rafmacalaba/datause-encoder-data huggingface dataset:
in the bottom two quintiles of the propensity score, practically no applicant is eligible for the program and vice versa in the top two quintiles. In the middle quintile, almost half of the control group should be eligible to receive the program, while approximately 40% of the treatment group should be ineligible. Hence, the strictly algorithmic application of the eligibility formula using the registration information available in the integrated databases of the ISAS system, which contained discordances with the actual household compositions of applicants at baseline, explains why we are able to match households in the treatment and control group quite well across the propensity score distribution. While these discrepancies are not correlated with observable characteristics of applicant households, we nevertheless cannot rule out unobservable differences between the treatment and control groups that might bias impact estimates.

**Table 3: Attrition per survey round.**

||**6 months**|**12 months**|**18 months**|
|---|---|---|---|
||(1)|(2)|(3)|
|Treatment|-0.0581***|-0.0736***|-0.0725***|
||(0.0169)|(0.0180)|(0.0193)|
|Propensity score|0.0416|0.00738|0.0744|
||(0.0457)|(0.0472)|(0.0498)|
|Treatment X Propensity score|-0.105*|-0.0608|-0.185***|
||(0.0602)|(0.0640)|(0.0685)|
|Control group mean (and standard|0.275|0.332|0.468|
|deviation)|(0.447)|(0.471)|(0.499)|
|F-test of joint orthogonality (F-stat. / p-|12.753|25.655|13.979|
|value)|0.001|0.000|0.000|
|Strata dummies|Yes|Yes|Yes|
|Sampling weights|Yes|Yes|Yes|
|Number of observations|7,745|7,745|7,745|

Notes: significance level p< 0.01 - ***, 0.05 - **, 0.1 - *; Robust standard errors in parentheses; F-test shows the F-statistics and the p-value for the joint significance of the coefficients on treatment, demeaned propensity score, and their interaction; the propensity score variable is centered by subtracting the sample mean; all regressions include strata dummies (n=5); sampling weights are representative at the population level.

# _2. Attrition from the study sample_

As discussed briefly in Section 3, the study suffered from a high rate of attrition from the baseline sample. The problem worsened over time: 27.5%, 33.2%, and 46.8% of the survey respondents in the control group were lost to follow-up at the 6-, 12-, and 18-month follow-up surveys, respectively (Table 3). Not only are these high rates of attrition, albeit perhaps not surprising considering the study sample of international refugees, but the attrition is differential in levels between the treatment and the control groups – with the treatment group 6-7 pp more likely to be interviewed at each of the three follow-up rounds of data collection.

To examine whether attrition is also differential in baseline characteristics, we include the propensity score and its interaction with treatment in the regression model for Table 3. This allows us to avoid interacting treatment with a large number of baseline characteristics and use a summary statistic instead. The propensity score is positively correlated with attrition, but this relationship is not statistically significant. However, the interaction 16
<!-- Start of picture text --> 150<br>100<br>50<br>Q<br>-50<br>200641 2008q1 2010q1 2012q1 201491<br>—— BoP<br>— Fill<br><!-- End of picture text --> <!-- Start of picture text --> 400<br>200<br>a<br>-200<br>2006q1 2008q1 201091 2012q1 201491<br>—— BoP<br>— Fill<br><!-- End of picture text --> <!-- Start of picture text --> 1500<br>1000<br>500<br>0<br>-600<br>-1000<br>2006q1 2008q1 201091 2012q1 201491<br>—— BOP<br>— Fill<br><!-- End of picture text --> <!-- Start of picture text --> 500<br>8<br>-500<br>2006q1 2008q1 201091 2012q1 201491<br>—— BoP<br>— Fill<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>“0<br>20<br>Q<br>-20<br>-40<br>2006q1 2008q1 2010q1 201291 201491<br>—— BOP<br>— Fill<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>20<br>10<br>5<br>-10<br>20081 2008q1 201091 201291 201491<br>— BoP<br>— Fill<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>fl<br>-50<br>2006q1 2008q1 2010q1 201291 201491<br>—— BOP<br>— Fill<br><!-- End of picture text --> <!-- Start of picture text --> 150<br>100<br>50<br>Q<br>-50<br>20061 2008q1 201091 201291 201491<br>— BoP<br>— Fill<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>5<br>0<br>5<br>2006q1 2008q1 201091 2012q1 201491<br>—— BOP<br>— Fill<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>2<br>0<br>-2<br>2006q1 2008q1 201091 2012q1 2014q1<br>—— BoP<br>— Fill<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>1<br>0<br>a<br>2<br>2006q1 2008q1 201091 2012q1 201491<br>—— BOP<br>— Fill<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>5<br>0<br>5<br>2006q1 2008q1 201091 2012q1 2014q1<br>—— BoP<br>— Fill<br><!-- End of picture text -->
# **3.2. External Costs**

