rafmacalaba/datause-encoder-data huggingface dataset:
higher in the India data compared to that in the Bangladesh data, thus the resulting truncation bias is likely to be relatively lower in India. For example, in the father-son sample the coresidency rate is 79 percent in India, while the corresponding rate is only 52 percent in Bangladesh. In the mother-daughter samples, the coresidency rates are lower: 39 percent in India and 26 percent in Bangladesh, reflecting the fact that women leave the natal family following marriage in both countries.

For each country we present three graphs: (i) son-father, (ii) daughter-mother, and (iii) all children-father. The figures show that the slope of the fitted line is _smaller_ in the coresident sample which is consistent with Hausman and Wise (1977). The widely held belief that the coresidency bias in the estimates of IGRC is substantial thus appears clearly visible in the graphs.

In the graphs for the “all children” sample (both sons and daughters), the coresident line intersects the full sample line from above (see Figures 2.A for Bangladesh and 3.A for India). This implies that the surveys miss less educated children from households with low parental education, but miss better educated children from households with high parental education. We thus have both truncation from above and from below.

A closer look at the other graphs reveals some interesting differences across gender and countries. In Bangladesh, the fitted lines in father-son sample (see figure 2.B) intersect each other at a very low level of father’s education, implying that most of the coresident line lies below the full sample line. This implies that, for most of the distribution, the better educated sons leave the parental household. For Mother-daughter sample in Bangladesh (figure 2.C) the pattern of sample selection is different; the line for the coresident sample intersects the full sample line from above at about 5 years of mother’s schooling which is very high given that the average education for mothers is only 1.47 years. This implies that that coresident line lies above the full sample line for most of the cases; the girls with relatively lower education leave the parental household (presumably following marriage, they relocate to husband’s house). Also, the gap between the coresident and full sample lines becomes smaller as the parental education increases, which suggests that the probability of a less 10
**<u>Annex Table 7. Marginal Effects from probit regressions. Dependent variable: Participating in school, ages 6 to 17</u>**

||Burundi<br>2000|Cambodia 199|9 Cambodia 200|0 Indonesia 2003|Jamaica<br>1998|Jamaica<br>2000|Mongolia 2000|<br>Myanmar 2000|<br>Romania 1995|Sierra Leone<br>2000|
|---|---|---|---|---|---|---|---|---|---|---|
|Disabled (0,1)|-0.163|-0.581|-0.414|-0.620|-0.363|-0.632|-0.449|0.069|-0.682|0.131|
||(1.49)|(5.85)***|(5.20)***|(6.89)***|(4.32)***|(3.65)***|(5.57)***|(0.50)|(5.17)***|(0.61)|
|Age (years)|0.510|0.457|0.507|0.269|0.036|0.002|0.549|0.237|0.340|0.104|
||(21.00)***|(32.69)***|(59.38)***|(75.49)***|(11.94)***|(0.85)|(38.25)***|(21.20)***|(51.76)***|(7.33)***|
|Age - squared|-0.023|-0.019|-0.023|-0.012|-0.002|-0.000|-0.023|-0.012|-0.014|-0.005|
||(19.25)***|(31.95)***|(60.35)***|(80.28)***|(15.60)***|(2.10)**|(36.61)***|(21.55)***|(51.43)***|(8.32)***|
|Male (0,1)|0.091|0.083|0.119|-0.007|-0.012|-0.003|-0.074|-0.001|-0.008|0.101|
||(6.65)***|(6.77)***|(15.56)***|(2.28)**|(4.87)***|(3.42)***|(6.08)***|(0.17)|(1.55)|(7.91)***|
|Urban (0,1)|0.189|0.097|0.043|0.059|-0.001|0.001|-0.094|0.123|0.095|0.074|
||(4.33)***|(7.48)***|(3.06)***|(16.34)***|(0.38)|(1.21)|(5.62)***|(13.77)***|(15.66)***|(3.83)***|
|Quintile 2|0.005|-0.044|0.034|0.044|0.013|0.002|0.055|-0.000|0.068|0.022|
||(0.24)|(2.24)**|(3.11)***|(11.52)***|(4.92)***|(2.30)**|(3.07)***|(0.01)|(10.75)***|(1.06)|
|Quintile 3|0.125|-0.007|0.094|0.077|0.018|0.002|0.146|0.019|0.091|0.093|
||(5.46)***|(0.35)|(8.50)***|(19.87)***|(6.58)***|(2.70)***|(8.20)***|(1.90)*|(14.61)***|(4.46)***|
|Quintile 4|0.162|0.067|0.198|0.103|0.021|0.002|0.201|-0.010|0.092|0.172|
||(7.18)***|(3.44)***|(17.95)***|(25.81)***|(7.76)***|(3.54)***|(10.79)***|(0.98)|(14.12)***|(8.10)***|
|Quintile 5|0.263|0.234|0.323|0.134|0.024|0.002|0.221|0.002|0.101|0.271|
||(10.80)***|(10.67)***|(25.60)***|(30.40)***|(9.22)***|(3.60)***|(10.37)***|(0.16)|(15.15)***|(11.17)***|
|Disability*Male|0.025|0.179|0.096|-0.008|-0.011|0.001|0.094|0.031|0.086|-0.270|
||(0.17)|(1.47)|(1.26)|(0.18)|(0.45)|(0.46)|(1.47)|(0.20)|(2.35)**|(1.47)|
|Disability*Urban|0.291|0.056|0.131|-0.092|0.009|-0.001|0.171|-0.651|-0.034|-0.301|
||(1.03)|(0.45)|(1.29)|(1.53)|(0.58)|(0.26)|(2.54)**|(2.13)**|(0.50)|(1.39)|
|Disability*(Quint. 1 or 2)|-0.167|0.196|0.153|0.005|0.012||0.091|-0.018|0.008|0.001|
||(1.17)|(1.59)|(2.18)**|(0.10)|(1.07)||(1.23)|(0.10)|(0.13)|(0.00)|
|Observations|5834|10880|23719|64136|6952|1543|7593|25796|13777|7339|

