rafmacalaba/datause-encoder-data huggingface dataset:
migrated, while columns (3) and (4) split this sample between male and female migration. It appears that the differences between nonmigrant households (column 5) and migrant households are very stark: households with no migrants are more likely than migrant households to reside in urban areas, they generally have less children and are richer and more educated. The comparison between households in which the mother migrated and the father stayed and households in which the father migrated and the mother stayed (column 6) further reveals that these differences are amplified for households with migrant mothers; these are indeed more likely to live in rural areas, to be less educated and poorer, and to have more children, though these are on average older than those of migrant fathers. The last row of table 4 finally shows differences in terms of degree of risk aversion of the head of household.<sup>13</sup> This measure is a 1–4 score derived from the answers to a sequence of questions in which respondents are faced with hypothetical lotteries with high stakes.<sup>14</sup> It appears that migrant households are on average more risk averse than nonmigrant ones, while there is no significant difference between households who choose female migration and those who choose male migration.

**Table 4.** Descriptive Statistics, Households with Children. IFLS 2007.

||(1)<br>Nonmigrant<br>households|(2)<br>Migrant<br>households|(3)<br>Migrant<br>father|(4)<br>Migrant<br>mother|(5)<br>Difference<br>(1)-(2)|(6)<br>Difference<br>(3)-(4)|
|---|---|---|---|---|---|---|
|Size ofhousehold|4.623<br>(1.797)|4.590<br>(1.933)|4.570<br>(2.014)|4.558<br>(1.790)|0.033|0.012|
|Rural|0.545<br>(0.498)|0.643<br>(0.480)|0.587<br>(0.493)|0.711<br>(0.455)|-0.098***|-0.124*|
|Number ofchildren|1.728|2.066|2.050|2.079|-0.338***|-0.029***|
||(1.009)|(1.114)|(1.135)|(1.077)|||
|Age ofchildren|8.456<br>(4.701)|10.05<br>(3.502)|9.680<br>(3.706)|10.39<br>(3.299)|-1.594***|-0.71*|
|Share of female<br>children|0.494<br>(0.403)|0.474<br>(0.363)|0.462<br>(0.363)|0.499<br>(0.360)|0.02|-0.037|
|Mother’s years|8.301|7.250|7.325|6.994|1.051***|0.331*|
|ofeducation|(4.333)|(3.364)|(3.593)|(2.789)|||
|log totalexpenditure|15.22<br>(0.937)|15.08<br>(0.849)|15.15<br>(0.905)|14.93<br>(0.659)|0.14***|0.22**|
|Risk aversion ofhead|3.693|3.757|3.738|3.784|-0.064*|-0.046|

13 Or the spouse when no measure for the head is available. Indeed the data show that the head’s and her spouse’s degree of risk aversion are significantly positively correlated,  0.34 . 14 For example the first question asks: “Suppose you are offered two ways to earn income. With option 1, you are guaranteed an income of Rp 4 million per month. With option 2, you have an equal chance of earning either the same income, Rp 4 million per month, or, if you are unlucky, Rp 2 million per month, which is less. Which option will you choose?” 13
Here, _a_ ( _g_ ) represents the ”Administrative Level 1” that the grid is mapped to. _Y_ ˆ and _Y_ ˆ _D_ are the admin 1 level averages of predicted _Y_<sup>ˆ</sup> and _Y_<sup>ˆ</sup><sup>_D_</sup> , respectively.

# **5.2 Simulating Production Gains**

We then use the YLD obtained from each of the two methods described above to simulate production gains.

However, we only calculate these production gains under three key conditions. First, the mean aquifer productivity or yield (= _η_ ) for the GAEZ grid must be at or above the low to moderate threshold. Adequate aquifer productivity is essential for fulfilling irrigation needs, and this condition ensures its consideration. Second, the ratio of groundwater extraction to recharge, _ν_ , must remain below 70%.<sup>8</sup> This condition is crucial to ensure gains are computed only for regions with sustainable access to groundwater, addressing concerns regarding current or future over-exploitation of this resource. To set the threshold, we rely on the guidelines set in Chapter 22.2.1 by the Ground Water Resource Estimation Committee (2015), Government of India. Third, to account for ease in accessibility of groundwater, we consider aquifers that have a depth of 7 meters or less ( _ζ_ ). For more details on the parameters used, refer to Appendix 9. We summarize all the parameters used in Table A1.

