rafmacalaba/datause-encoder-data huggingface dataset:
Friedberg, Rachel. 2000. “You Can’t Take it with You: Immigrant Assimilation and Portability of Human Capital: Evidence from Israel.” Journal of Labor Economics, 18(2): 221251.

Funkhouser, Edward and Stephen J. Trejo. 1995. “The Labor Market Skills of Recent Male Immigrants: Evidence from the Current Population Survey.” Industrial and Labor Relations Review, 48(4): 792-811.

Green, David A. 1999. “Immigrant Occupational Attainment: Assimilation and Mobility over Time.” Journal of Labor Economics, 17(1): 49-77.

Jasso, Guillermina and Mark R. Rosenzweig. 1982. “Estimating the Emigration Rates of Legal Immigrants Using Administrative and Survey Data: The 1971 Cohort of Immigrants to the United States.” Demography, 19(3): 279-290.

Jasso, Guillermina and Mark R. Rosenzweig. 1986. “What’s in a Name? Country-ofOrigin Influences on the Earnings of Immigrants in the United States.” In: Research in Human Capital and Development vol. 4, Oded Stark (ed.), Greenwich.

Jasso, Guillermina and Mark R. Rosenzweig. 1988. “How Well Do U.S. Immigrants Do? Vintage Effects, Emigration Selectivity, and Occupational Mobility.” Research in Population Economics, 6:229-253.

Jasso, Guillermina and Mark R. Rosenzweig. 1995. “Do Immigrants Screened for Skills So Better than Family Reunification Immigrants?” International Migration Review, 29(1): 85111.

Jasso, Guillermina Mark R. Rosenzweig and James P. Smith. 2000. “The Changing Skill of New Immigrants to the United States: Recent Trends and Determinants.” in G. Borjas (ed.), Issues in the Economics of Immigration. Chicago, IL: University of Chicago Press.

Heston, Alan, Robert Summers and Bettina Aten. 2006. Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, September.

Hu, Wei-Yin. 2000. “Immigrant Earnings Assimilation: Estimates from Longitudinal Data.” The American Economic Review, 90(2): 368-372.

Kim, Seik. 2009a. “Economic Assimilation of Foreign-Born Workers in the United States: An Overlapping Rotation Panel Analysis.” Working Paper, University of Washington.

Kim, Seik. 2009b. “Sample Attrition in the Presence of Population Attrition.” Working Paper, University of Washington.

LaLonde, Robert J. and Robert H. Topel. 1990. “The Assimilation of Immigrants in the US Labor Markets.” 1990. NBER Working Paper No. 3573.

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China, Argentina and Canada each increases its OTRI by 0.3 percentage points. Such increases in the overall level of tari¤ protection could signi...cantly disrupt trade if imports are elastic. Back of an envelope calculations suggest that, once we take into account the import demand elasticities of these countries, increases in OTRI_BE in Malawi, Russia and Turkey jointly may have led imports to drop by US$6.7 billion. The trade impact of Canada, China and Argentina is even larger, close to US$7 billion.

Countries that do not raise their MFN or bilateral tari¤s are not necessarily less protectionist. In fact, there is evidence suggesting that during the crisis period, countries such as the USA, the EU and India actively levied AD duties on their partners to protect domestic producers. Based on data from the Global Anti-dumping Database, we calculate the change in OTRI_AD for a group of 13 countries where data are available. Given that AD duties are imposed at the tari¤ line level, which for many countries is at the 8 or 10 digit HS level, we ...rst need to identify the share of these goods in each HS 6 category in the bilateral trade of each of the 13 countries, and only impose AD duties on the goods a¤ected. In doing so, we avoid imposing AD duties on all HS 8 goods within the HS 6 categories, even though we are still making the assumption that AD duties a¤ect all bilateral trade within HS 8 goods and are not distinguishable among di¤erent ...rms that export. For some countries, such as Turkey and India, only a portion of AD cases have information on the actual AD duties imposed (see Table 2 last column). For the missing AD duties, we use our bilateral import demand elasticity estimates to infer the minimum prohibitive AD duties.

