rafmacalaba/data-use-mentions-extended huggingface dataset:
11

# SOCIA:. SECURITY

The effect of education and infant and child mortality on fertility have been well established with household level data. While debate exists over the exact form of the relationship and the magnitude of the effects in various circumstances, the relationships are not in doubt. The effect of social security systems, however, is not well established. There are four kinds of data that have been brought to bear on this issue: (1) cross country data, (2) anthropological analysis of fertility and social security in certain countries, (3) survey data on people's stated motives for having children and (4) experimental data on savings programs designed to provide incentives for reduced fertility. These data sources vary enormously in there richness and their findings. In addition to the empirical work, there are a number of theoretical arguments on the probable effect of the old age security motive for having children. These are reviewed by Nugent and Gillaspy, (1983).

The anthropological data is difficult to review thoroughly in a paper of this length. Vlassoff and Vlassoff provide an excellent piece of work on the old age security arrangements and attitudes among men in rural India. The theoretical and anthropological studies raise several questions (1) granted that children may be an important source of old age support, how many children are needed to ensure adequate support? (2) given that men tend te have very short periods of inactivity prior to death in many poor countries and women tend to be considerable younger than their husbands and are longer lived in most areas, are women more likely than men
sector of employment overseas back home experience a significant wage premium compared to returnees who find employment in other sectors. Those, however, are a minority in our sample: only 6%, 28% and 15% of return migrants to Bangladesh, Nepal and Pakistan, respectively, are employed in the construction sector.

# **6 Conclusion**

This paper reported systematic differences in the labor market outcomes of return migrants compared and nonmigrants in South Asian countries, which are major senders of temporary migrants globally. While some of these differences are partly driven by the selection of return migrants among the working age population, they subsist when controlling for workers’ observable characteristics. In particular, we find that return migrants who come back at an older age are less likely to participate to the labor force compared to nonmigrants with similar observable characteristics. Return migrants are also more likely to be self-employed compared to nonmigrants, and to hire paid employees among those that become entrepreneurs. Those who are wage employees upon return are more likely to be employed in the construction sector, the main sector of employment at destination, compared to nonmigrants. Return migrants who are paid employees earn a significant wage premium compared to nonmigrants in Pakistan, but not in Bangladesh and Nepal. The returnee wage premium, however, is larger and statistically significant in the construction sector, suggesting that return migrants who find employment in this sector benefit from their experience abroad.

This paper however also illustrates the limitations of standard nationally-representative data sets when looking at the relationship between past migration overseas and labor market outcomes back home. Given the parsimonious information on past migration overseas included in those surveys, the evidence reported in this paper is descriptive rather than causal. To investigate in detail the causal link between temporary migration overseas and labor market outcomes back home, much more comprehensive information on past migration overseas needs to be collected in nationally representative surveys of South Asian countries, in the spirit of the Egyptian Labor Market Survey (Wahba 2015). In addition, dedicated migrant surveys that collect
intervention.

> 26 Esther Duflo is a professor of economics at the Massachusetts Institute of Technology. Parker (2010) paraphrases an email sent by Duflo to MVP creator and Columbia University professor Jeffrey Sachs in 2009 stating that in her opinion it was “too late” to use rigorous evaluation methods on the existing program, though “the methods could be used in any later expansion.”

> 27 This could be done using widely-available rainfall data plus rich information contained in the most recent census. Recent censuses are available for many countries, including Ethiopia (1994, 2007), Ghana (2000), Kenya (1999, 2009), Malawi (1998, 2008), Mali (1998, 2008), Nigeria (1991, 2006), Rwanda (2002), Senegal (2002), Tanzania (2002), and Uganda (2002). Access policies for the census microdata vary by country. For many countries that do not make their microdata publicly available, given the extremely high profile of the MVP, we believe that it is likely that access to the data sufficient to conduct the procedure we describe could be negotiated.

25
Our results certainly offer suggestive support for the view that large, directed public investments can make a significant contribution to low-carbon development. However, we cannot discount the potential role of endogeneity in this exercise. The implied relationship between mass transit investment and CO2 emissions has no explicit endogeneity, because reducing CO2 emissions has not been a goal of mass transit investments until very recently. However, traffic congestion has been a target, with local air pollution as a correlate. Some element of endogeneity may therefore be present, because CO2 emissions and local air pollutants have common sources in vehicle traffic, heavy industry, power generation, and residential heating. At the same time, the heterogeneity of global cities makes it unclear whether simultaneity is an important consideration in this case. Our database includes cities with and without subways in 138 countries, with some subway installations dating back more than a century, in political and economic regimes as varied as the former Soviet Union, other COMECON countries, social democratic regimes in Western Europe, military and populist authoritarian regimes in Latin America, relatively laissez-faire regimes in the United States and Australia, and regimes in Asia that range from authoritarian in the Democratic People’s Republic of Korea and mainland China to relatively laissez-faire in Thailand and Taiwan, China. In light of these multiple, disparate factors, we believe that the exploratory results in Table 5 and Figure 13 offer reasonably strong evidence in favor of the public investment hypothesis. In future research, we hope to revisit this question in an econometric exercise with an explicit treatment of potential simultaneity.