We calculate gains in production using equation (7).

Here, _QGA_ is the production gain for crop _c_ in GAEZ grid cell _g_ . _HAR_ is the harvest area of rain-fed croplands, and _Y LD_ refers to the yield difference between irrigated croplands and rain-fed croplands. _Share of Irrigable Area_ denotes the fraction of the rain-fed area within the grid where the crop’s water demand can be sufficiently met with the crop’s water supply.<sup>9</sup> It equals one if the crop’s water supply is greater than or equal to the crop’s water demand; otherwise, it is set as the crop’s water demand divided by the crop’s water supply. This variable can be thought of as a discount on the achievable production gain, where we scale down the gain in production to consider only the share of the grid where groundwater availability can fill in existing crop water deficits or the extent to which the existing crop water deficits can be filled using groundwater.

## **5.2.1 Estimating Water Supply**

We overlay water-volume gridded data with gridded agricultural data (referred to as GAEZ grids). Subsequently, we estimate the volume of local shallow groundwater in each GAEZ grid _g_ using equation (8).

> 8To compute this ratio, we divided the total water use by the mean recharge rate of each grid. For each country, we divided total water use across grids within the country weighted by the GAEZ grid’s area and multiplied by, _φ_ , the share of water use that is attributable to groundwater use (=67.5%) (Rodella et al., 2022)).

> 9In the water-supply calculations, we only consider groundwater availability after accounting for sanitation needs.

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Policy Research Working Paper 5374

# **Abstract**

The World Bank has produced a huge volume of books and papers on development—20,000 publications spanning decades, but growing appreciably since 1990. This paper finds evidence that many of these publications have influenced development thinking, as indicated by the citations found using Google Scholar and in bibliographic data bases. However, the authors also find that a non-negligible share of the Bank’s publications have received no citations, suggesting that they have had little scholarly influence, though they may well have had influence on non-academic audiences. Individuallyauthored journal articles have been the main channel for scholarly influence. The volume of the Bank’s research output on development is greater than that of any of the comparator institutions identified, including other international agencies and the top universities in economics. The bibliometric indicators of the quality and influence of the Bank’s portfolio of scholarly publications are on a par with, or better than, most of the top universities.

This paper—a product of the Director’s office, Development Research Group—is part of a larger effort in the department to assess the impact of World Bank research. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at mravallion@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
SADC (less the EAC) and Rest of the World.<sup>30</sup> In our model, we sum the shares of EAC and SADC (less EAC) to obain the shares of our Africa region.

# **Ownership Shares of Banks**

Each bank’s market share was then allocated among geographic regions according to the shares of ownership of the bank. We then summed across the banks to obtain total market shares by region. In many cases, however, the Bankscope data was inadequate to allocate ownership shares by region. In these cases, we investigated bank websites, to obtain the required ownership information. The results of our supplementary inquiries are listed below.

The results we get are that owners of the banking sector of Tanzania are as follows (in percent): Tanzania, 77; EU, 6; EAC, 2; SADC, 1; ROW, 14 (see Table B2).

## **Supplementary Information on Ownership Shares of Tanzanian Banks**

- Tanzania Postal Bank – 100% Tanzanian: “United Republic of Tanzania , 45.3%; Revolutionary Government of Zanzibar, 11.0%; Tanzania Posts Corporation, 33.2%; TP & TC SACCOS, 10.5%.” http://www.postalbank.co.tz/about.htm.

- Azania Bank Limited – “The Current shareholders of the Bank are as follows: National Social Security Fund (NSSF), 40%; Parastatal Pension Fund (PPF), 35%; Public Service Pension Fund (PSPF), 14%; East African Development Bank, 7%; Tanzanian Individual including staff, 4%.” <u>http://www.azaniabank.co.tz/azania/about.htm</u> .

- Consolidated Bank of Kenya Limited –- “The bank is fully owned by the Government of Kenya with the majority shareholding in the bank (51%) held by the Treasury through the Deposit Protection Fund. The remaining shareholding is spread over twenty five (25) parastatals and other -

- government related/controlled organizations.” <u>http://www.consolidated bank.com/index.php?option=com_content&task=view&id=12&Itemid=57.</u>

- Faulu Kenya Limited – “Faulu Kenya Limited is incorporated in Kenya under the Companies Act and is a 70% owned subsidiary of Food for the Hungry International, which is registered in Switzerland.” <u>http://www.faulukenya.com/images/stories/pdf/Faulu_Annual_Report_2007.pdf</u> .

