rafmacalaba/datause-encoder-data huggingface dataset:
that have joined the EU.<sup>22</sup>

# **3.1 Czech Republic/Slovakia Case Study**

## **3.1.1 Method**

The goal of this section is to assess the impact that EU accession negotiations had on the GER of the Czech Republic. To do this I follow Abadie’s methodology using a difference-in-difference estimation but rather than create a synthetic counterfactual, I utilise the disparate timing of the opening of EU accession negotiations with the Czech Republic (in November 1997) and Slovakia (in January 2000) to use Slovakia as a Czech counterfactual. If the brain gain hypothesis is correct, and agents incorporate information relating to the probability of a successful migration into their investment decision, I expect to see a rise in the disparity between Czech and Slovak GER following the 1997 announcement of negotiations with the Czech Republic and a fall following the 2000 announcement of negotiations with Slovakia.

I have chosen these countries for two reasons. The first is obvious; until 1993 they were just one country and whilst there were economic differences<sup>23</sup> they were at least subject to the same policy environment and political shocks. The second motivation behind this choice is the micro level survey data (IOM 1998) suggesting these are the two countries with the highest potential migration.

Ultimately the Czech Republic and Slovakia joined the EU at the same time, and so this example can only be utilized to look at the effect of accession negotiations on enrollment. This creates a number of problems with the timing.

> 22Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, Slovenia, Greece, Portugal, and Spain.

> 23Prior to their separation on January 1st 1993 the Czech and Slovak republics operated under similar institutions and policies and by the time of their separation were considered to have progressed more rapidly toward a market system than their East European neighbours (with the exception of East Germany). However differences do remain: the Czech Republic privatized faster, offered less generous social security benefits, and had a higher proportion of smaller firms offering more opportunities for entrepreneurial activities. In addition the Czech Republic’s long border with Germany enabled many workers to commute beyond its borders. And, whilst the supply of more educated workers, following the fall of communism, increased in both the Czech Republic and Slovakia; labor demand did not. The Slovakian economy experienced higher unemployment than the Czech Republic.

16
corruption index and developing countries dummy variable are multicolinear, and because the institutional quality indices are highly and significant correlated, we do not include the developing country dummy in the following regressions.

Specification (5) includes as explanatory variables: HIV prevalence, the rule of law index constructed by Kaufmann et al. (2006) and data on ethnic fractionalization from Alesina et al. (2003). As expected, both the estimated coefficients for HIV prevalence and ethnic fractionalization present negative signs, although the latter is not statistically significant, whereas the rule of law index presents a positive and statistically significant coefficient. Under this specification, a 1 percentage point increase in HIV prevalence would result in a 0.76 percentage point decrease in trust. Alternatively, a one standard deviation increase in HIV prevalence will lead to just over 2.5 percentage point decline in trust. The RESET test rejects the null hypothesis (model is linear) at the 10% level but not at 5%, which is acceptable given the small sample size. Including linguistic instead of ethnic fractionalization does not qualitatively change the results. We also tested the regressions including a quadratic term for ethnic or linguistic fractionalization because according to Zak and Knack (2001) in settings with a large number of small groups, no single group represents much of a threat to others; therefore, the effective social distance is greatest at an intermediate range of the fractionalization measure. The results were qualitatively similar<sup>11</sup> , the fractionalization variables were statistically insignificant but HIV prevalence and the rule of law variable are statistically significant at conventional levels.

Specifications (7) through (11) regress trust on HIV prevalence, rule of law index, the Gini coefficient for income inequality as a proxy for social distance, the log of initial income, population density and average age of the population. Rule of law has always a positive and significant effect on trust except in (9) when we include (the log of) initial income because, as stated earlier, both variables are highly correlated. Unexpectedly, the estimated coefficient of educational achievement has a negative sign although statistically insignificant. Similar to other studies, we find that the Gini

> 11 Not reported in table1 for ease of exposition , but available upon request

13
independence of the agency from political authorities. The low position of Uruguay in the Formal Autonomy Index may reflect an institutional design that lacks provisions to guarantee the political independence of the agency (which, according to the informal index, is quite high) or ensure the autonomous management of its resources; or it may reflect the existence of limited regulatory powers in the agency’s responsibilities.

