rafmacalaba/datause-encoder-data huggingface dataset:
financial dependence, asset tangibility, employment turnover, and the natural rate of establishment entry obtained from secondary sources (e.g. Braun, 2003, and Micco and Pages, 2004), and information on provincial level minimum wages (World Bank, forthcoming).

In addition, we complemented the data with two measures of political connectedness constructed by Mobaraq and Purbasari (2008). The first builds on an insight by Fisman (2001) and identifies firms traded on the Jakarta Stock Exchange (henceforth JSX) whose stock returns responded negatively to news reports about Suharto‟s health. Mobaraq and Purbasari (2008) identify the major shareholders on the boards of these firms, and all conglomerates run by these entrepreneurs, as well as firms owned by these conglomerates and classify those as connected. This measure, however, may only identify those firms for whom connections mattered or spuriously include firms for whom an adverse stock market valuation spuriously coincided with news reports about Suharto‟s health. The second proxy, which is an indicator of whether a firm has a relative of Suharto on its board, overcomes these limitations. For more information see Mobaraq and Purbasari (2008).

The survey design impacts the definitions of key explanatory variables. Entry is defined as entry into the survey; it is when establishments cross the 20 employee threshold, not necessarily when they began operations. Conversely, exit is defined as exit from the survey; we cannot distinguish whether firms go out of business or continue operating with fewer than 20 employees.<sup>7</sup> Information on the capital stock was not collected in 1996. We use data from 1991-95 to predict the capital stock based on output, investment, material inputs, labor usage, ownership characteristics, whether or not the firm exports, province and lagged capital.<sup>8</sup> We also confirmed the robustness of our results by omitting 1996 from the regressions.

> 7 As establishments are not required to report their closure, exit is inferred from establishments ceasing to file reports to BPS.  We do not count as exits temporary lapses in reporting. Temporary exits account for 0.6% of all the data.

> 8 For firms that enter in 1996, we use data from 1997-2000 to backcast their capital stock using the same set of explanatory variables (but using leads rather than lags where appropriate).

15
robust to controlling for GDP per capita.

The visual evidence displayed in Figure VI shows that for secondary completion, the mean hypothesis predicts a very early reversal of the gender gap, while the tail hypothesis and the joint hypotheses predict the reversal to take place at a much higher value of the secondary completion rate. This late reversal is closer to what is observed in the Barro-Lee dataset, where the global gender gap reversal in secondary completion occurs when the global secondary completion rate is of about 0.50.

# **6.2 Robustness**

The sample restrictions we apply to the original Barro-Lee dataset of over 140 countries aim at increasing the precision and reliability of our estimates. However, one may argue that those could affect the composition of our sample and drive our results. To assess whether our results are robust to using a broader sample, we estimate the model parameters on a larger pool of countries where less restrictive sample restrictions are imposed. In particular, we include countries for which a large drop or jump in the gender ratio is observed in specific years, which increases the estimation sample from 98 to 113 countries. The results of the model estimates in this larger sample are reported in Table VII. As shown in the table, the estimation results in this expanded sample are similar to the ones reported for the baseline estimation in Table VI.

# **6.3 Model parameter estimates by level of economic development**

The respective predictive power of the tail and mean hypothesis may vary by level of economic development. We assess whether our baseline results hold in both OECD and non-OECD countries by splitting the sample into these two country groups and separately estimating the model parameters in these two subsamples.

Table VIII and IX report the model estimates in the sample of OECD and non-OECD countries, respectively. The respective explanatory power of the mean ad tail hypothesis somehow differ in 26
97 _Cost data_ - on emission control devices, switching to cleaner fuels, making transportation system changes, enforcement, air quality monitoring, applying emission standards, inspection and maintenance compliance, and making changes in the manufacturing processes, as well as other private and public costs related to alternative control strategies.

# **Economic Considerations**

For developing countries to make a serious commitment of resources to combat air pollution, economic impacts of the environmental pollution have to be much greater than the cost of abatement. Only if it can be clearly demonstrated that environmental pollution is in fact a serious drag on the economy including the damaging effects on health and public welfare, can major ameliorative actions be expected.