- Commercial Bank of Africa –“wholly Kenyan owned.” <u>http://www.cba.co.ke/default2.php?active_page_id=117</u> .

- Micro Provident Tanzania Limited – BW 85% – <u>http://www.letshego.com/index.php?id=8</u> Accessed 3 April 2009.

- Tanzania Investment Bank – 99.3% TZ – “Subscribers to the share capital of the bank are the Government of the United Republic of Tanzania (99.098%), Consolidated Holding Corporation (0.676%) and the National Insurance Corporation of Tanzania Limited (0.226%)” http://www.tib.co.tz/incorporation.html.

> 30 Although we calculated data for the U.S. and the U.K. separately, these were aggregated into the Rest of the World and the European Union, respectively.

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|**Table C.13. Estimated rho (**ρ**) from**<br>|**cross-sectional data**<br><br>||
|---|---|---|
|**Country**|**Period**<br>ρ|**Alternative**ρ|
||2012-2015<br>0.84|0.52|
|**Egypt, Arab Rep.**|2015-2017<br>0.89|0.46|
||2017-2020<br>0.79|0.61|
||2007-2009<br>0.54|0.56|
|**West Bank and Gaza**|2009-2011<br>0.62|0.66|
||2011-2017<br>0.34|0.59|
||2005-2010<br>0.57|0.67|
|**Tunisia**|2010-2015<br>0.73|0.65|
||2015-2021<br>0.89|0.61|
||2004-2008<br>0.77|0.57|
|**Mauritania**|2008-2014<br>0.63|0.56|
||2014-2019<br>0.70<br>|0.61<br>|
|**Iraq**<br><br>|2007-2012<br>0.68<br><br>|0.37<br>|
|**Jordan**<br><br>|2010-2013<br>0.63<br><br>|0.63<br>|
|**Note:** ρare estimated using Equati<br>education. Alternativeρ’s are estima|on (C5) for all countries with cohorts being defined by age i<br>ted using Equation (C6).|nteracted with household heads’|

91
same household can consume a larger amount of education at Q<sup>*</sup> 2 since the supply curve of education in this case is not constrained by the vertical part rising at point A.

This setup, which we refer to as the “standard framework”, underlies the discussion below on the determinants and welfare consequences of tutoring. The framework incorporates certain assumptions that will not always be valid, and so we return to those below. One assumption is that the market for private tutoring is competitive, and indeed that households are allowed to choose whether to purchase tutoring services. A second is that public schooling reaches a strict capacity constraint after a certain point, which is likely a better description of the short run than of the long run. In section 6, we will explore how relaxing these assumptions would change our analytical and policy conclusions.

# **4. Drivers of Private Tutoring**

To understand the equity and productivity effects of the large and growing private-tutoring sector, and to design policy, it is essential to understand what factors, both micro and macro, lead to demand for tutoring. Factors at the micro level may include the different characteristics of individuals, households, schools, and communities. Macro-level factors may include the share of public education expenditure in a nation’s GDP, the characteristics of its education system and labor market, and its cultural values. Together, these factors determine the level and slope of the tutoring demand curve for the society as a whole – specifically, the size of the gap between D1 and D2, in our simplified two-class model depicted in Figure 1. In this section, we present the evidence from the literature on these two sets of explanatory factors.

## **4.1. Micro factors**

From standard economic theory, we would expect certain factors to increase household’s demand for education (or shift households’ demand curve for education outward in Figure 1): household income, their tastes for education, and their expectations about the returns to education for their children. These same factors therefore explain heterogeneity of demand across household types, as in the simplified two-type model in Figure 1. Compared to households on the demand curve D1, which consume no private tutoring at the given supply curve for private tutoring S2, households on the demand curve D2 consume a positive amount (Q*2 - Q2) of private tutoring.

