rafmacalaba/datause-encoder-data huggingface dataset:
25 serious crisis of the external sector. However, no serious measures aimed at solving these problems were undertaken. For instance, it was argued that it was not convenient to reduce government expenditures, the policy of granting salary increases that exceeded inflation was maintained, and a significant devaluation was ruled out.<sup>17</sup> The combination of generalized price controls and unchecked fiscal and monetary policies acted as a major encouragement for the black market economy. What made things even worse was that the government completely lost control over wages. Unions in both nationalized and private firms demanded increasingly higher wage adjustments. This produced a dilemma for the government. If it had refused to grant the wage adjustments, it would help the macroeconomy but deeply hurt its political constituency and objectives. Under these circumstances, the government chose, every time, to uphold its revolutionary label. Moreover, a number of economists argued that higher real wages could be sustained as long as the government was able, via increased controls, to extract additional "surplus" from the private sector.<sup>18</sup> By mid-1972, it became apparent that the February stabilization program was a failure. The underground economy was now generalized, output began to fall, open inflation reached an annual rate of 70 percent in the second quarter, foreign exchange reserves were only $82 million, and the black market rate was climbing at a very fast pace (see Figure 2).

17In 1971 a small devaluation was followed by the instituion of four separate exchange rates. 18See Bitar (1979).
the regression estimates of $62,000-69,000 are a little bit lower than the simple difference in means of $76,000-77,000. Nevertheless, the simple difference in means is still well within the confidence intervals for the regression estimates even in this case.

Finally, we attempted to construct instrumental variables for migration. We examined three classes of potential instruments. The first was macroeconomic shocks and political events such as coups; the second was birth-order; and the third was shocks such as parental illness and extreme weather events that occurred when the individual was aged 18 to 22 (the prime age for migration in the sample). The latter two categories of variables were only collected for Ghana and Micronesia. We found these types of variables had very low predictive power for predicting migrant status, with the only (weakly) significant first-stage being in Micronesia, where individuals who experienced a typhoon in their home region when aged 18 to 22 were more likely to have migrated (F-statistic of 3.63). The two-stage least squares estimate using this as an instrument gave a similar income gain to that obtained using the other two approaches.

The estimates in Table 2 give the gross income gain from migration at market exchange rates. In Table 3 we examine how these gains change once we allow for cost of living differences and for net, rather than gross, income. The first column repeats the estimate of the gross income gain in U.S. dollars at the market exchange rate, based on regression estimate 2 in Table 2. We do not believe that the International Comparison Program (ICP) PPP rates are the appropriate adjustment for cost-of-living differences among the countries in our study. First, Papua New Guinea, Tonga and Micronesia were not covered by the ICP, and the model used to impute PPP rates for countries not directly surveyed takes no account of population size, remoteness, or ruggedness of terrain, all of which serve to increase prices.<sup>9</sup> Second, the goods and services demanded by these high-earning individuals are likely to differ substantially from the basket of goods used for calculating PPP. For these reasons we consider an alternative cost-of-living adjustment.

The cost-of-living adjustment we use comes from custom tables kindly provided by Xpatulator.com, a commercial service that collects cost of living in 276 global locations, which it uses to assist companies in determining expatriate pay levels and international salary levels in

> 9 International Comparison Program (2008).

11
# Sources to Graph 1.

1. World Bank, February 1975, Stat. Appendix Table 1.2, 1962-72. World Bank, 1982, Annex II, Table 1.2, 1972-80. World Bank, int. information, 1977-84. OECD,Economic Surveys, Turkey, 1987, Table 3, 198U-86.

2 and 3. Statistical Yearbook of Turkey, several issues

- 4 and.5. See Table 1.4;.-..

6. OECD, Main Economic Indicators, According to Yearbook of Labor Statistics, ILO, 1987, this series shows registered unemployed-at end of year at Placement Offices.

-25-
The production processes and organizational technologies of the worker’s employer can be captured by the type of industry the worker is employed in. Decker and others (2014) and Hyatt <u>and Spletzer (2013)</u> find that changes in industry composition result in changes in working practices, which, in turn, may reduce tenure. Country characteristics and unemployment rates also affect the probability of a job’s termination.