There is a wide variation among developing countries (i) in the severity of the impact of air pollution (health effects, acid deposition, deterioration of lakes and forests, and so on), (ii) in the assimilative capacity of the environment, (iii) in public attitudes, (iv) in degree of urbanization, (v) in transportation systems, and (vi) in economic conditions. Consequently, the policies and their method of implementation will also vary, along with the urgency for tightening air pollution standards and pursuing attendant strategies.

A major constraint against meaningful programs to control air pollution in developing countries may be the economic and financial costs of abatement programs. The types of strategies pursued and the timing of their implementation would depend greatly on the financing implications of various actions. The question of how much and how soon to abate transportrelated air pollution in developing countries will be resolved not only on the basis of the severity of the pollution problem and its direct health and welfare impacts, but more importantly on the basis of expected consequences of various control strategies including "do-nothing", on the economic sectors.

The basic issue is represented by the question: What strategies should be adopted, how soon, and at what cost? The answer to this question can be approached by assessing the marginal cost of allowing the emission of a given quantity versus the marginal cost of abating that amount of air pollution. The optimal approach to pollution abatement programs and timing of implementation should be to select the strategy that equates the two marginal costs. Some of the financing options for air pollution abatement programs include mobilization of funding from the private sector, joint property developments, differential taxation schemes and various tax concessions, and public bonds.

# **Costs and Effectiveness of Alternative Strategies**

A careful cost-effectiveness analysis would be necessary before a particular set of strategies is adopted. Environmental problems related to land transport vary in severity from
# **Agricultural Trade: What Matters in the Doha Round?**

David Laborde* and Will Martin**

*IFPRI and **World Bank

JEL Codes and Keywords: F13, F17, O24. Agricultural trade; trade policy; WTO; Doha Agenda; tariff cuts; market access; domestic support. Sector Board: Agriculture and Rural Development.

The views expressed in this paper are those of the authors only and should not be attributed to their employers. Thanks are due to valuable comments from Kym Anderson, David Blandford, Ian Gillson and Gordon Rausser. The authors are responsible for any remaining errors. Financial support from the World Bank’s Multi-Donor Trust Fund for Trade is gratefully acknowledged. Please refer to Laborde, D. and Martin, W. (2012) ‘Agricultural trade: what matters in the Doha Round?’ _Annual Review of Resource Economics_ 4:265–160.
Within each panel, four specifications are included:

- **Column 1:** The basic double or triple interaction, and nothing else.

- **Column 2:** To column 1, we add 120 dummies for age in months 1-60, separately for boys and girls. This accounts for the unfolding of stunting over time, for any mean differences between our population and the WHO reference population, and for any differences in age structure across experimental groups. Adding these controls slightly increases the experimental point estimate in two cases and decreases it in two cases, but in no case makes an important difference.

- **Column 3:** To column 2, we add village fixed effects (constant across the three survey rounds). Because the treatment was randomly assigned to villages, we would not expect these to have an effect, and they do not, other than to slightly reduce standard errors.

- **Column 4:** As recommended in the WHO height-for-age reference table guidelines, we omit observations more than 6 standard deviations from the mean. Column 4 replicates column 2 using truncated regression<sup>18</sup> to demonstrate that this truncation has little effect. Section 3.4.4 further documents that mismeasured ages and dispersion in height-for-age _z_ -scores are not responsible for our results.

In all cases an effect of the program is seen, typically in the range of 0.3 to 0.4 heightfor-age standard deviations, or about 1.3 centimeters in a four-year-old. McKenzie (2012) recommends longer time series in experimental studies than simple before-and-after. Although we only have two post-intervention survey rounds, it is notably consistent with our interpretation of the results as representing an effect of the program that the point estimate for the endline is greater than the point estimate for the midline in every case, perhaps as the effects of reduced enteric infection have had an opportunity to accumulate. So, in panel A, the effect ranges from 0.236 to 0.278 at midline, and from 0.379 to 0.448 at the end. Without

> 18A’Hearn (2002) recommends this procedure for studying truncated height samples. Strong normality assumptions are required, although this might not be such a misleading assumption for normalized height.

