990 to the year listed on the x-axis, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with various controls (described in the text). Positive estimates imply fewer average months formally employed for workers initially employed in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

60
<!-- Start of picture text --> BChina @Highincome 8 Upper middle income Middle income & Lowincome<br>120<br>100 =<br>20<br>55 &- Sm= | = iL‘ =<br>& -— -— _ —— —— _- = —<br>860 Bee === =l=la =~<br>Fda = = = =4=|-= = = f= = =]==<br>40 =|-E}- ===: = = ==]- = =|- = =<br>ao = = = | = =f j= = = = = =| ={-<br>Pre-primary Primary Lower secondaryUpper secondary Tertiary<br><!-- End of picture text -->
in these circumstances is ethically indeterminate. Our second case-study presents the ethical dilemmas in this case using research on the behavior of health care providers. We illustrate these problems using on-going research on the interactions between health care providers and their patients in low-income settings, highlighting the difficult decisions that researchers face in such instances.

Our third category relates to review boards in low-income countries and the _absence_ of such a board at the World Bank, an organization that routinely conducts more than 500 surveys in low-income countries every year. This scale and the fact that all of the authors of this essay have conducted surveys and trials as World Bank staff (two are still working in that capacity at the time of this writing) motivate the inclusion of this topic in the paper.

This essay will concentrate on practical issues of data collection and of communication with respondents and participants both in observational studies and in impact evaluations of interventions. This largely reflects the expertise of the authors and the large number of surveys that they have conducted over the previous three decades. Each of us has faced the problems that we highlight here in several instances. We have struggled with the answers, and in most cases, muddled through trying to keep the best interests of poor people around the world in view in making our decisions. Our hope here is that by recounting the issues of surveying populations in low-income countries, we do justice to the tough problems that researchers face and the need for a negotiated solution in each case. Many times, these problems (and their solutions) fall in the interstices of the human subjects courses that we take, and more often than not, these departures and a relative lack of guidance are part and parcel of surveying populations today. We recognize, of course, that there are many related ethical considerations in the choice of field studies to undertake and in the assignment of treatments and control groups in evaluation-based 5
part of the city (Figure 1B-D). The positive correlation between wealth and air pollution is similar to many other cities in low- and middle-income countries (Behrer and Heft-Neal, 2024).

The Georgian Ministry of Environmental Protection and Agriculture (MEPA) maintains four pollution monitors in Tbilisi, concentrated in the central part of the city. The location of these monitors is shown in Figure 1E. The data from these monitors is publicly available on MEPA’s website but does not seem to be regularly accessed by residents of Tbilisi.<sup>4</sup> Despite not frequently checking outdoor pollution levels, residents of Tbilisi regularly report that pollution is a major issue for them. For instance, a 2017 survey found that 42% of the residents of Tbilisi named pollution as the most important infrastructure issue facing the city (CRRC, 2017). A 2021 follow-up survey found that air pollution followed only unemployment and traffic as the most important issue in Tbilisi (CRRC, 2021).

Tbilisi’s built environment was established over three major modern phases of development: the pre-Soviet period in the late 19<sup>_th_</sup> and early 20<sup>_th_</sup> century, the Soviet period from the 1920s to the early 1990s, and the post-1990 period (Figure A1) (Salukvadze and Golubchikov, 2016). Each period was associated with construction of unique housing types with quality, in particular insulation from the outside environment, tending to increase over time.<sup>5</sup> Housing built prior to 1920 is low rise, 2-3 story structures with large windows and 2<sup>_nd_</sup> story balconies. Housing built during the Soviet era largely resembles the large apartment blocks common in most Post-Soviet Republics. Post-1990s housing is similar to much of the modern high rise development throughout Europe with mid-to-large towers that may or may not have small balconies for each unit. In general, housing quality is lower than in the United States or Western Europe but higher than in many other lowand middle- income countries. Our baseline survey indicates that most houses use natural gas for cooking and gas or electric sources for heating and that less than 0.5% of households own an indoor air quality monitor or an air purifier.