Our estimation strategy is based on the two-stage approach proposed by Jager and Huff Stevens <u>(1999) for the United States and Gregg and Wadsworth (2002) for the United Kingdom.</u><sup>14</sup> In the first stage, we estimate the separate probabilities of a worker having short ( _T_<sup>_*_</sup> < 1 year), medium (5 < _T_<sup>_*_</sup> ≤ 10 years), or long ( _T_<sup>_*_</sup> > 10 years) tenure as a function of **X** , a vector of individual characteristics, **E** a vector of job characteristics, and a set of country dummies: P P + ∑ + ∑29 + ∑2019 Y �. (2) KK kk JJ jj ii kk=1 kk ii jj=1 jj ii aa=1 aa ii tt=1995 tt ii ii (TT <> TT<sup>∗</sup> ) = ff�∑ αα XX αα EE γγ CCPPCCnnCCPPCC ππ aaPP + εε We use a binary probit model to estimate equation (2). Consistent with previous studies, we estimate the probability of being on the job for less than a year on the sample of individuals 20 and older. The probability of being on the job for more than five years is estimated on the sample of individuals 25 and older, and the probability of job tenure exceeding 10 years is estimated on the sample of individuals 30 and older.<sup>15</sup> To account for the effect of individual characteristics on job tenure, we include in **X** a dummy for the worker’s gender, the worker’s age and age squared, education-level dummies, a dummy if a worker is married, the share of children, and household size. Regarding job characteristics, we include a dummy for the nature of the job contract (temporary or permanent) and whether the job is full- or part-time. A temporary job could capture the differential ability of firms to release workers (Gregg and Wadsworth 2002). Workers employed part-time or self-employed are more likely to break their tenure than full-time workers (Garcia-Cabo and Mudero 2019). We control for occupation by a set of dummies based on the two-digit International Standard Classification of

> 14 <u>Bratberg, Salvanes, and Vaage (2010)</u> used a similar methodology to analyze changes in job stability in Norway. 15 Data on tenure derived from the recall questions on the year when a worker started her current job might contain systematic errors correlated with the duration of tenure (Duncan and Hill 1985, <u>Bollinger et al. 2019). These errors</u> might bias estimates of tenure duration. We are unaware of the analysis of such biases for the EU LFS, but typically these errors could lead to a slight overestimation of the longer-term tenure, which, qualitatively, should not affect the main conclusions of our analysis.

16
**Table 24 Proportion of own anualized lifetime earnings**<sup>**replaced by different hybrid**</sup> **pension systems, when contributions are based on**<sup>**own income and joint income**</sup> in **marriage, ranked by educational experience**

|EDU|POP|PAYG=O<br>DC=20|PAYG=5<br>DC=15|PAYG=10<br>DC=10|PAYG=15<br>DC=5|PAYG=20<br>DC=0|
|---|---|---|---|---|---|---|
|Own C|ontributio|ns|||||
|EDUl|ALL|.60|.67|.74|.81|.88|
|EDU2|ALL|.58|.61|.64|.67|.69|
|EDU3|ALL|.57|.55|.53|.51|.49|
|Joint Co|ntributio|ns|||||
|EDU1|ALL|**.75**|.72|.82|.85|.88|
|EDU2|ALL|.68|.68|.69|.69|.69|
|EDU3|ALL|.58<br>-|.56|.53|.51|.49|

Given that we would expect earnings to be related .o educational<sup>acheivement the</sup> results are not suprising, mirroring the trends described<sup>above. The greater the</sup> dependency on past earnings the lower the replacement<sup>rate for the less well</sup> educated. The most striking feature of the tables is the split<sup>between individuals with</sup> a university level education and all others. Those who have<sup>experienced higher</sup> education have much higher average lifetime earnings and<sup>so they face a lower</sup> replacement rate relative to _own_ earnings but a much higher rate<sup>relative to average</sup> male earnings.

The effect of joint contributions has limited impact on replacement<sup>rates relative to</sup> average wage. Its effect is more marked with regard to own lifetime wage.<sup>Since the</sup> proportionate change in pension income is much greater for those<sup>with low lifetime</sup> average earnings (i.e. wives) than high (i.e. husbands) the<sup>net effect of joint</sup> contributions is to raise the average replacement rate of each educational<sup>group. The</sup> replacement rate of persons with higher education is least sensitive<sup>to the sharing</sup> assumotion in part because that group contains fewe women<sup>than the other</sup> categories.

