rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 1). Finally, the home economy is 1% of the world economy, representing a small SSA country ( .<sup>21</sup> **Persistence and volatility of commodity prices.** The most important nn= 0.01) parameter is the persistence of commodity price shocks, . To calibrate this parameter, we first construct a commodity price index for SSA. The index is a ρρ weighted average of prices of commodities that countries in SSA export. Each commodity is assigned a weight based on its average share of export value out of total goods exports in 2008–2012.<sup>22</sup> We consider 61 commodities for which price data is available either at the World Bank Commodity Price Database (The Pink Sheet) or from Jacks (2013). Export trade data is taken from the UN Comtrade Database (four-digit HS 2002 code). Column (1) of Appendix Table 2 provides the list of 61 commodities we consider.

For each commodity price, we estimate an AR(1) process with OLS using the longest time series available at an annual frequency.<sup>23</sup> Appendix Table 2 reports the implied half-life, which is our baseline measure of persistence, and the price standard deviation. The table shows that commodity prices are highly volatile and shocks are persistent: the median half-life is 5.4 years, and the price annual standard deviation is 42%.<sup>24</sup> Next, we simulate 1,000 observations from the estimated AR(1) process for each commodity price and compute the implied trajectory of the SSA commodity price index. We then use this time series to estimate the autocorrelation and standard deviation of the SSA commodity price index––by OLS with an AR(1) model. Panel B of Table 1 reports the results. The quarterly autocorrelation of the commodity price in the NK model is set to 0.975 (which implies a half-life of 6.
28 lower than those of operating unofficially. Hence, the official liberalization effort has to be sufficiently bold (and credible) so as to lower expected official costs of doing business to a level below those of operating unofficially. Small liberalizing steps will affect only the marginal unofficial activity and, in addition, will generally undermine the credibility of the reformns in the eyes of economic agents. Consequently, partial or gradual liberalization is not an effective policy-making approach, particularly where there is a large unofficial economy.<sup>2 7</sup> The most visible (and aggregate) economic liberalization measures, such as removal of price ceilings and export quotas, while necessary, are insufficient in themselves to sufficiently lower the cost of operating unofficially. Deregulation, at both the micro and regional levels, is also required in order to address issues of harassment and the costs of discretionary enforcement of old regulations and laws (licenses, health and fire inspections, etc.). Thus, the concept of full economic liberalization should include deregulation as an integral component.

# 3. **Macroeconomic Stability**

The Ukrainian entrepreneurs' survey responses point to macroeconomic stability (and very low inflation) as one priority in ensuring that they operate more effectively, and officially. Lax budgetary and monetary policies, and the resulting high inflation, raise the expected benefits of unofficial activities and reduce their perceived risks and costs. Furthermore, they increase the costs of operating officially. Budgetary and credit handouts breed vested interests, which further fuel and perpetuate unofficial income flows, domestically and abroad. For instance, it appears evident that in Russia the episodes of large expansion of credit in 1992-93, which were channeled by the central bank into many large state enterprises, were also associated with a very significant (multibillion dollar) capital flight abroad.

Lax monetary policies and high inflation cloud financial accounting, making it easier to distort official reports. At the same time, they reduce the probability of getting caught (or of proving illegal behavior). Since domestic currency
br>4.684<br>4.592|4.904|
|**Panel D: Husband worked last week**||
|Fraction of female leaders<br>0.056<br>0.059|0.088|
|(0.078)<br>(0.097)|(0.084)|
|Observations<br>44,713<br>31,478|13,235|
|Outcome mean<br>0.915<br>0.902|0.947|
|State fxed efects<br>x<br>x|x|
|District characteristics<br>x<br>x|x|
|Individual characteristics<br>x<br>x|x|