18
WPS3957

# **Do Workers’ Remittances Promote Financial Development?**<sup>∗</sup>

Reena Aggarwal McDonough School of Business, Georgetown University <u>aggarwal@georgetown.edu</u> Asli Demirgüç-Kunt The World Bank <u>ademirguckunt@worldbank.org</u> Maria Soledad Martinez Peria The World Bank <u>mmartinezperia@worldbank.org</u>

# **Abstract**

Workers’ remittances to developing countries have become the second largest type of flows after foreign direct investment. This paper uses data on workers’ remittance flows to 99 developing countries during 1975-2003 to study the impact of remittances on financial sector development. In particular, we examine whether remittances contribute to increasing the aggregate level of deposits and credit intermediated by the local banking sector. This is an important question considering the extensive literature that has documented the growth-enhancing and povertyreducing effects of financial development. Our findings provide strong support for the notion that remittances promote financial development in developing countries.

<u>Keywords: remittances, financial development, financial institutions JEL Classification: F37, G21, 016</u> World Bank Policy Research Working Paper 3957, July 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ∗ We benefited from comments and suggestions from Thorsten Beck, Caroline Freund, Aart Kray, David Mackenzie, and L. Alan Winters. We thank Nicola Spatafora, Caroline Freund, and Angela Cabugao for providing us data. We are grateful to Florencia Moizeszowicz for excellent research assistance.  Corresponding author: Reena Aggarwal, McDonough School of Business, Georgetown University, G-04 Old North, Washington, D.C. 20057.  Email: aggarwal@georgetown.edu.
<!-- Start of picture text --> Chronic poverty<br>Peninsular Bumiputera Pen. non-Bumiputera East M. Bumiputera East M. non-Bumiputera<br>mI<br>a<br>2 7<br>fy<br>2<br>zn<br>=<br>=<br>eTM<br>a<br>S — ae<br>a<br>PEeea! PSS“oe SEegyee SSa:x ESSee KCetetSPSeea’ SSSoe SKegySSx KKKoe SKet2 8<br>Sharesin the firstare roundbasedofoneachsyntheticinterval.panel The estimatespoverty lineforusedhouseholdis RM heads527. aged between 25 and 60<br><!-- End of picture text -->
with each other. Hence, this result should probably be taken as an indication that differences in data quality are largely, but probably not entirely, responsible for all of difference in inequality between Russia and Poland.

**Figure 3. Instrumenting Ginis to Eliminate Measurement Error**

<!-- Start of picture text --> Russia Russia Poland Poland<br>Income Gini Expenditure Gini Income Gini Expenditure Gini<br>0.50 0 50 0 5 i 0 50<br>0.45 * i l 40.45 0.45<br>0.40 0.0 0.0 0.40<br>0.35 0.35 CD~~~~~~~~~~~~~~(_ 0.35 0.35<br>0.30 0.30 0300 0 0.30<br>0.25 . 0.25 ID 0~25 0.50<br>E~~~~~~~~~~<br>0.20 .2 5 0 0.20 0.20<br>* ~0.15 0. 0.15 .E0.15 01<br>x 0.1<br>* 0.05 0.05 0.05 0.05<br>0.00 0.00 0.00 0.00<br>Note: Data refer to 1995 for Russia and 1994 for Poland. Measured Ginis include possible measurement error. Ginis are<br>instrumented in order to eliminate measurement error. Instruments for log income are a measure of subjective living conditions and<br>log expenditure. Instruments for log expenditure are a measure of subjective living conditions and log income. The calculations<br>assume log normal income and expenditure distributions and assume that measurement error in income, expenditure and subjective<br>living conditions are independent. To the extend measurement error is correlated, the instrumented Ginis are biased upwards.<br>Standard errors are less than 0.02 for both measured and instrumented Ginis. Differences between measured and instrumented Ginis<br>are highly statistically significant in all cases.<br><!-- End of picture text -->

# 5. **Economic Security**

Following families over time also allows us to examine the stability, or security, of their economic situation. Figure 4 shows for Russia and for Poland the distribution of income and expenditure shocks, measured as percentage changes between the reference month and the same month one year later.<sup>9</sup> These percentage changes are reported in deviation of the national mean. The figure shows that families experience huge 9 The reference month is the month in which the household was observed in wave 2 of the survey. In Russia, wave 2 was fielded in November and December of 1995 while in Poland wave 2 was fielded between January and December of 1994.

