rafmacalaba/datause-encoder-data huggingface dataset:
within 2-digit industry _j_ at time _t_ . _AnnualEarningsijt_ measures annual earnings of individual _i_ working in industry _j_ ’ at time _t_ . _GGExpjt_ and _GGImpjt_ measure the share of green goods export and import, respectively, in total exports and imports within 2-digit ISIC industries. _σj_ is the industry fixed effect; _μ_ is the constant term; _AD_ is the average duty; and finally, _εjt_ / _εijt_ is the error term.

To facilitate interpretation, these percentages are multiplied by 100. The calculations are based on the Labor Force Survey, incorporating appropriate weights. While incorporating additional control variables would enhance the precision of our estimation, we encountered limitations in doing so due to data constraints. Despite attempts to merge the data with the firm-level survey conducted by the Philippines Statistical Authority, the lack of identical sampling frames between the Labor Force Survey and the firm-level survey posed challenges in the merging and weighting procedures.

The annual earnings data is obtained from a cross-sectional Labor Force Survey. The richness of this database allows us to incorporate various observable characteristics in a Mincer-style regression, including gender, age, marital status, education, and industrial affiliation. However, due to the cross-sectional nature of the data, we are unable to control – for unobservable characteristics that may impact earnings, such as inherent talent which could introduce biases. The annual earnings variable is represented in natural logarithm form, while the green goods trade variables are expressed as shares out of 100. The gender variable assumes a value of zero for male workers and one for female workers. Education level is categorized into eight different groups, ranging from no schooling to incomplete elementary education, elementary graduate, incomplete high school, high school graduate, vocational education, incomplete college, and college graduate and above. The marital status variable is a dummy variable that takes a value of one if an individual is married and zero otherwise. Age is a self-explanatory variable that represents the age of the individual.

Estimating the impact of green goods trade on labor market outcomes is susceptible to endogeneity issues. Firstly, firms make decisions regarding both their trading activities 25
PAYING THE PRICE FOR UNRELIABLE POWER SUPPLIES: GENERATION OF ELECTRICITY BY PRIVATE FIRMS IN AFRICA The own-generation phenomenon varies across the geopolitical regions of Sub-Saharan Africa (figure 12). It is significantly more prevalent in the 15-country Economic Community of West African States (ECOWAS).<sup>14</sup> In Mali, Ghana, and Togo own generation accounts for more than 10 percent of installed capacity; in Niger, Guinea, and Nigeria, the share exceeds 20 percent. In the regional context, the high shares of own generation in Nigeria and Ghana— the second- and fourth-largest electricity producers in Sub-Saharan Africa—are especially important.

**Figure 11 Additions to  own generation as a percentage of gross owngeneration capacity, 1990–2005**

<!-- Start of picture text --> 18.0%<br>16.0%<br>14.0%<br>12.0%<br>10.0%<br>8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> Source: UDI World Electric Power Plants Data Base (WEPP).

Own-generation is much more prevalent in low-income countries than elsewhere (figure 13).<sup>15</sup> The share of own-generated capacity in low-income countries is 12 percent, twice that of lower-middleincome countries, and five times that of upper-middle-income countries. Of the 12 countries where own generation accounts for more than 10 percent of installed capacity, 8 are low income. Low-income countries account for 74 percent of all own-generation capacity, with upper-middle-income countries accounting for all but 3 percent of the rest.

**Figure 12 Own-generation as share of total installed capacity, by subregion, 2006**

**Figure 13 Own-generation as share of total installed capacity, by national income, 2006**

<!-- Start of picture text --> 18% 14%<br>16% 12%<br>14%<br>10%<br>12%<br>8%<br>10%<br>6%<br>8%<br>6% 4%<br>4% 2%<br>2% 0%<br>0% Low Income Low Middle Income Upper Middle<br>Income<br>Central Africa EAC ECOWAS SADC<br><!-- End of picture text --> Source: UDI World Electric Power Plants Data Base (WEPP).