_Notes_ : Data are from the 2015-16 National Family Health Survey (NFHS) of India. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. The dependent variables are husband’s years of schooling (Panel A), the difference in years of schooling between husband and wife (Panel B), the age difference between husband and wife (Panel C), and an indicator for whether a husband worked last week (Panel D). Column (1) presents estimates for the full sample, while columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and control for the share of seats in the district that had close elections between women and men. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls include the share of female population, the share of urban population, and the share of SC/ST population in the district, and the female and male
/or investment, migration also plays an important role in mitigating risks, serving as an _ex-post_ response to a realized shock and an _ex-ante_ strategy for income diversification. A long literature in development economics has recognized that the ability to diversify income sources provides a means for poorer households to reduce exposure to consumption risk (Dercon, 2002; Morduch, 1995; Morten, 2019). Matching the RCRE village and household surveys to local rainfall data, Giles (2006) finds that increased access to migrant employment

> 18This finding is not inconsistent with the idea that poorer households may limit migration due to credit constraints.

> 19 In contrast, two studies examining the direct impact of remittances, without adequately addressing migration selection based on unobservables, argue that rural households use remittances largely for consumption, not investment, purposes (Démurger and Wang, 2016; Zhu et al., 2014).

> 20 These findings are somewhat consistent with an early study showing that during 1995-2000, households with migrant members in non-poor villages spent more on housing and durable goods but not productive investments, while migration does not affect either type of investment for households in poor areas (de Brauw and Rozelle 2008).

17
. Third, there is the potential presence of spatial correlation due to omitted weather variables. We account for this problem by constructing a measure of drought during the agricultural season, using the Standardized Precipitation-Evapotranspiration Index (Vicente-Serrano et al., 2010; Miao and Popp, 2014; Harari and Ferrara, 2018). The final issue is the potential presence of unobserved time-constant and time-varying factors that can influence the relationship between our covariates of interest and crop yields. We account for this problem by controlling for enumeration area (EA) fixed effects, and time-variant household-level and community-level observables. Additionally, identification of the resilience effect of polygyny is obtained under the assumption that, upon controlling for EA fixed effects and over a relatively short time ( _i.e._ , 3 years), differences across Mali’s communes in the 3
Figure B5: Changes in APA and Government officials’ Survey indices (2016-2018) <!-- Start of picture text --> Urban planning Fiduciary responsibility<br>0 1 2 -2 0 2<br>Changes in DLI Changes in DLI<br>(a) Urban planning (b) Fiduciary responsibility<br>Infrastructure Accountability<br>-4 -2 0 2 -2 0 2 4<br>Changes in DLI Changes in DLI<br>(c) Infrastructure (d) Accountability<br>4<br>4<br>3<br>2<br>2<br>1<br>0<br>Changes in Survey Index Changes in Survey Index 0<br>-2 -1<br>3<br>4<br>2<br>2 1<br>0<br>0<br>Changes in Survey Index Changes in Survey Index -1<br>-2<br>-2<br><!-- End of picture text --> _Note:_ This figure presents a scatter of changes in DLI subcomponent scores (x-axis) and survey indices (x-axis) by LGA. DLI scores are normalized so that changes can be interpreted as standard deviations in the distribution of the 4th APA scores.

Figure B6: Correlation between APA and government officials’ survey measures <!-- Start of picture text --> Planning Baseline<br>Fiduciary Baseline<br>Infrastructure Baseline<br>Accountability Baseline<br>Planning Endline<br>Fiduciary Endline<br>Infrastructure Endline<br>Accountability Endline<br>Planning Change<br>Fiduciary Change<br>Infrastructure Change<br>Accountability Change<br>-.5 0 .5 1<br>Correlation<br><!-- End of picture text --> _Note:_ This figure presents correlations across the 18 ULGSP LGAs for baseline, endline and changes
|899|0.3|181|0.6|
|Sugar|756|0.2|0|0.0|
|Sheet metal|505|0.1|0|0.0|
|Chemicals|487|0.1|10|0.0|
|Ground nuts|483|0.1|-|-|
|Corrugated iron|314|0.1|5|0.0|
|Other|35,372|10.3|2,237|7.6|
|_Total_|_343,333_|_100.0_|_29,500_|_100.0_|

Officially recorded goods only. Source: Port of Brazzaville (PABPS).