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Table 4: Impact of PROBEMS on EMS Graduation by Levels of Household Assets

|Outcome variable|ENLACE (Y/N)<br>Household index (0, 1 & 2)<br>Household|index (3 & 4)|
|---|---|---|
||(1)<br>(2)<br>(3)|(4)|
|Treatment|0.006<br>0.005<br>0.015|0.010|
||(0.020)<br>(0.020)<br>(0.013)|(0.013)|
|Controls|Yes|Yes|
|N|2,201<br>2,176<br>5,120|5,044|
|Mean Dep. Control group|0.67<br>0.68<br>0.69|0.69|
|SD Dep. Control group|0.47<br>0.47<br>0.46|0.46|
|***|_p <_0_._01, ** _p <_0_._05, * _p <_0_._1||

The full set of controls include age, dummies for sex and area (urban-rural) and test scores on Enlace 9, one for spanish and other one for mathematics. ENLACE (Y/N) takes the value of 1 if the student took the 12th grade exam in 2012 and 0 otherwise. The houseld index takes a value from 0 to 4 depending if the student’s household has cement roof, cement floor, piped water and piped water toilet.

survey ENIGH, which measures incomes and expenditures, to identify, self-declared, scholarship holders and divide them between recipients of “Oportunidades” and “other government program”. Using ENIGH 2010<sup>12</sup> we identify households with students enrolled in EMS and, among this subsample, those that declared having an “Oportunidades” scholarship or a scholarship from another government program. Figure 5 shows the share of scholarship beneficiaries by decile of the distribution of per capita household income. Overall, “Oportunidades” scholarships seem to be well-targeted, benefiting the poorest EMS students. However, beneficiaries of “other government programs”, a category basically capturing PROBEMS, tend to be located in deciles 5 to 9 of the income distribution. The lack of targeting combined with the marginally positive effects among the most marginalized suggest that PROBEMS could increase the likelihood of finishing high school among the poorest students, but the program’s targeting mechanism was not very effective in reaching the poorest, or they were already covered by the conditional cash transfer program (Oportunidades).

> 12ENIGH is collected every two years and there was no survey in 2009, the year when PROBEMS applicants filled the online format with the socio-economic information.

18
# **1. INTRODUCTION**

Income differences between developed and developing countries are vast: in 2014, GDP per capita in high-income OECD countries was 9 times that in middle-income countries, and 68 times the per capita income in low-income countries.<sup>1</sup> These differences hold even within narrowly defined occupations, with Ashenfelter (2012) showing that McDonalds’ workers’ wages differ by as much as a factor of 10 across countries. How much of this difference can someone migrating from a poorer to a richer country hope to gain? The answer depends on the sources of these income differentials. If better institutions, higher quality capital, and other factors serve to raise the productivity of all workers, then the same worker will be immediately vastly more productive working in a rich country than in a poorer one, and therefore should earn more as soon as they move. In contrast, to the extent that benefiting from these factors requires language skills, higher education, country-specific knowledge, and other attributes that are embedded in native workers, immigrants may be no more productive abroad than they were at home, and their scope for immediate income gains from migrating much more limited. Longer-term income gains will then only occur to the extent that immigrants can assimilate and gain these country-specific skills.

Determining the gain in income possible through international migration is complicated by the fact that migrants are self-selected, differing in a wide range of skills, wealth, ambition, and many other attributes from those who do not migrate. One approach is to try to control for as many observable differences as possible between migrants and non-migrants, and then argue that the amount of self-selection on unobserved factors needed to overturn the measured gains is too extreme to be likely. Using such an approach, Clemens et al. (2009) document that a typical individual from the average developing country should expect to earn 2.5 to 3 times

> 1 Source: World Bank World Development Indicators online query 23 September 23, 2015: high income OECD countries had a per-capita GDP of US$43,619, middle-income countries US$4,694 and low-income countries US$639.