Note: EAC = East African Community; ECOWAS = Economic Community of West Africa States; SADC = Southern African Development Community.

14 For a complete list of ECOWAS countries, see <u>http://www.ecowas.info/.</u> 15 The income categories are defined according to the World Bank’s classification, available at <u>www.worldbank.org/data/countryclass/classgroups.htm.</u> 10
measure.<sup>26</sup> The interest rate is measured by the e↵ective federal funds rate and the inflation rate is defined as the growth rate of the consumer price index.

In practice, labor productivities and the real price of capital investment are nonstationary. To overcome this problem, we follow the common practice of first di↵erencing. The measures of per capita hours also exhibit some nonstationarity. This feature is also documented in previous studies that examine quarterly data (e.g. Gal ́ı, 1999, Francis and Ramey, 2005, Gal ́ı and Rabanal, 2005, and Fisher, 2006). The nonstationarity of per capita hours is even more evident at annual frequencies. As Fisher (2002) points out, the appropriate way to include per capita hours into the analysis is a matter of some controversy. Christiano, Eichenbaum and Vigfusson (2003) provide an extensive discussion on the treatment of per capita hours in the VAR. In this paper, we stationarize the hours measures by removing a linear trend from the log series. As in Collard and Dellas (2007), this approach avoids the criticism of Christiano et al. (2003), that hours should not be di↵erenced. Using hours in levels or first-di↵erences produces confidence intervals for hours and other variables that diverge to infinity as the horizon increases.

# **7 Empirical Results from the VAR**

In this section we discuss our results from the estimated VAR. With quarterly data, four is the common choice for the number of lags which adequately captures the medium-run dynamics in the data.<sup>27</sup> This corresponds to one lag at annual frequencies. The one year lag

> 26Aggregate real output in the consumption-good sector is defined as aggregate nominal output net of R&D investment divided by the implicit GDP deflator from the BEA-NSF satellite account.

> 27For instance, see Christiano, Eichenbaum and Evans (2005), Altig, Christiano, Eichenbaum and Linde (2005) and Fisher (2006).

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The second main data source was the _Regional Accounts Statistics_ , which contains GDP levels disaggregated by state and sector, from 1985 onwards.<sup>21</sup> Output statistics are presented in fourteen broad sectors, which we grouped into primary (largely agriculture), secondary (largely industry) and tertiary (largely services), according to the classification described in Appendix I. These state-level GDP statistics were not spatially deflated, since the spatial price deflator used for the PNAD differentiates between rural, urban and metropolitan areas, and the GDP statistics do not provide a breakdown of output by those areas within states. They were temporally deflated using the INPC/IPC consumer price index, which was also used to deflate household incomes, as noted above.<sup>22</sup> Table 2 gives growth rates for GDP and for each of its three sector components, for each year in our sample. All three sectors contributed to a slightly higher and more sustained growth in the period after 1994. Figure 2 gives the evolution of the implied sector shares in GDP. The most notable feature of the series in Figure 2 is that the services sector generally experienced a rising share of total GDP in the period up to about 1994, but that its share declined (from about 63% to about 53%) after that date. The other two sectors followed roughly the opposite pattern .

When controlling for time-varying correlates of poverty reduction, we use state level public expenditure statistics, disaggregated by type. These come from our third main data source, the _State and Municipal Expenditure Accounts (Despesas Municipais e Estaduais por Função)_ of the Treasury Department of the Ministry of Finance. We grouped a subset of the expenditure types presented there into “social expenditures” (including education, health, sanitation and social security) and “investment expenditures” (including equipment, installation, real estate and infrastructure purchase and construction). A detailed classification is provided in Appendix III. Moreover, we collected data on _Federal_ expenditures on Social Security and Social Assistance, to control for the rapid changes in these transfers over the period, as depicted in Figure 1. We

> 21 The state of Tocantins (TO), which was created as a split-off from Goiás (GO) in 1992, is merged with Goiás throughout the analysis.