Figure 4 compares OTRI_BE in 2008 to OTRI_AD in 2009, where OTRI_AD is OTRI_BE with AD included. For the most part, adding AD does not change the results in Figure 3. However, for selected economies, the di¤erences are signi...cant. Incorporating AD duties during the crisis period increases the OTRI_BE of the US by half a percentage point. This seemingly small number 12
# **1 Introduction**

Epidemics and pandemics, including the Coronavirus Disease 2019 (COVID-19), have large impacts on human life and livelihoods. The 1918 influenza, for example, caused large declines in economic output as many productive workers died (Fenske, Gupta, and Yuan 2020; Tumbe 2020). During a pandemic, governments typically intervene to slow its spread, as well as to mitigate the economic impact and revive activity. Thus, understanding the impact of government restrictions on reviving or hindering economic activity is important.

This paper estimates the impact of differential containment policies implemented by the Government of India (GoI) during the COVID-19 pandemic on aggregate economic activity. The first COVID-19 infection in India was reported at the end of January 2020. In March, the GoI implemented one of the most stringent lockdowns globally (Hale et al. 2020). After five weeks of nationwide lockdown, uniform restrictions were replaced with targeted measures that introduced variation across districts in May. The differential relaxation of restrictions across districts permits us to examine the impact of the heterogeneous containment measures. Districts were classified into three zones: those with the most severe restrictions (Red), those with intermediate restrictions (Orange), and those with the least severe ones (Green). Using a difference-indifferences approach, we compare the speed of recovery in the three zones after the uniform national lockdown. Using monthly data of man-made nighttime lights at the district level, we examine the impact of these differential relaxations of restrictions on aggregate economic activity. We also examine the period after the differential lockdown to understand whether the containment measures yielded subsequent gains in economic activity. We validate and explain the aggregate impact by analyzing mobility patterns across districts and household income and consumption. In addition, we compare the impact across different districts by accounting for their population density, the share of services sector employment, outstanding credit per capita, and the average age.

We use nighttime light intensity as our main proxy of economic activity for a number of reasons. First, a number of studies have reported the high correlation of man-made nighttime light intensity with other measures of economic activity (Donaldson and Storeygard 2016). Most notably, Henderson et al. (2012) argue that for countries with poor national income accounts, the optimal estimate of growth is a composite measure with roughly equal weights on conventionally measured growth and growth predicted from nighttime lights. Nighttime lights track economic activity in India closely (Prakash et al. 2019; Beyer et al. 2020), and provide a useful approximation of economic activity at high spatial granularity (Gibson et al. 2017; Chanda and Kabiraj 2020). In a recent study, Chodorow-Reich et al. (2020) use cross-sectional differences 2
executive not only with regard to the execution and implementation of policies but also with regard to the preparation of policies. This is why, paraphrasing Lees’ definition of legislative oversight, we refer to legislative oversight of the executive as to “the behavior of legislators and their staff which affects executive behavior”<sup>10</sup> .

In overseeing the executive, Parliaments have several different oversight tools at their disposal. The most common oversight tools are committee hearings, hearing in plenary sessions of the parliament, the creation of commissions of inquiry, questions, question time, interpellations, the ombudsman, auditors general, and the public account committees<sup>11</sup> .

These oversight tools can be grouped along two dimensions. The first dimension pertains to the timing of the oversight activity. If legislative oversight is performed before the government enacts a specific policy or becomes engaged in a specific activity, then the oversight tools are “instruments of control ex ante”<sup>12</sup> . Hearings in committees, hearings in the plenary sessions of the Parliament, along with the request of documentation are all tools that can be used ex ante. If the legislative oversight is performed after the government has enacted a policy to check whether the policy is properly implemented, then the oversight tools are instruments of control ex post.

Questions, interpellations, the creation of committees of inquiry are the tools that are

> 10 Lees defined oversight as “the behavior by legislators and their staffs, individually or collectively, which results in an impact, intended or not, on bureaucratic behavior”, see John D. Lees, “Legislatures and Oversight: A Review Article on a Neglected Area of Research”, _Legislative Studies Quarterly_ , vol. 2, n. 2, (May) 1977, pp. 193-208.

> 11 A description of some of these tools can be found in National Democratic Institute, “Strengthening Legislative Capacity in Legislative-Executive Relations”, Legislative Research Series, Paper # 6, Washington, DC, 2000, especially pp. 19-32.