Absolute value of t statistics in parentheses. *, **, *** indicates significant at 10%, 5%; and 1% levels. Models include region dummies 23
- Hoff, K. and A. Sen (2011): “The Kin System as a Poverty Trap?” in _Poverty Traps_ , ed. by S. Bowles, S. N. Durlauf, and K. Hoff, Princeton, NJ: Princeton University Press, 95–115.

- Jakiela, P. and O. Ozier (2016): “Does Africa Need a Rotten Kin Theorem? Experimental Evidence from Village Economies,” _The Review of Economic Studies_ , 83, 231–268.

- Karaivanov, A. and R. M. Townsend (2014): “Dynamic Financial Constraints: Distinguishing Mechanism Design from Exogenously Incomplete Regimes,” _Econometrica: Journal of the Econometric Society_ , 82, 887–959.

- Kaur, S., M. Kremer, and S. Mullainathan (2015): “Self-Control at Work,” _The Journal of Political Economy_ , 123, 1227–1277.

- Kennedy, P. (1988): “African Capitalism: The Struggle for Ascendancy; and Dietz, J. and D. James (eds)(1990),” _Progress Toward Development in Latin America_ .

- Lewis, A. W. (1955): _Theory of Economic Growth_ , Routledge, 1 edition ed.

- Ligon, E., J. P. Thomas, and T. Worrall (2002): “Informal Insurance Arrangements with Limited Commitment: Theory and Evidence from Village Economies,” _The Review of Economic Studies_ , 69, 209–244.

- McMillan, M. and A. Zeufack (2022): “Labor Productivity Growth and Industrialization in Africa,” _Journal of Economic Perspectives_ , 36, 3–32.

- McNeill, K. and R. Pierotti (2021): “Reason-giving for resistance: obfuscation, justification and earmarking in resisting informal financial assistance,” _Socio-Economic Review_ .

- Miracle, M. P., D. S. Miracle, and L. Cohen (1980): “Informal savings mobilization in Africa,” _Economic Development and Cultural Change_ , 28, 701–724.

- Mobarak, A. M. and M. Rosenzweig (2012): “Selling Formal Insurance to the Informally Insured,” _Unpublished Manuscript_ .

- O’Brien, R. (2012): “Depleting Capital? Race, Wealth and Informal Financial Assistance,” _Social Forces_ , 91, 375–396.

- O’Donoghue, T. and M. Rabin (1999): “Doing It Now or Later,” _The American Economic Review_ , 89, 103–124.

- Platteau, J.-P. (2000): _Institutions, Social Norms, and Economic Development_ , vol. 1, Psychology Press.

- ——— (2014): “Redistributive Pressures in Sub-Saharan Africa: Causes, Consequences,

- and Coping Strategies,” in _Africa’s Development in Historical Perspective_ , Cambridge University Press, 153–207.

