rafmacalaba/datause-encoder-data huggingface dataset:
**—** **<u>Table 3. Matching results alternative matching estimators</u>**

|**Treatment Variable:**<br>**Non-Cooperation**|**Bias**<br>**Corrected**<br>**Matching**|**Augmented**<br>**Inverse-**<br>**Probability**<br>**Weighting**|**Inverse-**<br>**Probability**<br>**Weighted**<br>**Regression**<br>**Adjustment**|**Inverse-**<br>**Probability**<br>**Weighting**|**Regression**<br>**Adjustment**|
|---|---|---|---|---|---|
|**ATT**|-2.408***<br>(0.459)|-2.492***<br>(0.553)|-2.445***<br>(0.672)|-2.366***<br>(0.713)|-2.113***<br>(0.647)|
|Number of observations|513|513|513|513|513|
|N. obs. for the Control group|409|409|409|409|409|
|N. obs. for the Treated group|104|104|104|104|104|
||||**Robustness chec**|**ks**||
|[1] Using Rule of Law|-1.841***<br>(0.482)|-2.805***<br>(0.549)|-2.335***<br>(0.674)|-2.183***<br>(0.711)|-1.987***<br>(0.635)|
|[2] Using Control of Corruption|-2.361***<br>(0.460)|-2.530***<br>(0.533)|-2.235***<br>(0.701)|-2.172***<br>(0.735)|-1.935***<br>(0.659)|
|[3] Using Regulatory Quality|-2.647***<br>(0.449)|-2.687***<br>(0.598)|-2.278***<br>(0.670)|-2.118***<br>(0.701)|-1.910***<br>(0.632)|
|[4] Using Political Stability|-1.937***<br>(0.537)|-2.355***<br>(0.636)|-1.745***<br>(0.669)|-1.692**<br>(0.690)|-1.665***<br>(0.611)|
|[5] Using Voice and Accountability|-2.524***<br>(0.495)|-3.002***<br>(0.560)|-2.357***<br>(0.620)|-2.430***<br>(0.667)|-2.089***<br>(0.615)|

Note: Standard errors in parentheses computed using 500 bootstrap replications. * _p_ <0.10, ** _p_ <0.05, *** _p_ <0.01.

# _6.2. A different look at endogeneity_

The use of propensity score matching and—as illustrated by the previous subsection—of alternative matching methods revealed a robust effect of our treatment—being included on the FATF list—on tax revenues. Such an event analysis is an appealing way to estimate a causal effect in the absence of robust external instrumental variables. However, an alternative way to control for possible endogeneity in the effect of IFFs on tax revenues is to use the SystemGMM method coined by Blundell & Bond (1998) that draws upon internal instrumental variables—it instruments the variables using their lagged values as instruments.

Following the recent literature (see e.g. Combes et al., 2018), when applying SystemGMM we restrict and collapse the instrument set to avoid the proliferation of instruments (Roodman, 2009) and we correct standard errors for the finite sample bias (Windmeijer, 2005). As illustrated by the bottom part of Table 4, in addition to an appropriate number of instruments (namely, lower than the number of cross-sections), the Hansen J-test does not reject the null hypothesis that our instruments are valid (i.e. uncorrelated with the error term). Next, the AR(2) test showing the absence of second-order autocorrelation of the error term supports the quality of our fitting. Moreover, the fairly large lagged coefficient of tax revenues (see the top of Table 4) supports our use of a System-GMM model to cope with the important persistence of tax revenues. In addition, whenever significant control variables present the expected sign, namely a positive effect of trade and GDP growth.

13
Table 2. City Comparisons for Industiral Pollution and Socioeconomic and Environmental Conditions, 2001

||Huaian|Wuxi|Yangzhou|Zhenjiang|
|---|---|---|---|---|
|**Socioeconomic conditions**|||||
|GDP per capita (Yuan)|14,359|37,700|21,311|18,852|
|Economic growth rate (%)|11.05|12.20|7.30|11.10|
|Unemployment rate (%)|3.84|3.62|3.60|2.30|
|Population (1,000)|558|2,131|1,097|628|
|**Environmental conditions**|||||
|TSS: Total suspend solids (mg /m3)|0.158|0.144|0.237|0.105|
|SO2: sulfur dioxide (mg /m3)|0.037|0.056|0.023|0.024|
|NO2: nitrogen dioxide (mg /m3)|0.027|0.034|0.035|0.038|
|% of drinking water meeting standards|93.00|97.96|98.80|96.43|
|% of surface water meeting standards|83.00|91.67|62.00|88.89|
|Noise (dB(A))|55.80|56.90|53.20|55.50|
|**Total industrial pollution emissions**|||||
|Waste water (10,000 tons)|1,674|14,010|3,774|4,544|
|COD: chemical oxygen demand (tons)<br>|1,708|N.A.|6,787|25,200|
|Waste gas (100 million m<sup>3</sup>)|129|471|461|1,895|
|Smoke (tons)|11,063|8,611|5,385|47,421|
|SO2 (tons)|8,863|21,492|35,765|96,377|
|Solid waste (10,000 tons)|1|8|N.A.|265|