4
be a result of the targeting strategy, the enrollment prioritizing mechanisms of the Program, or the self-selection of beneficiaries. Using the Wooldrige robust regression-based test we conclude that _p_ pam_ is indeed an endogenous variable for the extreme poverty headcount, the extreme poverty gap, and the extreme poverty severity indexes at least at the 5% significance level, and at the 10% level for the multidimensional extreme poverty headcount index.

The same procedure was followed with the labor supply variables, including labor force participation and hours worked in the previous week. In this case, the Wooldrige test suggests that the individual’s labor supply decisions and expectations are exogenous.

Using OLS and equation (3) with _Y = labor supply_ , we estimate the effects of the expansion of PAM on labor supply variables. OLS results suggest that the expansion of PAM did not have an effect on labor supply, consistent with what we found in the ITT estimations. Even though OLS estimates are more efficient than the IV estimates in this case, results from OLS by themselves should be taken with caution. The OLS estimates of the expansion of the program on labor supply is hardly rigorous evidence that the program did or did not have an effect on labor supply of the treatment group because we are not controlling for unobserved characteristics, which in this case may be a concern (e.g. self-selection bias).

_Table 10: IV First-Stage Regression_

||_p_pam_|
|---|---|
|Treat*year 2014|0.451***<br>[-0.014]|
|Year 2014|-0.116***|
||[-0.042]|
|Treat|0.007**<br>[-0.003]|
|Observations|13,120|

F (63, 13056) = 29.4 Prob > F = 0.00 _Table 11: Treatment-on-the-Treated (TOT) Effect of PAM on Extreme Poverty IV Second-Stage Regression_

|||_Poverty_|_Measures_||
|---|---|---|---|---|
||**Head Count Index**|**FGT1**|**FGT2**|**Multi-dimensional**|
|p_pam|-0.115**|-0.079***|-0.061***|-0.132***|
||[-0.045]|[-0.022]|[-0.016]|[-0.033]|
|year2014|-0.053|0.000|-0.013|0.005|
||[-0.082]|[-0.038]|[-0.029]|[-0.067]|
|treat|0.039**|0.021***|0.015***|0.009|
||[-0.016]|[-0.008]|[-0.006]|[-0.012]|
|Observations|13,120|13,120|13,120|13,120|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in brackets. Standard errors heteroscedasticityrobust and clustered at the primary sampling unit level, which takes into consideration the sampling design of the survey. Income variables used were adjusted to per adult-equivalent at August 2014 prices. The following conditioning variables were used: village size range, years of schooling, literacy, household size, dwelling’s ownership status, sex, indigenouos language speaker status, disability condition, household structure, marital status, access to Seguro Popular, income from Conditional Cash Transfer Program - PROSPERA, income from PROCAMPO, overcrowding, access to drinking water, access to sewage, access to electricity, and a full set of the preceding covariates interacted with the time dummy.

26
# Dual and Multiple Exchange Rate Systems in Developing Countries: Some Empirical Evidence*

by Nita Ghei** and Miguel A. Kiguel***

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|A Model for a Dual Exchange Rate System|3|
|III.|The Empirical Estimation|6|
||a. Evidence from Individual Countries<br>b. Additional Cross Country Evidence|7<br>18|
|IV.|Insulation Properties on the Official Exchange Rate and Domestic Prices|20|
||a. The Impact of the Spread in Setting the Rate of Devaluation<br>b. Insulation on Domestic Prices|21<br>24|
|V.|Summary and Conclusions|_5|
|Ref|erences|28|
|App|endix Table 1: Parallel Market Premium|30|
|App|endix Table 2: Sample for Quarterly Estimation|31|

* This paper is part of the CECMG project on "Macroeconomic Implications of Multiple Exchange Markets in Developing Countries."

- * The World Bank and University of Maryland, College Park

- ** * The World Bank
employment in establishments with fewer than 10 workers are highly correlated, with a correlation coefficient of –0.82.<sup>12</sup> The last column in Table 3 reports the share of total employment in firms with foreign-ownership for each state in Mexico. The share of foreign-owned firms in total employment ranges from as low as 11 percent in Nuevo Leon to as high as 52 percent in Chihuahua. On average, about 24 percent of employees are employed by firms with foreign ownership. These employees are concentrated in the large firms.

