rafmacalaba/datause-encoder-data huggingface dataset:
of forced migrants, the PEP became a benchmark for a development-based approach to the current predicament.

Regularization (or legalization) programs give migrants legal residence and thus access to the rights and services enjoyed by citizens, including the right to work. These programs, which grant temporal benefits but typically exclude voting rights, have been implemented in the United States, Europe, Latin America, and LMICs in response to a sharp increase in irregular migration. Despite their prevalence, these programs are highly controversial. Critics claim they encourage irregular migration, lead to job displacement and unemployment for natives, increase crime, and burden social welfare and public finances. Yet, prior research has demonstrated negligible effects on employment opportunities for natives and shown reductions in migrants’ crime rates (Bahar et al. 2021; Pinotti 2017; Baker 2015). Furthermore, from a theoretical perspective, regularization programs may produce positive effects for both migrants and host societies. The right to work means migrants may rely less on social welfare and be more likely to pay taxes, more productive by working in occupations more closely related to their skills, and more inclined to invest in their children’s human capital. Research on long-term migration has established positive effects on host countries and shown that these effects are or faster if are to eco- larger migrants permitted integrate nomically (Abramitzky and Boustan 2022).

The features of PEP facilitate causal identification of its effects. First, the program was introduced decisions or ex-ante behavioral re- unexpectedly, thereby isolating anticipatory Unknown to both and for the sponses. migrants government officials, ex-post eligibility program was based solely on prior registration in a nationwide census of irregular forced migrants, the _Registro Administrativo de Migrantes Venezolanos_ (RAMV for its Spanish acronym), that was administered between April and June of 2018. According to the government officials who designed RAMV, the census was implemented to count the number of irregular Venezuelan forced migrants in Colombia and was not intended to precede or lead to a regularization program. However, in August 2018, Colombia’s president unexpectedly 3
FIGURE 12

# BRAZIL SAVING RATES

<!-- Start of picture text --> N lR= BY 2010<br><!-- End of picture text --> <!-- Start of picture text --> 0.09 -<br>006 0-<br>0.07<br>0.06<br>0.03 -<br>0.02<br>0.03<br>0.01<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 205G 2060 2070 2080 2090 2100<br><!-- End of picture text --> 0 Teens do not work + Teens work
# **6 REFERENCES CITED**

- 1) Abril, R. Maria Elena and Ofosu-Amaah A. Waafas <mark>. 2009. “Improving Gender Targeting of Public Expenditures: A Consolidated Note on Lessons and Policy Implications.” World Bank, Washington, DC.</mark>

- 2) Angrist, Joshua D., and Alan B. Krueger. 1999. “Empirical Strategies in Labor Economics.” In _Handbook of Labor Economics_ Volume 3A, edited by Orley C. Ashenfelter and David Card, 1277–1366. Amsterdam: Elsevier.

- 3) Annan, Francis, and Wolfram Schlenker. 2015. “Federal Crop Insurance and the Disincentive to Adapt to Extreme Heat.” _American Economic Review_ 105 (5): 262–66.

- 4) <mark>Beegle, Kathleen G., Emanuela Galasso, and Jessica Ann Goldberg. 2015. “Direct and Indirect Effects of Malawi’s Public Works Program on Food Security.” Policy Research working paper; no. WPS 7505. Washington, D.C.: World Bank Group.</mark>

- 5) Berhane, Guush, Daniel Gilligan, John Hoddinott, Neha Kumar, and Alemayehu Seyoum Taffesse. 2014. “Can Social Protection Work in Africa? The Impact of Ethiopia's Productive Safety Net Programme." _Economic Development and Cultural Change_ , 63(1): 1-26.

- 6) Bun, Maurice J. G., and Teresa D. Harrison. 2013. “OLS and IV estimation of regression models including endogenous interaction terms.” University of Amsterdam Discussion Paper 2014/02.

- 7) Deiniger, Klaus, and Yanyan Liu. 2013. “Welfare and Poverty Impacts of India's National Rural Employment Guarantee Scheme.” World Bank Policy Research Paper no. 6543.