# **2.3 Passenger Traffic**

**Passenger traffic on the river is dominated by traffic across Malebo Pool, the river basin between Kinshasa and Brazzaville, with the total number of fare-paying river crossings estimated at around 212,000 annually** .<sup>11</sup> Official data from the port authority in Brazzaville and from ONATRA in Kinshasa are presented in Table 6. Less than 5 percent of passengers arrive from or travel to upriver destinations. Oddly, according to these statistics, the number of passengers who arrive at Brazzaville

> 11 According to estimates given to us by ONATRA, their ferry “Matadi” carries some 600 paying passengers per average workday. This would correspond to around 190,000 passengers per year. As some additional travelers cross on the much smaller CNTF ferry and on private speed boats, a  reasonable estimate of the total number of fare-paying passengers crossing annually between the two capitals may be found by doubling the number of inbound passengers recorded by the port of Brazzaville, i.e. some 212,000 crossings.

14
, some data sets have included selected questions only, changed the answer scale or the wording of questions, and/or altered the introduction to the questions. There are thus data sets that have questions somewhat similar to the WGSS.

In addition, there are functional difficulty questions that are independent of the WGSS. Some internationally tested survey tools such as the Model Disability Survey (Cieza et al 2018) or WHO-DAS 2.0 (WHO 2020) and other recent tools (e.g. Trani et al., 2015, Eide et al 2006) do include functional difficulty questions. For instance, WHO-DAS 2.0 has questions on cognition, mobility, and self-care.

# **Activities of Daily Living**

Disability has been measured using difficulties in Activities of Daily Living (ADL). There are different types of ADLs, including basic and intermediate. Basic ADLs are fundamental for body functioning (e.g. walking a specific distance) and include self-care activities such as feeding oneself, going to the bathroom without help, and dressing without help, bathing, eating, walking, toileting, urination and defecation. Intermediate ADLs are more complex tasks such as shopping, housekeeping, food preparation, and managing money.

ADL questions were initially developed to capture the physical effects of aging, and, as such, in some surveys the questions are only administered to respondents above a certain age (e.g. 45 and above). Intermediate ADLs include role activities, such as caring for others, that can be broad 6
respondents implement consumption surveys and speak regularly with households about their economic choices—in essence, they constitute a panel of experts. We asked respondents to

> 18The story of one respondent in Bukoba can be paraphrased as follows: “We know that we need 1 kilogram of maize flour each evening. But if we buy a 50 kilogram bag, we may find that it is gone at the end of one month, because we use too much each day. So it is better to buy smaller amounts.” Despite this anecdote, in the forthcoming analysis maize was not deemed to be “temptation goods” in our survey (see next paragraph). This underscores the subjectivity of temptation and the importance of grounding any analysis of temptation in data sourced from more than one person.

25
real domestic credit growth (Chart P3) remained high. Moreover, the ratio of banks' total reserves to deposits does not indicate a noticeable decline; it instead exhibits a slight downward trend to be evaluated in the light of successive cuts to the required reserve ratio (Chart P4).

**Chart** P5

# EVOLUTION OF CREDIT **TO** THE PRIVATE SECTOR'<sup>5</sup>

(at constant exchange rate, quarterly moving average, monthly annualized percent change) <!-- Start of picture text --> :::100%°fiff- , , ; ,, D ;E. .. E A, ......... . ..<br>-i80°k%<br>60%0/<br>- E-4o0<br>00/0~~~~<br>20% /<br>A.:r May Jun Jul Aug Sep ,Oct Nov D -<br>-20%<br>-. peso _ $ - - - total<br><!-- End of picture text --> Disaggregated data on credit developments reveal that while domestic currency denominated credit has been growing (Chart P5), there has been a marked decline in foreign currency denominated credit.