# **2.2 Experimental design**

Our experiment was implemented in 3 stages: installation of outdoor air pollution monitors, baseline household survey, and treatment followed by the end-line household survey (Figure A3 shows the

> 4Our household survey indicates that fewer than 5% of households in Tbilisi regularly check outdoor pollution levels at baseline.

> 5We provide images of examples of each housing type in Figure A2.

6
owning a phone, but whose number does not match to any number in the CDR provided to us by the single mobile operator.<sup>9</sup> Results in Panels B and C of Table 2 show the performance of each targeting approach on the balanced and full sample, respectively. Note that as described in Section 2.5, different targeting quotas are applied for each panel based on the proportion of each sample that is ultra-poor. In the CDR-based and combined approaches, we report performance when the households without CDR are targeted first (i.e. households without CDR are targeted in a random order and then the households predicted to be poorest are targeted until the quota is reached) as well as when households without CDR are targeted last (i.e., after the 535 households with phones are targeted, households without phones are included in a random order until the quota is reached).

Unsurprisingly, these results suggest that CDR-based targeting is not effective when a large portion of the target population does not own a phone (e.g. Panel C of Table 2, where only 16% of the sample has matching CDR). However, when we simulate more realistic levels of phone ownership in Panel B (84% of the households, based on our survey data), CDR-based targeting is once again comparable to asset- or expenditure-based targeting, particularly when households without phones are targeted first (AUC = 0.72, 0.70, 0.68 for assets, consumption, and CDR, respectively). On the other hand, if households without phones are targeted last (for example, if program administrators base targeting wholly on CDR and provide no benefits to any household without a phone), the CDR-based method only improves marginally on random targeting.<sup>10</sup>

# **3.5 Additional Tests and Simulations**

Our main analysis considers the household head to be the unit of analysis. As described in Section 2.3, this analysis is based on matching survey-based indices to phone data from the household head, which is consistent with the design of the TUP program and the TUP survey

> 9These 1,807 households include households that report owning a phone on a different network (this network is estimated to have around 30% market share in Afghanistan), as well as phones on our network that were not active during the six-month period of CDR that we analyze.

> 10A key nuance in this analysis is that for the CDR-based and combined methods where households without phones are targeted first, the precision and recall measures in Table 2 correspond to programs that only target households without phones (at random), as the number of households without phones exceeds the budget constraint of the program. The AUC score, on the other hand, is a summary statistic that represents targeting accuracy at all counterfactual targeting thresholds, and thus is not sensitive to the budget constraint — which explains the contrast between AUC and precision and recall in Table 2 Panels B and C. The ROC curves (Figure 1) and Precision-Recall curves (Figure S5) highlight how budget constraint affects precision and recall.

14
Are the Children of Uneducated Farmers Doubly Disadvantaged? Farm, Nonfarm and Intergenerational Educational Mobility in Rural China M. Shahe Emran IPD, Columbia University Yan Sun World Bank

**Key Words** : Educational Mobility, Inequality, Rural China, Nonfarm, Education and Occupation, Family Background, Heterogeneity, Complementarity, Market Reform, Gender Gap

**JEL Codes:** O12, J62

______________________ Yan Sun is an economist in the Chief Economist Office of the World Bank's East Asia and Pacific Region (EAPCE). Shahe Emran is a Research Fellow in the Initiative for Policy Dialogue (IPD). We would like to thank Matthew Lindquist, Forhad Shilpi, Samantha Rawlings and participants in the NEUDC 2014 at Boston University for helpful comments on earlier versions. Special thanks to Li Shi, Zhan Peng, and Song Jin for help with the CHIP survey data. We are also grateful to Sudhir Shetty and Bert Hofman in the World Bank for their support and valuable comments on the later version.
40 'The Unproductive Housewifd':' Her Evolution in the 19th Century Economic Thought," mimeo, (University of Massachusetts (May) 1988).

