rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9981

# **Abstract**

Large arrivals of refugees raise concerns about potential tensions with host communities, particularly if refugees are viewed as an out-group competing for limited material resources and crowding out public services. To address this concern, calls have increased to allocate humanitarian aid in ways that (also) benefit host communities. This study empirically tests whether the presence of refugees in Uganda (one of the largest refugee-hosting countries) has improved public service delivery, and consequently, dampened potential social conflict. The data com- bines geospatial information on refugee settlements with unique longitudinal data on primary and secondary schools, road density, health clinics, and health utilization. This study reports two key findings. First, particularly after the 2014 arrival of over 1 million South Sudanese refugees, host communities with greater levels of refugee presence experienced substantial improvements in local development. Second, using public opinion data, we find no evidence that refugee presence is associated with more negative (or positive) attitudes towards migrants or migration policy.

This paper is a product of the Social Sustainability and Inclusion 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 ggros@upenn.edu.

_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
<!-- Start of picture text --> % of children under 5 years of age that are stunted, 2000 - 2016<br>Global leaders (Top 8 fastest declines .<br>in stunting) Regional comparators<br>53<br>50<br>42 43 43<br>40 40 42<br>36 36 .<br>31 32 33 v4 Indonesia33 aes<br>30 Lesotho fe N77 Philippines<br>30 27 27<br>Tajikistan Burkina Faso 25<br>Vietnam<br>29<br>Cote d'Ivoire 21 .<br>20 19 48 17 Malaysia<br>Ghana,<br>Bolivia<br>13 16<br>14 Peru 40<br>10 Mongolia 8 Thailand<br>China<br>2000 2005 2010 2016 2000 2005 2010 2016<br><!-- End of picture text --> Source: UNICEF, WHO, The World Bank Group: Joint child malnutrition estimates. Only countries with the following criteria are considered for inclusion in the 'Global leaders; group: (1) had stunting prevalences higher than 25% at the start of the period, (2) have at least three stunting estimates that cover more than a 5 year interval between 2000 and 2016, (3) Have populations exceeding 1,000,000.
wards were identified.

The next step involved matching the bridge with survey data to obtain treatment assignments for each ward surveyed. The survey data included 119 unique wards, all of which were successfully matched using the ward name as the unique identifier. The final step involved linking the survey bridge with TRA data using the street location name as the unique identifier. This reduced the sample size from 3,585 wards to 119 wards and from 408,106 taxpayers to 40,180 taxpayers. Throughout this process, matches were consistently ensured to be within similar tax regions.

Balance tests were conducted to assess payment trends across treatment and control wards. The results indicate no statistically significant differences in the average payment amounts between treatment and control wards during the 3- and 5-month periods preceding the intervention (see Table 2).

Table 2: Balance Tests

||(1)|Control|(2)|Treatment|(1)-(2)|Pairwise t-test|
|---|---|---|---|---|---|---|
|Variable|N|Mean/(SE)|N|Mean/(SE)|N|Mean diference|
|pay_trend_|3<br>17,938|87,330.319<br>(2,834.644)|22,239|80,620.056<br>(5,541.215)|40,177|6,710.263|
|pay_trend_|5<br>17,938|1.27e+05|22,239|1.18e+05|40,177|8,179.031|
|||(3,450.451)||(5,742.526)|||

If the table includes missing values (.n, .o, .v etc.), see the Missing values section in the help file for the Stata
# _Deflators_

Spatial price indices were calculated using a common food basket and spatial prices to make consumption comparable across regions. The Laspeyres index is chosen as a deflator due to its moderate data requirements. The deflator is calculated by analytical strata areas based on the price data collected in wave 2 of the SHFS. The Laspeyres index (Table 3) reflects the item‐weighted relative price differences across products. Item weights are estimated as household‐weighted average consumption share across all households before imputation. Based on the democratic approach, consumption shares are calculated at the household level. Core items use total household core consumption as reference while items from optional modules use the total assigned optional module household consumption as reference. The shares are aggregated at the national level (using household weights) and then calibrated by average consumption per module to arrive at item‐weights summing to 1. The item‐weights are applied to the relative differences of median item prices for each analytical stratum. Missing prices are replaced by the item‐specific median over all households.

