rafmacalaba/data-use-mentions-extended huggingface dataset:
it is lower than that of the census. The underestimation in the census does not seem to be confined to small establishments. One can infer this from the fact that output per person employed is generally lower in the census than in the national accounts figures. One usually expects small firms to have lower labour productivity than big ones (see table 2.4). The paradoxical productivity figures for the informal sector may be due to the fact that the national accounts includes only paid employees, whereas in the informal sector there is probably a fairly high proportion of unpaid family workers.

The Mexican census definition of gross value added, "Valor Aggregado Censal Bruto" is netter than the Brazilian "Valor de Transformacao Industrial" or the US census definition. However, the census contains enough informaUion to arrive at an estimate of value added which corresponds with the national accounts concept or to one which corresponds with the Brazilian and US industrial census definitions. As in Brazil, the Mexican output figures refer to production including output which goes to inventory, whereas US figures refer to shipments. The Mexican value figures in some cases include indirect taxes. The most notable cases, for which we have made a correction are alcoholic beverages and tobacco and tobacco products where the incidence of excise taxes was 28.1 and 192.7 per cent (see table 2.3). For petroleum refining and products we also deducted indirect taxes, which we derived from national accounts information. Elsewhere we did not think this problem was significant.
Ministers.

Table 15. Services Trade Restrictions Index in Eurasian Countries

|Country|Overall|Financial|Telecomm.|Retail|Transportation|Professional|
|---|---|---|---|---|---|---|
|Belarus|35.1|53.8|62.5|0|36.8|40|
|Ukraine|27.2|24.4|25|25|26.7|35|
|Russian Federation|25.7|46.7|50|0|14.2|32|
|Uzbekistan|23.4|24.6|50|0|32|31.5|
|Kazakhstan|17|22.1|25|0|16.4|28|
|Kyrgyzstan|15.2|8.4|0|0|45.7|28|
|Georgia|11.5|9|0|0|8.8|37|
|Armenia|11.4|2.6|0|0|25|32|

Source: World Bank’s Services Trade Restrictions Database (2012).

Restrictions in the financial sector are relevant in Belarus and Russia. In the case of Belarus, a license for providing some banking services can be obtained two years after registration of the bank. There is a limit on licenses, due to a 50% threshold on foreign investment in the banking sector. If this limit is reached, foreign investment in banking would not be possible. In the case of automobile and life insurance, entry through a branch is not allowed. For entry through a subsidiary and acquisition, the foreign ownership limit is 49%. For a single foreign investor, the foreign ownership limit is 35%. In the case of Russia, entry in the banking system is possible through a subsidiary and/or representative office. There is no limit on foreign ownership
# **The Poverty Impact of Rural Roads: Evidence from Bangladesh**

## **1. Introduction**

Improved roads and infrastructure can create opportunities for economic growth and poverty reduction through a range of mechanisms. Roads reduce transportation costs and the costs of consumption and production of goods and services (BIDS 2004). With easier access to markets and technology, improved roads expand farm and nonfarm production through increased availability of relevant inputs and lower input costs (Binswanger, Khandker, and Rosenzweig 1993; BIDS 2004; Levy 1996) as well as growth in rural enterprises (Lokshin and Yemtsov 2005). At the household level, road development contributes to higher productivity and demand for labor (Leinbach 1983; World Bank 2000), and improved education and health, including for women and girls (Bryceson and Howe 1993; Levy 1996). Road-related studies have also suggested that household consumption is likely to get a boost from increased household income, consequently reducing poverty (BIDS 2004; Fan, Hazell, and Thorat 2000).

It has been difficult to precisely quantify the benefits of roads, due to methodological constraints and data limitations. While transport investments have consistently represented 15-20 percent of the World Bank’s lending portfolio<sup>2</sup> , traditional estimates of the returns to such investments using the internal rate of return approach are generally so low that the investments do not appear viable. Such approaches have also been criticized for not being able to capture the true distributional benefits to the targeted population, particularly for the poor (van de Walle, 2002). Additional mechanisms also depend on the type of road improvement project.<sup>3</sup> These problems are compounded by the fact that the effects of rural roads are also long-term and thus cannot be captured through the use of cross-sectional data, particularly since unobserved fixed area characteristics influence the placement of road investment in a village or community (Binswanger, Khandker and Rosenzweig, 1993).

Since the timeline for the full impact
**_<u>________________________________________________________</u>_**

countries and countries in transition), and are broken down by major category of expenditure: capital projects, budget and balance of payments support, food and other commodity aid, technical co-operation and emergency relief. Dac Online, together with the Creditor Reporting System (CRS), comprise the OECD's International Development Statistics Online.

# _<u>SourceOECD</u>_

Provides online access, from 1998 onward, to the full-text of all OECD periodicals, monographs, annuals, outlooks, policy reviews, conference proceedings, analytical reports, guidelines, working papers, and 27 statistical databases.