# **4.3.2 Transparency:**

Transparency includes the procedures, mechanisms, and instruments aimed at guaranteeing the disclosure and publication of relevant regulatory and institutional information, the participation of stakeholders in the agency’s regulatory decisions and decision-making, and the application of rules aimed at governing the integrity and behavior of agency officials. It has a regional average of 0.69.

**Figure 13: Transparency Index**

<!-- Start of picture text --> Transparency Index<br>Source: LAC Electricity Regulatory Governance Database, The World Bank, 2007.<br>1<br>.8<br>.6<br>Index (0-1)<br>.4<br>.2<br>0<br>T&T T1 ESV PER MEX COL BOL BRA C R R D BAR ARG URU JAM NIC CHI GUA T2 PAN ECU HON<br><!-- End of picture text --> Leading countries in the Transparency Index are Trinidad and Tobago, El Salvador, Peru, and Mexico. Countries at the bottom are Panama, Ecuador, and Honduras. Results indicate a slight preference of electricity agencies in the LAC region for procedures and instruments aimed at allowing the involvement of non-institutional stakeholders and their access to agency decisionmaking and information (social transparency), as opposed to the use by agencies of procedures and tools to improve institutional transparency. Social transparency obtains an average of 0.76, compared to 0.64 for institutional transparency.

The division of countries in three tiers allows us to see that only Trinidad and Tobago meets the criteria to be included among agencies with a desirable level of transparency. Most countries in the region only meet the minimum conditions to guarantee the access of the different stakeholders to the agency’s information and its regulatory making and the transparent management of its resources (for instance, the publication of its job vacancies). Panama, Ecuador, and Honduras do not even meet the necessary conditions to guarantee its transparency.

# **A. Social Transparency:**

The social aspects of transparency are related to the involvement of stakeholders in the agency’s decision and rule-making processes and their access to the agency’s information. Social

- 26 -
Table A6: Labor force participation by race, immigration status, with PUMA fixed effects and cell (PUMA x race/immigration x education x age <u>group) level averages.</u>

||(1)|(2)|
|---|---|---|
||Males|Females|
|Dependent variable|Inthelabor force|Inthelabor force|
|HighSchool|0.0908***|0.1519***|
||[0.0011]|[0.0012]|
|Some college|0.1333***|0.2320***|
||[0.0011]|[0.0012]|
|College and above|0.1677***|0.2798***|
||[0.0011]|[0.0013]|
|married|0.0821***|-0.0376***|
||[0.0020]|[0.0027]|
|#ofchildren <5|-0.0104***|-0.1440***|
||[0.0016]|[0.0022]|
|Income ofother familymembers (log)|0.0029***|-0.0019***|
||[0.0002]|[0.0003]|
|disabled|-0.1374***|-0.0360***|
||[0.0026]|[0.0031]|
|White bornout US|-0.0183***|-0.0779***|
||[0.0014]|[0.0017]|
|Blackborn inUS|-0.0730***|-0.0158***|
||[0.0011]|[0.0012]|
|Blackbornout US|-0.0230***|0.0379***|
||[0.0028]|[0.0030]|
|Asianborn inUS|-0.0228***|0.0028|
||[0.0026]|[0.0034]|
|Asianbornout US|-0.0550***|-0.0826***|
||[0.0015]|[0.0019]|
|Hispanic born inUS|-0.0411***|-0.0272***|
||[0.0013]|[0.0015]|
|Hispanic bornout US|-0.0405***|-0.0810***|
||[0.0013]|[0.0016]|
|Inschool|-0.1405***|-0.0611***|
||[0.0034]|[0.0038]|
|Inmate|-0.7504***|-0.6338***|
||[0.0033]|[0.0092]|
|Constant|0.7863***|0.7024***|
||[0.0020]|[0.0026]|
|Observations|266666|271061|
|R-squared|0.63|0.55|

Robust standard errors in brackets. * Significant at 10%; ** significant at 5%; *** significant at 1%. Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The unit of observation is a cell defined at the level of the PUMA for each combination of age group, racial/immigration group and education group. The dependent variable of the linear regressions is the average labor participation in the cell. The regressors are indicators for each PUMA, education group, racial/immigration group and each group as well as the average at the cell level of the other X variables: marital status, number of children under age 5, the logarithm of income of other family members, disability, school enrollment and inmate status. The regressions use the sampling weights provided with the IPUMS data.

