rafmacalaba/datause-encoder-data huggingface dataset:
affiliates and improving basic infrastructure would drive up FDI”. Fung, Iizaka and Parker (2002) and Fung, Iizaka and Siu (2003) find positive evidence that Chinese provinces with better infrastructure are more likely to become an FDI destination. Our data for the corporate income tax rate are collected from various issues of Price Waterhouse Cooper’s Worldwide Tax Summaries and European Tax Handbook. The proxy for the host country’s infrastructure quality is the number of telephone mainlines per thousand people collected from World Development Indicators.

Factors that control for every host country’s FDI recipients include the current global supply characterized by world FDI outflows. Other things equal, a jump in FDI outflows worldwide is expected to have a positive effect on FDI inflows to all host countries. Therefore, annual world FDI outflow is added in our analysis to take care of this supply side effect. Data for world FDI outflows are drawn from the UNTCAD interactive database. (See summary table of data descriptions and sources in Appendix.) In addition, the fifth EU enlargement, by granting accession to eight CEECs, is likely to signal to foreign investors the credibility of these countries’ institutional and economic reforms. In recognition of their strong improvement, foreign investors are more confident choosing to operate their business in these economies. Follow the reasoning, a dummy, accession is constructed which assumes the value of 1 for all eight CEECs that are member states and 0 for the remaining seven. Since the dummy, accession is time invariant, estimation from either the fixed-effect model or first-differencing in the Generalized Method of Moments (GMM) will drop it out of the regression. In order to recover the estimates for accession, we transform by interacting it with a time trend<sup>4</sup> .

> 4 The accession dummy is assigned as country-specific and time fixed across the period.

17
For countries outside Europe and Central Asia, we use the GDIM dataset, which provides estimates of intergenerational educational mobility for four birth cohorts (individuals born in the 1950s, the 1960s, the 1970s, and the 1980s) across 114 countries (van der Weide and others 2023). It draws on microdata from social and household surveys, which contain retrospective information on parental education. Similarly to the LiTS, the GDIM relies on a correspondence between categorical information on attained levels of education and the equivalent number of years of schooling.

Our empirical analysis includes variables from other sources. GDP per capita and population come from the World Development Indicators <u>(World Bank 2023). Historical GDP per capita and</u> population (used to estimate GDP per capita and population at the decade of birth of the different cohorts) are from the 2020 edition of the Maddison Project Database (Maddison Project Database <u>2020; Bolt and van Zanden 2020). Historical infant mortality rates from 1950 onward are estimated</u> by the United Nations’ World Population Prospects Population Division (UNDESA 2022); rates for 1930–50 are from various historical sources.

Our cross-country panel of educational mobility and economic outcomes includes 1,419 countryyear observations in the period 2000-2020, covering 108 countries. However, we have 10 or more non-missing country-year observations only for 68 countries.<sup>14</sup> This sub-panel of 1,244 countryyear observations is the primary sample of our empirical analysis. <u>Table 1</u> presents the descriptive statistics of the main variables of our analysis for the cross-country sample.

# **6. Educational mobility across countries and generations**

This section describes the patterns of intergenerational educational mobility observed across countries and generations, using the enlarged sample of 115 countries for which we have estimates of mobility. We start our descriptive analysis by looking into the patterns of the upward mobility gap, the new oriented mobility measure that we apply in this work.

> by rounds of the LiTS. The oldest respondents from this cohort were 70 years old in the second round of the survey, 76 years in the third, and 83 in the last round of the LiTS. The numbers are especially low in Kazakhstan (29), Uzbekistan (37), and Azerbaijan (45). Sample sizes of younger cohorts, however, are large and more stable. 14 These are 18 countries in Europe and Central Asia, 16 countries in Latin America and the Caribbean, 24 highincome countries not elsewhere included, and 10 other developing countries in Asia and Africa.