The Mexico City basin is especially prone to local air quality problems, given its high altitude, and that it is surrounded by mountains on three sides, which limits pollution dispersion. We assume that local pollution damages per gallon of gasoline amount to 90 cents per gallon, where the huge bulk of the damage reflects mortality risk. This figure is based on a compromise between two sources. First, an extrapolation from a study for Los Angeles (another region with topography and climate especially favorable to pollution formation) by Small and Kazimi (1995), adjusting for differences in population exposure, the value of health risks, and vehicle emission rates between the two regions. Second, is a study of the health benefits from reducing future pollution concentrations in Mexico City (and two other cities) by Bell et al. (2006), based on pooling epidemiological evidence and the valuation of health risks from a variety of different sources (we consider only their mortality effects). As in other studies, we assume the health/environmental damages per gallon are the same for gasoline and diesel (see Appendix B).

From the mainstream climate damage assessment literature, we assume the damage cost per ton of CO2 is $10, which converts to 9 and 11 cents per gallon of gasoline and diesel respectively (given the carbon content of these fuels). A few studies estimate far larger damages either because they employ much lower discount rates, or because they assume a risk of arbitrarily large damages (e.g. through a climate feedback effect causing extreme warming). We therefore consider other values later.

Marginal congestion costs for autos are put at 17 cents per passenger mile. As discussed in Appendix B, this is based on observed travel delays of 0.037 hours per mile (relative to travel times under free flowing traffic), equation (15), and evidence suggesting the value of travel time is about half the market wage, currently about $4 per hour. (Using these figures, and annual mileage, also gives our figure in the introduction of $580 in congestion costs per capita). In addition, parameter  in equation (15) is taken to be 1.121; this implies, for example, that a 20 percent reduction in road traffic will lower average travel times by 26 percent. For microbus and public bus we assume passenger car equivalents of 3 and 5 respectively (again, based on US studies). However, given much higher occupancies of transit vehicles, marginal congestion costs per passenger mile are computed at 11 and 4 cents for microbus and public bus respectively.

22
PPR Working Par Series

||IiUa|Author|Date<br>pape|Contact<br>for<br>r|
|---|---|---|---|---|
|WPS32|Municipal Development Funds and<br>Intermediaries|Kenneth Davey|July 1988|R. Blade-Charest<br>33754|
|WPS33|Fiscal Policy in Commodity-<br>Exporting LDCs|John Cuddington|July 1988|R. Blade-Charest<br>33754|
|WPS34|Fiscal Issues in Macroeconomic<br>Stabilization|Lance Taylor|September 1988|R. Blade-Charest<br>33754|
|WPS35|Improving the Allocation and<br>Management of Public Spending|Stephen Lister|August 1988|R. Blade-Charest<br>33754|
|WPS36|Social Security Finance in<br>Developing3ountries|Douglas J. Puffert|August 1988|R. Blade-Charest<br>33754|
|WPS37|Black Market Premia, Exchange<br>Rate Unifi_ation, and Inflation in<br>Sub-Saharai Africa|Brian Pinto|July 1988|R. Blade-Charest<br>33754|
|WPS38|Intergovernmental<br>Grants in<br>Developing Countries|Larry Schroeder|September 1988|R. Blade-Charest<br>33754|
|WPS39|Fiscal Policy in Low-income Africa|Stephen A. O'Connell|July 1988|R. Blade-Charest<br>337b4|
|WPS40|Financial Deregulation and the<br>Globalization of Capital Markets|Eugene L. Versluysen|August 1988|R. Blade-Charest<br>33754|
|WPS41|Urban Property Taxation in<br>Developing Countries|William Dillinger|August 1988|R. Blade-Charest<br>33754|
|WPS42|Government Pay and Employment<br>Policies and Government Performa<br>in Developing Economies|<br>David L. Lindauer<br>nce|August 1988|R. Blade-Charest<br>33754|
|WPS43|Tax Administration in Developing<br>Countries: Strategies and Tools<br>of Implementation|Tax Administration<br>Division, IMF Fiscal<br>Affairs Department|August 1989|WDR Office<br>31393|
|WPS44|The Size and Growth of<br>Government Spending|David L. '<br>,auer|September 1988|R. Blade-Charest<br>33754|
|WPS45|How Much Do State-Owned<br>Enterprises Contribute to Public<br>Sector Deficits in Developing Coun<br>-- and Why?|Govindan Nair<br>Anastasios Filippides<br>tries|December 1988|L. Mattheisen<br>33757|
|WPS46|The Management of Public<br>Expenditures: An Evolving Bank<br>Approach|Robert Lacey|January 1989|L. Matthiesen<br>33757|
with the results of Amin and Islam (2015) who use the same data source as we do. As Amin and Islam (2015) argue, there are several possible reasons for the negative relationship. These include diminishing returns to labor or decreasing returns to scale, transformation costs that larger firms face who may be preparing to formalize, evasion costs associated with operating informally which increase with greater visibility and therefore with firm-size.