38
25 Table 1: Regressions aimed at understanding variations in product NRAs across 75 countries, 1955 to 2007 (no country fixed effects)

|||Log of real||% deviation|||||
|---|---|---|---|---|---|---|---|---|
||Log of real<br>GDP per<br>capita|GDP per<br>capita,<br>squared|Log of<br>arable land<br>per capita|of int’l price<br>from its<br>trend|Dummy if<br>exportable|Constant|No.<br>of<br>obs.|<br>Adjusted<br>R<sup>2</sup>|
|Rice|-2.022***|0.157***|-0.390***|-0.320***|-0.732***|5.988***|1281|0.514|
||(0.152)|(0.00988)|(0.0219)|(0.0532)|(0.0447)|(0.562)|||
|Wheat|-0.921***|0.0707***|-0.158***|-0.317***|-0.424***|2.823***|1661|0.347|
||(0.116)|(0.00728)|(0.0159)|(0.0529)|(0.0368)|(0.454)|||
|Maize|-0.432***|0.0334***|-0.167***|-0.236***|-0.195***|1.307***|1525|0.208|
||(0.0937)|(0.00602)|(0.0145)|(0.0504)|(0.0292)|(0.354)|||
|Soybean|0.957***|-0.0424**|-0.548***|-0.0372|-0.128|-<br>5.229***|703|0.310|
||(0.345)|(0.0212)|(0.0368)|(0.155)|(0.0893)|(1.366)|||
|Sugar|-1.021***|0.0843***|-0.244***|-0.582***|-0.414***|3.180***|1648|0.413|
||(0.178)|(0.0113)|(0.0255)|(0.0338)|(0.0554)|(0.670)|||
|Cotton|-0.370***|0.0320***|0.00829|-0.274***|-0.270***|1.057***|883|0.275|
||(0.0897)|(0.00607)|(0.0159)|(0.0363)|(0.0429)|(0.315)|||

*** indicates statistically significant at the 1% level

Source: Authors’ revision of Table 2.14 in Anderson (2010).
These results suggest that classroom observations that measure how teachers, students, and parents are interacting between and among each other (namely the ECERS-R) are capturing elements of playgroup quality beyond what can be predicted from student’s school readiness, teachers’ education and teaching experiences alone. Thus, observed playgroup quality is a strong predictor of whether project playgroups are sustained after the initial funding period.<sup>20</sup> Table 5 summarizes the relationship between sustainability and market conditions. First, we do not find support for the fact that distance to the village center or distance to neighboring playgroup services mattered in our setting. Second, project playgroups located in villages with a higher density of kindergartens during the project period were significantly more likely to be open (column 1) and remain open with two trained teachers (column 2) – holding constant all other variables in the model. As shown in Figure 4, below, the predicted probability that a project playgroup remains open increases from 82% to 97% as the number of kindergartens per 100 children increases from 0 to 1. Similarly, the predicted probability that a project playgroup is open with two trained teachers jumps from 29% to 41% as the number of kindergartens per 100 children increases from 0 to 1. We view kindergartens as services that complement playgroups given the age of students intended for each. Therefore, in villages where both kindergartens and playgroups are sought after, we would expect a positive relationship between the density of kindergartens and sustainability of project playgroups. Our results are consistent with this hypothesis.<sup>21</sup> <u>Table 5. Logistic regression of project playgroup sustainability on market conditions</u>