# _<u>UN Development Business Online</u>_

provides information on opportunities to supply goods, works, and services for projects financed by the United Nations, governments, and the following development banks: the World Bank, Inter-American Development Bank, African Development Bank Group, Asian Development Bank, Caribbean Development Bank, European Bank for Reconstruction and Development, and North American Development Bank. Searchable by region, country, sector, bank, and other parameters. Published by the United Nations Department of Public Information. Updated several times per week.

39 ____________________________________________________________________________________________________________
practitioners who may wish for more functionality and options may have to program it themselves. In an effort to simplify the process, we have created a family of Stata commands which implement small area estimation methods, and provide users with a valid, modular, and flexible alternative to PovMap. All of the results produced with `sae` have been bench-marked with the well established PovMap software, and are an exact match.<sup>6</sup> In the following section we discuss the linking model and focus on the first stage of the estimation. How the variance of the location effect present in the linking model is accounted for is discussed afterwards. This is initiated by presenting the ELL methodology for decomposing the first stage residuals’s variance parameters. Afterwards, this is followed by Henderson’s Method III decomposition of the residual’s variance parameters and the adaptation of Huang and Hidiroglou’s (2003) GLS and implemented by Van der Weide (2014) in an update to the PovMap software. Then we present Empirical Bayes prediction of the location effects. We finally present the command’s syntax along with examples of its use.

# **2 First Stage: Model**

When survey data is not sufficiently precise for reliable estimates at low geographical levels, small area estimation can be implemented. This commonly relies on borrowing strength from outside sources to generate indirect estimates (Rao and Molina, 2015). For example, when attempting to obtain welfare estimates at low geographical levels, it is possible to borrow strength from census data. Censuses in most cases do not collect sufficient information on incomes and/or expenditures. On the other hand household surveys tend to collect detailed information on incomes and/or expenditures which allows the generation of welfare statistics such as poverty and inequality measures. Although welfare statistics can be obtained using household surveys, these are rarely sufficiently large to allow for statistics corrsponding to small areas (Tarozzi and Deaton, 2009). Small area estimation methods attempt to exploit the large sample
Predicted probability of early childhood stunting (24-60 months of age) for simultaneous access to nutrition driver domains <!-- Start of picture text --> 35.8<br>Predicted baseline risk —-oO-<br>46.2<br>Adequate access to 0 domains —_o—<br>Adequate access to any 1 domain; —o—417<br>Adequate access to any 2 domains. —o—33.6<br>Adequate access to any 3 domains. —_o—.26<br>Adequate access to any 4 domains. —_—o——_—_-19.7<br>0 10 20 30 40 50%<br>Predicted probability of stunting (%)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: The predicted probabilities are based on model 3b (adjusted) and calculated at the means for all other predictors. 95% confidence intervals are shown.
percent less than soil salinity tolerances. Several more recent studies examine specific crops. For example, Rameshwaran et al. (2016) find that sweet peppers become sensitive to water salinity at levels exceeding 143 mS/m and decline by a slope of 11 percent thereafter. Arslan et al. (2015) found that yields of chickpeas, lentils, and fava beans fell by 50 percent when EC in irrigated water exceeded 420, 440, and 520 mS/m, respectively. This implies that based on the staple diet of countries, salinity may have an amplified impact on countries relying on agricultural production that is more salinity sensitive. While crop based studies are informative, they do not provide an indication of the global loss of food production due to salinity. To address this issue, this paper provides estimates of the average impacts on yields across all crops using a variety of recently available data sources and finds a remarkably stable yield response function that holds across wide geographies.

# 2. Data

To estimate the impact of water salinity on global agricultural productivity, we use several georeferenced data sets related to water quality, agricultural yields, land cover, and weather. A description of these data sets can be found below and summary statistics are provided in Table 1.

## 2.1 Water Quality

Data on electrical conductivity (EC) were used to measure salinity in water. EC measures the ability of an electrical charge to pass through water. Distilled, or deionized water is a very poor conductor of electricity and as dissolved ions in water increase, it becomes a better conductor. In surface and groundwaters, these ions are generally dissolved salts, which is why EC is a widely used indicator of water salinity (Miller, Bradford, and Peters 1988). EC as a measure of salinity has the added benefit of being easily measured at monitoring stations or with handheld monitors, making it a very widely available water quality indicator.

> 1 See Tanji and Kielen (2002) Table A1.1 for values and list of
# **IX. Figures**

## **Figure 1 – Plot Size Distribution**

<!-- Start of picture text --> Malawi Integrated Household Survey 2010/11<br>Plot Area Distribution<br>SR GPS<br>30<br>20<br>10<br>0<br>0 1 2 3<br>Acres<br>*Limited to plots <=3 acres<br>P ercen t<br><!-- End of picture text --> 42
active investors, _η_ , and (ii) changes in expected repayment, _α_ . Using the calibrated model, we can isolate the effects driven by changes in expected repayment to properly identify the structural demand elasticity.

