rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table 5.2. Impact of treatment on PqTRs in treated schools**

||**2016-17/ 2017-**<br>**18**|**2017-18/ 2018-**<br>**19**|**2018-19/ 2019-**<br>**20**|
|---|---|---|---|
|Control|111.067 ***|108.804 ***|106.133 ***|
||(1.066)|(1.284)|(0.860)|
|Time|5.231 ***|3.585*|1.845|
||(1.789)|(2.058)|(1.508)|
|Treatment|3.193**|1.433|2.392|
||(1.393)|(1.535)|(1.549)|
|DiD (Treatment and|-46.187 ***|-40.099 ***|-37.431 ***|
|Time)|(2.036)|(2.263)|(2.085)|
|Control (N)|415|299|470|
|Treatment(N)|912|714|330|

_Data Source: EMIS 2016-2019 Note: Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01_

**Table 5.7. Robustness check: Impact of PqTR reduction on repetition rate (PqTR 80)**

||**Overall**|**Lower Primary**|
|---|---|---|
|Control in 2018|0.260***|0.
 Quality standards for warehouse receipts  Performance guarantees on warehouse operator  Receipt negotiability _6) Getting credit_  Security interest granted to movable assets and future assets  Collateral registry  <u>Credit information from non-banks institutions</u> MFIs play an important role in promoting financial inclusion. They substitute for moneylenders and help provide financial services to those who are excluded from the traditional banking system (Dev, 2006). Accessing microfinance credits has significant impacts on improving the livelihood of the poor, by enabling them to smooth consumption, better manage skills and diversify their economic activities (Bakhtiari, 2011). The microfinance regulator’s challenge is to adopt a regulatory framework with appropriate prudential and non-prudential regulations to balance the multidimensional goals of financial access, financial stability, and consumer protection (Christen et al., 2003). The MFI indicator measures the regulatory environment for deposit-taking microfinance institutions, including prudential regulations such as minimum capital adequacy ratios and provisioning rules imposed on those MFIs, as well as consumer protection requirements focusing on interest rate disclosure and enrollment in a deposit insurance system.

Financial cooperatives serve large numbers of low-income customers; however, constrained by obsolete governance structure, low capacity, lack of an appropriate regulatory framework, and poor supervision, financial cooperatives in most developing countries are underdeveloped and not fulfilling their potential to serve the unbanked population (Nair and Kloeppinger-Todd, 2007). This indicator measures the existence and content of financial cooperative regulations, including the minimum requirements to establish a financial cooperative, prudential ratios and consumer protection requirements.

Agent banking allows agents to provide financial services on behalf of a financial institution in areas where physical branches do not reach. It provides the poor with more economical options of accessing financial services, as they do not need to spend out of pocket to reach a bank branch (Barasa and Mwirigi, 2013). Strong legislation fosters a positive customer experience that creates trust in the system. At present, the agent banking indicator examines the extent to which countries have enacted some good legal/regulatory
and cognitive development : We next examine how child health and</u> parenting quality are related to test scores. We focus on how much of the associations between cognitive development and SES can be explained by the inclusion of health and parenting measures. It seems plausible that health and parenting are routes through which wealth and parental education influence children’s cognitive development. Wealth is correlated with both measures of child health and parenting. For example, the correlation coefficient between our wealth index and the child’s hemoglobin level is 0.24; correlations between wealth and other key measures are 0.13 (height-for-age); 0.08 (weight-for-age); -0.20 (HOME score); and 0.39 (whether the child is read to). Similar correlations exist between parental education and measures of child health and parenting.

> 16 Similarly, Case, Lubotsky and Paxson (2002) use data from the US to show that associations between family income and child health increase as children age.

16
# **4 Empirical Strategy**

Our primary outcomes of interest are three measures of consumption: log of monthly per capita consumption expenditure: total, food, and non-food. We also show results on an indicator variable for whether the household is poor (headcount ratio). We focus on two measures of weather anomalies in 2018 growing season defined at the cluster level: NDVI, and land surface temperature z-scores, as measures of drought conditions. We also present the main result with rainfall z-scores with the caveat that it is not an ideal measure of drought in Afghanistan (details in section 5).