6. I.M.D. Little, "Small Manufacturing Enterprises in Developing Countries," The World Bank Economic Review 1:2 (January, 1987) 203-236; A. Portes and L. Benton, "Industrial Development and Labor Absorption," £pRulation and Development Review, 10:4 (December, 1'84) 589-612; D. Mazumdar, "The Urban Informal Sector," Working paper 211, (Washington, DC: The World Bank, 1975). See also note 4.

7. The World Bank data base was used for GNP and population structure (an updated version of World Bank Tables) and ILO data file (a machine readable version of the Yearbook of Labour Statistics. 1969-71 average foreign exchange rates are used to convert local constant price GNP series into 1970 dollar figures. Recourse to census publications were required in several cases where ILO figures were inadequately reported or wrong, e.g. Switzerland 1950, Pakistan 1971, and India 1971.

8. J.N. Sinha, "Comparability of 1961 and 1951 Census Economic Data,' Artha Vijnana (December 1964) and Durand. More recent data from Labor Force Surveys in Pakistan, India, and Bangladesh suggest a growing role for women in wage employment in come sectors. Until parallel data from the Population Census become available, these trends could not be evaluated here, but will be the focus of additional analysis.

9. Boserup and Durand (See Notes 3 and 5).

10. Seccoral disaggregations of the labor force present some problems
growth on poverty tend to dominate the impact of redistribution caused by reforms (see Demery, Sen and Vishwanath, 1995), and poverty can be substantially reduced if reforms actually do have the assumed impact on growth.

How might one incorporate growth within our simple methodology? In the absence of a model linking policy reform to sectoral growth rates one is forced to rely upon projections based upon historical performance. It is straightforward to incorporate a steady reduction in particular transaction costs, or productivity growth in line with that experienced in the past (see World Bank (2001)). Furthermore, if separate studies give some indication of how such parameters may be changed by policy reforms then these new values may be used instead. Certainly this approach leaves much to be desired, but it does have the advantage that, since the "growth" rates are exogenously determined, it is possible to conduct sensitivity analysis so that policymakers can see the likely distributional outcome for any set of growth rates which they deem to be plausible.

# **5. Other Ideas for Methodological Development**

In the longer term there are a number of other ways in which the analysis of the linkages between structural reforms and poverty could be improved. I have grouped these under two themes: dynamics and risk; and qualitative approaches.

# _Dynamics and Risk_

Poverty is not a static phenomenon. A large number of studies from both developed and developing countries show that there is a great deal of movement in and out of poverty (see Baulch and Hoddinott (2000) for a selection of papers on poverty dynamics in developing countries). However, much "poverty profile" analysis is based upon static regressions between income/consumption and a set of household and economic characteristics. Unsurprisingly we find that the poor have high dependency ratios, are poorly educated, have few assets and live in poor areas - in other words, they are poor! However, the policy implications resulting from such associations are much less clear: "removing" children or the elderly from households with high dependency ratios is not a policy option; neither in most cases is supplying significant private assets to poor households, while the problems associated with "moving" households to better areas are well known. Education _is_ a key policy option, but even here, it is not certain that, if everyone's educational level rises, the poor will be much better off, even if they are better educated.

Interventions based upon such static analysis may therefore be less effective than expected in reducing poverty. If, instead we think of poverty as a dynamic process, then the "equilibrium" poverty rate can be reduced either by reducing the probability of falling into poverty, or by increasing the probability of exiting poverty (or both). For example, if 25
motivation is compensated by the cost increasing effect of women requiring higher reservation wages to work in a rural area.) Attracting more students from rural areas, would also reduce the required budget increase slightly, as reported in column 4 of Table 8.

