rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10086

# **Abstract**

The literature suggests that one of the main factors behind the interstate inequality in economic development in India is the variation in the level of infrastructure development. However, unequal infrastructure development across the Indian States is less understood. This study empirically analyzes various factors (economic, fiscal, demographic, social, institutional, and political) to explain interstate infrastructure inequality using a panel data set for 18 states in India between 2004 and 2020. Employing the principal component analysis technique, three separate infrastructure indices are developed for physical, social, and financial infrastructures. The relationship of each index with its explanatory variables is estimated using System Generalized Method of Moments. The results show that economic factors— including economic performance, financial development, investment, and economic structure—are more influential on physical infrastructure. For social infrastructure, in addition to economic factors, fiscal and demographic factors are more influential. Meanwhile, economic and demographic factors are found to drive financial infrastructure. Financial development fosters physical infrastructure, while its impact on social infrastructure is weak.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gtimilsina@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
*|**Gaza**|
|---|---|---|---|
|**Government Revenue & Expenditure**<br>**(Million NIS, unless otherwise specified)**|Total (%<br>of GDP)|Included<br>(Yes/No)|Total<br>included as<br>% of GDP|

> 20 The CIT is better modelled with tax administrative data. Also, it is a challenge to distribute CIT burdens across households.

10
telecommunication and corn products are calculated using the ENIGH from 2014. Furthermore, the per capita income (welfare variable) measured using the monthly income is also calculated from the ENIGH for each household. WELCOM requires the poverty line as a variable in the data set to provide insights on impacts on poverty levels. The poverty line used in this case is based on the same survey, following the calculations of the Consejo Nacional de Evaluación de la Política de Desarrollo Social (National Council for the Evaluation of Social Development Policy).<sup>20</sup> For the market structure parameters, the market share of the largest firms in each sector is calculated based on the 2014 economic census produced by the Instituto Nacional de Estadística y Geografía (INEGI, National Institute of Statistics and Geography). The demand price elasticities are derived from existing research papers and from the forthcoming Stata module for WELCOM that will enable users to estimate price and income elasticities.<sup>21</sup>

# **4.1. The mobile telecommunications industry**

The telecommunication sector became a key element of Mexico’s economy during the process of liberalization in 2013. According to the OECD (2017), since the reform started, there has been a 75 percent price reduction in mobile broadband packages alone, adding 50 million subscriptions, more than the entire population of all (seven) Central American countries. Furthermore, Mexico now has the eighth lowest fixed broadband price basket in the OECD for low end users. However, despite the advances in the reform, Mexico still has the highest fixed broadband price basket in the OECD for high end users, demonstrating that higher quality and speed of service remain out of the reach among lower-income groups.<sup>22</sup> Since 2012, mobile cellular basket prices have decreased by 61 percent, well below any of the aforementioned income groups as its subscriptions per 100 population increased by 6 percent (figure 2). However, the market remains highly concentrated. Mexico has
context, in which trade liberalization is the policy lever --and the eventual culprit-- while market and institutional weaknesses are accepted as immanent characteristics.

The theoretical ambiguity on the effects of openness is reflected in the available empirical evidence. Some papers point to strongly positive growth effects by trade liberalization. This is the case of Dollar (1992) and Sachs and Warner (1995), who run cross-country growth regressions on composite indices of the stance of trade policy; as well as Edwards (1998), who prefers to base his positive evaluation by examining the robustness of various individual indicators of trade liberalization in cross-country growth regressions. But others, most notably Harrison (1996) and Rodriguez and Rodrik (2000) have cast doubt on the significance and robustness of the growth benefits of openness. Their critique starts with the openness measures used in practice; for instance, some purportedly openness indicators reflect general poor economic management (e.g., the black market premium) or are primarily affected by geographic characteristics (e.g., the trade volume). Other criticisms are based on econometric grounds, such as omitted-variable bias (particularly due to the exclusion of institutional and geography-related variables) and joint endogeneity bias (stemming from the effect of growth on certain policy regimes).

Recent empirical studies have addressed these criticisms by emphasizing the overtime variation in openness indicators and growth performance. (Harrison 1996 had already noted that panel studies rendered a more positive evaluation of the growth effects of openness than cross-sectional studies.) Dollar and Kraay (2004) and Loayza, Fajnzylber, and Calderón (2005) run growth regressions on panel data of large samples of countries. Both papers use indicators fo openness based on trade volumes and control for their joint endogeneity and correlation with country-specific factors through GMM methods that involve taking differences of data and instruments. This implies that, although they continue to use cross-country data, these papers favor within-country changes as the main source of relevant variation. Both papers conclude that opening the economy to international trade
Our findings on the arrival dates of adults and children within our sample indicate that, on average, adults arrived in Colombia 102 days prior to the children and adolescents. The distribution of arrival dates, as shown in Figure 5, corroborates the observation that adults tend to arrive earlier than their younger counterparts. This pattern aligns with the commonly held notion that adults initially migrate to secure accommodation or employment opportunities before arranging for their children to join them. This phased migration strategy reflects a practical approach to establishing stability and security in a new country before bringing over family members.

