rafmacalaba/data-use-mentions-extended huggingface dataset:
support or cultural assimilation) is provided by different network along the lines suggested by Massey (1986).

A final appealing feature is the possibility of finding suitable instruments for the network at destination. Since both the current migration flows and diasporas (i.e. past migration flows) are influenced by same factors, endogeneity and other statistical problems (see section on the reflection problem) arise in the estimation. Appropriate instruments should be strong predictors of the network but be uncorrelated with the size of the flows or their composition (i.e. the dependent variables). Munshi (2003) provides a good example for networks defined at the community level, again for the Mexico-US corridor. Rainfall in origin communities in Mexico are supposed to predict the rate of emigration of those migrants but are uncorrelated with labor market outcomes at destination (in the US) that are potentially affected by Mexican networks already present.

A major drawback of micro datasets is that they can consider only a limited number of corridors at a time. That is why a large number of the studies focus on the Mexican-US migration patterns since that is where the best datasets exist. Most other prominent corridors lack such detailed and high-quality data. Furthermore, destination selection effects are quite limited since the US is the destination for more than 99% of the Mexican migrants. This is unfortunately not the case for a majority of origin countries which send their migrants to a diversified set of destination countries. Even in origin countries where migrants have a limited number of choices, the patterns are likely to vary across destination countries. A good example is provided by Cape Verde, which sends a majority of its unskilled migrants to Portugal while sending the most skilled migrants to the US.

Pooling a large set of origin and destination countries in a macro dataset makes it possible to statistically assess the determinants of the various patterns in international migration which might not be easily captured in an analysis of a single corridor. Furthermore, a large number of cells in a migration matrix are filled with
# **5. Returns to Education**

The returns to education can be estimated using the human capital model (Becker, 1975 and Mincer, 1974), wherein one can estimate the returns to additional years of schooling by using the wage data collected from persons who have different levels of education. The model assumes that wage earners are paid according to their marginal product, which rises as more human capital is accumulated. Using the earnings function approach, we can write the wage earnings (W), a function of years of schooling(S), years of experience(E) and other factors.

ln(wi) _=aO+CClSi+ Uc2 Ei+cx3E2 +E_ (1) Years of schooling can be interpreted as an aggregate measure of human capital obtained from formal schooling, while experience is an indicator of human capital acquired while employed (or on the job training). One can interpret Cal as the private rate of return to schooling, based on mincer's earnings function. If we assume that the cost of additional schooling is simply the wages foregone (Ws-,), then the private rate of return (CC ) can be shown to be equal to the annual increase in income (W, - Ws-,) divided by the cost of investment (which is the wages foregone, W,_l). A more general variant of (1) allows different levels of schooling (e.g. primary, secondary and higher) to have different private returns, and can be written as: _ln(wi)= co+acpSpi +aSs,±i+C,Si +a2 Ei+ 3 E E+ui_ (2) where Sp;, S,i and Std are the number of years in primary, secondary and tertiary education ith respectively for the i individual.

**Table 29: Mean Wage Earnings by Educational level in Ghana (in cedis).**

|Education level|1|987|1|
# **Acknowledgments**

This paper draws on contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Heinrich Bofinger on air transport, Michael Minges on information and communication technologies, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank teams; notably Yusupha Crookes (director of the Africa Regional Integration Department), Andrew Roberts (Africa Regional Integration Department), Gael Raballand (transport), and Charles Kunaka (International Trade Department). The paper benefited from feedback provided by REC staff at a May 2010 Workshop organized by the Infrastructure Consortium for Africa in Tunis and at other meetings.
101,789) in 2013. Similarly, its share of manufacturing employment grew from 9 percent (10,386 out of 116,774) in 2003 to 16 percent (44,507 out of 271,863) in 2013. The evidence suggests manufacturing employment in the north has partially converged with that in the south over time.<sup>6</sup> We next look at regional trends in manufacturing subsectors. We distinguish between heavy manufacturing and light manufacturing.<sup>7</sup> Given the importance of food and clothing, we also examine them separately in selected analyses. Table A1 compares growth of firms and growth in the number of employees (“growth of employees”) at the district level between

> 6 Data are not available for nonindustrial sectors in NIC 2003. However, based on comparable national household sample surveys for 2005/06, 2012/13, and 2016/17 (rounds of the Ghana Living Standards Survey), services employment in the north grew faster than that in the south, with the north’s share of services employment increasing from 43 percent to 45 percent; in comparison, manufacturing-employment growth was much slower in the north (Paul and Raju 2021).

