rafmacalaba/datause-encoder-data huggingface dataset:
eliciting information about an illegal act. This raises questions of both research ethics (Duvell et al, 2008) as well as whether one can obtain truthful responses to questions about this sensitive issue. As a result of these issues, while there are a few exceptions (e.g. the Mexican Migration Project), most surveys of migrants (especially in destination countries) do not ask about their legal status.

Of course illegal migration is far from the only sensitive behavior of interest to social scientists, and a variety of methods have been developed to help obtain more reliable data on the incidences of these behaviors. One of the more widely used and simple to apply methods is that of list randomization. Under this method, a sample is randomly divided into two groups. The first group is given a list of _n_ statements (often _n=3_ ) and asked to say how many of the three statements they agree with. The second group is given a list of _n+1_ statements, consisting of the same _n_ statements as the first group, plus an additional statement which is about the behavior of interest. Since respondents only need reveal the number of statements they agree with, and not which ones, this can yield more truthful responses. The difference in the mean number of responses between the two groups then gives an estimate of the incidence of the behavior of interest.

Our purpose in this paper is to explore the use of list randomization as a means of measuring both the incidence of illegal migration, as well as the characteristics of illegal migrants. This is the first paper of its kind to use this method in a migration setting. We do this by adding a list randomization question to surveys being undertaken in four countries: Ethiopia, Mexico, Morocco and the Philippines. We illustrate how the incidence of illegal migration can be obtained via this method, how one can then obtain estimates by socio-demographic characteristic, and in the case of Ethiopia and Morocco, compare direct elicitation to this indirect method. Our results show both some promise, but also limitations, of this approach. They enable us to get estimates of the incidence of illegal migration which are broadly consistent with prior knowledge of relative differences in the share of migration which is legal across our four countries, and which are not too different from the direct measures for the two countries where this was not considered too sensitive a question to ask directly. However, we also show that confidence intervals can be wide unless samples are large, and there are some areas where future efforts could add additional tests.

3
**<u>Table 5: Descriptive statistics by land sale market participation</u>**

|||**Niger**|||**Uganda**||
|---|---|---|---|---|---|---|
||**Autarky or Sold**|**Purchased**||**Autarky or Sold**|<br>**Purchased**||
|**Basic household characteristics**|||||||
|Share owning land|0.98|1.00||0.95|1.00|***|
|Share agric. Cultivators|0.99|1.00||0.98|1.00|**|
|Household size|6.79|7.80|***|6.00|6.39|*|
|adults five years ago|2.48|2.73|*|2.34|2.24||
|adults|3.05|3.41|*|3.19|3.04||
|children|3.49|4.25|***|2.81|3.35|***|
|old people|0.24|0.15|*|0.35|0.13|***|
|<br>Female head|0.08|0.02|*|0.31|0.19|***|
|Age of head|44.98|42.65||48.04|40.86|***|
|<br>Head is illiterate|0.51|0.38|**|0.17|0.11|**|
|Head has primary|0.44|0.54|*|0.58|0.52|*|
|<br>Head has more than primary|0.05|0.07||0.21|0.29|***|
|<br>**Income and its sources**|||||||
|Mean household gross income (USD)|1381.66|1772.24|***|1155.10|1639.64|***|
|share from crops|0.44|0.44||0.46|0.44||
|<br>share from livestock|0.17|0.21|*|0.19|0.17||
|share from ag wage|0.02|0.00|**|0.06|0.06||
|share from non-age wage|0.06|0.05||0.10|0.14|**|
|<br>share from self employment|0.24|0.24||0.13|0.17|**|
|share from transfer|0.07|0.06||0.06|0.03|***|
|Gross inc.from crops (USD/ha)|269.25|209.32||528.80|697.97|*|
|Net inc.from crops (USD/ha)|230.11|119.68||268.19|379.12|***|
|**Land endowments**|||||||
|Owned area/adult (14-60) five years ago|1.94|1.09|***|0.65|0.30|***|
|<br>Owned area/adult (14-60)|1.93|1.96||0.54|0.47||
|No. of plots owned five years ago|2.20|1.68|**|1.69|1.26|***|
|<br>No. of plots owned|2.70|3.75|***|1.80|2.58|***|
|Owned area ha five years ago|4.43|2.72|***|1.38|0.70|***|
|Owned area ha|5.15|5.62||1.46|1.31||
|Operated area ha|5.16|5.33||1.41|1.35||
|<br>Operated area/adult (14-60)|1.93|1.81||0.51|0.49||
|<br>Has any type of document|0.09|0.46|***|0.16|0.43|***|
|Share of plots with gender of manager|0.96|0.97||0.95|0.97||
|male manager|0.53|0.68|***|0.10|0.06||
|<br>joint manager|0.36|0.24|**|0.51|0.58|*|
|female manager|0.10|0.08||0.39|0.35||
|<br>Share of plots with gender of owner|0.78|0.90||0.98|0.99||
|male owner|0.58|0.73|***|0.25|0.31|**|
|joint owner|0.26|0.11|***|0.45|0.52|**|
|female owner|0.09|0.07||0.30|0.16|***|
|**Production**|||||||
|Gross inc.from crops (USD)|513.49|611.57||394.94|510.19|***|
|# of days by family members|148.73|213.67|***|233.37|249.44||
|Value of days by hired labor (USD)|39.17|116.05|***|28.65|45.97|***|
|<br>Value of used fertilizer (USD)|44.90|64.66||6.89|24.03|***|
|<br>**Assets**|||||||
|Value of agricultural assets (USD)|481.79|536.08|*|24.64|23.03||
|<br>Value of animals (USD)|482.48|686.53|**|366.97|392.27||
|<br>Value of durable goods (USD)|4348.33|5856.95|***|2607.45|6145.21||
|<br>**Geo variables**|||||||
|Share of non-ag wage income (EA level)|0.06|0.07||0.10|0.10||
|<br>Distance to the nearest major road|12.92|12.90||8.08|7.87||
|<br>No. of households|2,182|81||1,924|205||