Figure 5. Arrival Date Distribution <!-- Start of picture text --> 2015m5 2016m3 2017m1 2017m11 2018m9 2019m7 2020m5 2021m3 2022m1<br>Arrival date<br>Children Adults<br>.03<br>.02<br>Density<br>.01<br>0<br><!-- End of picture text --> _Notes:_ The figure depicts the distribution of the arrival date to Colombia of adults and children in the VenRePs-Kids study. The arrival date measures the year and the letter ”m”, beside it, indicates the month when the individual arrived in Colombia. Therefore, January is symbolized by m1, February by m2, and so on, alongside the respective year. The average difference in the arrival date between adults and children is 102 days.

# **IV.D Service access for children and adolescents**

6 examines the access to services the minors in our Figure among sample, showcasing enrollment rates in school (Panel (a)), sisb ́en (Panel (b)), health insurance (Panel (c)), and regularization (Panel (d)) for children aged 5-10 and adolescents aged 11-17. The figure 28
# **Monsoon Babies: Rainfall Shocks and Child Nutrition in Nepal**

## **Sailesh Tiwari**<sup>**1**</sup> **Hanan G. Jacoby Emmanuel Skoufias**

The World Bank JEL Classification Codes: I12; J16; O15 Key Words: Income insurance; human capital; malnutrition Poverty Reduction and Economic Management Network Poverty Reduction and Equity

> 1 _Corresponding Author:_ <u>stiwari@worldbank.org. The World Bank Group (Mail Stop: MC4-415), 1818 H Street</u> NW, Washington DC 20433, USA. Jacoby: <u>hjacoby@worldbank.org; Skoufias: eskoufias@worldbank.org. We</u> are grateful to the Department of Hydrology and Meteorology (DHM) of Nepal for providing the rainfall data used in this study. Research support from World Bank’s Development Economics Research Group Research Support Budget is also gratefully acknowledged. We have benefitted greatly from discussions with Andrew Foster, Ana Aizer, Abla Safir and Mariano Rabassa. All remaining errors are our own. The findings, interpretations and conclusions in this paper are entirely those of the authors and do not necessarily reflect the view of the World Bank, its executive directors, or the countries they represent.
of an explicit deposit insurance scheme, the resolution of the mid-80s crisis reinforced the belief that depositors and banks would be bailed out if their investments proved umprofitable.

**_Treatment of Financial Institutions' Shareholders and Management._** Shareholders of insolvent financial institutions did see a (temporary) dilution of their investments, but they were not completely eliminated as shareholders.<sup>2 4</sup> While the chief executive officers of the failed and restructured institutions were removed, senior management was left in place. This allowed the weak banking culture to remain intact, and made the overall rehabilitation of the financial sector more difficult. Moreover, despite the fact that the financial crisis of the mid 1980s was caused by poor risk management and lending practices, relatively low average operating expenses in the afternath of the crisis seerm to suggest that financial institutions invested insufficiently into upgrading the skill base of their staff and their risk management practices.<sup>2 5</sup>

- 23 See for _a_ detailed account of 1983 crisis: Johnston (1991) and Caprio and Klingebiel (1996a & b).

- 24 Existing shareholders had a buyback option at a predetermnined price and under a five-year time horizon.

- 25 While the comparability of cross country data is limited because of differences in accounting conventions regarding the valuation of assets and loan loss provisioning, and interest rate accrual norms and tax regimes differ across countries, with an average of 1.9 percent operating expenses over average assets over 1990-1997,
(4,000) and the Internal Security Forces.

# Federal Republic of Yugoslavia (Serbia and Montenegro)

Data on military employment include conscripts (60,000).

Hungary Unemployment rate was taken from Economist Intelligence Unit 1995-96 and reflects unemployment as of November 1994. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994.