Sources: Municipal governments of the four cities.
- Education: based on answer to questions ar16 (“Highest level of schooling attended by HHM”) and ar17 (“Highest grade ever completed by HHM”) from Book K. Years of education computed combining information from Wikipedia and .

Follow-ups (wave 4 – 2007/08, wave 5 – 2014/15):

- Internal migration: we use information on province, kabupaten, and kecamatan of each household detailed in the htrack file of each survey round.

- International migration: from section AR of Control Book. A household member is classified as international migrant if she lives in a foreign country (from question AR18i ”Where does [...] live now?”) and moved out of the household after baseline (from question AR18e ”When did [...] move from/into this household?”). Note that we exclude from the group of international migrants in wave 5 respondents that had left Indonesia between baseline and wave 3 but then returned between wave 3 and 5.

- Occupation type: same as baseline.

# **B.5 Mexican Family Life Survey (MxFLS)**

Notes: a large fraction of migration instances comes from individuals who leave the household and are reported in the Control Book. MxFLS does not collect information on their occupation at destination.

Baseline (2002):

- status: a is as not if the answer

- Employment respondent classified working to question tb02 from Section TB (Employment) of Book 3A is different than “Worked or carried out an activity that helped household expenditures”.

- Self-employed: if the answer to question tb32 is either “Boss, employer, or business proprietor” or “Self-employed worker (with or without non-remunerated worker)”. Exclude self-employed who work in agricultural activities according to question tb25.

- Wage worker: if the answer to question tb32 is “Non agricultural worker or employee”. Restrict to non-agricultural workers as above.

- Agricultural worker: if the answer to question tb32 is “Peasant on your plot”, or “Rural laborer, or land peon (agricultural worker)”, or if the individual is mainly working on agricultural activities according to question tb25.

52
**Table 10** Stylised Facts on Trade Balance to GDP Ratio in India in the Preand Post-Reform Period

This table presents business cycle moments and the average value of the trade balance to GDP ratio for the pre- and post-reform periods.

||Pre-reform period (1951–<br>1991)|Post-reform period<br>(1992–2009)|
|---|---|---|
|Mean|1.99|3.48|
|Std. dev.|0.90|1.16|
|Rel. std.dev.|0.40|0.60|
|Cont. cor.|0.25|-0.69|
|First ord. auto. corr.|0.246|0.504|

_Source_ : National Accounts Statistics, authors’ estimates outlined in the Financial Development, Permanent Income Shock, and Relative Consumption Volatility in a Small Open Economy section 49
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7 per country = 6; mean = 7.5; min = 1; max = 44). We then include surveys from the 1990s to make sure we increase the number of countries in our sample. In particular, data is available for 176 survey/census samples in 63 countries in 1990-1999, 630 samples in 129 countries in 2000-2009 and 278 samples in 107 countries in 2010-2016. Note that several survey samples may be available for a same country-year.<sup>8</sup> Finally, the data is highly representative of the world economy. The 145 countries comprise about 95% of the world’s population today. The data is representative for both developed countries (76% of their population) and developing countries (98%).

**Main Variables.** For the 24,437,020 individuals, we know their monthly wage, age, and number of years of education. For a substantial subsample, we know the hourly wage and the number of hours worked. Lastly, we know their employment status (e.g., self-employed or paid employee) their sector (ISIC-1: agriculture, industry, services), their occupation (ISCO-08: managers, professionals, technicians, clerks, crafts, sales, machinists, elementary occupations, skilled agriculture, other), and whether they work in the formal sector or live in urban areas. Finally, note that I2D2 mostly calculated the wage of self-employed individuals as the amount of salary taken from the business.