# **7.  Future Research**<sup>**10**</sup>

The advent of satellite-based CO2 data has opened many research lines that could not be explored until the requisite information became available. In this section, we summarize some of the topics that have been identified in the course of our own research.

# **7.1 Quantifying CO2 Emissions**

Although satellite-based measures of CO2 concentration anomalies are extremely useful for comparative analyses
consumption or expenditure is measured in per-capita terms and has implications for other indicators of well-being such as housing, rents, or crowding. The Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and Living Standards Measurement Surveys (LSMS) are the most common household surveys used to produce comparative statistics on well-being across time and countries. The DHS and MICSs define household members as (i) usual residents or people who slept in the dwelling the previous night and who (ii) share living arrangements and (iii) share food (ICF International, 2012; UNICEF, 2013). The LSMSs define household members as (i) people who slept in the dwelling three or more months of the last 12 months and (ii) share food (Grosh and Munoz, 1996).<sup>7</sup> While the definitions differ, they are defined on similar concepts and are unlikely to lead to major differences in key household characteristics.

The UNHCR has definitions for household that resemble the definitions used by MICSs and LSMSs but uses the concept of “case” as unit of observation. The UNHCR defines a case as: “A processing unit similar to a family headed by a Principal Applicant. It comprises (biological and non-biological) sons and daughters up to the age 18 (or 21) years, but also includes first degree family members emotionally and/or economically dependent and for whom a living on their own and whose ability to function independently in society/in the community and/or to pursue an occupation is not granted, and/or who require assistance from a caregiver.” (Verme et al., 2016; see also https://www.unhcr.org/5ea81c114). This definition is different from the DHS, MICS and LSMS, may pose challenges when one compares cases with households in surveys, and lends itself to exploitation on the part of users, for example by spreading different household members across different cases to maximize benefits.

Some socio-economic
variables _ydi_<sup>_∗_(</sup><sup>_b_),</sup><sup>_i∈sd_,</sup><sup>_d_=</sup> 1 _, . . . , D_ . Fit the model (5.1) again to the bootstrap sample data **y** _s_<sup>(</sup><sup>_b_)</sup> and obtain the bootstrap EB predictors _δ_<sup>ˆ</sup> _d_<sup>_EB∗_(</sup><sup>_b_)</sup> , _d_ = 1 _, . . . , D_ .

5. Repeat steps 2–4 for _b_ = 1 _, . . . , B_ , and obtain the true values, _δd_<sup>_∗_(</sup><sup>_b_)</sup> , and the corresponding bootstrap EB predictors, _δ_<sup>ˆ</sup> _d_<sup>_EB∗_(</sup><sup>_b_)</sup> , for each area _d_ = 1 _, . . . , D_ , and for each bootstrap replicate, _b_ = 1 _, . . . , B_ .
Policy Research Working Paper 6349

# **Abstract**

This paper examines the extent to which policy restrictions on foreign direct investment in the Indonesian service sector affected the performance of manufacturers over the period 1997–2009. It uses firmlevel data on manufacturers’ total factor productivity and the OECD’s foreign direct investment Regulatory Restrictiveness Index, combined with data from Indonesia’s input-output tables regarding the intensity with which manufacturing sectors use services inputs. Controlling for firm-level fixed effects and other relevant policy indicators, it finds, first, that relaxing policies toward foreign direct investment in the service sector was associated with improvements in perceived performance of the service sector. Second, it finds that this relaxation in service sector foreign direct investment policies accounted for 8 percent of the observed increase in manufacturers’ total factor productivity over the period. The total factor productivity gains accrue disproportionately to those firms that are relatively more productive, and that gains are related to the relaxation of restrictions in both the transport and electricity, gas, and water sectors. Total factor productivity gains are associated, in particular, with the relaxation of foreign equity limits, screening, and prior approval requirements, but less so with discriminatory regulations that prevent multinationals from hiring key personnel abroad.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network; and Poverty Reduction & Economic Management Sector Department, East Asia and Pacific Region. 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://econ.worldbank.org. The author may be contacted at gvarela@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
history of bailouts; and the enactment of FRLs. During 2008–09 and 2009–10, countercyclical measures were taken by states to mitigate the impact of the crisis on domestic economic activity. These included relaxing the deficit levels to 3.5 percent of GDP (from 3 percent legislated under the FRLs). Further, the center allowed states a larger share of market borrowings to compensate for the unprecedented impact of exogenous factors on the fiscal situation. Concomitantly, states increased market borrowings by 34.6 percent and 28.6 percent during 2008–09 and 2009–10, respectively, compared with the increase of 23 percent during 2007–08. Interestingly, a positive impact of this was an improvement in the interest profile of states, with the share of high-cost market loans (with an interest rate over 10 percent) declining further during 2009–10.