Central and local government, Education and Health are taken from Barbara Nunberg's Administrative Reform in Hungary: _The Advanced Transition expenence_ September 1995 (draft), for the year 1993. Data on military employment include conscripts (47,500), but exclude personnel in paramilitary organizations, i.e., the Border Guards (730), and Internal security forces (police- 1,800). Data on wages in manufacturing (monthly basis) are taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1993. Wages and Salaries as percent of GDP and Average Government wages are taken from Barbara Nunberg's report of September 1995 (draft) Administrative Reform in Hungary: The Advanced Transition experience, prepared in conjunction with EMTPM's Seminar on State Transformation in the Transition (Salzburg, November 1995). Data on paid employment in non-agricultural activities are taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1992. Macedonia Labor force figures are drawn from Economist Intelligence Unit 1995-96 (p. 11). Unemployment rate is taken from Country Economic Memorandum dated 21 June 95. Note that employed labor force includes self-employed. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994. Central Govemment, Education and Health employment are taken from Country Economic Memorandum of July 20, 1995 and relates to 1994. The Republican Budget covers the expenditures of central government as well as education and research institutions. The Health care fund covers the financing for the services rendered by autonomously administered health management organizations. Local
Previous research that used the sample approach to provide an estimate of the extent of the Cambodian genocide by interviewing survivors and collecting information about death and survival in their families include Sliwinski (1995) and Kiernan (1996). However, their estimates were based on samples of convenience, limited by local constraints, which were not representative of the Cambodian population. The present paper uses data about siblings’ mortality collected in the 2000 Demographic and Health Survey of Cambodia (Kingdom of Cambodia 2001). With as primary goal the assessment of maternal mortality, the survey collects information about all the siblings (78,852 in total) of a nationally representative sample of 15,557 women, aged between 15 and 49 in 2000. The information includes gender, date of birth, whether the sibling is still alive and, in case the sibling died, the date of death.

Even with a nationally representative sample, estimates based on a sample of survivors are potentially subject to several shortcomings (Heuveline, 1998 (b), 2001 (a), 2001 (b)). First, the method relies on recall of past events and is therefore possibly prone to misreporting. Several tests of data quality have been performed and there does not seem to be systematic reporting biases (Kingdom of Cambodia 2001): the sex ratios at birth are in the internationally acceptable range suggesting that there is no serious under or over reporting of brothers or sisters. Similarly, the median year of birth is the same for respondents as for siblings, indicating that there is no underreporting of older siblings. Another issue is the possibility of double counting: if two sisters are interviewed, the same deaths and survivals will be reported twice. Although this possibility cannot be ruled out in the data set used, this does not appear to be a major issue in a random nationwide sample. More importantly, as long as the analysis focuses on relative rather than absolute numbers, double counting of some deaths and survivals should not affect most ratios.

A more serious
# **4.2 Assumptions and Implementation of the Fiscal Incidence Analysis**

# **_PIT paid by households_**

We assume that PIT is only paid on the labor income of the employed population in the formal sector. Because we do not have a direct measure of labor income, we use an approximation based on our definition of market income. The following steps were taken to calculate the PIT payment for formal workers using the SLCHBS from 2018: (1) we identify the disposable income; (2) we obtain the net market income, subtracting direct transfers; (3) we divide the resulting value by the number of employed adults; (4) we calculate the taxes paid based on the PIT rates corresponding to the brackets; and (5) we aggregate again at the household level.

# **_VAT paid by households_**

The VAT is levied on all forms of consumer spending—goods and services. The tax is computed on the value of imports and the value added (or markup) that one business charges another or the final consumer when a good or service is provided. The VAT rate is 15 percent on the selling price; however, hotels and dive operations are charged a rate of 10 percent. Some goods and services, for example flour, rice, sugar, and powdered and evaporated milk are not taxed, nor are postage stamps and supply of textbooks as prescribed by the regulations.

Expenses associated with cell phone services have a tax rate of 20 percent and water and electricity services are partially subsidized, based on consumption bounds. In the case of electricity, the quantities are obtained from the costs paid by the households and the exogenous estimates of the prices per kwH are obtained from the Grenada authorities. Households who consume less than 99 kwH per month are exempt from paying the VAT, while households who exceed this consumption pay the VAT on the excess over the limit. Similarly, the water consumption limit is 2,900 gallons per month, with households paying
numbers. There are other factors that play a role in how easy it is to travel from one city to Juba, such as the amount of formal and informal tolls needed to pay at checkpoints, as well as the number of checkpoints in given routes. Unfortunately, the systematic data required to add this element into the analysis are not available. However, an informative discussion is presented in Box .