2
49 Table 2: Health sector outcome variables: descriptive statistics

|||Full sample<br>|||FFS = 1<br>|||PBP = 1<br>||Hist<br>|orical budget<br>|= 1<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable|Mean|S.D.|Obs|Mean|S.D.|Obs|Mean|S.D.|Obs|Mean|S.D.|Obs|
|Inpatient admissions|16.01|5.89|389|18.65|3.28|62|16.29|6.59|71|15.29|6.00|256|
|Hosp discharges - circulatory|1851.99|1046.65|346|2436.78|698.97|60|2232.06|1420.10|67|1575.50|880.67|219|
|Hosp discharges - cerebrovascular|328.22|230.40|342|458.50|132.78|60|420.32|310.30|66|263.88|196.85|216|
|Hosp discharges - respiratory|2070.39|998.60|343|1910.45|656.14|60|1761.68|767.77|67|2210.57|1109.31|216|
|Hosp discharges - digestive|1599.84|609.19|346|1803.01|392.80|60|1493.78|630.65|67|1576.63|640.14|219|
|Hosp discharges - musculoskeletal|756.42|493.11|346|1014.15|315.49|60|872.02|676.23|67|650.44|430.99|219|
|Length of stay|12.77|3.07|390|11.55|2.13|62|10.73|2.01|71|13.62|3.14|257|
|Hospital beds|8.08|2.91|339|7.43|1.71|51|7.08|1.67|53|8.45|3.24|235|
|Bed occupancyrate|72.84|14.88|274|77.40|7.52|44|73.97|17.02|60|71.26|15.30|170|
|Health expenditures - Total|388.24|283.91|317|645.64|231.34|55|552.10|411.95|59|270.88|152.77|203|
|Health expenditures - Public|287.15|243.17|317|509.85|220.95|55|391.57|353.96|59|196.46|136.11|203|
|Health expenditures - Private|101.09|72.38|317|135.78|68.46|55|160.53|84.73|59|74.42|52.97|203|
|SDR ischemic heart disease|302.48|130.11|354|259.33|93.83|62|281.73|106.63|65|320.21|141.05|227|
|SDR diabetes|15.08|8.62|354|13.26|6.57|62|17.06|12.10|65|15.02|7.80|227|
|SDR cerebrovascular diseases|175.99|53.64|354|168.27|46.75|62|164.96|56.49|65|181.25|54.05|227|
|SDR female breast cancer|21.52|6.72|354|26.39|3.13|62|24.63|5.90|65|19.30|6.62|227|
|SDR appendicitis|0.30|0.18|341|0.22|0.14|62|0.22|0.11|63|0.35|0.19|216|
|SDR hernia & intestinal|2.24|0.75|344|2.28|0.57|62|2.26|0.61|64|2.22|0.83|218|
|SDR adverse effects|0.20|0.33|181|0.09|0.08|50|0.08|0.08|42|0.31|0.44|89|

_Note:_ Mean, standard deviation (S.D.) and number of observations (Obs) for the full sample and for the sub-samples of country-year combinations with fee-for-service methods (FFS = 1), patient-based payment (PBP = 1) and budgets (Historical budget = 1) as the predominant hospital payment method.
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54
ERADICATING POVERTY IN FRAGILE STATES: PROSPECTS OF REACHING THE “HIGH-HANGING” FRUIT BY 2030

# _CHEN/WORLD BANK [FORTHCOMING]_<sup>_13_</sup>

The poverty forecasts forthcoming from the World Bank from Chen closely follow those of Ravallion. Initial poverty estimates come from PovcalNet, and the base, or “Business-as-Usual”, forecast is derived from a linear extrapolation of current economic growth rates and constant inequality. The “Business-as-Usual” forecast assumes the average growth from the past 10 years for all developing countries. The more optimistic scenario (“simulation”) takes the optimistic growth rate<sup>14</sup> from Ravallion (2013). In the forthcoming work, there is no mention of how inequality is treated in these two forecasts.

> 13 Methodology for Chen/World Bank is based on personal correspondence, and we acknowledge that details regarding the methodology may change substantially from what is presented here in the coming months.

> 14 The developing world growth rate given in World Bank/Chen (forthcoming) is 4.2%, but how this rate is applied is not specified (i.e. whether this rate is used as a GDP growth rate (the usual rate represented by “growth rate”) or growth in household consumption (as specified in Ravallion (2013)).  There is also a slight discrepancy in the rates given in World Bank/Chen (forthcoming) and Ravallion (2013); optimistic rates are given as 4.2% and 4.5% respectively.

11