Results for the higher education and labor productivity relationship are along expected lines. That is, labor productivity is significantly higher (at 1 percent) when the owner has secondary or higher education versus when owner has only primary education or no education. This positive and significant relationship between labor productivity and owner education level holds in the full sample as well as in the subsamples of female- and male-owned firms. In terms of the magnitude, in the full sample, labor productivity is higher by 25 percent when the owner has secondary or higher education compared to when the owner does not. The corresponding figure is lower for female-owned firms than male-owned firms (20.6 and 29.7 percent). Analyzing labor productivity of informal firms, La Porta and Shleifer (2008, 2014) note that higher levels of education of managers (owners in our case) in the formal sector than the informal sector is what makes formal firms far more productive than informal firms. However, most of the literature in the area is focused on education in the formal sector and worker education rather than owner or manager education (see Bartelsman and Doms, 2000; Konings and Vanormelingen, 2015). Nevertheless, important insights can be gleaned from this body of work. At the basic level, increased education implies that valuable skills are learned which can enhance labor productivity in the firm (Becker, 1975). Furthermore, education for both workers and owners can widen the access to different sources of information and may increase their ability to learn from experience, potentially raising productivity (Rosenzweig 1995 presents the arguments for workers). Education or training directly 18
exemplified by the diffusion of internet and mobile phone use in selected countries of the region. However, the situation seems to be rapidly changing in Moldova’s favor.

Looking to the future, as noted by the study “Moldova 2020,” the prospects for rapid future growth in remittances are limited and uncertain, considering labor demands in the receiving countries and slow labor force growth in Moldova. Given this, rapid future growth may be best assured via a change in the development paradigm toward a model that is based on export-oriented production of goods and services with a focus on industrial sectors, supported by investments in roads and other forms of infrastructure as well as measures that contribute to an equitable distribution (Government of Moldova 2012a, pp. 5-8 and 21).<sup>**4**</sup>

**Figure 4. Internet users per 100 people**

<!-- Start of picture text --> 80<br>70<br>60<br>Slovak Republic<br>50<br>Hungary<br>40 Romania<br>Belarus<br>30<br>Moldova<br>Ukraine<br>20<br>10<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: World Bank 2012.

> **4** For a similar policy message, see World Bank (2011).

9
15

# IV. **Estimation Results**

The goal of our econometric estimation is to gauge how much of the growth rate of a cross-section of countries might be explained by the initial level of GDP per capita (i.e., pure and simple absolute convergence) and, since we allow for diverse environments across countries, by the share of investment in physical capital, the stock of human capital, the policy framework within which economic activities take place (i.e., the IEF index previously discussed), and the rate of growth of the labor force. In effect, these _environment factors_ determine different steady state levels for each country and will be referred in the rest of this paper as _growth determinants._ To achieve this goal we regress the annual average growth rates of per capita GDP (in PPP terms) on the four growth determinants mentioned above and the initial level of output per capita. **9** The econometric estimation follows the specification described in equation [5] . In Appendix 3 10 we present the list of countries included in each regression and the corresponding data . The period under study is in all cases 1965-89.