**Table 3: Spatial Laspeyres index**

|**Analytical strata**|**Foo deflator**|
|---|---|
|IDPs|0.856|
|Nomads|1.030|
|Banadir (Urban)|0.910|
|Nugaal (Urban)|1.058|
|Bari and Mudug (Urban)|0.976|
|Woqooyi Galbeed (Urban)|1.181|
|Awdal, Sanaag, Sool and Togdheer (Urban)|1.181|
|Hiraan, Middle Shabelle and Galgaduud (Urban)|1.119|
|Gedo, Lower and Middle Juba (Urban)|0.960|
|Bay, Bakool and Lower Shabelle (Urban)|0.931|
|Bari, Mudug and Nugaal (Rural)|0.960|
|Awdal, Togdheer and Woqooyi (Rural)|0.887|
|Hiraan, Middle Shabelle and Galgaduud
> Some of the Brazzaville prices were obtained from wholesalers, and are thus not directly comparable to the prices recorded at the retail level. All prices were recorded in local currency, between 26 August and 3 September 2010. We asked vendors for the origin of their goods, allowing us to classify the goods as locally produced, imported across the river, and imported from overseas. For our estimations, prices are converted into US dollars at the exchange rates prevailing locally in that period.<sup>15</sup> Finally, for goods sold in known weight units, we expressed all prices in terms of dollars per kilogram.

# _Results_

**We use panel data regression methods to estimate systematic price differentials for identical products across markets.** By using this technique, we can formally test the hypothesis that observed price differences across markets are systematic – i.e. “statistically significant” –, meaning that they cannot be explained by random price variation among individual vendors due to a myriad of reasons such as the time of day, the vendor’s available stock at the time of the transaction, the precise quality of the item(s) sold, the vendor’s location within a market, or the vagaries of the haggling process. Moreover, we can control for good-specific “fixed effects”, meaning that price differences across markets are estimated by using only differences within individual products, and thus by comparing like

> 13 Kinshasa center : Marché Central and Gambela; Kinshasa suburbs : Matadi-Kimbala and Liberté; Brazzaville center : Total and Poto-Poto; Brazzaville suburbs : Mikalou and Mongali.

> 14 We could not collect prices for each good in each market. While a balanced sample would comprise 1,539 observations (57 goods * 9 markets * 3 vendors), our maximum sample size is 970.

> 15 1 USD = 910 Congolese francs; 1 USD = 511 CFA francs.

16
**_Figure 15. Distribution of Households by Final Electricity Consumption Block by Market Income per Capita Decile_**<sup>**_66_**</sup>

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Household market income decile<br>1-160 161-300 301-500 501-600<br>601-750 751-1000 1001+<br>Percent<br><!-- End of picture text --> <!-- Start of picture text --> Note: Deciles of market income. The share of households per final<br>consumption block in HEIS has been adjusted to preliminary figures from<br>administrative data.<br>Source: HEIS 2017-18 and World Bank calculations.<br><!-- End of picture text --> Although PIT is the most progressive form of revenue generation and collections are particularly low in Jordan by international standards, the recent history of PIT reform suggests that efforts directed to increased compliance are more politically feasible in the short-term; in the longer-term greater reliance on direct taxation will be needed to increase public revenues in a progressive manner. Indirect taxes, however, can be simplified and significant revenue raised by eliminating GST exemptions and lower rates and unifying the sales tax rates for all items. Under this scenario, we assess the potential impact of using a unified single GST rate of 16 percent for all items.<sup>67</sup> While this raises the tax rate for some items with reduced tax rates and thus the direct impact of taxes, it also eliminates the cascading effects caused by exemptions, which were accounted for around 10 percent of the total GST collections in the baseline. Overall, this reform has only small poverty and distributional effects, increasing inequality by only 0.1 points as
# **5 Concluding remarks**

A considerable literature is concerned with assessing the extent to which countries share their consumption risk. Much of it, however, uses empirical models designed to test the null hypothesis of perfect consumption risk sharing. Once such hypothesis is rejected – as is almost invariably the case in practice – those models do not offer a rigorous basis for inferences about the degree of imperfect risk sharing present in the data. Drawing such inferences requires an empirical model of partial risk sharing. Furthermore, unless one is willing to assume that the extent of consumption risk diversification is the same across all countries in the world, the model needs to allow for cross-country heterogeneity in the degree of risk sharing.

This paper extends the existing literature by developing an empirical model that fits those two requirements. In the model, countries contribute possibly different fractions of their income to a common pool, in exchange for a claim on the aggregate income contributed to the pool by all countries. The fraction of income contributed to the global pool by each country can be naturally interpreted as its respective degree of risk sharing. Solution of the model yields a system of equations in which each country’s consumption path depends on the innovations to the present discounted value of income growth of all countries. Moreover, the system features nonlinear parameter restrictions across equations.