69
children at home.<sup>8</sup> This was not coincidental: The test applied in children’s homes built directly on the school-based test, although the questions in the test applied at home were on average somewhat easier (to account for the fact that some children would have dropped out of school after completing 6<sup>th</sup> grade). The Cronbach alpha values for all of our tests are reasonably high—0.74 for the home-based mathematics test, 0.86 for the vocabulary test, and 0.79 for the in-class mathematics test.<sup>9</sup> We begin by showing that test scores are associated with years of schooling. Specifically, we use the tests taken in the household survey and limit the sample to children who were not offered scholarships (non-recipients). Test scores are normalized so that they have mean zero and a standard deviation of one (in the sample of non-recipients). We then graph the distribution of test scores of non-recipients who dropped out before completing 7<sup>th</sup> grade (solid lines), and those who had completed 7<sup>th</sup> grade at the time of the follow-up survey (dashed lines). Figure 1 shows that, as expected, children who have completed more schooling have higher test scores—in both mathematics (left-hand panel) and vocabulary (righthand panel). This difference in distributions does _not_ have a causal interpretation. Children who dropped out of school before completing 7<sup>th</sup> grade may have lower ability than those who stayed in school, in which case the difference in the distributions could primarily reflect the impact of ability on test scores, rather than the impact of the additional schooling. Nevertheless, Figure 1 is reassuring in that it shows that our tests are not simply noise, and makes it at least plausible that CSP recipients, whose schooling levels are higher than they would have been in the absence of the program (as we show below), would also have higher test scores.

# **3. Identification strategy**

The basic identification strategy we use in this paper is based on regression discontinuity (RD).

These regressions take the following form:

> 8 Of the 24 questions 19 were multiple choice with the remainder requiring the student to enter a single response to the question.

> 9 Cronbach’s alpha is the average covariance across the item responses, divided by the sum of the average variance of all items plus the average covariance across all items. It is a widely used measure of the reliability of a test.

10
**Figure 3:  Logistics Performance is Mediocre**

(Logistics Performance Index, higher is better) <!-- Start of picture text --> 5.0<br>Top in the World<br>Top in Sub-saharan Africa<br>Median in Sub-saharan Africa<br>4.5<br>Nigeria<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>Overall Customs Infrastructure International Logistics Tracking & Logistics Timeliness<br>Shipments Competence Tracing Costs<br><!-- End of picture text --> _Source_ : World Bank (2007b).

A third area besides the trade regime and trade transactions costs that warrants the attention of policy makers is export promotion. Both market and government failures tend to afflict developing countries as they seek to expand exports and growth. Trade policies that provide low-tariffs are rarely sufficient to prompt dynamic export drives or overcome obstacles in other areas. In many cases, these constraints to competitiveness impinge more on higher quality and differentiated products and require specific interventions and institutions. A successful export diversification strategy should not just focus on developing products, but also look for ways to exploit new market opportunities for existing export goods.

Nigeria has pursued a number of initiatives to encourage non-oil exports in the past, including the establishment of Special Economic Zones, the operation of a duty drawback system, as well as bank guarantees and direct lending to facilitate exports (World Bank, 2007). Yet, the most prominent export incentive system that has been used in Nigeria in evolving forms since 1986 is the export expansion grant (EEG) program. This program subsidizes exports of qualifying companies through the issuance by Customs of negotiated certificates that can be redeemed 26
# **1. Introduction**

COVID-19 has spread relentlessly across the world, considerably affecting nearly all aspects of livelihood nearly everywhere. The health crisis quickly led to an economic crisis, forcing governments to issue support measures for households and the private sector to help cope. This paper complements the growing research into whether these support measures were useful. In this paper, we study whether the support measures provided during the COVID-19 pandemic by the governments of two rather distinct countries, El Salvador and Georgia, were effective in terms of some measures of the private sector performance. We use firm-level data from the World Bank’s Enterprise Surveys (ES) that were completed pre-pandemic, and two rounds of COVID Followup Surveys (COV-ES) which recontacted all the respondents of the ES to collect data about firms’ sales, employment, finances, and government support.<sup>2</sup> The ES are representative samples of each country’s private sector enabling analysis of patterns affecting a large part of each country’s economy.

We use data from only two countries, El Salvador and Georgia, to help alleviate concerns about selection bias, which are central to the estimation of effectiveness. Unlike in many others, the support measures in these two countries were not conditional on a specific set of firm outcomes during the pandemic. Given that fiscal capacity is not unlimited, governments across the world tried to target the neediest and often created eligibility criteria for support measures. These criteria often included specific parameters from the firms’ experience during the pandemic, such as a drop in revenue, or workforce, etc. While this may be a good way to target the neediest, it greatly complicates estimation of the effectiveness of the support as it becomes nearly impossible to form appropriate groups of firms to compare their performance. The Governments of El Salvador and Georgia also tried to target the neediest, but they used pre-pandemic fixed characteristics such as firm size and sector. In selecting the set of countries, we were also mindful of the number of firms that received government support in the COV-ES data. A lower bound on the number of observations and the nature of eligibility criteria for government support narrowed down the set of countries we could study to just El Salvador and Georgia.