- _5_ This chart was contributed by Bernard Funck (EACPF).

29
2018). Global Competitiveness Report 2018.

> 77 World Bank Enterprise Survey (2015).

> 78 Gashayie, A. and M. Singh (2016). “Development of Financial Sector in Ethiopia: Literature Review”, Journal of Economics and Sustainable Development, Vol 7, p 9‐20.

> 79 For instance, with capital of US$151 million and a network of 380 branches as of June 2016, Amhara Credit and Saving Institution (ACSI) is more capitalized and has a wider branch network than most of the private banks in Ethiopia.

19
trajectory. Four future climate scenarios in 2100 are then projected using this emissions path and four different climate models. A cyclone generator is then used to predict how hurricanes in the Atlantic Ocean may change (Emanuel et al. 2008). In both the current climate and the future climate, a total of 5000 hurricanes are generated that strike the United States, for each climate model. This provides an extensive data set concerning the distribution of hurricanes that would strike the country. It is equivalent to observing over two thousand years of data on hurricanes in each climate and for each model. Instead of using an average change for all hurricanes, we use the change in the entire distribution of hurricanes to predict the impact of climate change.

We estimate a hurricane damage function using historic data from storms that have hit the United States from 1960 through 2008. The damages are matched with characteristics of the storm, including minimum barometric pressure, maximum wind speed, and location at landfall (NOAA 2009). Estimates of county income and population density are generated from census data. A regression is then used to predict the damages caused by historic storms.

The damage function is then used to predict the damages that would be caused by each storm in the generated data set. The 40,000 storms generate a rich data set that describes the expected value and distribution of hurricane damages. It also describes how the risks vary along the coast of the United States given each climate scenario.

The analysis then compares the damages from hurricanes under the current climate versus the future climate. We perform two analyses. The first analysis examines the growth in hurricane damages caused by higher income and population along the coast. This analysis assumes no climate change. The second analysis examines the additional damages caused by climate change given the future baseline. The change in damages is predicted from the change in the entire set of future storms versus current storms.

The next section of the paper describes the methodology in more detail. The empirical findings of the paper are then
data can improve the targeting of social programs.<sup>8</sup> Less attention, however, has been paid to how predictive performance relates to the nature of the training data.<sup>9</sup> There are a few caveats to consider. The first is that we focus on one context: 10 districts in Malawi. While there can be some common features to other developing countries, additional research would be useful to confirm that the results apply in other contexts. Second, with respect to data availability, imputing per capita consumption into the 20 percent census extract was the best available measure of village-level welfare we could obtain. Nonetheless, because the welfare measure is imputed on the basis of slow-changing characteristics such as household size, head’s education, child dependency ratio, and household assets, the benchmark welfare measure is a longer-term measure of welfare that will only partially include transitory welfare shocks. A third caveat is that the sample of villages is constructed by matching village names by hand, which raises the possibility that the sample of villages used in the study is not fully representative. However, as demonstrated in table 3, most of key observed characteristics in the census are similar on average between matched and unmatched villages.

A final methodological issue is that, in villages included in the simulated partial registry, we utilize the same census model and data to construct both the benchmark measure and the partial registry predictions. As a result, the partial registry by construction perfectly predicts benchmark welfare in the 450 villages randomly selected for the registry. To address this issue, we show that replacing the perfectly accurate predictions from the partial registry with the imperfect predictions generated by the geospatial model leads to only a modest fall in predictive performance. This indicates that the vast majority of the improvement from utilizing the partial registry, relative to the other three methods considered, derives from its ability to training a much richer and more accurate predictive geospatial model, rather than the increased predictive accuracy in the villages it covers
, 1997, 1999, and 2001.<sup>9</sup> For each municipality in El Salvador, we identify the main US destinations for Salvadoran migrants using data on migration patterns for Salvadorans from the North American Integration and Development Center (NAID).<sup>10</sup> We then construct, for each Salvadoran municipality, a weighted

> 7 Our survey measures the distance to the closest bank office, but only for the last two waves: 1999 and 2001. We find that _Electricity_ has a negative correlation with the distance to the closest bank (-0.13) and is significant at 1 percent.

