rafmacalaba/datause-encoder-data huggingface dataset:
who were not employed were nonetheless looking for work and in the labor force, so that labor force participation rates were quite similar in 2001, 2005 and 2010 for women under 45. By 2010, however, the CULS suggests a sharper drop in labor force participation among urban women over age 45 than we observed in earlier years.<sup>10</sup> The decline in older women’s labor force participation raises an important question for labor research in China: Does the decline in women’s employment reflect a resurgence of gender discrimination in post-reform China or the effects of increases in household wealth and the ability of women to exit the labor force at a younger age? Differences across genders in mandatory retirement ages likely creates an institutional bias against women’s employment. Even in the absence of discrimination, the employment decision of older women in urban China reflects a constrained choice. Women may return to work as consultants or in self-employed activities after reaching mandatory retirement age, but retired women are frequently receiving pensions, which raises reservation wages for new employment. Those urban women uninterested in working in typical self-employed activities held by blue collar workers (e.g., nannies or housekeepers), may choose to stay out of work if their pension incomes are sufficient.

Earlier research on labor force participation in China has noted the drop in employment rates of urban residents, and urban women in particular (Cai et al, 2008; Maurer-Fazio et al, 2011) and attributed the drop to the effects of state sector restructuring after 1997. Restructuring reduced the state sector through two mechanisms: for younger state-sector workers, a special one-time urban layoff ( _xiagang_ ) program provided up to three years of living subsidies (and continued pension and health insurance contributions), as well as training and job placement assistance through reemployment centers. Workers within five years of retirement age were encouraged to take early retirement with pension benefits. As women were much closer to retirement age than men, particularly blue collar workers facing retirement at age 50, a far higher share of women took early retirement than men.

After losing work during state sector restructuring, men, the young and the well-educated, generally faced a shorter duration out of work (Appleton et al, 2002; Giles et al, 2006a; Maurer-

> 10It should be remembered that the CULS sample used for these comparisons are representative samples from five large cities (Fuzhou, Shanghai, Shenyang, Wuhan and Xian), and may not be representative of work decisions in medium and small cities.

6
**Table 2. The Impact of India’s Integration with the World Economy (% changes)**

|**Product**|**Output**|**Producer Prices**|**Exports**|**Imports**|
|---|---|---|---|---|
|Rice<br>|1.12|0.50|24.83|15.04|
|Wheat<br>|0.44|0.23|12.71|2.75|
|Grains|0.14|0.65|0.98|3.48|
|Vegetables and Fruits|-0.42|0.49|12.15|6.35|
|Oils and Fats|-1.75|0.10|11.18|8.23|
|Sugar<br>|0.31|0.73|11.34|13.73|
|Plant Fibers|-1.89|-0.07|12.05|1.94|
|Other Crops|-0.10|0.59|8.46|11.46|
|Livestock and Meat|-0.03|0.76|5.23|9.66|
|Dairy<br>|0.34|1.01|-6.57|13.80|
|Other Processed Foods|0.70|0.55|4.37|5.85|
|Energy|-0.83|-0.87|42.47|-0.20|
|Textiles|-1.90|-0.83|35.70|234.58|
|Wearing Apparel|12.78|-0.81|26.55|257.38|
|Leather|11.57|-1.34|48.70|241.71|
|Wood and Paper|-8.85|-0.27|30.17|90.69|
|Minerals|-3.28|-0.62|38.35|46.31|
|Chemicals, Rubber, & Plastics|-8.82|-3.42|90.22|128.04|
|Metals|-11.76|-3.25|108.29|209.06|
|Motor Vehicles and Parts|1.41|-2.31|59.51|30.91|
|Machinery and Equipment|20.98|-4.42|167.71|41.11|
|Electronics|34.97|-3.64|140.28|3.18|
|Other Manufactures|9.41|-3.19|56.48|82.57|
|Trade and Transport|-0.21|0.43|-1.81|1.51|
|Commercial Services|0.29|0.30|-0.62|1.46|
|Other Services|0.36|0.32|-1.09|1.75|
|_Food_|_0.02_|_0.55_|_9.85_|_7.23_|
|_Energy and minerals_|_-1.50_|_-0.80_|_39.47_|_6.27_|
|_Manufactures_|_-0.49_|_-2.74_|_67.63_|_84.17_|
|_Services_|_0.14_|_0.36_|_-0.68_|_1.51 _|
|**Total**|_1.14_|_-1.08_|52.36|50.46|
|Welfare represented as:|EV in US$2001|4989|Per capita utility|0.91|
|Real returns to:|Capital|3.26|Skilled labor|3.88|
||Land|1.70|Unskilled labor|3.28|

Source: Authors’ simulations with modified GTAP model; see details in text. The simulation includes introduction of duty drawbacks, a drop in manufacturing tariffs to 7%, and a reduction in transport costs to and from India by 20%..