_Note:_ Tests of differences of means between lessees and non lessees are reported with * notifying significant at 10%, ** notifying significant at 5%, and *** notifying significant at 1%.

19
# **Appendix: Comparison with administrative records**

Comparing the survey aggregates on _Jefes_ participation for the EPH with the administrative records is complicated by the fact that the sample frame for the survey does not coincide with the (national) coverage of _Jefes_ . This can be dealt with by confining the analysis of the administrative data to those areas included in the EPH sample frame. We consider two ways of doing this. In the first, we only use the administrative data for those municipalities included in the EPH sample frame, based on the location of the participants’ _Jefes_ registration. In other words, we only consider participants registered in the geographical areas where there is an overlap of municipalities with the sample frame of the EPH. In the second, we restrict the administrative data to those who have their recorded domicile in the EPH sample frame, i.e., we only consider participants whose residence is in the same conglomerate (according to the zip code) as where they receive their payment ( _boca de pago_ ). This second method deals with cases in which a person registers in a nearby city in which she is not in fact resident.

Table A1 gives the results. The grossed-up estimate of _Jefes_ participation represents about 80% of the registered applicants from the administrative data. The aggregate from the administrative data is outside the 95% confidence interval of the survey-based estimate. When we breakdown the aggregates by urban areas, the administrative count is outside the 95% confidence interval (CI) for 18 areas. And these are all cases in which the survey estimate of participation is lower than the administrative data suggest.

As one would expect, switching to the residence-based assignment of _Jefes_ participants to urban areas reduces the discrepancy. The tighter matching by residence puts the administrative data close to the upper bound of the 95% CI in the aggregate, but it still leaves 14 areas for which the survey gives a significantly lower count.

26
Figure A2: Specification Charts for Median Daily Rainfall <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.15 1<br>.1 0<br>.05<br>0 -1<br>-.05 -2<br>* Weather Product TAMSAT* -.1 * Weather Product TAMSAT* -3<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>1 .1<br>.5 0<br>0 -.1<br>-.5<br>* Weather Product * -1 * Weather Product * -.2<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.2 .3<br>.1 .2<br>.1<br>0 0<br>-.1 -.1<br>* Weather Product * * Weather Product * -.2<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

50
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86
Policy ReseaRch WoRking PaPeR 4708

# **Abstract**

This paper studies the effects of labor-regulation reform using data for 10,396 firms from 14 Latin American countries. Firms are asked both how many permanent workers they would have hired and how many they would have terminated if labor regulations were made more flexible. I find that making labor regulations more flexible would lead to an average net increase of 2.08 percent in total employment. Firms with fewer than 20 employees would benefit the most, with average gains in net employment of 4.27 percent. Countries with more regulated labor markets would experience larger gains in total employment. These larger gains in total employment, however, would be achieved through higher rates of hiring and higher rates of termination. These results may explain why there is substantial opposition to labor reforms despite the predicted gains in efficiency and total employment.