In Table 3 we present the average values of our five coefficient estimates (i.e., Xo, Xi, X 2 , X3 , and X 4 ). From these estimates we derive the implied values of (p, c, D and 4 using, respectively, equation [viii] and equation [xi] of Appendix 1 and Appendix 2. Recall that cx and ,B are the shares of both capital types in total output and 4 is a parameter that affects the policy framework of the economy . Also, from Appendix I we know that p is the speed with which a 9 A total of eight different regressions are estimated in this paper, half of which are based on the original IEF index while the other half use the modified IEF index discussed in Box 3. A thorough discussion of the estimation procedure is presented in Appendix 2 of this paper.

10 We begun with a total sample of 100 countries. However, as can be observed in Appendix 3, this number got reduced to slightly over 80 since some data was not available for our original sample, particularly that on the level of educational attainment. Note that CEE countries have been excluded from the econometric estimation. Also, we modify in all regressions the data from Johnson and Sheehy by inverting the index ordering so that higher numbers --the range is from I to 5-- indicate less government intervention or a higher degree of economic freedom. The goal of this modification is to make this index consistent with equation [4].

11 Note that the sum of a and a in Table 3 leads to a share of capital, in its two types, equal to approximately 60 percent of total output. This is consistent with the finding that high shares of capital are required to explain slower speeds of convergence in neoclassical growth models. In this respect, the usual shares of capital of close to 30-40
The time trends in demographic and socio-economic characteristics of these flows are not very encouraging and are indicative of little ‘brain gain’ (Figure 21). Returnees’ average years of education is decreasing steadily since 1995 and at a steeper rate than the downward trend observed in the education level of new migrants (Figure 9). Furthermore, although the peak in the average age of returnees around 1999 could be expected in view of the massive outflows of older migrants following the 1996-97 pyramid scheme crisis in which the majority of Albanians lost most of their savings, the spike in most recent years may be a matter of concern (Figure 22).

**Figure 21** _Average education of last-time_ **Figure 22** _Average age of last-time migrants by migrants by gender gender_

<!-- Start of picture text --> year of ret migration from last time year of ret migration from last time<br>all males all males<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>12<br>36<br>11<br>34<br>avg education years 10 avg age at return 32<br>9 30<br><!-- End of picture text --> Even more disturbing are the trends in terms of welfare: in recent years, returnees are coming back to poorer households compared with earlier years. All these trends combined are suggestive of some negative selection among migrants, with increasingly older, less educated individuals returning to poorer households. As such, and as put forth anecdotally by Barjaba (2000), return migration may reflect failure to succeed abroad, thus making it a less likely catalyst of growth at home. De Coulon and Piracha (2003) also find evidence of negative self-selection among returnees. Not surprisingly, the lion share of returnees comes from nearby Greece (Figure 23).

16
especially in countries where these regulations are likely to be enforced. They show that higher levels of job security decrease productivity growth roughly by one percent.

In this study, I empirically show how stringent EPL have detrimental effects on exporting using data from a detailed firm level survey that covers 26 countries from Eastern Europe and Central Asia (ECA) region. Most studies that analyze the effects of labor regulations resort to cross country data in institutions and regulations using different data sources. This might create problems due to differences in measurement across countries. These problems are ruled out in this study because the data used is homogeneous in unit of observations, the measures of firm performance, and labor regulations.

Another novel feature of this study is that it does not only look at the cross-country differences in labor regulations but also looks at the variation of their effects across sectors like construction, manufacturing, and retail. Although in most countries labor laws don’t vary across sectors, differences in the intrinsic demand and supply shocks can lead to differential effects of labor regulations across sectors. On the other hand, most of the existing studies analyzing exports focus on export of products in manufacturing sector. Services are the fastest growing sector of the global economy and the growth in service trade has surpassed the growth in goods trade. Hence it is important to incorporate firms in service sector in the analysis. Data from Enterprise Surveys show that in both 2002 and 2005 roughly 20% of firms in service sector export part of their services. The analysis is also performed across industries within the manufacturing sector. There are only a few studies that perform cross-industry analysis on the differential impacts of labor regulations on firm performance within a country. Two such studies are Micco and Pages (2007) and Haltiwanger, Scarpetta, and Schweiger (2008). Both studies show that high hiring and firing costs are detrimental to job flows particularly in those industries that require more frequent labor adjustments.