12
**Table 12. Oil Prices and the Breakdown of Expenditures among Fuel Exporters**

|Dependent variable:|Consumption<br>expenditure/GDP (%)|Capital<br>expenditure/GDP (%)|Wage<br>expenditure/GDP (%)|
|---|---|---|---|
|Constant|-1.39<br>(-0.26)|-9.05***<br>(-3.01)|0.0097<br>(0.76)|
|Lagged dependent<br>variable|0.814***<br>(19.16)|0.950***<br>(20.66)|0.936***<br>(26.47)|
|Ln (real $ oil price)|-3.50**<br>(-2.21)|0.186<br>(0.30)|-2.57***<br>(-6.69)|
|Lagged ln (real $ oil|4.51***|2.43***|2.41***|
|price)|(3.18)|(3.17)|(6.53)|
|Hard peg dummy|-1.26**|-0.0030|-0.131|
||(-1.99)|(-0.01)|(-1.36)|
|Political stability|0.0444**|0.00206*|0.00589|
|(0 to 100)|(2.06)|(1.69)|(0.94)|
|Time trend|-0.0794|-0.314***|0.0053|
|(=0 in 2000)|(-0.43)|(-3.15)|(0.11)|
|n|288|265|254|
|R-squared|0.741|0.809|0.884|
|RMSE|3.60|1.88|0.891|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries with fuel > 25 percent of total exports. RMSE = root mean square error, or sample standard deviation.

21
compared to the transport sectors of other LAC countries. However, during 1989-1994, Mexico exceeded Brazil for the transport sector CO2 emissions even though its transport sector energy consumption remained below than that of Brazil’s (see Figure 2). Four large economies in the region – Argentina, Brazil, Mexico and Venezuela – alone have accounted for between 76-79% of the region’s CO2 emissions from transportation since 1980.

**Figure 1: CO2 Emission Mix by Sector**

<!-- Start of picture text --> Power Industry Transport Other Sectors<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Argentina Bolivia Brazil Caribbean Chile Colombia Costa Rica Cuba Ecuador El Salvador<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Guatemala Honduras Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela Other Latin<br>America<br>1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005<br>1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005 1980 1990 2005<br><!-- End of picture text --> Source: IEA (2007)
different model specifications. Furthermore, we did not find any systematic bias generated from any major event that took place during the survey.

The usefulness of subjective well-being data to understand numerous public policy debates is becoming increasingly recognized in the literature. As Clark et al (2008) mention, “the analysis of subjective well-being data provides a valuable alternative, but complementary, approach to a revealed preference framework that dominates the discipline of economies”. In the context of this analysis, the revealed policy preferences would imply desire for lower social inequality and the need to provide protection from shocks.

23
## **5.4 Judges’ workload**

We adapt the event study specification (1) to document changes in judges’ workload and report overall changes in judges’ incoming and ongoing caseload around the decree introduction cutoffs (Figure 8). While aggregating our data to the judge level weakens the precision of our estimates, we observe that the number of cases heard at each hearing increases in line with the upward trend in judge-level incoming caseload (Panels A and C, Figure 8). Interestingly, judges’ ongoing caseload remains relatively flat (Panel B, Figure 8). Although we lack statistical power to precisely measure these effects, the patterns indicate that the reform did not lead to a meaningful increase in judges’ backlogs, corroborating the notion that the reform improved judicial efficiency.