This paper—a product of the Enterprise Analysis Unit, Financial Private Sector Development Department—is part of a larger effort in the Financial Private Sector Development VPU. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dkaplan@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Trade Agreements and countries, our focus on the effect of RTA on inflows to and outflows from the region, and on our account of the role of Global Value Chains.

Also related is Baier and Bergstrand (2007) and Baier and Bergstrand (2015) who address the problem of endogeneity related to free trade agreements and trade flows by using panel data and average treatment effects (ATEs). They find positive estimates and conclude that free trade agreements increase members’ international trade.

Anderson and Yotov (2016) refer to a gravity model to estimate the effects of trade agreements on terms of trade and global efficiency. They face two main problems: heteroskedasticity in trade flows data, and endogeneity due to the two way causality. To address the first one, they use the Poisson pseudo maximum likelihood (PPML). To address the second one, they introduce two variables, one for trade agreements between countries with low most-favored-nation tariffs (MFN), the other for trade agreements between countries with high MFN tariffs. They find an increase in the global efficiency of manufactures trade over the period 1990-2002.

The behavior of trade flows following a regional trade agreement is also impacted by the depth of the agreement, as analyzed in Mattoo et al. (2019). Using a sample of 96 countries for the period 2002-2014, they show that deep agreements lead to more trade creation and less trade diversion than shallow agreements.

Additionally, beyond complementarity in trade policy, GVCs also create strong interdependence of GDP fluctuation at business cycle frequency and are associated with network propagation effects of any reform aiming at reducing the cost of crosscountry trade.<sup>2</sup> Moreover, the segmentation of production across countries also has significant implications for the magnitude of estimated trade elasticities (Amiti et al. (2014), de Soyres et al. (2018)).

Finally, Estevadeordal et al. (2008) study the impact of regional trade agreements on trade liberalization toawrds non-members. Focusing on Latin American countries, they found “complementary effects” in the sense that a preferential tariff reduction leads to a reduction in the external tariff. Freund and Ornelas (2010) provide an insightful review of the literature on regionalism. The empirical findings of this paper, combined with theories of optimal trade policy suggest two opposing forces towards greater global integration: the response of trade flows to an RTA gives members an incentive to reduce trade barriers towards non-members; however

> 2See Gunnella et al. (2019) and de Soyres and Gaillard (2019) for the former point, Boehm et al. (2019) and de Soyres et al. (2019) for the latter.

4
17

# VII. **DISCUSSION AND CONCLUSIONS**

On the program’s coverage, there is good news and bad. The good news is that HCFP is very well targeted on the Vietnam’s poor: the poorest 20% of the population accounts for just over 50% of HCFP beneficiaries. The bad news—known already to the government—is that the program (at least as of 2004) was not covering all the intended groups: coverage ought to have been 31% or so in 2004; in the event, it was around 15%. On impact, there is also good news and bad. The good news is that the program appears to be increasing the utilization of services quite considerably, and reducing the risk of catastrophic out-of-pocket spending. The bad news (or potentially bad news) is threefold. First, there is no perceptible impact on (average) out-of-pocket spending, and even with HCFP coverage poor households are left spending a high share of their modest income on out-of-pocket health expenses and at considerable risk of catastrophic spending. Second, the utilization impact is far more pronounced for inpatient care than outpatient care. This may not necessarily be the most cost-effective way of improving the health of poor Vietnamese households, and may leave them facing costs—including transport costs, informal payments, etc.—that are higher than necessary. Third, the impacts on utilization are larger among the better off: among the poorest decile, utilization impacts are rarely significant.