> 22 Although the choice of the consumer price index for deflating both the household incomes that underlie the poverty statistics and the sectoral output series that underlie the growth statistics appears natural, it did require a validation process. The Census Bureau (IBGE) recommends that the GDP deflator, rather than the CPI, be used to deflate the GDP series. However, since the poverty statistics used as dependent variables in our main analysis are based on incomes deflated by the CPI, avoiding biases requires correcting for the ratio of the two deflators. When this is done, one can not reject the hypothesis that the resulting model is identical to a model that deflates the GDP series by the CPI directly. Details of these procedures are recorded in Appendix II.

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or two migration episodes and less than 5% had more than two migration episodes. In Table A.17 in the Online Appendix, we also check the robustness of our results to excluding individuals who had more than two migration episodes and our results are also robust to this check.

Finally, we check whether the differential results that we observed are due to differences in the reasons of return between documented and undocumented migrants. In Table A.18 in the Online Appendix, we estimate two distinct models. Columns (1), (2), and (3) are the same across the two models, while in the first model, we focus on voluntary return migration (column 4a), and in the second model, we focus on involuntary return migration (column 4b). Voluntary reasons are the following: individuals return to get married, to set up business at home country, to take over family business or farm, due to poor working conditions, to care for family, to study, or due to an appointment. On the other hand, involuntary reasons include contract ended, sudden termination by employer, retirement, health problems, the war in Iraq or Kuwait, accident or illness, death, or compulsory military service. The estimates in columns (4a) and (4b) allow us to compare documented and undocumented migrants who returned due to voluntary or involuntary reasons, respectively.

Interestingly, we find that voluntary returns are associated with a wage penalty for undocumented migrants and a wage premium for documented migrants. On the other hand, we find that undocumented migrants who returned involuntarily do not witness a wage penalty, while documented migrants still witness a wage premium. Indeed, we find that undocumented migrants who returned involuntarily have lower migration duration (average migration duration is 3.6 years with a standard deviation of 3.7), while undocumented migrants who returned voluntarily have on average 5.8 years of migration duration with a standard deviation of 6.6. In line with our results in Table 12, these differential effects might be associated with the length of the migration spell abroad. These results confirm once again that the longer undocumented migrants stay abroad, the more their human capital is depleted.

# **7 Concluding Remarks**

This paper examines the impact of the legal status of temporary overseas migration on migrants’ wages upon return to the home country. Using unique data from Egypt, we distinguish between return migrants according to their type of international migration—documented versus undocumented—and examine the impact of temporary migration on wages upon return. Relying on a conditional mixed process model, which takes into account the triple selection into emigration, illegality, and return migration, we examine the effect of illegality on wages upon return.

Our findings show that controlling for the various layers of selection, return migrants who had an illegal status while abroad have significantly lower earnings upon 28
# **Measuring the Technical Efficiency of Airports in Latin America**

Sergio Perelman CREPP, University of Liege Bd. du Rectorat 7 (B31), 4000 Liège, Belgium +32 43663098, sergio.perelman@ulg.ac.be Tomas Serebrisky<sup>1</sup> The World Bank WORLD BANK - MSN I6-601, Washington DC 20433, USA +1 202 458-2872, tserebrisky@worldbank.org

**JEL classification:** L25, L33, L90

> 1 Corresponding author: Tomás Serebrisky (tserebrisky@worldbank.org). We are grateful to Antonio Estache for the useful discussions and to Germà Bel for the comments he provided as discussant in the International Conference on Infrastructure and Development, Toulouse, 2010. Raquel Fernandez, Juan Ortner and Matías Herrera Dappe provided excellent research assistance. The financial support provided by PPIAF is acknowledged. All errors are exclusively our own.
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54. The results of the analysis are shown in Table 2 below. At the end of 1997, Zambia's fiscal balance remained far from the level required for solvency and sustainability, and fiscal adjustment equivalent to about 2.4 percent of GDP would be needed to bring about solvency in the central government deficit (Table 2). However, achieving fiscal solvency for the public sector as a whole (the quasi-fiscal deficit) would require fiscal adjustment on the order of 16.1 % of GDP. Because of a substantial real devaluation in 1997, fiscal adjustment to achieve sustainability was about 25.3% of GDP, a huge requirement beyond the ability of any government. A comparison of Table 1 and 2 also shows that while the conventional measure of fiscal deficit (Table 1) shows a decline from 1996 to 1997, the solvency and sustainability indicators (Table 2) show that an increase in fiscal adjustment was needed.