44
8 migration, the Docquier and Marfouk (2005) data contain several important limitations. Most notably, by focusing on information collected from census and register data, these data do not capture the very large number of illegal and irregular migrants living and working in the OECD. For example, in 2002 the stock of illegal immigrants in the United States was estimated at 9.3 million, or about 26 percent of the total stock of the “foreign-born” population in the United States (Passel, Capps, and Fix, 2004). Since it focuses on OECD countries, the Docquier and Marfouk (2005) data also do not include the large number of migrants from the Middle East and South Asia who are currently working in the Persian Gulf. In 2000 the total stock of migrants working in the Persian Gulf was estimated at 9.6 million. Finally, the Docquier and Marfouk (2005) data only relate to the stock of migrants, not flows of migrants. In other words, these data only provide information on the numbers of migrants currently living and working in the OECD, and not on the flows of migrants going to (and returning from) the OECD.<sup>8</sup> Appendix Table 1 shows the countries, poverty, international remittance and skill composition of migrant variables included in the new data set. The data set includes one observation for each of the 76 countries and is notable in that it includes 14 observations for Sub-Saharan Africa, a region for which poverty, remittances and skill composition of migrant data are relatively rare.

# � Put Appendix Table 1 at end of paper –

Appendix Table 1 reports two different poverty measures. The first, the headcount index, set at $1 per person per day, measures the percent of the population living beneath that poverty line at the time of the household budget survey.<sup>9</sup> However, the headcount index ignores the “depth of poverty,” that is, the amount by which the
(significant between 10-20% level). However, even in these weak cases, the relationship is large in magnitude. A move from the smallest to the largest value of land area per person decreases the probability of reform by 18.6 percentage points controlling for regional fixed effects and 23.8 percentage points controlling for ethnic, linguistic and religious fractionalization. The corresponding decline with no additional controls equals 35.1 percentage points (p-value of 0.017).

Finally, we performed a number of additional robustness checks on the results reported in Tables 3-6. These checks include following Rodrik (1999b) in controlling for the primary and secondary gross enrollment rates (from World Development Indicators, World Bank) and the initial (year 2003) level of regulation using the Heritage Foundation’s Business Freedom index; controlling for a measure of openness equal to the fraction of years during the period 1970-1990 in which the country is rated as an open economy according to the criterion in Sachs and Warner (1995); controlling for GDP per capita in all the specifications; controlling for the (log of) settler mortality rate from Acemoglu et al (2001) and controlling for net foreign aid as percentage of GDP (average of annual values over 1998-2002 and current and lagged annual values during 20032008).<sup>5</sup> The relationship between the three resource abundance measures and reform remains robust to all these checks except when we control for settler mortality, a result most likely due to the smaller sample size (we lose 87 countries, which have no data on settler mortality).

> 5 Foreign aid is defined as net official development assistance (ODA) which consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Data source for the variable is World Development Indicators, World Bank.

15
# **1 Introduction**

Quantifying the ex-ante effects of trade liberalization, e.g., the impact of free trade agreements (FTAs) and the expansion of the European Union (EU), or trade protection, e.g., Brexit or the break up of the free trade agreement between Estonia and Ukraine due to Estonia’s accession to the EU, are important but difficult tasks. The transmission channels for the impact of trade protection and trade liberalization are clear and well understood from a theoretical perspective, c.f., Arkolakis, Costinot and Rodriguez-Clare (2012). However, difficulties in such evaluation efforts arise in the empirical implementation and, more specifically, with the definition and quantification of the initial change in trade costs that triggers ripple effects in the global economy. While ex-post evaluation analysis usually allows researchers to obtain estimates of the impact of liberalization or protection efforts, e.g., the impact of NAFTA or the impact of applied tariffs, this is not the case with ex-ante studies, e.g., the formation of a new trade agreement or Brexit. Therefore, researchers have adopted one of two approaches to quantify the impact of “shallow” vs. “deep” trade liberalization efforts, e.g., the “shallow” vs. “deep” effects of FTAs, or the impact of “soft” vs. “hard” impact of protection, e.g., the “soft” vs. “hard” of Brexit.<sup>1</sup> Policy makers and academics seem to agree on the modeling choice for the measurement of the initial trade cost changes in the “shallow” and “soft” scenarios. Specifically, these changes are captured through changes in observed trade policies, e.g., tariffs. However, it is more difficult to find appropriate measures for the initial change in trade costs in the case of “deep” and “hard” trade liberalization and protection scenarios, respectively. To this end, two popular approaches have been adopted. The first approach is to rely on additional observable trade policy measures (e.g., nontariff trade measures, NTMs) and to add the changes in those measures to the changes in tariffs from the “soft” scenario. This approach has two drawbacks. First, data on NTMs are often scarce and patchy, and the measurement and aggregation of NTMs can be difficult, c.f., UNCTAD-WTO (2012). In addition, even if NTM data were available, the combination of changes in observable