What do the results suggest for fine-tuning of HCFP? The probit results in **Table 3** suggest that the shortfall of coverage from the target rate has something to do with regional differences in implementation of the HCFP program, while the concentration curves in **Figure 1** suggest that tighter targeting could be achieved by dropping residence in a Decision 135 commune as a qualification. A number of factors could help explain the negligible impact of the program on (average) out-of-pocket spending. It is possible that facilities may be permitted to levy some copayments. Some spending recorded in the survey may include spending for hotel and ‘nonessential’ items used by inpatients (e.g. blankets, dressings, etc.). It is also highly plausible that the out-of-pocket payments recorded in the survey include informal payments, which of course are not reduced by the program. With inpatient care increasing so dramatically as a result of the program, such spending may well increase, offsetting at least in part the fall in fees and other costs
have become important drivers of aggregate convergence in labor productivity (Table 1 and Figure 5). This reflects larger productivity improvements in many sectors in EMDEs (especially the LICs) compared to advanced economies as well the fact that many EMDEs experienced rapid sectoral shifts from agricultural sectors over the last few decades.

Looking across the sectors, agricultural productivity growth has been a significant contributor to aggregate convergence, whereas catch-up in other sectors has only contributed a small amount to convergence. Given the share of value-added in the agriculture sector in LICs is large (Figure 1), the strong growth of the agriculture sector has been the most important driver of the aggregate productivity convergence. Our result is in line with Ivanic and Martin (2018) and Ligon and Sadoulet (2018) which illustrate that the increase in agricultural productivity has a larger poverty-reduction effect than increases in other sectors.

# **3.2.3 Sectoral convergence**

The same exercise is undertaken to examine convergence across sectors (Table 2 and Figure 5). Examining this using this study’s extensive data suggests the following:

- **Agriculture sector:** Over the first half of the sample and the over the entire sample, there is no evidence for unconditional convergence in the agriculture sector. This is line with Kinfemichael and Morshed (2019). However, over the second half of the sample (1995-2018) there is convergence. This seems to some degree to be due to the commodity price boom during the 2000s. Nonetheless, the estimated convergence rate is very low, implying that it would take about 750 years for the bottom 10 percent of the productivity level to reach the top 10 percent.<sup>9</sup>

- **Industry sectors (Mining, Manufacturing, Utilities and Construction):** There is evidence of unconditional

- convergence in many of the industry sectors. The finding of unconditional convergence in the manufacturing sector is line with Rodrik (2013) using UNIDO data.<sup>10</sup> However, the estimated convergence rate is low. Diao et al. (2021) reveal a dichotomy between larger firms in the manufacturing sector that exhibit

> 9750 years ≒(ln(0.9)/ln(0.1)-1)/0.00127, using Table 2.

> 10However, Rodrik (2013) acknowledges that the “convergence results that follow should be read as applying to the more formal, organized parts of manufacturing and not to micro-enterprises or informal firms. In developing countries, enterprises with fewer than 5 or 10 employees are often not included.” UNIDO reported that there is a significant difference between UNIDO and the national account.

8
Table C.4. Impacts of Extremism on Educational Attainment Excluding Provinces with more than 50% of Taliban Control in 2017

||Education<br>Index|Literate [=1]|Some<br>Education [=1]|Primary [=1]|Secondary [=1]|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|_Panel A. Taliban Exposure_||||||
|_ETi_|-0.105**|-0.070|-0.109*|-0.050|-0.192*|
||(0.038)|(0.047)|(0.058)|(0.062)|(0.088)|
|FDR q-values|[0.031]|[0.110]|[0.079]|[0.198]|[0.062]|
|R-squared|0.185|0.172|0.174|0.053|0.071|
|_Panel B. Contested Territories Exposure_||||||
|_ECi_|-0.045**|-0.034*|-0.033|-0.032|-0.080**|
||(0.015)|(0.018)|(0.023)|(0.024)|(0.035)|
|FDR q-values|[0.016]|[0.068]|[0.118]|[0.118]|[0.042]|
|R-squared|0.185|0.172|0.174|0.053|0.071|
|_Panel C. U.S. - Allies Exposure_||||||
|_EAi_|0.037**|0.027*|0.030*|0.023|0.066**|
||(0.012)|(0.014)|(0.018)|(0.019)|(0.027)|
|FDR q-values|[0.011]|[0.059]|[0.074]|[0.101]|[0.027]|
|R-squared|0.185|0.172|0.174|0.053|0.072|
|Mean Dep. Variable|0.602|0.913|0.855|0.140|0.500|
|Observations|5,383|5,383|5,383|5,383|5,383|
|_Controls in All Panels_||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Dependent variables: Education Index is constructed using the average of the variables in columns (ii) to (v). Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

64