**Data Limitations.** While I2D2 is particularly useful in terms of external validity, the fact that all these surveys/censuses had to be harmonized implies that few variables are available (about 70). Also, the I2D2 team does not provide details on how the harmonized variables were created for each survey, and thus the level of consistency across surveys. However, given the wide use of the data across flagship World Bank reports, we feel compelled to trust that the I2D2 team did a good enough job that the results generated in our study do not capture statistical artefacts. Having said that, the only significant issue comes from the potential mismeasurement of wages. There could be issues with how self-employed individuals calculate and self-report the amount of salary taken from the business. As a result, we will examine the robustness of our results when excluding self-employed individuals. Next, 99% of these samples are surveys because censuses rarely include data on wages. In addition, we measure _potential_ work experience as I2D2 does not include direct measures of work experience. Ability (e.g., test scores) is also not measured. Finally, the data are not panelized.

8Web Appx. Fig. A.1 shows the number of countries with available data for each year from 1990-2016. The Web Appendix is available on the website of Remi Jedwab: www.remijedwab.com.
an important insight: the premium received by unions is not mirrored in the wage differential between the unionized and non-unionized high-wage sectors. In the absence of unions, wages in both these sectors would decrease and employment in the two sectors would increase, while the third low-wage sector would remain unaffected.

In the analysis above, we have not included such a third sector since the data do not allow it. Instead, we have what can be considered as the two high-wage sectors. Hence, to the extent that such a spillover effect exists, our measure of the impact from unions must be considered a lower bound, since any such spillover effect will lead to a downward bias in the returns to unionization.

The idea of a third sector may also explain part of the establishment-size wage effect since, presumably, small non-unionized firms are less prone to have their wage setting tied to the wage setting in the unionized firms. This does not explain the difference between the premium to blue vs. white-collar workers that we observed above and, hence, cannot be the only reason for the establishment size wage effects.

# **7. Conclusion**

# _Summary of findings_

The objective of this paper has been to analyze possible channels of distortions in the labor markets in Côte d’Ivoire by using enterprise micro-level data. The tool kit used is the technique of quantile regressions, which has proven its usefulness by providing a much more detailed analysis across the distribution than least square techniques could have accomplished. The main findings are the following: (1) Basic education: The wage premium related to basic education is insignificant across all quantiles when the entire sample is used as well as for all sub-samples.

(2) Higher education: The wage premium from higher education is very significant and positive at all quantiles for all sub-samples, except for firms with less than 40 employees, and the premium is relatively high.

(3) Occupational groups: The effect from occupational groups on wages, on the other hand, are generally very significant, and the heterogeneity both within and between these groups

> 26 See Schaffner (1998).  We define blue collar workers as quality production workers or support staff, while white collar workers include managers, adm/sales, and tech/maintenance.

20
arrival in Brazil and mean age of selected yellow race foreign-born. The mean year of arrival is much earlier for Japanese than other races, meaning that a larger proportion of their ethnicity should be second or later generation. Thus our overstatement from misclassifying on race should be considerably less than 35 percent.

The 2000 Census was then used to estimate the number of Nikkei in each municipality, área de ponderação, and census tract. An área de ponderação (AP) is the smallest geographical unit used for public reporting of the results of the Census, and consists of a grouping of census tracts. There are 1913 APs in São Paulo state and 596 in Parana state. A second source of estimation error occurs from the fact that questions on race, birthplace, and migration are only asked on the long form of the Census questionnaire, which is applied to only 10 percent of households in municipalities with more than 15,000 inhabitants, and to 20 percent of households in municipalities with less than 15,000. Therefore an additional source of prediction error arises from this sampling. This sampling error will be small at the level of a municipality and AP, but will be greater at the level of the census tract. An Appendix to the paper provides an example modeled on our survey, where the overestimation of the number of Nikkei households at the census tract level is 50 percent.