> 1While “shallow” vs. “deep” FTAs can be distinguished by the (number) of provisions included, for example, the concept of “soft” vs. “hard” effects is a bit vague. One way to think about the difference between “soft” vs. “hard” trade protection, which is consistent with our approach in this project, is as the difference between directly observable measures that can be included explicitly in quantitative analysis vs. obstacles to trade that are not directly observable and for which data for the econometric analysis are not available.

1
**Figure 3.7. Consumption and investment growth by simulation (%‐age point deviation for average annual growth from base)**

<!-- Start of picture text --> ‐2 ‐1 0 1 2 3 4<br>ginv‐tdir<br>ginv‐dbor<br>ginv‐fbor<br>pwe<br>remit<br>Consumption, private Investment, private<br>Investment, government Consumption, government<br><!-- End of picture text --> Source: Authors’ calculations based on simulation results.

**Figure 3.8. Real household consumption per capita growth by simulation (%‐age point deviation for average annual growth from base)**

<!-- Start of picture text --> ‐0.3 ‐0.2 ‐0.1 0 0.1 0.2 0.3 0.4<br>ginv‐tdir<br>ginv‐dbor<br>ginv‐fbor<br>pwe<br>remit<br><!-- End of picture text --> Source: Authors’ calculations based on simulation results.

‐25‐
paper 4492.

Klein, R and F. Vella (2009a), “A Semiparametric Model for Binary Response and Continuous Outcomes under Index Heteroscedasticity”, Journal of Applied Econometrics, v. 24, iss. 5, pp. 736-62.

Klein, R and F. Vella (2009b), "Estimating the Return to Endogenous Schooling Decisions via Conditional Second Moments," Journal of Human Resources, University of Wisconsin Press, vol. 44(4): 1047-1065.

Lewbel, A (2012) “Using Heteroskedasticity to identify and estimate mis-measured and endogenous regressor models,” Journal of Business and Economic Statistics, vol. 30.

Mehlum, Halvor, Edward Miguel, and Ragnar Torvik (2006), “Poverty and Crime in 19th Century Germany”. Journal of Urban Economics 59(3): 370-388.

Miguel, E., S. Satyanath, and E. Sergenti (2004): “Economic Shocks and Civil Con‡ict: An Instrumental Variables Approach," Journal of Political Economy, 112.

Millimet, D and Tchernis (forthcoming), “Estimation of Treatment E¤ects Without an Exclusion Restriction: with an Application to the Analysis of the School Breakfast Program?”, forthcoming in Journal of Applied Econometrics.

Mocan, N (2008), “What Determines Corruption? International Evidence From Micro Data”, Economic Inquiry, 46(4): 493-510.

Olken, B. (2006).“Corruption and the Costs of Redistribution: Micro Evidence from Indonesia.’Journal of Public Economics, 90(4-5):853-870.

Olken, B. (2007). “Monitoring Corruption: Evidence from a Field Experiment in Indonesia." Journal of Political Economy, 115: 200-249.

Olken, B. (2009). “Corruption Perceptions vs. Corruption Reality." Journal of Public Economics 93, (7-8): 950-964.

Olken, B. and Barron, P. (2009). “The Simple Economics of Extortion: Evidence from Trucking in Aceh.”Journal of Political Economy, 117(3): 417-452.

Olken, B., and R. Pande (2011). “Corruption in Developing Countries”, Abdul Latif Jameel Poverty Action Lab.

Plomin, R, DeFries, J, P. McGu¢ n, G. McClearn (2008): Behavioral Genetics, Worth Pub39
have a Nikkei name. Thus such a method would only cover one-fifth of the target population in our application.

# **3. The Experiment**

Each of the three main methods of sampling migrants or migrant-sending households has its theoretical advantages and disadvantages in terms of cost, time, coverage, and representativeness. However, comparing the practical performance of the three methods is made difficult by the fact that they have all been used in different country contexts, at different times, with different questionnaires and survey teams. Nevertheless, knowing how the different methods perform in practice is a question of large importance for the design of new surveys of migrants or migrant-sending households. We therefore designed an experiment to compare how the three main methods perform in practice. In particular, we compare a census-based stratified random sample, an intercept survey, and a snowball survey.