Empirical evidence supports our intuition about what factors tend to increase demand. Table 2 lists the most important micro determinants of tutoring for Egypt (Assaad and El-Badawy, 2004), Japan (Baker, 1992), Korea (Kim and Lee, 2004), Turkey (Tansel and Bircan, 2006), and Vietnam (Dang, 2007b). We highlight these studies because they all use nationally representative data, but the choice of countries has other benefits as well: the private tutoring sector is relatively prominent in all of them, and together they capture some of the geographical variation in the phenomenon.<sup>9</sup> 5
Figure 1. Actual Onset over Kerala as deviations from June 1<sup>st</sup> (1978-2006) <!-- Start of picture text --> 1975 1985 1995 2005<br>years<br>20<br>10<br>0<br>Number of days since June 1st<br>-10<br><!-- End of picture text --> Note: Data for this Figure come from Indian Meteorological Department (http://www.imdpune.gov.in/)
Regulation vulnerability is proxied by the natural labor turnover rate in the corresponding industry in the United States. They find that stringent hiring and firing regulations reduce job turnover, especially in industry and size-class cells that inherently exhibit more job turnover.

Finally, deregulations in labor are found to facilitate factor adjustments and to enhance productivity. In the 1990s, Colombia reduced dismissal costs by 60 to 80 percent, made the social security system more portable and minimized controls over capital market (by liberalizing foreign direct investment). Using the Colombia Annual Manufacturing Survey between 1982 and 1998, Eslava et al. (2004 and 2006) find that market-oriented reforms were associated with increasing employment adjustments (especially on job creations) and investments, but less capital deployments. And, market reforms are associated with rising overall productivity that is largely driven by reallocation away from low- to high-productivity businesses.

# **6. The Effects of Entry Regulations**

Starting a business is costly in many countries. In Mozambique, the owner must go through 19 procedures, take 149 days, and pay US$256 in fees. In Canada, it takes only two procedures, two days, and US$280 (Djankov et al. 2002). Combining time and out-of-pocket costs, the world average of the full cost is 66 percent of per capita GDP, varying from 1.7 percent in New Zealand to 495 percent in the Dominican Republic. Partly due to the overall change in prevailing beliefs about what is best for growth, partly due to new measurements of the costs of registering businesses across the world, entry deregulation has become a major area of reforms in the past decade.<sup>22</sup> There is evidence that entry deregulations improve productivity and macro performance (Crafts 2006; Barseghyan 2008, Loayza et al. 2005a, Chari 2007). Moreover, the positive effect of deregulation is found to differ by the initial level of regulation. Gorgens et al. (2003), using the Index of Economic Freedom, find that deregulation from a high to a moderate level of regulation has a large effect on growth of about 2.5 percentage points, but further deregulation has no effects. This explains why deregulations in countries such as China and India have spectacular effects, but barely noticeable effects in OECD countries. Recent firm-level evidence of entry regulation shed light on the specific channel through which entry regulation affects economic outcomes.

An important channel for deregulation effects is by allowing for an easier entry. Investigating the effects of entry regulations with a database of firms in Western and Eastern Europe, Klapper et al.

> 22 Djankov (2009) surveys how entry deregulation affects economic performance in the past decade. More skimpy than usual due to the existing survey by Djankov, this section differs in that I survey many papers not covered by Djankov (2009) and emphasize the heterogeneity and complementarity issues on which he does not focus.

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we use average effective tax rates as marginal effective tax rates. The main advantage of following this method is that all exemptions and deductions of the tax code are embedded in our measure. Hence, we do not have to model the intricate details of the tax code. The main drawback of our approach is that the observed tax rate is potentially endogenous to certain firms’ characteristics and past behavior. We conduct several robustness checks to verify that our results are not driven by other specific characteristics and behavior of the firm.

We set the rental rate of capital to _r_ = 0 _._ 05 and the depreciation rate to _δ_ = 0 _._ 05, to make our results comparable with Hsieh and Klenow (2009). The elasticity of substitution between varieties is fixed at _σ_ = 3, so that firms’ price is 50 percent higher than their marginal cost. In Section 5.1, we evaluate the sensitivity of our results with respect to these assumptions. The capital share _αs_ in industry _s_ is equal to 1 minus the labor share in that corresponding industry for the United States.<sup>10</sup> These shares are obtained from the NBER Productivity Database.<sup>11</sup> Using the data and parameter values described above, we back out the capital and output wedges in the following manner. For firms with positive accounting profits, we use equations (5) and (6) to obtain the firm-specific wedges. Since _μ_<sup>_t_</sup> _si_<sup>= 0,theoutputandcapitalwedgesare</sup> On the other hand, for firms with negative accounting profits the capital and output wedges are obtained from equations (7) and (8). In this case, _μ_<sup>0</sup> _si_<sup>=0</sup> 10. Following Hsieh and Klenow (2009), we set the capital shares for each industry equal to those of the United States as we suppose that the US economy is less distorted than Chile’s economy.