# **6 Firm-level impacts**

## **6.1 Event study of pre-trial entry and speed**

### **6.1.1 Empirical strategy**

Merging court data with tax administration records, we estimate a party-level event study model to capture the evolution of firms’ revenues after they enter pre-trial. Letting _yi,j,t_ be log monthly revenues at time _t_ for firm _j_ that is party to case _i_ , and _τi,j,t_ the number of months since _j_ ’s case _i_ started pre-trial (normalized to zero at -1), we estimate a flexible functional form that estimates one treatment effect per month relative to pre-trial start, as 22
Figure 9. Scatter plot of under-five mortality inequality versus income per capita <!-- Start of picture text --> .3<br>Braz I<br>Peru o<br>Bolivia Egypt a<br>hidonesia ° Dominican Rep. Turkey<br>.2 Philippines °3<br>nldia<br>Ke 8§MbdiT Morocco<br>o Ghana Colombia<br>Mambs Rep. Guatemala D<br>in T * r F a °bwe Namibia<br>ChOad () ° ° Kazakhstan<br>0.0 -<br>0<br>a)<br>a) cm<br>0 1000 2000 3000 4000 5000 6000 7000<br>GNP/capita PPP 1999<br>plot of under-five mortality inequality versus public share of health<br>.3<br>Brazil<br>Peru o<br>o Egypt Bolivia<br>DominihpWFvrkey a<br>.2 0 pl1lPippings<br>Senegal<br>0313 c a Ghana Colombia N<br>Cote d'lvoir&d Guaemal Kyrgyaz'u Mozambique<br>.1 * bia<br>Of 13 J?jwadn0Nal?iibia Ubksa<br>IE Burkina Faso llibia Uzbekistan<br>a~~~~~~~~Ca<br>Kazakhstan Chad<br>C0 0.0 2<br>a)<br>10 20 30 40 50 60 70 80 90<br>Public share of health spending<br><!-- End of picture text -->

**Figure 10. Scatter plot of under-five mortality inequality versus public share of health spending**

_20_
**<u>Table 1: Summary Statistics for key Variables</u>**

|||Obs.|Mean|SD|
|---|---|---|---|---|
|Employment Rate||228,423|0.73|0.44|
|Hourly Wage||85,591|10.71|11.00|
|Work Days Per Month||96,056|21.62|6.40|
|Work Hours Per Day||92,899|8.78|6.57|
|Male||450,488|0.51|0.50|
|Schooling||450,381|9.79|3.96|
|Age||450,488|32.20|12.48|
|Married||450,488|0.58|0.49|
||City|450,488|0.48|0.50|
|Locality type|Refugee camp|450,488|0.08|0.27|
||Village|450,488|0.44|0.50|
|Tenure (Months)||96,326|80.86|85.13|
||In Same District in WB|191,770|0.79|0.41|
|Pl f Wk|In other District in WB|191,770|0.07|0.25|
|ace o or|Israel|191,770|0.12|0.33|
||Settlements|191,770|0.02|0.14|
|Manufacturing||209,899|0.14|0.34|
|Working Out of Locality||143,568|0.50|0.50|
|WorkingOut of District||209,899|0.20|0.40|

_Source: Authors’ elaboration on Palestinian Labor Force Surveys_ 37
WPS3708

# **Brain Gain: Claims about its Size and Impact on Welfare and Growth Are Greatly Exaggerated***

# **Maurice Schiff World Bank and IZA**

## **ABSTRACT**

Based on static partial equilibrium analysis, the “new brain drain” literature argues that, by raising the return to education, a brain drain generates a brain gain that is, under certain conditions, larger than the brain drain itself, and that such a _net_ brain gain results in an increase in welfare and growth due to education’s positive externalities. This paper argues that these claims are exaggerated. In the static case, and based on both partial and general equilibrium considerations, the paper shows that i) the size of the brain gain is smaller than suggested in that literature; ii) the impact on welfare and growth is smaller as well (for any brain gain size); iii) a positive brain gain is likely to result in a smaller, possibly negative, human capital gain; iv) an increase in the stock of human capital may have a negative impact on welfare and growth; and v) in a dynamic framework, the paper shows that the steady-state brain gain is equal to the brain drain so that a ‘beneficial brain drain’ cannot take place, and a net brain loss is likely during the transition.