The context of our experiment is a survey that the World Bank was requested to perform of the Japanese-Brazilian population (Nikkeis) in Brazil. Japanese migration to Brazil began in 1908 with a ship carrying bonded labor to the coffee plantations (Goto, 2006). High rates of migration from Japan to Brazil occurred from 1925-36 as the Japanese government subsidized emigration, and again from 1955-1961 as the Japanese government again promoted emigration during post-war rebuilding. Many of these workers settled in Brazil, and the population of Japanese descent in Brazil was estimated to have reached 1.2 million by 1987-88 (Tsuda, 2003). Following a revision of Japanese immigration law in 1990, many of these Nikkei began migrating back to Japan to work: In 2004 there were 190,000-265,000 Brazilians in Japan, who were estimated to be sending US$2 billion in remittances back to Brazil (Beltrão and Sugahara, 2006).

The Japanese-Brazilian population is now relatively concentrated in the upper part of Brazil’s income distribution, and work in relatively skilled occupations. They migrate to undertake less skilled jobs in Japan than they were doing in Brazil, but for higher wages. McKenzie and Salcedo (2007) use the data from the census-based survey described here 9

-  -
predictive power of the auxiliary geospatial indicators. Additional research, drawing on data from different contexts, will help inform the choice between using more recent estimates obtained by combining survey and geospatial data, and older small area estimates derived from a census.

# 2. Small area estimation

Small area estimation deals with methods to reliably estimate parameters of interest in areas without adequate sample sizes (Molina and Rao, 2015). Here, we discuss the basics of small area estimation, with a particular focus on the Mexican context we study in this paper. Interested readers can find more details in Rao (2003), Molina and Rao (2010, 2015), and Jiang and Rao (2020). Ghosh (2020) details the evolution of small area estimation over the last five decades.

In Mexico, it is worth differentiating four separate levels in the data: households, AGEBs, municipalities, and states. Households are nested within AGEBs, AGEBs are nested within municipalities, which are nested within states. We are interested in estimating poverty rates for municipalities – the target areas – but the only national surveys available are representative at urban-rural areas within states. Therefore, although these estimates are unbiased over repeated samples, a single municipality-level sample is generally too small to provide a reliable estimate of poverty rates.

To estimate these poverty rates, we need to build a model at the level of the municipality or lower. In theory, we could directly estimate poverty rates at the municipality level – that is, taking poverty rates at the municipality level and estimating the model using municipalities as the unit of analysis – which is referred to as an area-level model. We could also estimate poverty in AGEBs and then aggregate AGEB-level poverty rates up to the municipality based on AGEB populations, referred to as a sub-area model. Finally, we could estimate poverty at the household level and then aggregate household-level poverty rates up to the municipality, using a household-level (or unitlevel) model.

These methods share some characteristics but also offer different advantages. One similarity across all three methods is that they combine survey data with auxiliary data. This is distinct from much of the recent literature on satellite estimation of poverty (see, for example, Blumenstock et al. (2015)), which tends to estimate poverty using a purely synthetic regression prediction. Small area 8
# **<mark>Table A3C – Comparison of Means Between Firms in Municipalities with Low and High Bank Exposure to Public Local Debt</mark> Before FD Law**

This table displays the summary statistics of firms given the exposure to local public lending of the banks they borrow from. All statistics recorded in March of 2016, one month prior to the implementation of the FD law. A bank is defined as with low (high) public debt if its share of lending to local governments in March of 2016 is below (above) the median. _Liabilities, Assets, Fixed Assets_ and _Sales_ are the volume (in logs) of firms’ total liabilities, total assets, fixed assets, and total sales. The first two columns in each panel show the mean and standard deviation of each variable. The last column— _Standardized Difference—_ shows the normalized differences of variables between banks with high and low public debt (based on Imbens and Wooldridge, 2009).

||**Banks wi**<br>Mean|**th Low Local Public Debt**<br>Standard Deviation|**Banks with Hi**<br>Mean|**gh Local Public Debt**<br>Standard Deviation|Standardized<br>Difference|
|---|---|---|---|---|---|
|Liabilities (logs)|19.13|1.8|19.19|1.87|-0.02|
|Assets (logs)|19.85|1.79|19.93|1.8|-0.03|
|Fixed Assets (logs)|17.93|2.48|18.34|2.38|-0.12|
|Sales(logs)|20.17|1.39|20.2|1.47|-0.01|

68