**Table 2: Zambia: Public Sector Solvency and Sustainability (percentage of GDP)**

||**1995**|**1996**|**1997**|
|---|---|---|---|
|Solvency||||
|Primary Balance|6.6|6.3|5.3|
|Real GDP Growth|-2.3|6.5|3.5|
|Domestic Debt|7.5|5.8|4.6|
|External Debt|207.5|188.1|178.5|
|Primary Balance for Solvency|22.9|6.7|7.7|
|Central Government Solvency Adjustment|16.3|.5|2.4|
|Non-financial Public Sector Solvency|23.7|12.6|12.4|
|Adjustment||||
|Quasi-Public Sector Solvency Adjustment|32.8|18.8|16.1|
|Sustainability||||
|Grants|5.5|5.9|4.8|
|Sustainable Primary Balance|15.0|11.2|30.6|
|Actual Central Government Primary|6.6|6.3|5.3|
|Balance||||
|Central Government Sustainability<br>Adjustment|8.5|4.9|25.3|
|Non-financial Public Sector Sustainability<br>Adjustment|15.8|17.1|35.4|
|Quasi-public Sector Sustainability<br>Adjustment|24.9|23.2|39.0|

_Source:_ Zambia Fiscal Management Report, World Bank Report 18552ZA _55._ We also ran additional simulations that examined the impact of slower export growth, lower grants, and a reduced external debt burden (Table 3). Reducing the annual rate of export growth from 5.8 percent to 2.9 percent in 1998 would require an adjustment effort of 18.5 percent of GDP. This result reveals just how difficult the situation is in Zambia and highlights the need for external debt relief. If both the stock of external debt and the
<!-- Start of picture text --> 1,000<br>800<br>600<br>400<br>200<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-11<br>Own Farm NFE Wage Labor<br>4000 4000<br>3000 3000<br>2000 2000<br>1000 1000<br>0 0<br>0 1000 2000 3000 4000 0 1000 2000 3000 4000<br>Own farm (12 month, crop only) NFE (12 month)<br>4000 4000<br>3000 3000<br>2000 2000<br>1000 1000<br>0 0<br>0 1000 2000 3000 4000 0 1000 2000 3000 4000<br>Wage labor (12 month) Total labor supply (12 month)<br>Ethiopia Malawi Tanzania Uganda<br>7 day recall12 month recall 7 day recall12 month recall 7 day recall12 month recall 7 day recall12 month recall<br>Mean hours (annual equiv)<br>NFE (7 day)<br>Own farm (7 day, crop + livestock)<br>Wage labor (7 day)<br>Total labor supply (7 day)<br><!-- End of picture text --> Figure 11: Figure 11a (top) depicts a comparison between mean labor supply aggregates using only 7-day recall and those using 12-month recall. Figure 11b (bottom) contains a non-parametric regression of 7-day recall based labor supply aggregates on 12-month recall based labor supply aggregates for farms, NFEs, wage labor, and total labor supply. The regressions are visually weighted, so that the predictions fade at the tails of the distribution.

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-30 -

# Joint Exchange Rate and **Interest** Rate **PoUcy Action**

4.9 Table V presents the key economic indicators when joint exchange rate and interest rate policy action is taken to eliminate a public sector borrowing requirement which is additional to an external financing gap. Exchange rate action retuces the residual public sector borrowing requirement compared to a situation in which no such action is taken (para. 4.5). In principle, the interest rate policy action also reduces the overall demand for credit in the economy relative to the base projections. From this perspective therefore, the required exchange rate adjustment would be expected to approximat that of the independent exchange rate policy solution while the required interest rate adjustment would be less than in the independent interest rate policy solution (Figure Two). These expectations are confirmed by the simulation results presented in Table V.