The relationship between consumption and weather may be nonlinear: while high temperatures may result in crop loss, low-temperature realizations may also adversely affect the harvest. To allow for flexibility in the relationship between consumption and weather anomalies, we use a semiparametric partially linear model, in which we assume a flexible functional form on weather anomalies, whereas the other regressors (controls) enter linearly: _Yicdp_ is the outcome variable for household _i_ in cluster _c_ in-district _d_ of province _p_ in 2019-20 based on past 12 month consumption data (see Appendix B for details). We add district dummies and include several explanatory variables (explained below). _zcdpg,_ 2018 is the weather anomaly constructed as described in Section 3.5. We do not assume a functional form on _f_ ( _._ ). We use Yatchew (1998)’s method to fit a partially linear model. The data is sorted on _z_ ( _weather_ ) _cdpg,_ 2018, and a first difference model from equation 1 is estimated.<sup>21</sup> Under the assumptions that _f_ ( _._ ) is smooth, single-valued with bounded first derivative and _z_ ( _weather_ ) _cdpg,_ 2018 has a compact support, _plim_ ∆ _f_ ( _._ ) = 0 as the sample size increases. In this case, parameters _γ_ 1 and _γ_ 2 can be consistently estimated with OLS, withˆ out explicitly modeling _f_
credit, and the business climate.

These explanatory variables are also used in an extensive literature that has examined the determinants of investment growth. These include weak output growth, the terms‐of‐trade shocks caused by the slide in commodity prices since 2011, slow FDI flows, and intermittent bouts of political and policy uncertainty.

_Weak output growth._ The weakness in investment growth has coincided with weakness in output growth and a deteriorating growth outlook for EMDEs (Didier et al. 2015). The growth slowdown in EMDEs has reflected both structural factors and cyclical components. Weak growth prospects signal reduced opportunities for firms selling their goods and services and thus lead to lower investment. This is captured in the “accelerator model,” which assumes that firms aim to maintain a constant capital‐to‐output ratio, in line with their expectations of future output growth (Jorgenson 1963; Jorgenson and Siebert 1968). Recent work on advanced economies has shown that output growth captures broad trends in investment, but actual investment often falls short of the model predictions.<sup>16</sup> In the regression, weak growth prospects are proxied by lagged output growth to reduce concerns about endogeneity.<sup>17</sup>

> 16 Lewis et al. (2014); Barkbu et al. (2015); Banerjee, Kearns, and Lombardi (2015); and Leboeuf and Fay (2016).

> 17 Ideally, growth prospects would be captured by forecasts for several years ahead. However, these are highly endogenous to investment and highly correlated with FDI inflows. Alternatively, a truly exogenous source of output growth would be used, such as changes in public investment. However, the available panel data on public and private investment are sparse to conduct a panel regression. Some authors include measures of foreign demand into similar types of panel regressions. However, when included here, export growth is insignificant as its effect is captured by domestic output growth.

19
escalin|g|New - N|o Res|caling|

Note: 1 Corresponds to the _old model_ , 2 to the _new model with rescaling_ , and 3 to the _new model without rescaling._ Source: Own elaboration based on SEDLAC <!-- Start of picture text --> A) Bolivia 2019 B) Dominican Republic 2018<br>35 18<br>16<br>30<br>14<br>25<br>12<br>20<br>10<br>15 8<br>10 6<br>4<br>5<br>2<br>0<br>0<br>-5 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 6<br>Upper bound Lower bound Upper bound<br>Actual growth Old Model Lower bound<br>New - Rescaling New - No Rescaling New - Rescaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

24
Nabli, M. K. 2007. Breaking the Barriers to Higher Economic Growth: Better Governance and Deeper Reforms in the Middle East and North Africa. Washington, DC: World Bank.

Novta, N., and J. C. Wong. 2017. “Women at Work in Latin America and the Caribbean.” IMF Working Paper 17/34, International Monetary Fund, Washington, DC.

OECD (Organisation for Economic Co-operation and Development). 2014. “Growth Prospects and Fiscal Requirements over the Long Term.” In OECD Economic Outlook. Paris: Organisation for Economic Co-operation and Development.

OECD (Organisation for Economic Co-operation and Development). 2015. Education at a Glance 2015: OECD Indicators. Paris: Organisation for Economic Co-operation and Development .

OECD (Organisation for Economic Co-operation and Development). 2016. Boosting Productivity and Inclusive Growth in Latin America. Paris: Organisation for Economic Co-operation and Development.

OECD (Organisation for Economic Co-operation and Development). 2017. OECD Economic Surveys: Mexico 2017. Paris: Organisation for Economic Co-operation and Development.

OECD (Organisation for Economic Co-operation and Development). 2018. “Climateresilient Infrastructure.” Policy Perspectives series. OECD Environment Policy Paper 14, Organisation for Economic Co-operation and Development, Paris.

OECD (Organisation for Economic Co-operation and Development). 2021a. Education in Eastern Europe and Central Asia. Paris: OECD Publishing.

OECD (Organisation for Economic Co-operation and Development). 2021b. Education at a Glance 2021: OECD Indicators. Paris: OECD Publishing.

OECD (Organisation for Economic Co-operation and Development). 2022. “2018 PMR database.” Organisation for Economic Co-operation and Development, Paris.