The model is implemented empirically using panel data for industrial and developing economies. Estimation results show that consumption risk sharing varies significantly across countries. On the whole, rich countries exhibit higher degrees of risk sharing than developing countries, and the gap between both country groups has widened over the period of financial globalization. Moreover, the pattern of consumption risk sharing across countries is significantly related to their degree of financial openness. Countries possessing larger stocks of international assets and/or liabilities, and more developed domestic financial sectors exhibit larger degrees of risk sharing. In contrast, the size of the economy and its degree of trade openness (after controlling for financial openness) seem to hinder consumption risk sharing
# 1. Introduction - summary

Pension systems around the world are facing serious, multidimensional challenges. Policy makers are grappling with the demographic phenomenon of aging societies, a low-interest financial environment that provides insufficient returns on pension savings, and policy legacy issues such as low contribution rates, generous retirement age and early withdrawal rules. All these issues affect the replacement rate of pensions and raise the risk of old-age poverty.

Policy makers in many developing countries must take additional factors into account. Higher rates of informality and greater fluctuation in the labor market lead to relatively low contribution density, and a considerable portion of society is not covered by public pension systems at all. In addition, the risk of old-age poverty is rising as urbanization and demographic changes lead to an erosion of traditional family support systems.

The academic literature on the topic suggests that reverse mortgages (RM) can be a welfare-enhancing tool to supplement pension income, or as a form of insurance **.** In particular, low-income senior homeowners may tap into their accumulated housing wealth to smooth consumption and increase their resilience against financial shocks. The welfare-enhancing potential of this instrument has been modeled in academic studies using data from a variety of jurisdictions. While estimates of potential benefits vary widely by location and model calibration, RM are estimated to be especially beneficial for the very elderly, women (due to higher longevity and lower pensions on average), and “house-rich, cash poor” households. They might also serve to prevent financial exclusion of retirees who can no longer meet the income requirements of a common mortgage.

However, this brief lays out market failures and risk factors that act as constraints on the supply side of the RM market. The risk that housing equity falls below the outstanding loan balance, adverse selection, moral hazard, the high cost of insurance based on imperfect actuarial models, the absence of risk pooling mechanisms that reduce the economic viability of RM for lenders, regulatory disincentives in some countries and market barriers. The combination of these factors may help explain why
Unfortunately, Sub-Saharan Africa, the region with the lowest agricultural productivity, is the one region in the world where this gap does not appear to be closing.

Beyond general educational attainment, higher education for women in agricultural science and technology is particularly important in regions where women constitute a large part of the agricultural sector. The number of women working in science and technology research in industrialized and developing countries has increased substantially in recent decades, but this number remains low in most countries. Women scientists, research managers, lecturers, and professors can provide different insights and perspectives and can help research agencies to better address the unique and pressing challenges faced by African farmers. They may also serve as role models for students and for other women in agriculture. Progress has been made in increasing the share of female professional staff in agricultural higher education and research institutions in Africa (see the discussion on extension below), but the gap persists.

# **_Technology—Improved Varieties_**

The evidence on gender and the adoption of improved varieties is mixed. For example, Doss and Morris (2001) find that in Ghana, female-headed households<sup>14</sup> are less likely to adopt high-yield maize, but women in male-headed households are not. Bourdillion et al. (2003) use data from Zimbabwe (2003) and find no significant difference between maleand female-headed households in the adoption of improved seed. Gilbert, Sakala, and Benson (2002) document an extension and new technology trial in Malawi. Although this study has selection issues (extension agents disproportionately chose male-headed households), they find that among participating female-headed households, maize plots 25
# **5. Estimating Program Impacts**

Given the coverage and size of and access to social safety-net schemes supported by both the government and NGOs, we raise an important policy question: Do safety-net programs help to alleviate starvation? Constructing a counterfactual is key to answering this policy question and evaluating the intervention; that is, what would have happened to the starvation status of safety-net program beneficiaries had they not received such support? Randomized design of an ex-ante intervention, where beneficiaries are treated randomly with a program intervention, can help to create the counterfactual against which this key policy question can be evaluated. IFPRI, in collaboration with the Mexican government, adopted such a rigorous, randomized evaluation technique to evaluate PROGRESA’s impact on education, nutrition, health, and rural poverty. This evaluation was also based on repeated surveys of individuals from 24,000 households in 506 localities in randomly assigned PROGRESA and non-PROGRESA areas. Formal surveys, structured and semi-structured interviews, focus groups, and workshops were held in seven states where the program was first piloted. However, such an extensive and innovative evaluation design is beyond the scope of this study.

In the absence of randomized design, we can rely on non-randomized evaluation techniques. For example, Ahmed et al. (2009) applied a propensity score technique to a cross-sectional survey of safety-net beneficiaries and non-beneficiaries to determine program effects based on observed characteristics. The weakness of this method is that it omits the bias due to unobserved characteristics influencing participation in safety-net programs. A more defensible non-randomized method that accounts for the unobserved-variable bias is panel data, where the key assumption for identification is that unobserved heterogeneity is time invariant. Ravallion, van de Walle, and Gautam (1995) used this method in their study of safety-net programs in Hungary. Unfortunately, we do not have panel household survey data for handling this endogeneity.