12
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33
_Table 3: Program Effects on Extreme Poverty Headcount Index_ ( _FGT_ 0)

|||_No controls_|||_Controls_||
|---|---|---|---|---|---|---|
||**LPM**|**Probit**|**Logit**|**LPM**|**Probit**|**Logit**|
|Treat * year 2014|-0.046**|-0.043**|-0.042**|-0.054***|-0.051***|-0.051***|
||[-0.021]|[-0.02]|[-0.019]|[-0.020]|[-0.019]|[-0.018]|
|Treat|0.056***|0.054***|0.054***|0.034**|0.034**|0.033**|
||[-0.016]|[-0.016]|[-0.016]|[-0.015]|[-0.016]|[-0.015]|
|Year 2014|-0.006|-0.006|-0.006|-0.029|-0.038|-0.034|
||[-0.016]|[-0.016]|[-0.016]|[-0.083]|[-0.087]|[-0.088]|
|Observations|13,120|13,120|13,120|13,120|13,120|13,120|

_Table 4: Program Effects on Extreme Poverty Gap Index_ ( _FGT_ 1) _and Extreme Poverty Severity Index_ ( _FGT_ 2) _(Ordinary Least Squares)_

||_Poverty Ga_|_p Index_|_Poverty Severi_|_ty Index_|
|---|---|---|---|---|
||**No controls**|**Controls**|**No controls**|**Controls**|
|Treat * year 2014|-0.032***|-0.035***|-0.025***|-0.026***|
||[-0.010]|[-0.010]|[-0.007]|[-0.007]|
|Treat|0.029***|0.018**|0.019***|0.012**|
||[-0.008]|[-0.008]|[-0.006]|[-0.005]|
|Year 2014|-0.005|0.007|-0.005|-0.011|
||[-0.007]|[-0.039]|[-0.005]|[-0.030]|
|Observations|13,120|13,120|13,120|13,120|

_Table 5: Program Effects on Multi-dimensional Extreme Poverty Headcount Index_

|||_No controls_<br>|||_Controls_<br>||
|---|---|---|---|---|---|---|
||**LPM**|**Probit**|**Logit**|**LPM**|**Probit**|**Logit**|
|Treat * year 2014|-0.054***|-0.052***|-0.051***|-0.058***|-0.043***|-0.038***|
||[-0.016]|[-0.013]|[-0.013]|[-0.015]|[-0.009]|[-0.008]|
|Treat|0.021|0.018|0.018|0.006|0.002|0.002|
||[-0.013]|[-0.012]|[-0.011]|[-0.012]|[-0.008]|[-0.007]|
|Year 2014|-0.023*|-0.022*|-0.022*|0.014|-0.006|-0.014|
||[-0.013]|[-0.013]|[-0.013]|[-0.069]|[-0.051]|[-0.045]|
|Observations|13,120|13,120|13,120|13,120|13,094|13,094|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in brackets. Standard errors heteroscedasticityrobust and clustered at the primary sampling unit level, which takes into consideration the sampling design of the survey. Income variables used were adjusted to per adult-equivalent at August 2014 prices. The column titled "controls" shows the results of estimations in which the following conditioning variables were used: village size range, years of schooling, literacy, household size, dwelling’s ownership status, sex, indigenouos language speaker status, disability condition, household structure, marital status, employment condition, access to Seguro Popular, income from Conditional Cash Transfer Program - PROSPERA, income from PROCAMPO, overcrowding, access to drinking water, access to sewage, access to electricity, and a full set of the preceding covariates interacted with the time dummy.

21
growth enhances shared prosperity even with there is a substantial “shift” in “sharing.” This is probably the most useful element of the algebraic decomposition: it reveals that, to a certain extent, the share is like a “shift” factor between steady states, while growth in income per capita is more akin to a steady-state growth rate of shared prosperity. It is interesting to note that the (admittedly small sample) empirical experience presented shows that, in many cases, growth has a larger role to play than “sharing” in increasing this measure of shared prosperity – even over a relatively short timeframe.