> 7 Heavy manufacturing refers to manufacturing of coke and refined petroleum, chemicals, basic metals and fabricated metal, machinery and equipment, motor vehicles, and other transport vehicles, among others. Light manufacturing includes manufacturing of wood, paper, printing and reproduction of recording, pharmaceuticals, rubber and plastic, other nonmetallic items, computers, electronics, electrical equipment, furniture, food, and clothing.

9
true for employment in the financial sector. Further geographic heterogeneity is captured by interacting selected independent variables with region dummy variables, in addition to region fixed effects.

We will work with both the 2001 and the 2007 HESs data. While we allow the models to be different, by building models that best fit the data for any given year, the models we identified are found to be almost identical (in the sense that they include nearly the same set of explanatory variables).

As an experiment, a second set of models is obtained by adding a handful of variables on durable asset ownership and housing conditions that are available in the HES but not in the LFS. Because these are not LFS variables, we cannot consider these models for imputation into the LFS. We can however use them to impute consumption poverty into the HES, and subsequently assess how imputed poverty data compares to observed poverty data. The purpose of this exercise is to verify whether adding these variables to the model significantly improves the statistical precision of the imputed data. Other studies have found that durable asset ownership and housing conditions are particularly powerful predictors of poverty (see e.g. Christiaensen _et al._ , 2012). If the same holds true for Morocco, then an argument can be made for adding these variables to future rounds of the LFS.

Table 1 shows selected descriptive statistics for the four regression models (urban versus rural plus 2001 versus 2007). A number of characteristics are apparent: (a) the models provide good in-sample fits of the data judging by the high adjusted R-squared, (b) the urban models fit the data better than the rural models, which is typical for these type of regression models, and (c) as expected, adding the five durable assets and housing variables significantly improves the in-sample fit. Whether this also translates into better out-of-sample fits is examined in the next section.

**Table 1 – Summary Statistics, Urban and Rural Models 2001 and 2007**

||**20*
**|
||(0.030)|(0.028)|(0.023)|(0.029)|(0.032)|
|_N_|<br>1,449|<br>1,449|<br>1,449|<br>1,449|<br>1,449|
|_R_<sup>_2_</sup>-statistic<br>_Source:_Authors’ estimat|<br>0.027<br>es based on data from the|<br>0.019<br>2014 Integrated Bus|<br>0.027<br>iness Establish|<br>0.050<br>ment Survey.|<br>0.028|

_Note:_ This table presents estimates for equation 2. The dependent variable is an indicator variable indicating entry of at least one new firm in year _t_ in district _d_ in the given sector. The database used consists of a district-year panel comprising 119 districts and 24 years (1990–2013) based on the location and year of entry of the firm. Other industry = industries other than mining; they include manufacturing, construction, and utilities. WRT = wholesale and retail trade. Other services = services other than wholesale and retail trade; they include transport, communications, finance, commerce, government services, and private services. Standard errors are reported in parentheses. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

26
, the 1998 flood indicator identifies historically flood prone areas for the whole nation. We identify an Upazila to be historically flood prone if 50% or more of the area in the Upazila were flooded during any of the three periods of August 26, September 10, and September 17 of 1998 for which the percent of Upazila flooded information is available. By this measure about 51 percent of the 333 Upazilas in the survey are historically flood prone. Historically flood prone and non-flood prone Upazilas are identified in **Map 5** . The map shows that the flood prone Upazilas are concentrated around the GangesBrahmaputra-Meghna river-basins.