Keywords: Brain Gain Size, Welfare, Growth, Exaggerated Claims JEL Codes: D61, D62, F22, H20, H41, I12, J61

## World Bank Policy Research Working Paper 3708, September 2005

_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_ _<u>http://econ.worldbank.org.</u>_

*An early version of the paper was presented at the Royal Economic Society (RES) Meetings, March 21 - 23, 2005. A second version was prepared during my stay at ECLAC (CEPAL) as Visiting Scholar in April 2005, and I’d like to thank José Luis Machinea and Andrés Solimano for their kind invitation. Thanks are also due to| Mohammad Amin, Frédéric Docquier, Maurice Kugler, Caglar Ozden, Pierre Pestieau, Hillel Rapoport, Alan Winters and participants at the RES session on “The New Economics of the Brain Drain: View and Counterview” for useful comments, to Vlad Manole for research assistance and to Maria Kasilag for logistical support.
closely associated with modern migration and remittances, since the early migrants formed the foundation for migration networks that facilitate migration and remittances to the present day.<sup>8</sup> Of course, because rail lines stimulate economic activity9, they may also have a direct effect on the development of the banking sector. Hence, we separate the direct effect of rail lines from the migration effect, using differences in the rail network in 1900 and the rail network in 1998. Since migration flows depend on networks developed early in the 20<sup>th</sup> century, only the early rail network should affect migration, while modern rail lines constructed any time before 2000 will have an effect on the level of economic activity in the _municipio_ s.

We find that remittances have produced a broader and deeper banking sector in Mexico. Our most conservative estimate suggests that a one standard deviation change in the percentage of households receiving remittances is associated with an increase of 1 branch per 100,000 inhabitants (against a mean of 1.79), 31 accounts per 1,000 residents (relative to mean of 42 accounts), and an increase of 3.4 percentage points in the deposit/GDP ratio (compared to a mean of 4.2). The results on bank credit (as a percentage of GDP) are much less robust and do not survive after we instrument for remittances.

We are aware of only one existing study that investigates the causal relationship between remittances and banking sector development. Using aggregate cross-country data, Aggarwal, Demirgüç-Kunt, and Martinez Peria (2006) find evidence that remittances are associated with banking sector development across a broad set of countries.10 However, several concerns bear mention. First, the study only captures remittances reported in balance of payments statistics, which often neglect remittances received through means other than banks and are, therefore, measured with error. Second, the cross-country estimations in Aggarwal et al. (2006) are subject to at least one source of endogeneity which our within-country data avoid: the fact that countries

> 8 For example, Woodruff (2007) shows that the correlation between migration in the 1950s (during the second Bracero program) and migration in the 1990s was 0.7.

> 9 See Donaldson (2008) for an interesting analysis of the role of rail networks on early 20th century development in India.

> 10 Giuliano and Ruiz-Arranz (2006) also shows a positive correlation between the level of remittance flows and measures of bank deposits, but much weaker correlations between remittances flows and bank credit. Orozco and Fedewa (2007) show that households receiving remittances in five Latin American countries are more likely than non-recipient households to have bank accounts. The differences are large in Guatemala, El Salvador, Ecuador and Honduras, but much smaller in Mexico, where 19% of remittance-receiving households have accounts compared with 16% of non-recipient households. Neither study makes any claim about the causality of the associations they report.

5
of countries; the median country in upper-middle income category has the lowest concentration ratio, while the median country in the low-income category has the highest ratio (Figure 8).There has been little variation in concentration ratios over time, with no clear trend (Figure 9). While concentration is often seen as indicator of competitiveness of a banking system, recent evidence has shown a very low correlation with other measures of banking competitiveness (Claessens and Laeven, 2004). Nevertheless, in the absence of more detailed banking level data, concentration ratios are still the most readily available market structure indicator across countries and over time.<sup>8</sup> We include two indicators of profitability, **Return on Assets** and **Return on Equity** . As the previous measures, they are computed as unweighted averages across all banks in a given year. While banks in the median country in high and middle-income country have a return on equity around 15%, with little variation between high, upper-middle, and lower-middle income countries, the median return on equity was over 20% in 2007 in low-income countries (Figure 11). Return on Equity shows quite some variation over time, declining from 12% in 1995 to 8% in 2002, before rising again to over 15% in 2007 (Figure 12). While returns on equity in high and middle-income countries have converged over time, returns on equity in the median low-income country have been volatile with a decreasing trend over time (Figure 13). Returns on Assets on the other hand, have been first decreasing, then increasing, a trend that is similar across median countries of all income groups.