- 8) Dercon, Stefan and Luc Christiaensen. 2011. “Consumption Risk, Technology Adoption and Poverty Traps: Evidence from Ethiopia.” _Journal of Development Economics_ , Elsevier, 96(2): 159–173.

- 9) De Vreyer, Philippe and Sylvie Lambert. 2014. “Intrahousehold Inequalities and Poverty in Senegal.” Working Paper, mimeo.

- 10) De Weerdt, Joachim and Stefan Dercon. 2006. “Risk-sharing Networks and Insurance Against Illness.” _Journal of Development Economics_ 81(2): 337–356.

- 11) Dutta, Puja, Rinku Murgai, Martin Ravallion, and Dominique vandeWalle. 2014. “Right to Work? Assessing India's Employment Guarantee Scheme in Bihar.” World Bank Working Paper.

- 12) FEWS NET, 2016. Accessed July 7. <u>http://www.fews.net.</u>

- 13) Gilligan, Daniel, John Hoddinott, and Alemayehu Seyoum Ta_esse. 2009. “The Impact of Ethiopia's Productive Safety Net Programme and its Linkages. “Journal of Development Studies, 45(10): 684-1706.

- 14) Grosh, Margaret, Carlo del Ninno, Emil Tesliuc, and Azedine Ouerghi. 2008. For Protection and Promotion: The Design and Implementation of Effective Safety Nets. Washington DC: The World Bank.

- 15) Handa, Sudhanshu, and Frank Otchere. 2017. “Unconditional Cash Transfer and Household Resilience: Results from the Malawi Cash Transfer Program.” CSAE Meetings.

- 16) <mark>Hasan, Amer. 2010. “Gender-targeted Conditional Cash Transfers: Enrollment, Spillover Effects and Instructional Quality.” Impact Evaluation series; no. IE 44, Policy Research working paper; no. WPS 527. World Bank.</mark>

38
funded individual accounts managed both privately and publicly are being developed while the previous system is being gradually phased out.

2.11 Contributions are now split in two: **10** percent of gross wage is paid to a pension fund of contributor's choice, whereas another **15** percent is paid to the old State Pension Fund. The old State Pension Fund continues to pay existing benefits and provides new benefits based on the number of years worked under the old system. Contribution rates to this fund are expected to decline as the expenditures related to the old system decrease. This decline will occur gradually, as the reform did not reduce acquired rights. Disability and survivor's benefits have been converted to _flat_ allowances unrelated to service or wage history, and are financed **by** general revenues (Andrews **1999).** 2.12 The government guarantees a minimum old-age pension for those workers who complete **25** years of service if their combined pay-as-you-go and funded pension falls below such minimum. The minimum pension was equivalent to **25** percent of the nation-wide average wage when the reform began, and was expected to be price indexed. Those who do not qualify for the minimum will receive an old-age allowance close to the social assistance level that is approximately **7** percent of the nation-wide average wage.

> **2.13** _The Kyrgyz Republic_ took the first step toward pension reform in **1996,** with the implementation of individual contribution record keeping and the elimination of many early retirement provisions. Then, in **1997,** The Kyrgyz Republic introduced a **NDC** system (see box above). The reform was accompanied **by** a generous recalculation of all existing pensions and no increase in the retirement age. The system, however, was not sustainable in the short-term, and the retirement age was finally gradually increased to **58** for women and **63** for men. There are no plans for a funded system.

> 2.14 Acquired rights in the old system were redefined and scaled down. Years of service accrued before **1996** give right to a replacement rate of 1 percent of worker's average wage.

> The later is calculated on workers **5** best consecutive years before **1996** although at **1997**

**7**
GDP in Macedonia by 2008, compared to more than 50% of GDP in Slovakia (see Figure 2 panel 1). Especially in the earlier years covered in this paper, a large part of the Macedonian economy had relatively limited access to financing due to less competitive banking sector, while those with access to financing were financed at unreasonably high costs.