As mentioned above, a fully specified growth model would – in principle – make both the share and the growth rate endogenous. On that basis, one might hypothesize that there is a systematic positive or negative relationship between the two variables. There is, in fact, a vast literature on both the theoretical<sup>8</sup> and empirical<sup>9</sup> bases for a relationship between inequality and growth. In a well-known paper, Dollar and Kraay (2002) found empirical evidence that there is no systematic relationship between income per capita growth and the share of the bottom 20 percent. In other words, the income per capita of the bottom quintile (on average) grew in line with the income per capita of the total population. They used this finding to declare in the title, “Growth Is Good for the Poor.” More recently, Dollar, Kleineberg and Kraay (2013) looked at the income per capita of the bottom 40 percent, using additional data now available, and reached a similar conclusion: “Growth Still Is Good for the Poor.” The original Dollar-Kraay paper focused on bottom quintile since many national poverty lines tend to settle at the 20 percentile. At the subnational level, Virmani (2007, 2008) has studied the relative performance of both economic growth and poverty levels across India’s states. He finds that, among other structural factors, the consumption share of the bottom 40 percent of a state’s population has a significant effect on a state’s poverty rate. In other words, promoting shared prosperity is consistent with reducing poverty: the World Bank Group’s twin goals, in this case, are complementary.

A key remaining issue is that the coverage, frequency and comparability of household survey data need to improve if one is to do more detailed work on the empirics of shared prosperity. This paper only presented some illustrative data for understanding how the basic algebra works.

# **REFERENCES**

Asano, Akihito, 2012, “Is There a “Double Bonus” From Reducing Inequality?,” _Economic Inquiry_ , Vol. 50(2), pp. 551-562.

Banerjee, Abhijit and Esther Duflo, 2003, “Inequality and Growth: What Can the Data Say?” _Journal of Economic Growth_ , 8(3), pp. 276-99.

> 8 See Asano (2012) for a recent model in this literature. 9 See, for example, Perotti (1996), Deininger and Squire (1998), and Banerjee and Duflo (2003).

14
<!-- Start of picture text --> Pollcy Research<br>Trade and Flnance<br>WPS 882<br><!-- End of picture text --> This paper-a product of the Trade and Finance Division, Technical Department, Europe and Central Asia - and Middle East and North Africa Regions is part of the department's regional study on financial sector reform in Eastern European countries. This paper was presented at a conference on Creating Capital Markets in Eastem Europe, organized by the Woodrow Wilson Center in Sofia, Bulgaria, in September 1991. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Lanha Ly, room H9-071, extension 37352 (April 1992, 42 pages).

What difficulties do Eastern European countries face in reforming their financial systems? What should their refonn priorities be? Can financial reform make the supply response more positive?

Thorne addresses these questions using the situation in Bulgaria to illustrate the financial system most Eastern European countries have inherited. Reforming these financial systems is especially difficult because of the problems inherited from a centrally planned economy (CPE). The financial system in a CPE is completely different from the financial system in a market economy. It is only a slight exaggeration to say that reforming the financial systems in these countries means creating a financial system from scratch.

Thome illustrates the types of problems Eastern European countries face in reforming their financial systems. He argues that these countries can stimulate the supply response by giving the emerging private sector more access to credit and by increasing the savings deposited in the financial system. He argues that the authorities should:

* Link reform of the financial sectcr to the privatization of banks and enterprises.

* Quickly privatize a group of banks.

* Encourage privatized banks to lend exclusively to the emerging private sector.

* Tum the rest of the banks into investment banks and make them participate in the restructuring and privatization of state-owned enterprises.