# **6. Conclusion**

This paper analyzes the willingness to work in a rural area of final year nursing and medical students in Ethiopia. We use contingent valuation data, obtained from "payment cards" questions. We find that there are two main determinants of the willingness to work in a rural area: the income of the parents’ household and the students’ willingness to help the poor. The first result is in line with results from labor economics which show that income from other household members affects labor decisions (Mroz, 1987). The second result indicates that a health worker’s intrinsic motivation has a strong influence on her preference for a rural posting. We also find that medical students are less likely than nursing students to prefer to work in a rural area. The results are very robust: they are very similar when using OLS estimation or when using maximum likelihood estimation that uses the full information of the "payment card". The latter method also allows a direct estimate of the standard deviation, which appears to be constant across different specifications, a further sign of the robustness of the results.

Given the high significance of the variable, we attempt to further unravel who is motivated to help the poor. Behind it is the question whether intrinsic motivation is predetermined or whether it can be shaped - i.e. whether motivated health workers are a result of socialization or selection. If intrinsic motivation is an internalization of norms, as argued by Deci (1975) a central question for public policy is when internalization takes place: before they enter the profession or after. Our data are by no means rich enough to address this question in a conclusive way, but give some indication. The key result is that women are significantly more likely to want to help the poor than men, but we cannot determine whether this is due to inherent differences or has come about through socialization. Having attended an NGO school also has an effect, although this is less significant. It may suggest that health students attending an NGO school are socialized towards helping the poor or that the prospective health students with high intrinsic motivation self-select into NGO schools. Other psycho-social characteristics seem to have limited effect.

Since two-thirds of the nursing students and 90 percent of the medical students want to work in an urban area in the long run, we investigate what makes an urban job so attractive. Analyzing the premium to work in a rural area, we find that this is explained especially by access to education for children and promotion opportunities. For doctors 19
Table 3a: Tests for discontinuities in covariates: environmental data

||(1)|(2)|(3)|
|---|---|---|---|
||**Full**|**2011-2012**|**2013-2014**|
|||**Panel A: Community survey**||
|% baseline forest|0.011|0.019*|0.007|
||(0.007)|(0.010)|(0.010)|
|Control Mean|0.893|0.880|0.907|
|Observations|11035|5859|5197|
|Ln(elevation)|0.121**|0.260***|-0.028|
||(0.059)|(0.068)|(0.087)|
|Control Mean|6.250|6.293|6.208|
|Observations|11031|5856|5196|
|Ln(km to any road)|-0.012|-0.034|-0.060|
||(0.077)|(0.098)|(0.120)|
|Control Mean|7.922|7.886|7.956|
|Observations|11035|5859|5197|
|Ln(km to city_>_5000 people)|0.024|0.030|-0.027|
||(0.039)|(0.046)|(0.063)|
|Control Mean|3.268|3.212|3.326|
|Observations|11035|5859|5197|
|Ln(km to major city)|0.028|0.013|0.052|
||(0.034)|(0.036)|(0.057)|
|Control Mean|4.530|4.537|4.523|
|Observations|11035|5859|5197|
|Deforestation risk|-0.002|-0.005|0.001|
||(0.003)|(0.005)|(0.005)|
|Control Mean|0.042|0.047|0.038|
|Observations|10708|5669|5060|
|% agricultural land (NFI 2000)|-0.002|-0|0.001|
||(0.007)|(0.011)|(0.009)|
|Control Mean|0.043|0.053|0.033|
|Observations|11035|5859|5197|
|Marginality 2000|0.015|0.082|-0.023|
||(0.050)|(0.054)|(0.083)|
|Control Mean|0.238|0.198|0.280|
|Observations|11035|5859|5197|

> 1 To test for the same discontinuity statistically, we run OLS of the covariates on the treatment threshold, a quadratic of the running variable with slopes that vary on either side of the threshold, polygon size, a ejido dummy variable, ecosystem indicator variables, and state and year fixed effects. Standard errors are clustered at the ejido level for ejidal polygons, and at the municipal level for private properties.

34