# _Legal Institutions_

The second major component of our data is the quality of legal institutions. These come from a survey conducted under the direction of the Center for the Study of Law at the Instituto Tecnológico Autónomo de México (ITAM/GMA 1998). The ITAM project focused on collection of bank debt through local courts in each of Mexico’s 32 federal entities. Bank debt was chosen as the focus of the ITAM/GMA study because banks are centralized, but must collect debts in the location of the debtor; that is, they must operate in the courts of each state. From our perspective, the focus of the study on the legal enforcement of financial contracts is fortunate, since finance is clearly an important channel trough which the legal system affects firm growth. The data gathered come from

> 12 The use of plant level rather than enterprise level data has a clear impact on the weighted average firm size. The impact on the percentage of employment in firms with fewer than 10 employees is less clear. Firms with operations in multiple states are also likely to have operations which clear our minimum size level (10 employees) in each state in which they operate. If so, then they will show up as medium rather than small size even in our data, and will not affect the results using this measure of firm size. However, if there are a significant number of firms with multiple very small operations, either within or across states, then these will bias the data against finding the pattern we are looking for, because such small operations that are part of larger firms are unlikely to suffer to the same extent from a weak legal environment as small independent firms do. These divisions of large firms are likely to benefit from the increased access to finance of large firms and possibly from the internal capital markets of large firms (Lamont 1997 and Shin and Stulz 1998). Large firms are also generally thought to be able to deal better with an inefficient judicial system because they are better connected. To the extent this is true, every division of a large firm is expected to benefit from these connections.

22
was limited and spillovers were mostly expected in rural areas with a relatively higher coverage.

For all specifications, we use double-selection Lasso to include relevant baseline controls, starting with the full set of variables in balance Table A5 (for direct effects) or Table A6 (for local spillovers). To account for multiple hypothesis testing, we also calculate p-values controlling for the false discovery rate (FDR) following the step-up approach (Benjamini and Hochberg, 1995). The adjusted p values for outcomes in the main tables are presented in Table A9. Results are generally robust, but we note deviations in the text.

Lastly, we analyze treatment heterogeneity by estimating specifications in equations 1 and 2 with an added linear interaction between the treatment variable and the group of interest (and a control for the group of interest). We do so for rural areas (as this dimension is used in the stratified randomization), as well as for key subgroups of program participants such as women, youth and natives.

# **3.2 Timeline and Surveys**

**Enrollment and Baseline survey.** Figure 2 summarizes the study timeline. Enrollment took place in the 207 sample localities between December 2015 and January 2016. The NGO visited each locality at least twice. First, it organized a public meeting to describe the program and explain eligibility criteria. Second, the NGO collected enrollment (baseline) data for individuals interested in the program.<sup>24</sup> Baseline data was collected for 14,880 (interested) individuals, and 12,692 fulfilled the eligibility criteria.

As explained above, and key to the integrity of the experimental design, baseline data was collected prior to assigning localities to the three treatment and control groups. The baseline questionnaire included simple variables on employment, assets, education, and household characteristics.

The second public lottery took place in March 2016. The list of the 5,116 selected beneficiaries was publicly released in each locality between July and September 2016.<sup>25</sup>

> 24The baseline instrument was designed by the research team. NGO staff were trained as enumerators. An independent team of experienced enumerators was hired to perform field supervision and data quality checks. A double-blind data-entry process was set-up.

> 25The lag between the second lottery and the release of the beneficiary lists was due to delays in the

13
the sample and the results of the regressions are very similar when they are dropped.<sup>10</sup> As expected, _non-NGO/NBFI status_ is strongly positively linked to our primary supervisory dummy variable, onsite supervision at regular intervals (correlation .40, p-value 0.000). Because charter status was determined at the outset of the creation of each MFI, prior to and without substantial consideration for whether the MFI would face supervision, _nonNGO/NBFI status_ can be viewed as exogenous. In some specifications, we interact _nonNGO/NBFI status_ with the _Big Bank Supervision_ variable or the _OS index_ .<sup>11</sup> The intuition behind the interaction is that supervision should be especially likely for non-NGO/NBFI MFIs located in countries where the general propensity to regulate and supervise providers of financial services is high.