Figure 6 decomposes the channels outlined in Equation (19). The black line shows the reduced-form (inverse) demand elasticity _η_ ˆ, while the blue line depicts the model-implied structural elasticity, _η_ . The vertical differences between these two curves (red lines) indicate the portion of the reduced-form elasticity attributable to endogenous changes in the repayment ˆ function, _α_ . We find that the magnitude of _η_ is always higher than that of _η_ . This is consistent with our empirical analysis based on CDS spreads (described in Section 3). The difference can be substantial, particularly for larger values of _B_<sup>_′_</sup> and for higher bond spreads.

The first column of Table 6 shows the unconditional average for both the reduced-form ˆ elasticity, _η_ , and the structural elasticity, _η_ . On average, the structural elasticity accounts for less than two-thirds of the reduced-form elasticity. The rest is explained by endogenous changes in the expected repayment, _α_ . Despite the fundamentals ( _y, B_<sup>_′_</sup> ) being fixed and the ∆ _T_<sup>_′_</sup> shocks being exogenous, the reduced-form elasticity significantly differs from the structural one.

The magnitudes of the documented biases critically depend on the persistence of the 28
u>Section 1: presents the rationale for private participation in the highways sector,</u> alternative options, and a guide to conducting a sector diagnostic.

- <u>Section 2: sets out the key parameters needed in the design of a public-private</u> partnerships and presents detailed case studies of well-known highway contracts.

- <u>Section 3: stresses the important role of the public sector in ensuring the success of the</u> reforms and presents the tools at government's disposal for performing such roles.

- <u>Section 4: provides guidance on the design of legal and contractual frameworks for</u> private participation in highways, with boilerplate provisions.

- <u>Section 5: outlines the key steps for introducing private sector participation, bringing</u> elements from all previous sections and distinguishing by type of private sector contract.

# _<u>World Economic Forum:  Global Competitiveness Report (GCR)</u>_

It includes an index based on quality perception for rating railroad services, port facilities and air transport services. (Ranking from 1 to 7, with 1=underdeveloped, 7=developed as the world’s best).

# _<u>World Bank: Railway Database</u>_

This database provides information on scale, output and performance for over 90 railways worldwide permitting comparisons of performance and facilitating target setting by individual 26 __________________________________________________________________________________________________________
also been low by comparison with other reforming countries, as shown in Table 13.<sup>10</sup>

**Table 11: Net Rates of Return, Wage Growth, and Inflation Rates, 1998 – 2004**

||**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**<sup>**1**</sup>|
|---|---|---|---|---|---|---|---|---|
|Net Nominal Rate of Return|15.7|17.1|7.9|8.0|7.4|3.4|16.3|13.0|
|Wage Growth|16.8|12.1|12.7|13.2|15.0|12.7|6.3|6.2|
|Inflation Rate (Aver)|14.2|10.0|9.8|9.2|5.3|4.6|6.8|3.9|
|Inflation Rate(Dec)|10.3|11.2|10.1|6.8|4.8|5.7|5.5|3.0|

Notes: /1 estimates Source: NBH and HFSA raw data (wage and return), and IMF (inflation).

9 We use December inflation to calculate the real rate of return and average inflation to calculate the real wage growth as the rate of return is realized in December of each year while wages are a flow over the year. 10 Rates of return in Hungary are reported net of asset management fees, while in other countries rates of
and non-located households (as available from the bank data). Tested jointly, balanced attrition cannot be rejected at conventional levels of significance ( _p_ = 0 _._ 24). Similarly, attrition is not systematically related to landholding or relief amount.

The average surveyed household is a family of seven with total landholdings of 1.82 hectares, an annual gross income of Rs 72,429 (US$ 1,610) and total pre-program debt of Rs 92,676 (US$ 2,059). For all households in the sample, agriculture is the main source of income. Surveyed households are extremely dependent on credit to support investment in agricultural inputs, such as irrigation, fertilizer and pesticides. Households in the sample spent an annual average of Rs 13,254 (US$ 295) on agricultural inputs, and 72% of households relied on external borrowing to finance this investment. Before debt relief, 86.7% of this credit was provided by banks and credit cooperatives.

17
gion, geographic distance, poverty and rural share (see Figure 2). The two main religions are Christianity and Islam. I calculate the share of the population practicing each religion from the National Demographic and Health Survey 2008. For distance, I calculate geographic distance from the university town to the capital of each state using ArcGIS. I obtain poverty data for each state from the National Poverty Profile Report 2010 from the National Bureau of Statistics. I calculate rural share for each state from the rural/ urban classification of local governments from Census file 2006. Random assignment to states also creates exogenous exposure to these state attributes, some of which are highly correlated with ethnicity. In particular, compared to Yoruba majority states, non-Yoruba states are further away and more rural.