> 2 Both ES and COV-ES data sets are publicly available on data portal: <u>https://login.enterprisesurveys.org/. As of June</u> 2021, COV-ES has covered 44 countries, some of which have three rounds of surveys available. For further information on COV-ES, please visit https://www.enterprisesurveys.org/en/covid-19.

2
This argument implies that there are not likely to be “quick wins” of the sort identified by de Soto. Among other practical problems that reduce the seemingly large gains that could be realized are the following. First, titling is often a costly process. It is not just a matter of formalizing informal arrangements that already exist. Very often, contradictory claims of ownership succeed the announcements of titling programs. As Woodruff (2001) shows, the costs of adjudicating these claims may abrogate the gains from titling. Second, an apparent paradox accompanies any titling program for informal residents. Much of the land on which informal houses are built is obtained through illegal squatting on private property without any compensation paid to existing owners. Therefore, any titling program will have to consider providing amnesty for those who benefited from invasions. Whether such a process will result in greater respect for property rights is open to question. Third, as LeeSmith’s (1997) analysis of property contracts in Kenya shows, this contract’s value depends in large part on other existing and often unwritten contracts such as the degree of access women are allowed to exercise with respect to property. This broader web of societal contracts may well reduce the value given to property titles in isolation.

Besides, there is also less value to a title if it cannot offer value as collateral. Such a result occurs in most of Sub-Saharan Africa – and many other parts of the developing world – because there is no effective formal financial system that can help realize the value of the collateral. Moreover, even if a formal financial sector is functioning, very often many of those who live in informal housing are self-employed or work in the informal sector, with the result that it is difficult for them to show proof of income – a necessary condition to obtaining credit from formal financial institutions. The result is that in most of the developing world the collateral value of property title remains low.

These problems do not mean that housing policy should not include policies that improve tenure and the legal framework for individual ownership. Rather, it is to say that it 22
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23
more homogeneity, again, for good or bad.<sup>7</sup> Finally, emigration increases the home country population’s exposure to democratic values and norms, be it directly, through contacts with return migrants and relatives abroad, or indirectly, through the broader scope of migration and diaspora networks. Such networks have been shown to foster trade (Gould, 1994, Rauch and Trindade, 2002, Rauch and Casella, 2003, Iranzo and Peri, 2009) and FDI in‡ows (Kugler and Rapoport, 2007, Javorcik et al., 2011) and to contribute to the di¤usion of technology (Kerr, 2008, Agrawal et al., 2011) as well as to the transfer of norms of low fertility (Fargues, 2007, Beine, Docquier and Schi¤, 2008) and, in the case of foreign students, to the di¤usion of democracy (Spilimbergo, 2009). In particular, Spilimbergo (2009) shows that foreign-trained individuals promote democracy at home, but only if foreign education is acquired in democratic countries. While he does not identify the exact mechanisms through which such an in‡uence may materialize, he suggests a number of possibilities (e.g., access to foreign media, acquisition of norms and values while abroad that di¤use at home upon return, etc.) that can be generalized to other migration experiences.

Two recent micro studies come in support of this claim. The ...rst context we report on is Cape Verde, a nine-island tropical country o¤ the coast of West Africa with a population of half a million, good institutional scores by African standards, and a long tradition of migration (current migrants represent one-...fth of the population, and skilled emigration rates are extremely high).<sup>8</sup> In this context, Batista and Vicente (2011) set up a "voting experiment" along the following lines: following a survey on perceived corruption in public services, respondents were asked to mail a pre-stamped postcard if they wanted the results of the survey to be made publicly available in the national media. Controlling for individual, household and locality characteristics, they regressed participation in the voting experiment, which they interpret as demand for accountability, on migration prevalence at the locality level. They show that current

> 7In the penultimate paragraph of their article on "arti...cial states", Alesina, Easterly and Matuszeski (2008) write: "probably the single most important issue that we have not addressed is that of migrations. One consequence of arti...cial borders is that people may want to move, if they can. ... In some cases, migrations that respond to arti...cial borders may be partly responsible for economic costs, wars, dislocation of people, refugee crises and a host of undesirable circumstances. ... But sometimes the movement of people may correct for the arti...cial nature of borders."

> 8Brain drain ...gures for Cape Verde are 67 percent in Docquier and Marfouk (2006) and remain very high (60 percent) even after excluding people who emigrated before age 18 and acquired their tertiary education abroad (Beine et al., 2007).

4