# **Figure 2: Financial Deepening and Access to and Cost of financing as a constraint of doing business**

Panel A: Domestic Credit as % of GDP Panel B: Access and Cost of financing as a constraint of doing business <!-- Start of picture text --> 70<br>2002<br>60<br>50<br>2005<br>40<br>30<br>2002<br>20<br>10<br>2005<br>0<br>0% 20% 40% 60% 80%<br>Slovakia Macedonia Access to financing Cost of financing<br>Slovakia<br>in % of GDP<br>Macedonia<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> _Source:_ IMF IFS _Source:_ BEEPS 2002 and 2005.

Fourth, the extent of the euro-ization (formerly dollarization) is much larger in Macedonia than Slovakia, making the actual lending/deposit base affected by the changes in the monetary policy rate on local currency denominated instruments much narrower, and the changes in the monetary policy rate thus less effective in managing aggregate demand. At the end of 2006, only 1.5% of loans to households and 33% of loans to the corporate sector were extended in foreign currency in Slovakia, compared to 45% of loans to the household sector in Macedonia and 58% of loans to the corporate sector.

_(iii) Why could the exchange rate effect on net exports be weaker in Macedonia?_ Several factors could explain the weaker output response to changes in the real exchange rate in Macedonia compared to the Slovak Republic that we have estimated.

17
<!-- Start of picture text -->
1.00 1.00 1.00 A: Survival aD B: Profits<br>3 4 0.93 9.94 0.93 0.91 0.89 0.88 OD 350 286<br>2 I I ® 255 255<br>ec8 og 300 204 218 202 218 203<br>yanz 2Z 250200 it I 173<br>& 4 = 150<br>3 .5 £xfe) 10050<br>0 2013 (Baseline) 2016 (2-Year) 2021 (7-Year) g 0 2013 (Baseline) 2016 (2-Year) 2021 (7-Year)<br>a C: Profits conditional on Survival D: Employees<br>2 294<br>3 30 240 aa g ¢ ST og 29 «29 29<br>2& 300 204 218 999 224 228 196 ry 3 28 2.8 2.7 A . : A 27<br>a 260 a | l |<br>2 200 l | a<br>=n 150 5<br>2<br>& 10050 ZzE 1<br>EB 0 2013 (Baseline) 2016 (2-Year) 2021 (7-Year) 0 2013 (Baseline) 2016 (2-Year) 2021 (7-Year)<br><!-- End of picture text -->

MMH Personal Initiative [EE Traditional Training Control
<mark>industries include agriculture, fishing, manufacturing, mining, construction, and services. These variables are all based on respondent reporting. Moreover, anthropometric data were collected by measuring children’s height and weight during survey fieldwork. Using this information, a series of variables were computed for children that range from 0 to 5 years of age: length/height-for-age, weight-for-height, BMIfor-age and weight-for-age Z-scores. These estimates are calculated using the 2006 WHO child growth standards, and an indicator for wasting was created for children who have a weight for height values under -2 standard deviations from the WHO child growth standards (</mark> Onis et al., 2006).

# Coding steps

The construction of HP datasets was done in Stata. It consists of a series of structured Stata scripts (“dofiles”), made available in a dedicated public GitHub repository (Bentze, 2024). A consistent processing protocol was executed in each wave. As a first step, plot-crop, plot, individual, and household frames were created, using the crop harvest modules (for the plot-crop and plot frames), the cover information of the household questionnaire (for the household frame), and the household roster (for the individual frame). Following a modularized approach, each variable in the HP was then cleaned and processed separately. This approach guarantees a stable sample size and facilitates reuse by data users, who can download the code and include new variables as they see fit (see “Usage Notes” below). The coding operations described above are undertaken separately for each country-wave pair, in do files which are given the following naming structure: country ISO code, survey name, and wave number. For example, the do file for the first wave of the Ethiopia ESS is named _ETH_ESS1.do_ .For each of the plot-crop, plot, household, and individuallevel datasets, survey waves were then appended (i.e. combined) for each country, and panel unit IDs were created. During this process, variables were merged to their appropriate frames. Finally, variables were converted from LCU to USD, where relevant. These steps were undertaken is a series of do files named according to the following structure: ISO code followed by “append” (e.g. _ETH_append.do_ ). Finally, country-level datasets were appended to obtain the final cross-country HP datasets. The flow of Stata dofiles is summarized in Figure 3.