4.10 The required real domestic loan inerest rate is 11.1 perce' a 1991, 10.1 percent in 1993 and 8.3 percent in 1996. In comparison, it is 11.2 percent in 1991, 10.9 percent in 1993 and 13.0 percent in 1996 when only interest rate policy action is taken. The equilibrating interest rate is lower because the net foreign assets increase to - US$1119.4 million in i996 aoint exchange rate and interest rate action) compared with - US$104.2 million (mterest rate action only). Because the pool of domesdc credit increases when joint exchange rate and interest rate action are taken, more private investment can be accommodated for a given level of residual public sector borrowing. Thus, private investment is only 27.7 percent of GDP in 1996 when interest rate policy action is used (33.1 percent with joint policy action).
Let γk = ln p∗k and γi = ln(1 + λi). Using this result, and letting uik be the error term, the basic estimation equation is: where γk and γi are product and country fixed effects, respectively. Note that the algebraic interpretation of the estimated coefficient β<sup>�</sup> ȷ is: or where a�ȷ is the estimated ad-valorem equivalent (AVE) of NTM type j.

Note that since we do not observe the compliance costs of NTMs (we only know if an NTM exists and the number of such measures), the pass-through parameter for NTMs cannot be identified. Thus, the estimated AVE for NTMs, a�ȷ, is the fraction of the compliance cost that is ‘passed through’, e.g. if a�= 0.2, ȷ an NTM imposed on product k translates, on average, into a 20% rise in its domestic price across products and countries in the sample.

A second series of regressions are also run, using counts rather than dummy variables to capture the presence of NTMs.

# **c. Results**

NTMs are positively and significantly related to domestic prices across our sample of countries. Table 6 shows the baseline regressions results. The first and third specifications use a dummy variable to indicate the presence of an NTM, while the second and fourth specifications use the count of NTMs applied at the product level. Standard gravity type controls are used. Bilateral data on distance between two countries, common language, contiguity and common colonizer since 1945 are sourced from CEPII, and GDP figures come from the World Bank’s World Development Indicators database. Three variables on endowments are used, physical capital stock per worker, arable land per worker and education per worker. These figures are sourced from Shirotori et al (2010). After controlling for systematic cross-country costof-living differences, and partner and product specific unobservables, we find that the presence of at least one NTM increases domestic prices by 10.6% (exp(0.101)-1) on average across all 33 countries for which data are available. If we interpret the coefficient with the number of NTMs as the increase in domestic prices of adding one additional NTM (regardless of type), then an additional NTM increases domestic prices by an average of 2.2% (exp(0.022)-1).

18
Policy Research Working Paper 6536

# **Abstract**

This paper assembles a panel data set which measures cognitive achievement for 128 countries around the world from 1965 to 2010 in 5-year intervals. This data set is constructed from international achievement tests, such as the Programme for International Student Assessment and the Trends in International Mathematics and Science Study, which have become increasingly available since the late 1990s. The authors link these international assessments to regional ones, such as the South and Eastern African Consortium for Monitoring of Educational Quality, the Programme d’Analyse des Systemes Educatifs de la Confemen, and the Laboratorio Latinoamericano de Evaluacion de la Calidad de la Educacion, in order to produce one of the first globally comparable datasets on student achievement. In particular, this dataset is one of the first to include achievement in developing countries, including 29 African countries and 19 Latin American countries. This data set is an extension of an earlier data set constructed by Altinok and Murseli (2007). The authors provide a first attempt at using this dataset to identify causal factors that boost achievement. The results show that key drivers of global achievement are civil rights and economic freedom across all countries, and democracy and economic freedom in a subset of African and Latin American countries.

This paper is a product of the Education Unit, Human Development Network. 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 hpatrinos@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