# **4. Methodology**

## **_Occupational focus_**

We consider two types of occupational choices made by household members. First, a member may choose between the economic sector to work in such as agriculture, construction, transport and public utilities, manufacturing, trade and business, services, and others. The household member may also decide on the type of employment, and choose between wage and self employment. Sectoral diversification may be ideal when the risks are sector specific, such as flood and rainfall variability may affect agricultural and non-agricultural sectors differently. However, opportunities, access to human, 4 Coefficient of variation is the ratio of standard deviation and mean. We also tried the mean and standard deviation as two separate indicators of rainfall parameters, and they provide similar results and are not reported.

7
Table 14: Oaxaca Blinder Decomposition of effects

||(1)|(2)|
|---|---|---|
|Prediction sales/worker change|||
|Low competition|0.02|0.02|
|High competition|0.12|0.12|
|Diference|-0.09|-0.09|
|Endowments|||
|IT|-0.03*|-0.03*|
|Other factors||0.11*|
|Coefcients|||
|IT|-0.05*|-0.03|
|Other factors constant|-0.04|-0.05|
|Interactions|||
|IT|0.02|0.02|
|Other factors constant||-0.11|
|Observations|701|701|

Standard errors in parentheses * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 Source: Authors’ calculations using data from ENTIC, INEGI 35
Policy Research Working Paper 10077

# **Abstract**

Better understanding the geography of women’s labor market outcomes within countries is important to inform targeted efforts to increase women’s economic empowerment. This paper assesses the extent to which a method that combines simulated survey data from urban areas in Mexico with broadly available geospatial indicators from Google Earth Engine and OpenStreetMap can significantly improve estimates of labor force participation and unemployment rates. Incorporating geospatial information substantially increases the accuracy of male and female labor force participation and unemployment rates at the state level, reducing mean absolute deviation by 50 to 62 percent for labor force participation and 25 to 52 percent for unemployment. Small area estimation using a nested error conditional random effect model also greatly improves municipal estimates of labor force participation, as the mean absolute error falls by approximately half, while the mean squared error falls by almost 75 percent when holding coverage rates constant. In contrast, the results for municipal unemployment rate estimates are not reliable because values of unemployment rates are low and therefore poorly suited for linear models. The municipal results hold in repeated simulations of alternative samples. Models utilizing Basic Geo-Statistical Area (AGEB)–level auxiliary information generate more accurate predictions than area-level models specified using the same auxiliary data. Overall, integrating survey data and publicly available geospatial indicators is feasible and can greatly improve state-level estimates of male and female labor force participation and unemployment rates, as well as municipal estimates of male and female labor force participation.

This paper is a product of the Poverty and Equity Global Practice and the Social Protection and Jobs Global Practice. 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 dnewhouse@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
Ashraf, Nava, Dean Karlan and Wesley Yin (2006), “Tying Odysseus to the Mast: Evidence from a Commitment Savings Product in the Philippines,” _Quarterly Journal of Economics_ , 121(2), pp. 635-672.

Ashraf, Nava, Dean Karlan and Wesley Yin (2010), “Female Empowerment: Impact of a Commitment Savings Product in the Philippines,” _World Development_ , Vol. 38, No. 3, pp. 333344.

Atkinson, Jesse, Alain de Janvry, Craig McIntosh, Elisabeth Sadoulet (2010), “Creating Incentives To Save Among Microfinance Borrowers: A Behavioral Experiment From Guatemala”, University of California at Berkeley, mimeo.

Baland, J. C. Guirkinger and C. Mali (forthcoming), “Pretending to be poor: borrowing to escape forced solidarity in credit cooperatives in Cameroon,” _Economic Development and Cultural Change_ .

Banerjee, Abhijit, Esther Duflo, Rachel Glennerster and Cynthia Kinnan (2010), “The Miracle of Microfinance? Evidence From a Randomized Evaluation,” MIT, _mimeo_ (June).

Bruhn, Miriam, and David McKenzie. 2009. “In Pursuit of Balance: Randomization in Practice in Development Field Experiments.” _American Economic Journal: Applied Economics_ , 1(4): 200–232.