14
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|IDP|0.259***||0.260***|0.257***|0.248***|0.258***|0.247***|0.243***|
||(0.000)||(0.000)|(0.000)|(0.002)|(0.001)|(0.002)|(0.003)|
|Head female||-0.008|-0.018||-0.036|0.000|-0.,003|-0.013|
|||(0.877)|(0.734)||(0.558)|(0.995)|(0.955)|(0.863)|
|Head widow||||0.065|||||
|||||(0.318)|||||
|IDP x Head female|||||0.036|0.049|0.049|0.041|
||||||(0.565)|(0.447)|(0.445)|(0.575)|
|Household size||||||0.052***<br>(0.000)|||
|No. of adults|||||||0.030*|0.028*|
||||||||(0.052)|(0.058)|
|No. of children|||||||0.067***|0.060***|
||||||||(0.000)<br>|= (0.000)|
|Sample|Full|Full|Full|Full|Full|Full|Full|Restricted|
|Ward FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|N|2947|2947|2947|2947|2947|2947|2946|2616|
|R?|0.078|0.064|0.078|0.078|0.078|0.096|0.098|0.091|
- _A. Main results based on country-year averages_

To calculate country- and year-specific infant mortality rates we estimate the following:

- (1) D imtc = D tc _Z tc_ + ε _imtc_

where Dimct is an indicator variable for whether child _i_ born to mother _m_ in country _c_ in year _t_ died; Zct is a set of country-year fixed effects, and ε imct is the regression error term. For reporting convenience, we scale the coefficients Dct so that they estimate the mean infant mortality rate (expressed in infant deaths per 1000 live births) in country _c_ for the various years of observation.<sup>2</sup> With the estimates of Dct in hand, we then address the possibility of spurious correlation between infant mortality and GDP that can arise when relating two trending series. We adopt several standard approaches to this problem.

The first approach we take is to use first-differenced data. Differencing creates a stationary series if the underlying data series follow a random walk or is integrative of order 1. This approach, common in the growth literature, is shown in equation (2).<sup>3</sup> To account for the fact that the estimates of infant mortality are calculated more precisely for some countries and years than others, largely as a result of the much larger sample sizes in some surveys, this regression (as well as all of the other specifications that use the country-year averages) is weighted by the inverse of the variance of the estimated coefficient on the year dummy for a given country-year observation.

> 2 All calculations of infant mortality for a particular country-year observation use the weights provided in the survey. In some cases, more than one survey is available for a given country-year observation—for example, the infant mortality rate in Peru in 1990 can be calculated on the basis of the DHS for 2000, 1996, and 1992. In these instances, we first calculate the infant mortality rate for a given year separately for each of the surveys, using the appropriate weights, and then take a simple average of the three estimates.

> 3 Nelson and Plosser (1982) present evidence that macroeconomic aggregates such as GNP are better modeled as integrated process of order one (I(1) processes) rather than stationary processes with a deterministic trend.

8
where _Y_ is the measure of production, _L_ is the total land size harvested by the household, _Pi_ and _Dj_ represent the amounts of physical and labor inputs (respectively) used by the household, **X** is a vector of other household characteristics, state fixed effects are captured by λ, and ε is the idiosyncratic error term.