11. Most data on firm labor payments do not include labor benefits such as social security contributions. In the same manner as Hsieh and Klenow (2009), we scale each industry’s labor share by 3/2.

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last two decades. The graphs show the reductions in mortality on average. The reduction is the smallest in NMR; the curves are less steep when compared to the curves in IMR and U5MR. Figure 1b also shows that the reductions by income group first exhibited reversals from a pro-poor trend (2000-2005) to a pro-rich inequality trend (2005-2011) and then back to pro-poor inequality trend (2011 to 2014). Overall, the trend presented in Figure 1b does not show a clear distinction in the distribution of reductions in mortality by income group.

<!-- Start of picture text --> Figure 1b.Trends in child mortality by wealth ranking, residence and mother's education<br>(2000-2014)<br>NMR IMR U5MR<br>200 200 200 200 200 200<br>Rich/Poor Wealth Quintile Rich/Poor Wealth Quintile Rich/Poor Wealth Quintile<br>poorest<br>160 top60%bottom40% 160 poorestpoormiddle 160 top60%bottom40% 160 poorestpoormiddle 160 top60%bottom40% 160 poormiddlericher<br>richer richer richest<br>120 120 richest 120 120 richest 120 120<br>80 80 80 80 80 80<br>40 40 40 40 40 40<br>0 0 0 0 0 0<br>2000 2005 2011 2014 2000 2005 2011 2014 2000 2005 2011 2014 2000 2005 2011 2014 2000 2005 2011 2014 2000 2005 2011 2014<br>200 200 200 200 200 200<br>Urban/Rural Mother's Education Urban/Rural Mother's Education Urban/Rural Mother's Education<br>160 160 160 160 160 urban 160<br>120 urbanrural 120 no educationprimarysecondary+ 120 urbanrural 120 no educationprimarysecondary+ 120 rural 120 no educationprimarysecondary+<br>80 80 80 80 80 80<br>40 40 40 40 40 40<br>0 0 0 0 0 0<br>2000 2005 2011 2014 2000 2005 2011 2014 2000 2005 2011 2014 2000 2005 2011 2014 2000 2005 2011 2014 2000 2005 2011 2014<br>Note: Combiled from DHS data<br><!-- End of picture text --> In Figure 1c, the trends in immunization services show that the expansion has been for all groups by income, place of residence and mother’s education. The curves 15
severity. More specifically, the change in poverty (measured by any poverty index P<sup>35</sup> ) over dates t and t+n<sup>36</sup> is decomposed according to the Datt-Ravallion (1991) method into three components: Pt + n – Pt = G(t, t + n; r) + D(t, t + n; r) + R(t, t + n; r) (1) with r being the reference date with respect to which the change in poverty is decomposed, and where: G refers to the “Growth component”, D refers to the “Redistribution component”, and R is a “Residual component for r=t”. The “growth” component is defined as the measure of the change in poverty due to a change in the mean of the welfare distribution while holding the Lorenz curve constant at a pre-defined reference level. The “redistribution” component is determined as the measure of the change in poverty due to change in the Lorenz curve of the welfare distribution while holding the mean consumption at constant reference level. The “residual” component typically exists when the changes in the mean of the selected poverty measure are dependent from the precise Lorenz curve, or vice versa<sup>37</sup> . Quantifying the contribution of growth vs. redistribution components in poverty reduction in Jordan enables the paper to reveal whether the growth in household welfare and shifts in income distribution has helped or hurt the poor during the period of strong economic growth between 2002 and 2010.

A three-fold decomposition of the overall Gini measure of inequality is also performed with the objective of delving further into the question of regional disparity. This follows a framework proposed by Lambert and Aronson (1993) and Essama-Nssah et al (2010) in which the overall

> 35 This paper will decompose the Poverty Headcount (P0), the Poverty Gap (P1), and the Squared Poverty Gap (P2) indices.

> 36 n being the period of choice.

> 37 According to Datt-Ravallion (1991) this occurs when the poverty measure is not additively separable. This paper does not dwell on the decomposition methodology and the mathematics behind it. For further details on the latter aspects, refer to the Datt-Ravallion (1991) paper.

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