Burgess, Robin and Rohini Pande, “Do Rural Banks Matter? Evidence from the Indian Social Banking Experiment,” _American Economic Review_ , 95(3), 2005.

Chandrasekhar, Arun, Cynthia Kinnan, and Horacio Larreguy (2011), “Informal Insurance, Social Networks, and Savings Access: Evidence from a Lab Experiment in the Field,” Northwestern University, mimeo.

Collins, Daryl, Jonathan Morduch, Stuart Rutherford and Orlanda Ruthven (2009), _Portfolios of the Poor: How the World's Poor Live on $2 a Day_ , Princeton, NJ: Princeton University Press.

Daley-Harris, Sam (2009), _State of the Microcredit Summit Campaign Report 2009_ , Washington, DC: Microcredit Summit (November
**1**

# I N V E S T I N G A C R O S S B O R D E R S : D O F D I - S P E C I F I C R E G U L AT I O N S M AT T E R ?

“Happy families are all alike; every unhappy family is unhappy in its own way.” – Lev Nikolayevich Tolstoy, _Anna Karenina_ , 1878

# 1.1 introduction

Foreign Direct Investment (FDI) remains one of the most important forms of crossborder capital flow into developing countries: in 2009, FDI inflow amounted to more than US$510 billion, exceeding inward remittance (US$307 billion) and development aid (US$91 billion).<sup>1</sup> As shown in Figure 1, however, just nine countries have accounted for about 60 percent of FDI inflow into developing countries over the past decade.<sup>2</sup> In an era when almost all countries in the world welcome FDI, allowing an average foreign equity ownership of 90 percent across all sectors (Table 22), this cross-country asymmetry deserves explanation that is beyond the obvious such as countries’ sizes and growth prospects.

In this paper, I focus on the policy and institutional determinants of FDI using a new cross-country data set, Investing Across Borders (IAB) 2010 _,_ drawn from World Bank Group (2010). The data set consists of indicators of FDI regulation that

> 1 The figures on FDI, remittances, and development aid are from UNCTAD (2011), World Bank (2010), and World Bank (2011), respectively.

> 2 With the onset of the global financial crisis in 2007, FDI inflow into developed countries fell from a peak of US$1.31 trillion in 2007 to US$602 billion in 2009, whereas the more modest fall in developing countries occurred with a lag of one year, from a peak of US$658
of interest, financial depth (measured as the ratio of outstanding commercial bank credit and gross State GDP) decreases with institutional quality, as does (albeit mildly) the completion rate of legal cases by courts; finally, higher shares of entrepreneurial projects are delayed by environmental clearances.

# 6.1 The dynamics of governance: Is there convergence across states?

Given our focus on time trends, we would be ideally interested in providing at least some evidence on the evolution of governance patterns across Indian States over time. Since we do not have data on the governance score by Transparency International India and CMS for years other than 2005, we try to do so by exploring a range of additional sources. First, we use an indicator of State government effectiveness in the delivery of core public services as developed by Mundle et al. (2016), who focus on infrastructure, social services, fiscal performance, justice, law and order, and quality of the legislature. Then, we turn to another work by the Centre for Media Studies (2017) and borrow their statistics on the proportion of households who were forced to pay bribes in order to obtain public services. Finally, we consult the yearly reports by the Indian National Crime Records Bureau, and retrieve the number of corruption records to be investigated by the Anti-Corruption and Vigilance Departments of each State (both as a stock and as the number of new cases reported each year).

The exercise delivers two stylized facts: (i) governance is sticky; and (ii) there nonetheless seems to be convergence in governance. In other words, it seems that states that did not have robust governance capabilities might be effectively catching up with the rest of the country, despite still displaying worse outcomes overall.