17
database, mostly covering the 1990s and 2000s.<sup>3</sup> Changes in poverty are estimated across consecutive household surveys that vary in duration (i.e., based on survey frequency). To avoid mixing very long with very short changes in poverty, we focus on observation in which the gap between consecutive surveys varies between one and five years of duration and test sensitivity to restrictions within that range. The poverty measures we focus on are changes (first differences) in the $1.25/day and $2/day headcounts, measured at 2005 international dollars. We also drop countries with very low levels of initial poverty (less than 5% in the $1.25 per day headcount) to exclude upper-middle income countries in which food is less important for both consumption and production, and to prevent the sample from being heavily overloaded with such countries.<sup>4</sup> While there are many challenges in measuring comparable poverty rates across countries and over time, one particular concern with POVCAL estimates is the possibility that they suffer from biases stemming from the use of consumer price indices (CPIs) to deflate nominal estimates of household expenditure or income (or equivalently, to deflate poverty lines). Here there are two sources of bias. First, if the weights in the CPI consumption basket differ substantially from the expenditure patterns of the poor, there is a possibility of CPI bias during periods of real increases in the price of food. However, the POVCAL series used herein contains adjustments for this problem whenever food weights in the CPI were deemed to be excessively low. Moreover, a recent World Bank study of poverty in Africa in fact found evidence that CPI bias during periods of high food inflation typically works in the opposite direction: corrections for CPI bias in Africa would lead to faster poverty reduction during the recent period of rising food prices (Beegle et al. 2016).<sup>5</sup> Second, when food prices increase,
Public facility|31|35|32|48|−12|1.5<br>(0.7–<br>3.3)|
|Public or<br>private<br>facility|59|74|45|69|−9|0.9<br>(0.5–<br>2.0)|
|PNC|45|59|51|52|13|0.5|
||(_N_=|(_N_=|(_N_= 82)|(_N_=||(0.3–|
||397)|573)||216)||1.1)|

_<mark>Source:</mark>_ <mark>Population Council, Household Survey 2010–11.</mark> a Based on differences in the proportions of poor and nonpoor women who used the health services in villages with and without a voucher client: a negative sign means the difference between the poor and nonpoor was greater in the comparison group.

29
**Figure 4:  Number of firms that export a non-negligible amount**

<!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>No. of firms exporting > 10 000 USD / year<br>No. of firms exporting > 100 000 USD / year<br>No. of firms exporting > 1 million USD / year<br><!-- End of picture text --> _Source:_ Statistics in this an all following figures and tables are calculated on the firm-level dataset that is constructed by merging administrative information from three data sourced that covers the universe of registered firms in the agriculture, mining, and manufacturing sectors of Rwanda (for details see description in the data section).

In this perspective, it can be considered a positive development that the ratio of small to large exporters has increased over time. In particular, while the number of firms that exported more than 1 million USD grew from 20 to 36 over the 9 year sample period, the number of firms that exported less than 1 million USD almost tripled, increasing from 23 to 65. This can be seen directly from the widening gap between the black and the orange lines in Figure 4.

We show the number of small and large exporters separately by sector in Figure 5. Not surprisingly, the number of large exporters, i.e. firms that sell more than 1 million USD abroad in a year, is relatively stable over time. The right panel of Figure 5 barely shows any change for agricultural firms, rising from 8 firms to a maximum of 12 in 2015. The pattern is similar for mining firms between 2008 and 2013, but in the last three years the number of large exporters suddenly jumped. Quite remarkably, only a single manufacturing firm managed to sell more than 1 million USD
Korea. The NIESR in London intends to extend its previous comparisons for Germany, the UK and USA (Smith, Hitchens and Davies, 1982) to cover also France and the Netherlands. Work of this type can be self reinforcing, can be extended to incorporate capital productivity, more refined measures of labour inputs (adjusted for differences in working time and education per head) and total factor productivity.

- 2) For the three countries we covered, we would like to extend the comparison to cover the other main sectors of the economy, so that we can arrive at estimates for GDP, strengthen the methodological foundation of the industry of origin approach, and make a more careful comparison with the results of the ICP. Some work has already been done in this direction for agriculture and mining so the main task here would be to analyse the service sector. Coverage of the whole economy would make it possible to look afresh at the problem of double deflation, and to make better tests of the reliability of short-cut approaches.

# Recommendations on Official Statistics

# a) Censuses of Manufacturing

At present, the definitions of value added in manufacturing censuses are often anachronistic as they ignore inputs of services which are large and growing proportionately. They reflect the statistical practice of yesteryear, before the introduction of the more rigorous concepts of national accounts. Furthermore, these census concepts differ across countries in a way which is not adequately stressed in standard UN publications such as the Yearbook of Industrial Statistics, and this leads