As noted by CLR (2003), prudential supervision should generally be triggered when an MFI accepts retail deposits from the general public so as to safeguard the savings of relatively poor depositors. We therefore use a dummy variable indicating whether an MFI accepts retail deposits ( _savings dummy)_ as our second MFI-level instrument.12 Like _non-NGO/NBFI status_ , we presume that the decision to accept deposits was, in most instances, taken at the outset of the creation of the MFI. Also similar to _non-NGO/NBFI status_ , we interact the _savings dummy_ with the _Big Banks Supervision_ variable and the _OS index_ . MFIs that accept deposits and operate in countries with a high propensity to regulate should be among those most likely to face supervision.<sup>13</sup> Most of the control variables in the _X_ matrix in equation (1) are the same as those used in other studies of MFI performance and outreach (Ahlin and Lin, 2006; Cull, Demirgüç-Kunt, and Morduch, 2007). One new variable is _Premium_ , the difference

> 10 Dropping credit unions and cooperatives from the regressions reduces the sample by only 8-13 observations depending on the specification. Credit unions and cooperatives are mutuals with an objective to achieve operational sustainability (returning any surplus to members), rather than seeking profit in order to remunerate shareholders as in the case of private banks. Regression results for the sample that excludes the credit unions and cooperatives are available from the authors.

> 11 The correlation between _non-NGO/NBFI status_ * _Big Bank Supervision_ and regular onsite supervision is 0.51 (p-value 0.000). The correlation between _non-NGO/NBFI status_ * _OS index_ and regular onsite supervisions is .43 (p-value 0.000).

> 12 The correlation between the savings dummy and regular onsite supervision is 0.49 (p-value 0.000).

> 13  The correlation between _Savings*Big Banks Supervision_ and regular onsite supervision is 0.44 (p-value

> 0.000). The correlation between _Savings*OS index_ and regular onsite supervision is 0.48 (p-value 0.000).

15
the location, time, and percentage juvenile caught by non-SNP sets. After obtaining length distributions for all sets in the electronic logbook data, I match sets to landings events. I then use the percentage juvenile measured by third-party inspectors at landing to update length distributions in the electronic logbook data and calculate juvenile catch for each set.

Specifically, I first identify sets in the full electronic logbook data (reported to the regulator) that are also in the SNP data based on unique vessel identifiers and the time each set occurred. I calculate the number of individual anchoveta (both juveniles and adults) caught by these sets based on their length distribution and tons caught. When percentage juvenile for a set in the SNP data does not match its counterpart in the full electronic logbook data (e.g., the vessel reported a different percentage juvenile to its owner than to the regulator), I shift the length distribution up or down in half-cm increments until I minimize the absolute difference between the implied percentage juvenile (percentage of individuals that are less than 12 cm, as implied by the updated length distribution) and the percentage juvenile reported to the regulator (i.e., a one unit shift of the length distribution in either direction would result in a larger absolute difference between implied and reported percentage juvenile).

I then impute length distributions for non-SNP sets as follows. For each two-weekof-sample by two-degree grid cell, I calculate the individuals-weighted average proportion of individuals in each half-cm length interval caught by SNP sets. Given the percentage juvenile value for each non-SNP set, I adjust the length distribution for that set’s two-week-of-sample by two-degree grid cell to match the set’s percentage juvenile value. For sets with percentage juvenile above (below) the individuals-weighted average percentage juvenile for their twoweek-of-sample by two-degree grid cell, I inflate (deflate) the proportion of individuals below 12 cm and deflate (inflate) the proportion of individuals above 12 cm so that the imputed length distribution for each set implies a percentage juvenile equal to the percentage juvenile reported for that set.<sup>44</sup> My results are robust to imputing length distributions at the two-

> 4496.5% of non-SNP sets occur in a two-week-of-sample by two-degree grid cell that also contains SNP sets. For the remaining 3.5% of non-SNP sets, I use the average length distribution at the two-week-of-sample

34