|<br> <br>|<br>(1)<br>Playgroup is open<br>in 2016 (1=Yes)|<br>(2)<br>Playgroup is open in 2016 with<br>two trained teachers (1=Yes)|
|---|---|---|
|**Market conditions**|||
|Number of kindergartens per 100 children in 2010|0.15**<br>(0.07)|0.12*<br>(0.06)|
|Change in number of kindergartens per 100 children|-0.09|-0.11|
|from 2010-2013|(0.06)|(0.07)|
|Number of other playgroups per 100 children in|||
|2010|-0.11<br>(0.07)|-0.10<br>(0.08)|
|Change in number of other playgroups per 100|||
|children|0.10|0.08|
|from 2010-2013|(0.06)|(0.07)|
|Distance to village center (km)|0.00<br>(0.03)|0.01<br>(0.02)|
|Distance to nearest neighboring playgroup (km)|0.04<br>(0.03)|-0.01<br>(0.01)|
|Log-likelihood|-63.83|-155.81|
|Observations|245|245|

* p<0.10,  ** p<0.05,  *** p<0.01

Note: Robust standard errors clustered by center. Coefficients are average marginal effects.

> 20 This finding is in close agreement to the findings in the broader literature on preschool center quality. See for instance, Brinkman et al, 2017. 21 See Appendix Figure 6.

17
Policy Research Working Paper 6466

# **Abstract**

The paper shows how Labor Force Surveys can be used effectively to estimate poverty rates using Household Expenditure Surveys and cross-survey imputation methods. With only two rounds of Household Expenditure Survey data for Morocco (2001 and 2007), the paper estimates quarterly poverty rates for the period 2001–2010 by imputing household expenditures into the Labor Force Surveys. The results are encouraging. The methodology is able to accurately reproduce official poverty statistics by combining current Labor Force Surveys with previous period Household Expenditure Surveys, and vice versa. Although the focus is on headcount poverty, the method can be applied to any welfare indicator that is a function of household income or expenditure, such as the poverty gap or the Gini index of inequality. The newly produced time-series of poverty rates can help researchers and policy makers to: (a) study the determinants of poverty reduction or use poverty as an explanatory factor in cross-section and panel models; (b) forecast poverty rates based on a time-series model fitted to the data; and (c) explore the linkages between labor market conditions and poverty and simulate the effects of policy reforms or economic shocks. This is a promising research agenda that can expand significantly the tool-kit of the welfare economist.

This paper is a product of the Poverty Reduction and Economic Management Department, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at pverme@worldbank.org or rvanderweide@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
the crisis. Allowing for differential productivity-survival dynamics for entrants does not reduce the attenuation effect. Thus, the attenuation effect documented in this paper is not simply an artifact of reduced entry.

# _6.2.4 Connectedness and Creative Destruction_

Another possible explanation of the attenuation effect is that the fall of Suharto may have hurt firms affiliated with the Suharto regime disproportionately (see Fisman, 2001, and Mobaraq and Purbasari, 2008). If these firms were highly productive, their exit may result in an attenuation of the link between productivity and survival. Table 10 examines this possibility. The data on political connectedness are available for firms in 1997, constructed using JSX and board membership information from 1995-1997. Thus, to test the role of political connections, we look at survival patterns from 1997 to 2001, controlling too for factors in our baseline specifications presented in Table 3.<sup>14</sup> Also note that relatively few firms are identified as being politically connected to Suharto; 210 firms that are connected based on the stock market response to, and 97 firms have a Suharto family member on their Boards, which may make it hard to estimate the effects with precision. Row A uses the connectedness measure that is based on the response of the Jakarta Stock Exchange to news about Suharto‟s health and the business networks of those adversely affected by this news, while Row B uses as a proxy for connections whether a firm had a Suharto family member on its board. Row C combines these two definitions. The baseline regressions presented in columns 1 and 3 merely control for connectedness, while the extended specifications presented in columns 2 and 4 additionally include interactions between connectedness and productivity.

The baseline regressions demonstrate that firms with political connections to Suharto were not, on average, more likely to exit over the period 1997-2001 ceteris paribus. However, the extended models suggest that, amongst firms with connections to the Suharto regime, the least

> 14  Since the proxy for connectedness is in part based on stock market reactions over the period 1995-1997, conditioning on connectedness in prior periods might thus induce survivor bias.