Finally, we include an indicator of banking stability. The **z-score** is the ratio of return on assets plus capital-asset-ratio to the standard deviation of return on assets. If profits are assumed to follow a normal distribution, it can be shown that the z-score is the inverse of the probability of insolvency. Specifically, z indicates the number of standard deviations that a bank’s return on assets has to drop below its expected value before equity is depleted and the bank is insolvent (see Roy, 1952, Hannan and Henwick, 1988, Boyd, Graham and Hewitt, 1993 and De Nicolo, 2000). Thus, a higher z-score indicates that the bank is more stable. There is little variation in bank stability, as measured by the z-score, across different income groups, while there is significant variation over time (Figures 11 and 12) The z-score has been continuously falling over the past 13 years, from 8.9 to

> charges on deposits).  The main difference between spreads and margins are lost interest revenue on non-performing loans, so that spreads are normally higher than margins.

> 8 The original version of this database also contained indicators of foreign and government ownership of banks. However, the ownership information provided by Bankscope has been shown to be inaccurate in many cases, so that we do not include these indicators here.  Ownership information on a less than annual frequency can be obtained from Barth, Caprio and Levine (2008).

9
> <sup>28</sup>

little impact, whether or not one controls for legal origins. The variables they use to test their political hypotheses are subjective indicators from the Polity IV database. The results presented earlier already indicate that political measures different than the ones they employ are highly significant determinants of financial sector development. There remains the question, therefore, of whether these political results survive the introduction of controls for legal origin.

Given the potential political roots of legal institutions, it is not obvious that one should examine the robustness of tests of political hypotheses of financial sector development with respect to controls for legal origin. Political variables may be insignificant in the presence of legal origin variables simply because the latter embed political circumstances that more directly capture political incentives towards the financial sector than the political variables themselves. With this caveat in mind, however, we can nevertheless ask whether the direct political measures used here are “robust” to the presence of legal origin controls.

As in Beck, et al. (2001), legal origin dummies are simply added to the base specifications – in this case, to the specifications in Tables 1a and 1b, and 2a and 2b. Here, in a slight variation on their approach, the Scandinavian legal dummy is substituted for the French. This allows all of the legal traditions that are thought to be most conducive to financial sector development, the British, French and German traditions, to be compared to those that are thought to be less conducive.

Results in the first three columns of Table 4 verify that the effects of legal origin variables in the particular specifications used here are similar to those found by Beck, et al. (2001). The last three columns of Table 4 present results substituting Scandinavian for French legal origin, confirming that Scandinavian legal origin is (somewhat) better than
hinge on the particular spatial distribution. For countries with more extreme spatial concentration of poverty than Indonesia, it is plausible that the benefits of geographic targeting will be relatively higher, thereby making a case for alternatives to the purely individual-level targeting being promoted through the expansion in national targeting registries. However, it is also possible that in countries with less within-locality inequality, the exclusion errors arising from full-enumeration strategy will be much more limited.

Nevertheless, we acknowledge the possibility that the reduction in leakage when moving to a national targeting registry (based on full enumeration) may be disproportionately larger than the reduction in undercoverage. Ultimately, this reduction in leakage has important implications for the political economy of support for redistribution that should be reconciled with the persistent exclusion of some subset of poor households from such schemes.

Overall, though, our evaluation should provide new evidence to ongoing debates about the relative merits of different types of registry-based targeting schemes. Understanding how and why these schemes may vary in their effectiveness across countries is an important task for future research.

28
22

# **Figure 1. Number of Democratizing Countries, 1961–96**

<!-- Start of picture text --> 16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1961 1966 1971 1976 1981 1986 1991 1996<br>Year<br><!-- End of picture text -->

# **Independent Variable: Intervention**

To measure intervention we considered several possible datasets, such as those used by Tillema (2000) or Regan (2000). Tillema’s Overt Military Intervention dataset is being updated, but so far is only available up to 1991. The Regan dataset covers the period up to 1999, but is limited to interventions in on-going civil wars. We therefore chose to use Jeffrey Pickering’s update of the Pearson & Baumann (1993–94) dataset on International Military Interventions (Pickering, 1999, 2002). The original dataset spans the years 1946 to 1988, but was updated by Pickering through 1996, using the original coding guidelines.<sup>23</sup> The IMI dataset defines military intervention as ‘the use of troops or forces to cross borders or the employment of forces already based in a foreign country in pursuit of political or economic objectives in the context of a dispute’ (Pearson, Baumann & Pickering, 1994: 209). It includes friendly as well as hostile military interventions. Pickering’s updated dataset includes 827 individual military interventions between 1946 and 1996, and 673 military interventions between 23 These data were obtained directly from Pickering.