The Policy Research Working Paper Series disseminates the findings of work under way in the Bank. Anobjective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
19 welfare, through the provision of quality improvements<sup>and time-saving consumer</sup> goods and services, whose values are not<sup>captured by the national accounts. And</sup> the other is the leading role services<sup>play in systemic change.</sup> There are several aspects to this role in systemic<sup>change.</sup> One is the provision of a market infrastucture by<sup>the expansion of trading activities,</sup> including import/export trade. Such activities<sup>must replace the old centralized</sup> allocation and distribution systems which<sup>have deteriorated fast. Competitive</sup> trading activities can reduce the costs<sup>of transactions and can facilitate</sup> backward and forward linkages in production. It is perhaps obvious that the creation of a market infrastructure<sup>during stabilization is particularly</sup> important, as other forces are discouraging<sup>supply.17</sup> A second aspect is the role that services can play as a testing<sup>ground for beginner entrepreneurs, who</sup> are creating a new small business sector.'8 In most services, especially business and consumer services, investment<sup>requirements are low. A third aspect,</sup> employment generation, is a by-product of the<sup>other two. Employment absorption</sup> will be particularly important for<sup>white collar workers with high levels of</sup> education--a majority of whom will be women.

The gap between expected and actual levels<sup>of service activity in Russia</sup> and other former Soviet states can be<sup>used to provide a comparative statics</sup> simulation of the potential contribution<sup>of services to value added and</sup> employment as these countries move toward<sup>a market economy. Expected levels of</sup> service activity are estimated from the<sup>pooled cross-country OECD and "world</sup> economies" data; and the percentage differences<sup>from the actual data are applied</sup> 7 Lipton and Sachs 1990 and 1992 provide<sup>some evidence on these points.</sup> ***** For a study of new private service firms<sup>in Russia, see de Nelo and Ofer</sup> 1994.
- **McKenzie, David, Steven Stillman, and John Gibson.** 2010. “How important is selection? Experimental vs. non-experimental measures of the income gains from migration.” _Journal of the European Economic Association_ , 8(4): 913–945.

- **Miller, Douglas L.** 2023. “An introductory guide to event study models.” _Journal of Economic Perspectives_ , 37(2): 203–230.

- **Mobarak, Ahmed Mushfiq, Iffath Sharif, and Maheshwor Shrestha.** 2023. “Returns to international migration: Evidence from a Bangladesh-Malaysia visa lottery.” _American Economic Journal: Applied Economics_ , 15(4): 353–388.

- **Munshi, Kaivan.** 2020. “Social networks and migration.” _Annual Review of Economics_ , 12(1): 503–524.

- **Munshi, Kaivan, and Mark Rosenzweig.** 2016. “Networks and misallocation: Insurance, migration, and the rural-urban wage gap.” _American Economic Review_ , 106(01): 46–98.

- **Nagengast, Arne, and Yoto V Yotov.** Forthcoming. “Staggered difference-in-differences in gravity settings: Revisiting the effects of trade agreements.” _American Economic Journal: Applied Economics_ .

- **Nagengast, Arne, Fernando Rios-Avila, and Yoto Yotov.** 2024. “The European Single Market and Intra-EU Trade: An Assessment with Heterogeneity-Robust Difference-inDifferences Methods.” LeBow College of Business, Drexel University.

- **Orefice, Gianluca.** 2015. “International migration and trade agreements: The new role of PTAs.” _Canadian Journal of Economics/Revue canadienne d’ ́economique_ , 48(1): 310–334.

- **Ortega, Francesc, and Giovanni Peri.** 2013. “The effect of income and immigration policies on international migration.” _Migration Studies_ , 1(1): 47–74.

- ** ̈Ozden, C ̧ a ̆glar, Christopher R Parsons, Maurice Schiff, and Terrie L Walmsley.** 2011. “Where on earth is everybody? The evolution of global bilateral migration 1960–2000.” _The World Bank Economic Review_ , 25(1): 12–56.

- **Pence, Karen M.** 2006. “The role of wealth transformations: An application to estimating the effect of tax incentives on saving.” _Contributions in Economic Analysis & Policy_ , 5(1).

29
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Water supply and sanitation

Banerjee, Sudeshna, Vivien Foster, Yvonne Ying, Heather Skilling, and Quentin Wodon. 2008. ―Cost Recovery, Equity, and Efficiency in Water Tariffs: Evidence from African Utilities.‖ AICD Working Paper 7, World Bank, Washington, DC.