In addition to being estimated for the whole sample, the agricultural productivity model will be estimated for three pairs of subsamples: the bottom versus top land size quintiles, the bottom versus top consumption quintiles, poor households versus non-poor households. The land size quintiles are calculated from total land harvested by the household and is determined within the sample. Comparing the top to the bottom quintile will indicate whether the determinants of agricultural productivity are different for small and large farmers. The consumption expenditure quintiles are calculated from per capita household expenditures and are nationally determined (i.e. not within the sample). Comparing the top and bottom consumption quintiles will indicate whether there are appreciable differences between the richest and poorest agricultural households. Poverty is as defined above. Comparing the results for poor and non-poor will indicate whether the determinants of agricultural productivity vary with poverty status.

The relationship between agricultural productivity and poverty status is estimated using a Logit fixed effects model of the following general form: Where _P_ is equal 1 if the household’s per adult equivalent expenditures fall below the national poverty line and zero otherwise, _Y_ is harvest value per hectare (agricultural productivity). Therefore, Θ will reflect the relationship between agricultural productivity and poverty, _ceteris paribus_ . The terms in the summation capture the effect of three nonagricultural income sources: nonfarm enterprise (NFE) ownership, nonagricultural wage work, and rental or investment income. The set of _Ii_ are per capita incomes from these three sources (profits in the case of NFEs) whereas the _Pi_ are indicators for participation in these three nonagricultural activities. _Pi_ is equal to 1 if the household participated in nonagricultural activity _i_ and zero otherwise. All other household characteristics are contained in **Z** while λ again captures state fixed effects. The logit fixed effects estimator is used to account for the discrete nature of the poverty indicator and to avoid bias due to the incidental parameters problem associated with the probit estimator when using fixed effects (see Greene, 2002).

While equation 2 represents the general model to be estimated, three slightly modified specifications were also estimated. First, the model was estimated excluding the per capita nonagricultural income values ( _Ii_ ). This will provide a better understanding of the relative importance of participation in these activities and the amount of income received from them in terms of poverty status. Secondly, the two models (with and without _Ii_ ) were also estimated 8
14 (2003) indicate that convergence is still evident when one uses better data and an econometric method that allows for classical measurement errors in the inequality data.

Bénabou interprets inequality convergence as an implication of a neoclassical growth model. Ravallion points instead to an explanation in terms of the policy and institutional convergence that has occurred in the world since about 1990. Low-inequality socialist economies have become more market-oriented, which has increased inequality. On the other hand, non-socialist economies have adopted market-friendly reforms. In some of these economies pre-reform controls benefited the rich, keeping inequality high (Brazil is an example), while in others the controls had the opposite effect, keeping inequality low (India is an example). Thus liberalizing economic policy reforms can entail sizable redistribution between the poor and the rich, but in opposite directions in the two groups of countries. However, as Ravallion also notes, the process of convergence toward medium inequality implied by his finding is not particularly rapid, and it should not be forgotten that there are deviations from these trends, both over time and across countries.

The foregoing discussion has been about relative inequality. What about the competing concept of absolute inequality, which depends on the absolute gaps in levels of living between the “rich” and the “poor.”?<sup>19</sup> As Figure 6 shows, the two concepts give rise to completely different trends for international inequality: whereas relative inequality measures (such as the Gini and the MLD) fall from around 1980 onwards, absolute measures record substantial increases.<sup>20</sup> This figure is drawn for (population-weighted) international inequality, but the difference is as important when considering global inequality.

Although this paper (and the broader debate) has focused on income inequality and poverty trends, there should be no presumption that it is the only inequality that matters. Indeed, from some perspectives, international disparities in health status and educational achievement may matter inherently just as much (in addition to being instrumentally important to shaping income inequality and poverty). Since around 1930 there has been convergence in the intercountry and international distributions of life expectancy at birth (LEB). As (weighted) mean world LEB rose from 53.4 years in 1960 to 64.8 years in 2000, its distribution moved from

> 19 For further discussion of the role played by the concept of absolute inequality in debates about the distributional impacts of economic growth and trade openness see Ravallion (2004). 20 Although we include only two relative and one absolute measure, the opposing trends between relative and absolute measures over this period are robust to the choice of index. See Atkinson and Brandolini (2004)