It needs to be emphasised, however, that macroeconomic stabilization and countercyclical policy actions are the key responsibility of the center and not of subnational governments. Accordingly, the Thirteenth FC reccomended that instead of raising the borrowing limits for states in the event of such shocks, the center should assume the entire responsibility for the additional resource mobilization and pass these to states in the form of increased devolution. This (formula-based) devolution will meet the differential requirements of the states in terms of both fiscal capacity and fiscal need.<sup>47</sup> Much of the deterioration in the fiscal position of the states during that period was temporary, and thus could be attributed to deterioration in the share of central taxes because of the crisis and arrears of pay revision (Reddy 2011). Although the fiscal deficit had deteriorated to 3.3 percent of GDP during 2009–10, indications were positive for a turnaround in 2010–11, as reflected in the study of aggregate state budgets by the RBI. It also appears that fiscal discipline at the state level may have acted as a source of comfort for the market. This is corroborated by the fact
while focusing on annual stock measures of TTBs is an important and previously underemphasized area for research, my approach also allows for construction of other measures of TTB policy activity, including more precise “flow” measures based on product coverage. Capturing more information on the rate of new application of such barriers over time can better speak to the volatility of trade policy and important questions regarding policy uncertainty now emerging in the literature (e.g., Limão and Handley, 2010). While there is substantial variation in flows, in section 2 I also find that some major economies average 0.5% to 1.0% of imported

> 5 It is arguably not too early to begin to assess the stock of TTBs resulting from the crisis period given that increased flows of new investigations have shown signs of leveling off by the end of 2009, and my measurement of TTBs will account for flows and it will “time” the contribution to the stock of newly imposed TTBs as the year the first (even a preliminary) barrier is imposed. As of the latest Temporary Trade Barriers Database released in July 2010 (covering data through second quarter (2Q) 2010), there had been a substantial moderation in the count of newly initiated TTB investigations relative to the run‐up that took place in 2008‐2009. The count of new investigations began to taper off in 4Q 2009, and this has continued into 1Q and 2Q 2010. See, for example, Bown (2010b).

5
### **Figure 1: Drivers and macro-financial outcomes of bank lending rates and intermediation spreads**

<!-- Start of picture text --> Policy drivers and country characteristics Bank intermediation efficiency Macro-financial outcomes<br>Examples:<br>• Sovereign risk<br>• Monetary Macro-fiscal<br>conditions conditions Returns<br>• Crowding out<br>• Financial<br>Intermediation Oper-<br>openness ational Macro-economic<br>spread costs • Investment<br>• Scale Quasi taxes •• Savings<br>• Competition Banking Lending Monetary<br>• Leverage sector Risk rate policy<br>• Funding characteristics premia<br>Financial sector<br>Cost of • Depth<br>• Information funding • Access<br>and collateral Business • Stability<br>environment<br>environment<br>• Insolvency and<br>creditor rights<br>• Direct policy<br>interventions<br><!-- End of picture text -->

# 3. Empirical Results

## **A. Global trends and patterns of nominal lending interest rates and spreads**

Using information from 140 EMDEs, the following patterns of lending interest rates and spreads between 2003 and 2017 are found: a) <u>Nominal lending rates – In the 15-year period, nominal lending rates have consistently</u> declined across all regions and the EMDE median has fallen by a third -- however significant heterogeneity within and between regions persists (Figures 2A, 2B, and 3A). And the median lending rate has fallen by almost 5 percentage points between 2003 and 2017. The decline is consistent over time, except for the global financial crisis, when interest rates slightly increased. However, large variation across regions persist. The dispersion within region is also significant. In the decade 2007-2017, two-thirds of EMDEs saw their nominal lending rates decline.

9
’s international competitiveness, and – when operating efficiently – able to compensate substantially for the effect of geographical distance.