**Table 1: Yellow Race Immigrants in Brazil**

||co<br>Number|untry of birt<br>of foreign-|h<br>born by|Percent<br>Shares in 200|Percent of<br>0 Census|
|---|---|---|---|---|---|
|Country of Birth|1980|1991|2000|ofall immigrants|Yellow Race|
|Japan|139480|85572|70932|10.37|73.9|
|China|8799|8324|10301|1.51|10.7|
|South Korea|7258|8528|8578|1.25|8.9|
|Taiwan|2414|2737|4536|0.66|4.7|
|Indonesia|||693|0.10|0.72|
|Hong Kong|||376|0.05|0.39|
|Philippines|||360|0.05|0.38|
|North Korea|||66|0.01|0.07|
|Malaysia|||60|0.009|0.06|
|Macau|||27|0.004|0.03|

Source: 1980, 1991 and 2000 Brazilian Census 13

-  -
# Zble of Contents

||FISCAL STABILIZATION AND EXCHANGE RATEIN2AS LUTTY:<br>THEORETICAL APPROACH AND SOME POLICY SIMULATIONS<br>USING MEXICAN DATA||
|---|---|---|
|I.|INTRODUCTION ..................................................|...I|
|II.|EXCHANGE RATE DETERMINATION AND THE REDUCTIONOF ....<br>......<br>GOVERNMENT SPENDING:<br>BACKGROUND<br>AND INTUITION................|.......<br>5<br>...<br>5|
|_T.|THE MODEL.<br>...............................................|........<br>8|
||A. Production ................................................|... 9|
||B. Consumption ...............................................|... 13|
||C. Budget Deficits and the Adjustment of the Exchange Rate ...<br>D. Excess Demands ............................................|... 17<br>... 22|
|IV.|THE EXISTENCE OF AN EQUILIBRIUM ...............................|... 24|
|V.|AN APPLICATION TO MEXICO ......................................|... 28|
|VI.|CONCLUSION .<br>...............................................|........<br>45|
|REFE|RENCES ..........................................................|... 46|
|APPE|NDIX||
Table S.13: Description of Additional Data and Sources

|Variable|Description|Source|
|---|---|---|
|Log of Ruggedness|Log of Terrain Ruggedness Index, represent-<br>ing the average ruggedness for the road<br>buffer measured as meters of elevation dif-<br>ference for grid points 926 meters apart|Nunn and Puga (2012)|
|Log of Rainfall|Log of yearly precipitation in mm, averaged<br>monthly data from Jan 1984–Dec 2008|Harris et al. (2014)|
|Population<br>Den-<br>sity|Population Density (100 people per square<br>km), 2000|Gridded<br>Population<br>of<br>the<br>World version 3, Center for<br>International<br>Earth<br>Science<br>Information Network - CIESIN<br>-<br>Columbia<br>University,<br>and<br>Centro Internacional de Agri-<br>cultura Tropical - CIAT. 2005<br>(2005)|
|Log<br>of<br>Distance<br>to city (>100,000<br>ppl)|Log of average distance to nearest city of<br>more than 100,000 people|Esri;DeLorme Publishing Com-<br>pany, Inc. (2014)|
|Log of Distance to|Log of average distance to nearest port|World<br>Port<br>Index,<br>National|
|port||Geospatial Intelligence Agency<br>(2015)|

_Source_ : Datasets as outlined in table.

Table S.14: Summary Statistics

|Variable|Mean|Std. Dev.|Min.|Max.|N|
|---|---|---|---|---|---|
|Estimate|0.25|0.43|0|1|941|
|Contract|0.48|0.5|0|1|941|
|Actual|0.26|0.44|0|1|941|
|World Bank|0.41|0.49|0|1|866|
|Government|0.25|0.43|0|1|866|
|Bilateral Donor|0.22|0.42|0|1|866|
|Length_>_than 50 km|0.3|0.46|0|1|940|
|Missing: Length|0|0.03|0|1|941|
|East Asia & Pacifc|0.25|0.43|0|1|941|
|South Asia|0.25|0.43|0|1|941|
|Sub-Saharan Africa|0.5|0.5|0|1|941|
|Population Density|4.46|6.18|0.11|47.61|941|
|Log Ruggedness|3.45|1.29|-1.25|6.48|941|
|Log of Rainfall|4.74|0.33|3.81|5.64|941|
|Log distance to city|3.68|1.69|-3.28|6.12|941|
|Logdistance toport|5.17|1.52|-0.77|7.12|941|

_Source_ : Authors’ analysis based on data described in the text.

As many roads have a city of more than 100,000 people in the neighborhood of 30km, this variable would be zero for these observations. The results are not substantively different, but to use all the information possible we prefer to use the continuous measure of distance.

18
“public sphere,” the _gram sabha_ is hardly a place where participants engage in rational negotiation to reach a consensus with single-peaked preferences. Public deliberation in the _gram sabha_ is rather a competition between groups and individuals who want a piece of the public pie and employ a wide repertoire of discursive techniques to make their voices heard. Participants are not interested in reaching a consensus but rather seek to stake their particular claims to the “gifts” of the state.