OECD/CAF/ECLAC (Organisation for Economic Co-operation and Development/Corporación Andina de Fomento/Economic Commission for Latin America and the Caribbean). 2016. Latin American Economic Outlook 2017: Youth Skills and Entrepreneurship. Paris: OECD Publishing.

OECD/CAF/ECLAC (Organisation for Economic Co-operation and Development/Corporación Andina de Fomento/Economic Commission for
Despite the reduction of observations in the last two quartiles of checks and balances, the picture is broadly the same. The scatter plots in Figure 2 confirm the negative relationship.

[Table 2 about here]

[Figure 2 about here] Finally, Table 3 shows the averages of infrastructure quality per quartile of quality of governance and checks and balances. Three indicators that proxy the quality of infrastructure in transport, energy and telecommunications were selected: paved roads as a percentage of total roads, electric power system losses as a percentage of total power outputs, and faults per 100 main fixed lines per year. The latter two indicators were rescaled such that higher values mean higher quality.<sup>4</sup> The quartile analysis reveals that infrastructure quality is positively correlated with institutional quality. These findings are true for all the indicators, but the changes from quartile to quartile are more dramatic for paved roads as a percentage of total roads. Figure 3 corroborates these results by displaying the scatter plots with a positively sloped prediction line for all combinations of both infrastructure and institutional quality indicators. Interestingly, the shape of the probability density functions for the infrastructure quality indicators have significant differences. While there are very few observations recording low quality values for the energy and telecommunications sectors, the points for the transport sector show much more variance.

[Table 3 about here]

[Figure 3 about here]

# **3. A Review of the Literature**

Numerous papers have been written about the contribution of public investment to economic growth and poverty reduction, but the relationship, though generally positive, has not been conclusive.<sup>5</sup> Part of the reason can be attributed to the difficulty in differentiating between types of public investment. For example, one would expect the impact to be contingent upon the kind of public investment, the amount of investment, the initial stock of public capital, the economic context in which investment occurs (Anderson et al., 2006), as well as the quality
Policy Research Working Paper 9753

# **Abstract**

This paper examines the impact of the legal status of overseas migrants on their wages upon return to the home country. Using unique data from the Arab Republic of Egypt, which allows distinguishing between return migrants according to whether their international migration was documented or undocumented, the paper examines the impact of illegal status on wages upon return. Relying on a conditional mixed process model, which takes into account the selection into emigration, return, and the legal status of temporary migration, the analysis finds that, upon return, undocumented migrants experience a wage penalty compared with documented migrants, as well as relative to non-migrants. The results are the first to show the impact of undocumented migration on the migrant upon return to the country of origin.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa 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://www.worldbank.org/prwp. The authors may be contacted at nelmallakh@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
down from 325 percent in 1993. The 1995 figure is however somewhat higher than the rate required in 1992. P1HBK would have had to charge a 227 percent interest rate in fiscal 1995/96 to fully cover all costs attributed to the program. To cover all costs excluding the in-kind costs of salary and overheads BI attributed to the program, the program would have had to charge 159 percent. While PHBK's full-cost figure is the highest of the programs reviewed, its required interest rates have declined every year since 1992. Indeed, PHBK's required rates in fiscal 1995/96 are only about 65 percent as high as the rates required in the previous year. P4K's interest rate required to

- All five programs fund part of their portfolios from savings accounts required from borrowers. These required savings accounts earn an interest rate below that which the program would have to pay to obtain marginal additional resources from another source. For the purposes of this analysis, this practice is considered to be equivalent to generating additional fee income from loans and is not counted as a subsidy. An alternative approach would have been to assume that this forced savings policy constitutes a subsidy equal to the difference between the interest paid on the required savings and the interest the institution would have had to pay on marginal additional liabilities. Footnote 39 on page 47 reviews LKP's total subsidy if forced savings were counted as a subsidy in this way.

- 6 This analysis is based on a Subsidy Dependency Index (SDI) for each program. See Annex 7 for a description of how the SDI is calculated.

15
a phone but using a dierent mobile provider. Based on the Listening to Senegal Survey (LSS) done in 2014, 12.3% of adults age 18 and over never use a mobile phone. Sonatel is one of three mobile phone providers. Based on the LSS, Sonatel is the main provider for 80% of those surveyed with a cell phone, and 88% of those with a cell phone have a Sonatel SIM card (Agence Nationale de la Statistique et de la Démographie - Ministére de l'Economie 2014). Therefore, only around 12% of adults with a SIM are excluded. Combining the two types of missing adults, 77.2% of adults are represented in these data.