# **4 Methodology**

## **4.1 NYSC Randomized State Assignments**

NYSC seeks to encourage “the development of common ties among the youths of Nigeria and the promotion of national unity” (NYSC 2015b). As a result, a unique feature of the program is that participants are randomly assigned to one of the 37 states of the country, with the intention that the composition of the cohort in each state reflects the national diversity. This randomized assignment will be the main feature of the program that the research design will exploit.<sup>15</sup> NYSC randomized assignments provide that participants posted to different states are similar in expectation. Therefore, participants posted to states in which their ethnic group is the majority (the “control group”) should be otherwise similar to those posted to states in which they are not the majority (the “treatment group”). As a result, any differences

> 15An alternative identification strategy would be to measure the impact of participating in NYSC altogether by using the discontinuity in participation from graduating before age 30. The available sample in this study is not sufficient for this analysis and I plan to explore this in future research.

13
following then President Omar al-Bashir’s decision to end fuel and other subsidies. Many of the leaders of these protests, like many of the leaders of the earlier post-Arab Spring protests, were youth who had access to smartphones and social media. Kadoda and Hale (2015, 223) argue that “social media not only facilitated civil society’s mobilization and greater visibility, but they (the media) raised consciousness and drew in greater numbers.” Their research demonstrates that the youth activism that took place in 2013 on social media sites such as Facebook, Twitter, and YouTube succeeded in cross-cutting socioeconomic and other divides in ways that the ‘traditional’ protest movements in the mid-1960s and mid-1980s could not.<sup>26</sup> Building on that, access to social media in 2013 allowed many women who might not otherwise have been able to participate—due to the patriarchal norms discussed earlier—to actually participate, because social media enabled them to mask their identities. As mentioned earlier in this report, youth, including female youth, were active in the protests that led to the toppling of President Omar al-Bashir mid-2019, and they typically relied on social media to organize their protests.

The expansion of digital technologies and social media has the potential to contribute to expanding women’s and youth’s economic opportunities. Household possession of assets such as a computer and a phone has been shown to have a positive influence on labor force participation. Women living in households with access to a computer improve their chances of being in the labor force and employed by 33 percent and 77 percent, respectively, while phone availability is linked to a 34 percent better chance of employment for youth.<sup>27</sup> Based on the FGDs, a number of Sudanese are already relying on social media—in some cases exclusively so—to help them learn about job opportunities. For example, when asked about sources of job information, a female youth from Khartoum who was not working said, “Facebook
We use the special module on child labor available in the PNAD of 2001 to check whether the affected cohort is studying more hours per day than the comparison cohort and whether there is any evidence they are still in the labor force. Table A.3 in the appendix shows that the ‘treated’ cohort is 1.5 p.p. less likely to be looking for a job in the last 30 days than the comparison group. This means that, with the ban, the affected cohort left the labor force.

These results indicate that the law change made some boys who would combine work with schooling leave the labor force to dedicate full time to study. We perform the same estimates adding controls and the results are identical.<sup>26</sup> Finally, we investigate if the ban affected the occupations of banned boys. We perform the same exercise of equation (1) using the sample of working boys in 1999. Table A.4 presents the results. Among the comparison group, 36 percent work in the service sector, 25 percent in manufacturing, 15 percent in agribusiness, and 4 percent in administrative tasks. Although we do not find any statistically significant change in these compositions, the results suggests that there are some changes from service and agribusiness to manufacturing and administrative tasks. However, these exercises have low power due to the relatively small number of boys working for pay.

# **6.1. The Plausibility of the Identification Assumption**

The identification strategy used in this paper assumes that turning age 14 by the time the law changed (December 1998) was “random”. Thus, observed and unobserved characteristics are unexpected to be systematically correlated to both turning 14 around December 1998 and the outcomes of interest.

The official statistics for children at ages 14 and 15 in 1998 shows that 21.6 percent were in the workforce with about half (46 percent) in the formal sector. In 1999, the proportion of 14 and 15-year-olds in the labor force dropped
., 2020; Jeong et al., 2019; and Kang et al., 2018).

This paper applies recently developed methods to address threats to internal validity using individual child data from two countries, the Republic of Congo and São Tomé and Príncipe, for the purpose of deriving “causal” inferences about the effect of child stature and mother’s level of education on early childhood development (ECD). Stunting is the impaired growth and development that children experience from poor nutrition, and repeated infection, and is based on measuring the height (length) of children and comparing it to the height of well-nourished children of the same age. The level of education of a mother affects early childhood development through a variety of channels: improved child health and nutrition, increased labor force participation, and income earning opportunities 2