27
contrasted with the estimated subsistence minimum (based on wage data) which was between 5 and 6 taels (Chang). If we consider Maddison's (2004) estimated China GDI per capita of $PPP 540 and Chang's average income of 6.5 taels to be the same (as they should be), then the subsistence minimum of $400 works out to be 4.8 taels. This indirectly obtained subsistence minimum is quite close to the directly calculated one (from Chang) of 5 to 6 taels per annum. This further corroborates both the subsistence minimum and the average figures.

Derivation of incomes of the upper and lower gentry

|||Inco|me shares:|Estimatedt|otal income|
|---|---|---|---|---|---|
|Source of gentry income|Estimated<br>amonts (in<br>000_taels_)|Upper<br>gentry|Lower gentry|Upper gentry|Lower gentry|
||(1)|(2)|(3)|(1)x(2)|(1)x(3)|
|Office-holding|121000|1|0|121000|0|
|Gentry service|111000|0.18|0.82|20250|90750|
|Secretarial services|9050|0|1|0|9050|
|Teaching|61575|0|1|0|61575|
|Other services 1/|9000|0.2|0.8|1800|7200|
|Landholding|220000|0.7|0.3|154000|66000|
|Mercantile activity|113600|0.7|0.3|79520|34080|
|Total gentry income|645225|||376570|268655|
|_plus_Imputed rent|30000|0.34|0.66|10200|19800|
|m_inus_direct taxes|47500|0.14|0.86|6650|40850|
|(a) Disposable gentry income|627725|||380120|247605|
|(b) China-wide GNP|2781272|||||
|(c) Total taxes|332500|||||
|(d) Household disposable<br>income: (b)-(c)|2448772|||||
|(e) Gentry population<br> (in 000people)|7500|0.14|0.86|1050|6450|
|_Disposable income (in tael_<br>_per capitap.a.) =(a)/(e)_||||_362_|_38_|

Sources: Gentry incomes, Table 26, page 197. Imputed rent and GDP, p. 326. Number of gentry: p. 327 (average hopusehold size = 5). Direct taxes: p. 329. Upper and lower gentry shares in total gentry income: p. 330. All references to Chung-li Chang (1962). 1/ Upper and lower gentry’s shares for other services assumed.

48
allow for correlation between the random effects in wages and presence of wage information. As the bottom part of the relevant columns shows both _μ_ 1 and _μ_ 2 are – – positive indicating positive correlation between the random effects: for very low wages the probability to have wage information available is very small. As shown exp( _−_ 0 _._ 526) for males there is a probability of 37% ( 1+exp( _−_ 0 _._ 526)<sup>)thatthewageislowandthe</sup> presence of wage information is close to zero while there is a probability of 63% that – – the wage is relatively high and on average 75% of the wages is available in the exp( _−_ 0 _._ 249) dataset.<sup>11</sup> For females there is a probability of 44% ( 1+exp( _−_ 0 _._ 249)<sup>)thatthewageis</sup> low and the presence of wage information is close to zero while there is a probability of 56% that the wage is high and on average about 60% of the wages is available in the dataset. Despite of the selectivity in the presence of the wage information the parameter estimates of the wage determinants are hardly affected. This indicates that selectivity is not an important issue.<sup>12</sup> In addition to this analysis we followed a more traditional approach assuming a normal selection process including the inverse Mill’s ratio to account for possible selectivity in the available wage data. The calculation of the inverse Mill’s ratio is based on a probit estimate explaining the presence of wage information for all workers; the explanatory variables are the duration of unemployment, the ‘after change of law dummy’ and whether or not the job is temporary. The coefficients of the Mill’s ratios are insignificantly different from zero indicating that if we assume normality in the selection process potential selectivity in the availability of wage information is uncorrelated with the processes that determine the wage.

# **6 Concluding remarks**

Efficiency properties of unemployment insurance are theoretically ambiguous and empirically controversial. The paper sheds light on one such a controversial aspect – the impact of unemployment benefits on the quality of post-unemployment jobs.

> 11Calculated using the parameter estimates presented in Table 4 and average values of the explanatory variables.

> 12We also investigated whether the introduction of additional mass points – allowing for less than perfect correlation between the unobservables – improves the estimates but this turned out not to be the case.

21