Banerjee, Sudeshna, Heather Skilling, Vivien Foster, Cecilia Briceño-Garmendia, Elvira Morella, and Tarik Chfadi. 2008. ―Ebbing Water, Surging Deficits: Urban Water Supply in Sub-Saharan Africa.‖ AICD Background Paper 12, Africa Region, World Bank, Washington, DC.

- Banerjee, Sudeshna, Quentin Wodon, Amadou Diallo, Taras Pushak, Helal Uddin, Clarence Tsimpo, and Vivien Foster. 2009. ―Access, Affordability, and Alternatives: Modern Infrastructure Services in Africa.‖ AICD Background Paper 2, Africa Region, World Bank, Washington, DC.

- IBNET (International Benchmarking Network for Water and Sanitation Utilities). 2009. Downloadable from http://www.ib-net.org/

- Joint Monitoring Programme (JMP)–WHO. 2010. Benin estimates for the use of Improved DrinkingWater Sources. Updated March 2010. Downloadable from

http://www.wssinfo.org/resources/documents.html

- ———. 2010. Benin estimates for the use of Improved Sanitation Facilities. Updated March 2010. Downloadable from http://www.wssinfo.org/resources/documents.html

- Keener, Sarah, Manuel Luengo, and Sudeshna Banerjee. 2009. ―Provision of Water to the Poor in Africa: Experience with Water Standposts and the Informal Water Sector.‖ AICD Working Paper 13, World Bank, Washington, DC.

- Morella, Elvira, Vivien Foster, and Sudeshna Ghosh Banerjee. 2008. ―Climbing the Ladder: The State of Sanitation in Sub-Saharan Africa.‖ AICD Background Paper 13, Africa Region, World Bank, Washington, DC.

- World Bank. 2009d. ―Benin: Urban Environment Project. Project Information Document, Concept Stage.‖ Report AB5264. November 12, 2009.

48
do so. The comparison reveals that our impact estimate is many times higher than theirs. We refrain from commenting on the reasons for this difference beyond what we have stated in this appendix.

_Comparison with Chen and Whalley (2012)_ CW estimate the impact of introduction of the Taipei Metro (TM), in 1996, on pollution in Taipei City. Using a two-year window of very good quality data (they had 1 missing observation in a two year window), they find that the opening of the TM resulted in a 15 percent decline in _CO_ . Using a three year window and spanning multiple extensions, we find a much larger impact of 34 percent for Delhi.<sup>40</sup> One reason for why our impact is larger could be that _CO_ measurements in our study are from a single monitoring station located at a major traffic intersection, whereas CW use the average _CO_ across ten monitoring stations and they exclude the few stations located at traffic intersections (see footnote 20 on page 15 in CW). If traffic diversion is the main mechanism via which the metro impacts pollution, then one might expect to see bigger effects at traffic intersections.

Next, we compare the implied ridership-pollution elasticities across the two studies. From not having any metro in the city, CW report an average daily ridership of 40,410 in year following the TM introduction. Using data from the DMRC, we note that average daily ridership before and after the first extension of the Yellow Line was 119,855 and 385,866, respectively. Thus, a one percentage point decline in _CO_ is associated with an increase in average daily ridership of 2,694 in case of the TM, and an increase of 7,824 for the DM. Why should DM

> 40. Taipei today has a population density of about 15,200 persons per square kilometre, slightly higher than Delhi’s 11,050 (Data on population densities were accessed from City Mayors Statistics. Source url: http://www.citymayors.com/statistics/largest-cities-density-125.html. Last accessed on March 31, 2015). According to an inventory study for Taipei City (Chang and Lee, 2006, as cited in CW), 96 percent of CO was due to vehicles. This is comparable to the 86 percent for Delhi reported in Gurjar, 2004, but is much higher than the 58 percent reported in NEERI, 2010.

26