> 8 We follow McKenzie and Rapoport (2007), who in their study of the impact of migration on income inequality in Mexico, use economic conditions in the US as one of their instruments for migration from Mexico.

> 9 Appendix 1 describes other instruments we tried. Our main findings are not affected by our choice of instruments. 10 The mentioned database is available at http://gis.ats.ucla.edu/naid/. The NAID gathered information on the main destinations in the US of Salvadoran migrants during the last five years. We assume that migration patterns during

9
thereby also benefiting local host communities.

It is somewhat unclear how successful Uganda’s integration policy has been, because only a handful of studies has analyzed how the presence of refugees has affected welfare outcomes in host communities. With the exception of Kreibaum (2016), these studies have focused on general welfare (income and consumption) rather than service delivery. Other studies explore environmental outcomes. Based on focus group discussions in host communities near the Nakivale refugee settlement, Ronald (2020) reports that host communities are concerned about environmental degradation. Similarly, based on stakeholder interviews, IRRI (2019) also reports tensions over natural resources, especially around the allegation that refugees engage in illegal logging.<sup>10</sup> Zhu et al. (2016) assess the impacts of World Food Program aid within a 15 km radius of two refugee settlements in Uganda. They find that the average refugee household receiving cash food assistance increases the annual real income in the local economy. Here, locals around the settlements benefited from aid provided to refugees because on average, they are in a better position to increase their supply of goods and services as the local demand rises. Kreibaum (2016) corroborate the finding that host communities near refugee settlements in Uganda have relatively higher consumption levels using data on three south-western districts, though the effect is small in magnitude. d’Errico et al. (2021) find that the presence of refugees has only modest effects on local households’ consumption levels. The authors attribute this finding _not_ to an increase in demand for local produce, but to greater participation by host households in paid employment in aid agencies, and to the resulting increase in wage incomes. However, the effects they observe are small, and concentrated in areas very near refugee settlements.<sup>11</sup> Most relevant to our study, Kreibaum (2016) reports that access to private primary schools (but not public schools or health services) has increased at a greater rate as a function of
The transfer scheme makes use of our knowledge of the spatial distribution of poverty in such a way that poverty is minimized at the national level. We consider the gains from spatial targeting at alternative levels of disaggregation. As poverty measure we use the squared poverty gap, which is particularly sensitive to the severity of poverty by calculating the distance between a poor person’s income level and the poverty line and giving more weight to larger distances.<sup>6</sup> We specify a poverty line that accords with a poverty rate of around 20% nationally, in each respective year, and we postulate a modest hypothetical budget that would be insufficient, in and of itself, to eliminate all poverty, even if it were perfectly targeted at the household level. Our results show that in both 1999 and 2009 there are potentially large gains in targeting performance from disaggregating to the local level. These benefits are even more clearly seen when we examine the squared poverty gap as our poverty measure of choice. The impact on the headcount rate is, unsurprisingly, more muted, given that we are not able to “optimize” our transfer scheme with respect to this poverty measure.

The benefits from spatial targeting become increasingly evident as one makes use of more and more disaggregated data on poverty. We show that a given impact on poverty can be achieved at considerably less expense with detailed spatial targeting than with a uniform transfer. Importantly, we find that the benefits from spatial targeting, at any level of disaggregation, are more evident in 2009 rather than 1999. This follows from our earlier finding that poverty in 2009 is more spatially concentrated than was the case in 1999, and supports our assertion that in developing countries that experience uneven spatial progress geographic targeting can be a successful policy to reduce growing wealth disparities.

# **_7.1. Transfer scheme_**

> 6 We focus on the squared poverty gap because of its appealing properties from both a conceptual and technical point of view.  The basic approach explored here would