Table 4: Transport-Related <u>Conditions by State Capital</u>

||**State’s**<br>**Road**<br>**Length**|**Road**<br>**Grade**|**Road**<br>**Area**|**Road**<br>**Density**|**Distance**<br>**to Juba**|
|---|---|---|---|---|---|
|**Aweil**|1487|0.310|0.049|0.0021|794|
|**Bentiu**|1029|0.359|0.027|0.0018|736|
|**Bor**|3058|0.448|0.025|0.0023|199|
|**Kajok**|1527|0.319|0.034|0.0016|713|
|**Malakal**|2280|0.322|0.030|0.0024|588|
|**Rumbek**|1416|0.324|0.032|0.0020|418|
|**Wau**|1497|0.310|0.016|0.0045|643|

Note: Road length indicates the total length of roads in kilometers within each state; road grade is the fraction of primary roads
our sample. In Korea, 36.8% of establishments are managed by women, while only 13.5% of establishments have FTM in Senegal and 12.8% in Ethiopia. Although Chile has a relatively higher income, only 14.1% of Chilean firms are managed by women. Nevertheless, the share of FTM firms is positively correlated with GDP per capita at the country level. This suggests that as country grows there are more female participation in management of businesses.

Table 1: Percent of firms with FTM by country

||Percent of frms<br>with FTMs (%)|Observations|
|---|---|---|
|Korea, Rep.|36.84|1,520|
|Brazil|27.65|711|
|Viet Nam|25.77|1,499|
|Georgia|22.72|1,743|
|Poland|21.20|1,494|
|Kenya|16.54|1,297|
|Ghana|16.12|1,259|
|Chile|14.17|1,052|
|Burkina Faso|14.07|600|
|Senegal|13.47|1,785|
|Ethiopia|12.87|1,475|
|Total|19.44|14,435|

Note: This table shows the percents of firms with FTMs across countries. Shares are weighted by sampling weights. Number of observations are unweighted. Data from the FAT surveys in 11 countries including Brazil, Burkina Faso, Chile, Ethiopia, Georgia, Ghana, Kenya, Korea, Rep., Poland, Senegal, and Viet Nam. FTM = Female Top Manager.

There is a large sectoral heterogeneity in the firms having female top managers. Table 2 presents the percents of the firms with FTM across sectors. The results show that while only 2.4% of establishments in Motor Vehicles have FTM, the share increases substantially
Rosenthal, S. and W. Strange. 2012. Female Entrepreneurship, Agglomeration, and a New Spatial Mismatch. Review of Economics and Statistics, 94(3): 764-788.

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Storeygard, A. 2012. Farther on Down the Road: Transport Costs, Trade and Urban Growth. World Bank Policy Research Working Paper # 6444; _forthcoming_ in Review of Economic Studies Subramanian, A. and Roy, D. 2001. Who Can Explain the Mauriritan Miracle: Meade, Romer, Sachs or Rodrik? IMF Working Paper # 116.

Tewari, I. 2011. Is Small Beautiful? Evidence from India’s Product Reservation Policy for Small Industry. Unpublished mimeo.

Tewari I. and J. Wilde. 2014. Multiproduct Firms, Product Scope and Productivity: Evidence from India’s Product Reservation Policy. Unpublished mimeo.

Thomas, J. J. 2012. India's Labour Market During the 2000s. Economic and Political Weekly, 47(51): 39-51 Topalova, P. 2007. Trade Liberalization, Poverty and Inequality: Evidence from Indian Districts. In A. Harrison (Ed.), Globalization and Poverty (Cambridge
ineligible industries would have evolved similarly after the 2008/2009 economic crisis in the absence of the wage subsidy program.

# **4.5 Actual employment vs. formal employment**

Since the data on employment comes from the Mexican Social Security Institute (IMSS), I ask the question whether the measured effects reflect changes in actual employment vs. formal employment. For example, it may be the case that firms deregister employees with IMSS after the crisis, without actually laying them off. Then, eligible firms may have re‐registered employees during the recovery whereas ineligible firms may have kept employees being informal, i.e. not registered with IMSS.

A related study by Betcherman, Daysal, and Pagés (2010) that uses data on employment from the Social Security Administration in Turkey concludes that the effect of wage subsidies was to increase registration rather than boosting total employment. In the setting here, where subsidies are linked to keeping existing employees, not to hiring new employees, the distinction between formal employment and actual employment may be less relevant. It is presumably more difficult for firms to deregister employees that were previously registered than to register employees who were not previously registered. IMSS registration comes with benefits for the employee, such as medical care, which employees may be reluctant to give up. However, in a crisis situation employees may prefer relinquishing these benefits over losing their job.

I use data from the Labor Market Survey (ENOE) conducted by the Mexican Statistical Institute (INEGI), for the years 2005 through 2014, to examine whether registration rates with IMSS changed with the economic crisis. The ENOE is a quarterly survey that covers about 120,000 households. Unfortunately, the ENOE variable indicating industry of employment is quite broad. It is based on two digit codes from the North American Industry Classification System (NAICS), plus two more digits specific to the ENOE. There are nine industry codes that correspond to durable goods manufacturing. Appendix Table 2 lists these nine codes along with their names and