I conduct checks to see how representative the data are for the two types of users that are not includedthose without a SIM and those with a SIM from a dierent provider. I use the DHS survey to compare mobility patterns between women with and without a cell phone in the household.<sup>17</sup> There is no statistical dierence in whether a trip longer than a month was taken between those with and without a cell phone. The women with a cell phone in the household have only a slightly higher average number of trips taken in the last year. Using the LSS, I compare several indicators between people that have a Sonatel SIM card and those that only have a SIM card from another provider. Based on t-tests, there is no signicant dierence between these categories of individuals based on type of primary activity (p-value=0.344), sector of primary activity (p-value=0.863), source of drinking water (p-value=0.868), non-food expenditures over past month(p-value=0.193), and non-food expenditures over past 12 months(p-value=0.115). The third missing group is children. If children travel, they are likely traveling with adults (though
|<br>**Attrition Equations**<br>**Probit Estimates: Probability of Being in the Panel**<br>**(Marginal Effects, Standard Errors in Par**<br>**APPENDIX TABLE 1**|(1)<br>(2)<br><br><br>**Estimating Sample**<br>**entheses)**<br>In Panel Sample|
|---|---|
|Initial Village Inequality (Mean Log Deviation)|-0.516<br>-0.920|
||(1.698)<br>(1.726)|
|**Household Variables (in initial period, 1987-88):**<br>Log Initial Income|**_18.614_**<br>**_-0.195_**|
||(5.633)<br>(0.063)|
|Log Initial Income - Squared|**_-2.873_**|
||(0.908)|
|Log Initial Income - Cubed|**_0.144_**<br>(0.049)|
|Share of Income from Agriculture in 1987-88|0.180<br>0.197|
||(0.217)<br>(0.217)|
|Share of Income from Wages in 1987-88|0.082<br>0.093|
||(0.192)<br>(0.196)|
|Share of Income from Family Businesses in 1987-88|0.010<br>-0.012|
||(0.185)<br>(0.184)|
|Household Education in 1987-88|-0.004<br>-0.003|
||(0.010)<br>(0.010)|
|Log
but to estimate the true distance between actual and expected incomes that best measures the individual sense of deprivation.

# **IV) Concluding Comments**

This paper proposed two new indices of relative deprivation, derived from an extension of the concept of generalized Gini, originally proposed by Donaldson and Weymark (1980), to the measurement of distributional change. The idea was to consider the predicted income share of an individual, obtained from a regression of his income on his personal characteristics, as an "a priori" share and his actual income as an "a posteriori" share. Using such an approach we defined population- as well as income-weighted relative deprivation indices. Using panel data from the Consortium of Household Panels for European Socio-Economic Research (CHER), we then checked which of these various ways of defining individual deprivation best fitted the answers given by individuals on the degree of their satisfaction with income.

We first concluded that the generalized formulation of the deprivation indices studied in this paper performs better when the parameter  is equal to 2 (the case of the traditional Gini index) than when it is equal to 3. A deprivation aversion parameter of „two‟ seems to be better calibrated for capturing self-reported feelings of satisfaction. Then we concluded that the income weighted indices perform better than the population weighted indices. This would suggest that both income and rank are important for people but that absolute incomes are relatively more important than rank. Third we observed that indices that make use of predicted incomes are better than those that use income alone. The Silber-Verme (2010) index seems thus better than the Yitzhaki (1979) index in capturing feelings of deprivation, as already stressed by Silber and 20
Policy Research Working Paper 7862

# **Abstract**

The paper provides evidence of the effects of changes in transport costs on the geographic concentration of industries. The analysis uses micro-level commodity flow data and microgeographic plant-level data to construct industry-specific ad valorem trucking rates and continuous measures of geographic concentration. The findings show that, controlling for international trade exposure and input-output links, increasing trucking rates are significantly associated with declining geographic concentration. The effect is large: changes in trucking rates explain around 20 percent of the observed decline in geographic concentration of Canadian manufacturing industries between 1992 and 2008.

This paper is a product of the Environment and Energy Team, Development Research Group. 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 authors may be contacted at tbougna@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
the forward-looking component of monetary policy shocks is not correctly identified in standard empirical models, which typically exclude variables that help central bankers forecast inflation. Equations (1)-(3) imply that standard VAR models do not incorporate expectations for inflation and output gap. These models typically assume that the lagged output, prices, and financial variables—including interest rates, stock prices, and FX rates, which tend to incorporate news shocks on future economic developments— adequately capture inflation and output expectations. However, if the central bank considers a larger information set than VAR models capture, the estimated policy shock is then a combination of a genuine policy shock and an endogenous policy response. If the policy response only partially offsets current inflationary pressures, the VAR would deliver a spurious correlation between policy tightening and a rise in inflation.

The standard solution to this price puzzle was to include commodity prices in models estimated with U.S. data, as they help forecast inflation (e.g., Sims 1992). Other variables considered as potential correlates of future inflation include the output gap, house prices, and global variables.

4