The rest of the paper is organized as follows. In section two, I explain the methodology and the specification of the model. Then in section three, I describe the data used in the analysis. The empirical analysis is presented in section four and a sensitivity analysis using additional controls, different specification and the panel data is performed in section five. Finally, in section six I present concluding remarks.

4
Table 5: Coefficients on Main Variables when additional independent variables are included in regression (see Table 3, Column 2 for base regression).

|-<br> <br> <br>|DEPENDENT|VARIABL<br>|EISBRIBES<br>|PAIDTOUTI<br>|LITIESASP|ERCENTOF|REVENUE|
|---|---|---|---|---|---|---|---|
|Additioal<br>Indeendent<br>**ariablePrivatization**<br>AdditionalIndependentVariable|**Index**|**Landlocked**<br>**Dummy**|**Population**<br>**(naturalLog)**|AeaeIftin<br>**AverageInflation**|**Government**<br>**Expenditures**|beetaiain<br>**Decentralization**|**Overall**<br>**Corruption**|
|Number ofObservations|1822|1822|1822|1822|1822|1655|1737|
|_CoefficientonAdditional IndependentVariable_|<br>_0.4436***_<br>_(-3.29)_|_-0.2051_<br>_(-0.98)_|_0.0694_<br>_(0.58)_|_-0.0014_<br>_(-1.25)_|_-0.0037_<br>_(-0.18)_|_0.4047_<br>_(0.37)_|_-0.0556_<br>_(-0.66)_|
|_CoefficientsonothervariablesofInterest_||||||||
|FixedLineTelecom OperatorPrivatized|-0.4418*"|-0.5977***|-0.6948***|0.7413"'*|-0.6794***|-0.4498*|-0.6799"'*|
|(Dummy)|(-2.47)|(-3.34)|(-4.06)|(-4.24)|(-3.92)|(-1.71)|(-4.16)|
|FixedLines|-0.0312***|-0.0389***|-0.0358**|-0.0244*|-0.0361**"|-0.0301**|-0.0427"**|
|(Per1000people)|(-2.78)|(-3.43)|(-3.23)|(-169)|(-3.23)|(-2.02)|(-3.81)|
|CellularCompanies|-0.1617**|-0.2546***|-0.2744***|-0.2712"**|-0.2192"'*|-0.2006"*|-0.1533"*|
|(Number)|(-2.27)|(-3.44)|(-2.59)|(-3.50)|(-2.61)|(-2.39)|(-1.98)|
|Margin|0.0127**"|0.0125**"|0.0126"**|0.0123***|0.0126"**|0.0129"**|0.0119***|
|(Sales PricelessOperating Costs as%ofoperatingcosts)|(3.73)|(3.68)|(3.70)|(3.61)|(3.71)|(3.45)|(3.47)|
|Largest Shareholder- OtherPrivate-DeNovo|0.6495**"|0.6465***|0.6538***|0.6546"*'|0.6520**"|0.8611***|0.6103"*'|
|(Dummy)|(2.81)|(2.81)|(2.84)|(2.85)|(2.83)|(3.17)|(2.63)|
|Overdue PaymentstoUtilities|-0.1594**|-0.1601"|-0.1666**|-0.1652**|-0.1642**|-0.1669'|-0.1747*"|
|(Index- higher values meanlessoverduepayments)|(-2.02)|(-2.03)|(-2.11)|(-2.10)|(-2.09)|(-1.91)|(-2.17)|
|DataSource:TheWorldBusinessEnvironmnentSurvey(WBE<br>T-statisticsare inparentheses.<br>*Significantat10%level.|S)02000TheWorl<br>"*Significanta|dBankGroup.<br>t5%level.|"'Significant at1|0percentlevel.||||

> Note: Regressions include all variables included in Column 2 of Table 3 including sector and size dummies.

33