> 46 The NCAER study assesses the factors creating investment opportunities and driving investment decisions in Indian states, including the availability of factors of production, the efficiency in the use of these productive factors, States’ growth prospects, and firms’ perceptions of investment opportunities as
2005; Subramanian and Jayaraj, 2015; Sundaram and Tendulkar, 2003). The issue of diverging estimates from national accounts and household surveys is not limited to less wealthy countries. In the United States, per capita income from the two large national surveys, the Current Population Survey and the Consumer Expenditure Survey, are known to diverge from the national accounts (see for example the recent assessment by Schündeln, 2018). Nolan et al. (2019) recently reviewed gaps

> 1 Survey data for this example come from World Bank, Global Database of Shared Prosperity (GDSP) circa 2013 - 2018 (www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity) and national accounts data come from World Development Indicators, from latest data using the 2011 PPPs. 2 Estimates are from the 2009 to 2015 spell for Botswana in the Global Database of Shared Prosperity (GDSP) and growth is estimated in constant terms. The survey years are decimal years

> (2009.25 and 2015.85) and national accounts growth is estimated for the same period using weighted annual data.

2
where  _ic_ can be modelled either as a fixed effect or as a random effect and  _itc_ is supposed to follow a martingale that is with  _itc_ denoting an innovation term that is supposed to be normally, independently and 2 identically distributed, with mean zero and variance  _ct_ . Grouping households together by cohorts gives the possibility to estimate the model with repeated cross-section surveys. Estimating this model by focusing on second-order moments—as in Deaton and Paxson 2 (1994)—yields estimates of  _ct_  1 that can directly be used to predict the degree of household vulnerability in cohort _c_ . Indeed, by first drawing a value  ~ _itc_  1 in the normal distribution with mean zero and variance  ˆ  2 _ct_  1 , we obtain the probability to become poor in _t+1_ for household _i_ in cohort _c_ :

> where  (.) denotes the cumulative density of the standard normal distribution. Assuming,

> _c_ ˆ _c c_ ˆ _c_ for simplicity sake, that _xit_  1  _t_  1  _xit_  _t_ gives _v_ ˆ _itc_  Pr( _aitc_  1  _z_ | _xitc_ , _xitc_  1, _aitc_  1)  <sup></sup> ln _z_  lnˆ _aitc_   ~ _<u>itc</u>_ <u> 1</u> <sup></sup> , where  ˆ  2 _ct_  1 is the estimator of the   _ct_  1 

2 slope of the age profile for the asset disturbance term variance  _ct_ . Indeed, we propose to decompose the residual variance into age and cohort effects as follows: where  is a constant,  _ct_ is a cohort effect,  _at_ is an age effect, and _uct_ is an error term which is supposed to be independent and identically distributed and of mean zero. Then, assuming that the cohort effect is time invariant as it should asymptotically
We implement the methodology developed by Cusolito and Vranic (2020), which recreates the ownership trees of all firms in the ORBIS database, endowing us with the firm-level financial and ownership information required for our investigation. We focus on direct ownership links, as this is the most prevalent public-related ownership link for the European countries under study.<sup>1</sup> Our primary explanatory variable measures the percentage of direct shares owned by all the public authorities (PAs) that govern the country where a firm operates. Thus, it takes into consideration span-of-control issues.

Equipped with a distribution of firm-specific financing wedges, we use fixed-effect regressions to assess the role of state ownership in the cost of finance. Controlling for lagged values of firm size, age, physical productivity, firm, sector-time, and country fixed effects, we regress the idiosyncratic cost of finance (i.e., the wedge) against the lagged fraction of state ownership in a firm. Next, we leverage the theoretical foundations of our methodology for identifying financial wedges to assess the economic significance of SOEs’ subsidized access to capital markets. To this end, we conduct a series of counterfactual reforms, where SOEs are removed from their sectors of operation, and their financial resources are reallocated to surviving enterprises. How much would the aggregate productivity of the economy increase or decrease from these interventions?<sup>2</sup> The first reform imposed the shutdown of all SOEs in the economy and the reallocation of their financial resources towards their sectoral counterparts. Since these firms also face capital-market distortions, we reallocated the financing while preserving these distortions.<sup>3</sup> Next, we explored a reform that dismantled poorly performing

> 1We do not consider indirect linkages where the state is a shareholder of another company that holds shares in a particular firm. From this point of view, our results should be considered conservative estimates of the actual degree of financial subsidies confronted by state-related enterprises.

> 2Notice that our reforms could be considered a