Each petitioner argues that he or she most deserves the benefit, whether private necessities or public goods. Each strives to make the decision-makers hear and grant their plea. The result is a competition based on identity rather than reason. In this way poverty and social inequality have shaped a vernacular style of competitive discursive negotiation within _gram sabhas_ . The competition for state sponsored benefits, moreover, takes place in the context of changing governmental imperatives, which shift with amendments to policies designed to correct social and economic inequalities. Federal and state “schedules,” for instance, are used to target benefits to discriminated castes, and quantitative surveys to identify citizens living below the “poverty line.” Yet as Scott (1999) argues, the rush to classify human populations results in a Cartesian logic that forces geometric patterns on categories that are inherently fuzzy. The _gram sabha_ allows this fuzziness to be expressed and observed, if not by the “high state” far away in the state capital, then, at least, in the more proximate _gram panchayat_ . The _gram sabha_ also allows those affected by these policies to express their dismay when state categories fail to take into account the realities they are familiar with. Since one of the functions of the _gram sabha_ is to ratify BPL classifications and to voice complaints about the denial of 36
This paper reconsiders the claims about microfinance, both about its social and economic impacts on households and about microfinance institutions’ own profitability as business enterprises that serve low-income households. We argue that claims about large impacts and profits have been exaggerated, but so have claims about failures. There is important heterogeneity in both impacts and profit, and microfinance holds real appeal in some contexts, especially where communities remain fundamentally under-served.<sup>1</sup> Expectations for impacts were set high by microfinance pioneers. They argued that they were helping to overcome financing constraints for small-scale entrepreneurs and radically expanding their opportunities to earn. At minimum, they claimed, microfinance would provide capital at cheaper interest rates than moneylenders and diminish dependence on trader-lenders adept at extracting surpluses (U.S. House of Representatives 1986). But more optimistically, microfinance promised to raise income and, with newfound resources, improve education and health and empower women. In this vision, finance was seen as a tool of personal transformation, and it captured the imaginations of those seeking new modes of social and economic change (Yunus 2008).

Researchers, though, have so far failed to find sustained evidence that access to microfinance has _writ large_ done much to reduce poverty, improve living conditions, and fuel micro-businesses. Nearly all rigorous quantitative studies, beginning well before the use of randomized controlled trials, fail to fully support microfinance as a tool that can powerfully and single-handedly reduce poverty (see accounts in Armendàriz and Morduch 2010, Banerjee et al.

> 1 For related assessments, see Banerjee (2013), Field et al. (2016), and Hermes and Lensink (2011). See Beck (2015) for an assessment of microfinance within the broader contexts of financial inclusion and regulation.

3
the Mexican peso suffered a major devaluation making the exporting sector (mostly the manufacturing one) the most dynamic sector in the economy.<sup>4</sup> Between 1994 and 1996 the importance of international trade in the Mexican economy almost doubled, passing from a pre-crisis/NAFTA level of 38 percent to 63 percent in 1996.

<!-- Start of picture text --> Figure 3: Sectoral Composition<br>1990 1992 1994 1996 1998 2000<br>Year...<br>Services Manufacturing<br>Data source: INEGI<br>.2<br>.775<br>.77<br>.195<br>.765<br>.19<br>.76<br>.755 .185<br>Services/GDP .75<br>.18<br>Manufacturing/GDP<br>.745<br>.175<br>.74<br>.735 .17<br><!-- End of picture text --> The peso devaluation together with the trade opportunities brought about by NAFTA, had a significant sectoral redistribution impact on the Mexican economy. The proportion of GDP that was generated in the tradable manufacturing sector experienced an average annual expansion of 3 percent between 1994 and 1998. By the year 2000 the manufacturing sector accounted for 20 percent of the economy compared with a ratio of 17.5 percent in 1994. The counterpart of this increase was a reduction of the same scale in the proportion of GDP that was generated in the service sector, passing from 77 per cent in 1994 to 75 percent in 2000 (see Figure 3).

# **2.2 Inequality Levels**

The huge macroeconomic turbulence affected microeconomic agents (i.e. households, firms and individuals) especially through changes in overall income distribution. In this section, we will describe the distributional changes that took place during the 1990s and propose possible explanations for them. In the following section, we will quantify how much inequality is accounted for by these possible explanations.

> 4Between 1994 and 2000 manufacturing exports accounted for 95 percent of total exports.

6