The exponential expansion of the academic literature on the development impacts of infrastructure has been fueled by the availability of much richer sources of microeconomic data on firms and households, often with geospatial characteristics, as well as the associated analytical techniques. Compared to the earlier literature that focused primarily on cross-country panel data econometrics based on crude high-level measures of infrastructure, the new body of work provides a much richer and more granular understanding of the specific channels through which different kinds of infrastructure data can contribute to development overall. While the results can sometimes be mixed, and methodological controversies remain, the overwhelming balance of evidence suggests that improvements in infrastructure play a vital role in supporting the development process.

44
Following Deaton and Zaidi (2002), we constructed the expenditure aggregate excluding debt repayments, remittances, and celebrations. After analyzing elasticities, we decided to include expenditures on health and education. The aggregate was then converted into monthly per capita terms using household size. To align with the approach to monitor absolute poverty in Turkey, we used per capita rather than adult equivalent ratios.

The expenditure data and all monetary values were deflated to be expressed in average 2017 YTL. The deflation accounts for price variations over time and across regions, constructing deflators by expenditure type using Turkish Statistical Institute’s Consumer Price Index (CPI).

# _b) Food Consumption Score (FCS)_

The Food Consumption Score is an index of the diversity and frequency of foods consumed at the household level, calculated using the frequency of different food groups consumed by a household during the past seven days (WFP 2008). It is a standard indicator developed by the WFP, and used globally to measure food security and assess if households achieve acceptable or unacceptable food consumption. The score is a continuous variable with a possible range of 0 to 112, equal to the weighted sum of frequency of household consumption of each food group.

The food consumption groups include starches, pulses, vegetables, fruit, meat, dairy, fats, and sugar. In the survey, households are asked how many days each of the food groups were consumed within the past seven days. The formula for FCS, with the standard weights is:

# _FCS = (starches*2) + (pulses*3) + vegetables + fruit + (meat*4) + (dairy*4) + (fats*0.5) + (sugar*0.5)_

In the context of Turkey, households with a food consumption score greater than 42 were considered to have “acceptable” food consumption, those with FCS greater than 28 and smaller than or equal to 42 were considered to have “borderline” food consumption, and those with FCS
income. From this income, gross profit is derived by deducting the cost of goods sold, and net profit is calculated by further deducting allowable depreciation expenses, interest, and other expenses. Moreover, businesses have the option to deduct losses carried forward or backward from their net income to determine their taxable income. Subsequently, tax rates are applied to this taxable income to ascertain their tax liabilities. It is important to note that even if a business generates substantial revenue, its reported tax liabilities may appear lower if it reports higher costs of goods and services.As an illustrative example, Cronin et al. (2023) demonstrates that deductible expenses can vary based on demographic factors such as race. Given the potential for variation in reported deductible expenses by gender, this comprehensive dataset enables us to not only investigate the presence of gender disparities in reported expenses but also to explore gender differences in reported income and costs. Additionally, the data employed for this study covers a span of two years (2011 and 2012).

It is worth noting that this administrative data source does not explicitly indicate the gender of business owners. Instead, gender assignments were made by merging this administrative data with survey data collected through a field experiment, which collected information on the business owner by Shimeles et al. (2017). The study targeted 5,400 randomly selected taxpayers from approximately 86,000 taxpayers in Addis Ababa for the fiscal year 2013/2014.<sup>3</sup> The sampling approach employed a stratified random sampling method based on geographic location, known as sub-city, and economic sectors. The study included an equal number of taxpayers from three major sectors: wholesalers, agro-processing and manufacturing, and other services. These taxpayers were drawn from five of the ten sub-cities in Addis Ababa, where the majority of taxpayers operate. Specifically, the study encompassed 1,350 taxpayers from each of the following sub-cities: Addis Ketema, Bole,

> 3 th th The Ethiopian fiscal year starts on July 8 and ends on
**Table 2: A Systematic Review of Dimensions and Indicators Used in the Analysis of Social Sustainability**

|**Dimensions and Indicators**|**Number of**||**Group tha**|**t the indicat**|**or can be disa**|**ggregated by**|**:**||
|---|---|---|---|---|---|---|---|---|
||<br>**Countries**|**Age**<br>**A**<br>|**rea: urban/rur**<br>|**al**<br>**Gender**|**Disability**|**Ethnicity**|**Religion**|**Source**|
|**Access to Labour Markets**||**Social Incl**|**usion**||||||
|<br>Labor force participation rate|105<br>|X<br>|X<br>|X<br>||||GMD<br>|
|Unemployment rate (%)<br>Self-employed|104<br>71|X<br>X|X<br>X|X<br>X||||GMD<br>GMD|
|**Access to Financial Services**<br>Share of population with a bank account|34|X|X|X|X|X||Afrobarometer|
|**Access to Basic Public Services**|||||||||
|Share of households without access to adequate sanitation<br>Share of households with access to improved water|130<br>129|X