rafmacalaba/data-use-mentions-extended huggingface dataset:
block 1 residential|0.92||2.0|
|Mali|social tariff|n.a.|50|13.2|
||||100|20.3|
||||200|23.9|
||||>200|27.7|
|Mozambique|block 1 residential|n.a.||4.0|
|Namibia|n.a.|n.a.||n.a.|
|Niger|—|—||—|
|Nigeria|pensioners’ tariff|0.23||3.0|
|Rwanda|—|—||—|
|Senegal|tranche 1 residential|n.a.|150|0.24|
|South Africa|block 1 residential|n.a.||—|
|Sudan|—|—||—|
|Tanzania|n.a.|n.a.||3.0|
|Uganda|block 1 residential|1.09||3.4|
|Zambia|block 1 residential|1.31||1.6|
|Zimbabwe|tranche 1 residential|n.a.||0.6|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ For details see annex 13. n.a. Not applicable. — Not available.

Another important aspect of social policy in the power sector has been the growing use of prepayment meters. These have dual advantages. On the one hand, they help low income customers to control their expenditures on electricity by spreading them out into small frequent payments that they can more readily afford. On the other hand, they eliminate the commercial risk to the utility of serving low income 9
below -60 percent as genuine, reflecting seasonality and the high degree of idiosyncratic variability facing microenterprise owners.

As a result of the high variability and low autocorrelation in economic outcomes like firm profits, income, and expenditure among poor households, there is much to be potentially gained by taking multiple measures of these outcomes at relatively short intervals and averaging over them when estimating treatment effects. This is the approach used in de Mel et al. (2008), who use a baseline and 8 quarterly follow-up waves of business profits and in Fafchamps et al. (2010) which uses two survey rounds before randomization and a further 4 quarterly follow-up surveys.<sup>1</sup> However, this approach seems to be the exception rather than the rule. The premise of this paper is that many field experiments are making a suboptimal choice of how many rounds of surveys to collect, and that the default choice of a single baseline and single follow-up study is unlikely to be optimal in many cases. This paper aims to combine insights from the medical literature on repeated measurement, new analysis of the formulae underlying power calculations, and experience from these studies using multiple measures in economics to provide a practical guide for researchers designing experiments. While our discussion will be in terms of experiments, many of the same issues and therefore lessons also apply for design of non-experimental impact evaluation designs, such as matched difference-in-difference estimation.<sup>2</sup> An additional contribution of the paper is to draw to the attention of empirical researchers in economics the large improvement in power that can arise when estimating treatment effects via an Analysis of Covariance (ANCOVA) estimation compared to using the more common difference-in-difference specification. The improvement in power is greatest when the autocorrelation is low – intuitively when the baseline data have little predictive power for future

> 1 Bloom et al. (2010) provides an example of very large T, with 114 weekly observations on firm output, quality, and inventory levels.

> 2 Gibson and McKenzie (2010
.63|191.56|68.86|170.94|67.02|247.30|
|<br>Leases of comm. land in village|66.96|27.88|121.48|28.19|116.44|38.46|
|<br>**Time period of transaction**|||||||
|Year 2021|0.382|0.376|0.397|0.341|0.367|0.377|
|Year 2022|0.193|0.182|0.224|0.184|0.228|0.184|
|Year 2023|0.284|0.296|0.252|0.320|0.275|0.305|
|Year 2024|0.141|0.146|0.127|0.156|0.130|0.134|
|Numberofobservations|884,936|648,292|236,644|382,653|151,474|79,316|

_Source:_ Own computation based on land monitoring data reported by SGC, complemented by administrative and remotely sensed data as discussed in the text.

23
from the labor market and, together with those in the informal sector, declining earning opportunities.

- The “ **Socio-economic Assessment of the Impact of Syrians under Temporary Protection (SuTPs) on Turkish Hosting Communities** ” [ongoing], to be undertaken in partnership with the Government of Turkey, will include a nationally representative household survey with SuTP and local Turkish households including camp and non-camp environments. The questionnaire will cover welfare (assets, income, expenditure), municipal services, labor and employment, education, social networks and quality of life.

# **F. Options to improve forced displacement statistics**

Significant efforts are needed to enhance the reliability, comparability, quality and scope of the global data on forced displacement. In particular, more robust estimates are needed of the scale (stocks, flows and locations) and typology (demographics, location and accommodation) of forced displacement crises. This requires substantial improvements in the rigor of data collection and compilation methodologies including:

- (a) Harmonization of definitions and methodologies used in the collection and analysis of statistical data on forced displacement—covering stocks and flows of refugees, asylumseekers and IDPs—to ensure comparability across regions and countries;
that the adjustment occurred almost entirely at the intensive margin, through a decline in trade volumes of globally engaged firms with their participation in global markets along with the numbers of destination or sourcing markets per firm remaining stable (see Behrens et al. (2013) for Belgium and Bricongne et al. (2012) for France). Our paper adds to the literature by providing evidence on trade adjustments at the intensive and extensive margins by firms initially and in the pandemic recovery phase based on both BPS data and customs data for large samples of firms in developing countries. While the COVID-19 pandemic is a unique crisis, the evidence that we provide can be useful in preparing policy makers for complex events that affect firms through combined demand, supply, and uncertainty shocks.

The effect of shocks on trade outcomes is, however, not uniform across firms, sectors, and countries. For instance, in face of the GFC smaller firms in France were more likely to exit the international market and experience a large decline in their number of export destinations (Bricongne et al., 2012). French firms importing inputs from China suffered shortages due to the COVID-19 pandemic that disrupted their production and translated into a stronger decline of their exports until June 2020 (Lafrogne-Roussier et al., 2021). Services trade was much more resilient to the GFC than goods trade, for several reasons: services trade demand is less cyclical, less dependent on external finance, and less subject to protectionist measures (Borchert and Mattoo, 2010; Ariu, 2016). However, in the current pandemic, trade of services sectors requiring physical proximity (travel, transport, and construction) experienced substantial declines, while trade of financial, telecommunications, computer and information services was resilient or even thrived (WTO, 2021; World Bank, 2021). Sector characteristics related to the inputs used in production (e.g., whether inputs are imported and in particular from China and the intensity of unskilled labor use) are shown to drive the negative effect of
Policy Research Working Paper 10947

# **Abstract**

Climate change is increasing household exposure to extreme heat, floods and other natural disasters. This paper examines the differential exposure of poorer households to heat and floods in South Asia. The use of spatially detailed data on climate shocks and relative wealth allows the analysis in this paper to capture highly localized variation in wealth, heat and floods. It finds that poorer South Asian households experience more heat than better-off ones. In urban areas, poorer households also experience more recurrent flooding. Using spatially detailed data on the universe of firms in India, this paper also finds that smaller firms are more exposed to heat and flooding. The paper concludes by discussing potential mechanisms that could explain these disparities in exposure to climate shocks.

This paper is a product of the Office of the Chief Economist, South Asia 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 abehrer@worldbank.org, jrexer@worldbank.org, ssharma1@worldbank.org, and mtriyana@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
|0.327|-0.548*|
|(15-24/25-34/35-44/45-54)|(0.202)|(0.182)|(0.280)|(0.293)|
|Share of population with a primary|0.192***|0.197***|0.092**|0.066|
|education or less|(0.067)|(0.052)|(0.042)|(0.043)|
|Natural log of GRP per capita|6.703***|2.424***|-0.141|-0.627|
|(constant 2010 IDR)|(0.985)|(0.829)|(0.824)|(0.571)|
|Growth in GRP per capita|-0.308***|-0.070|-0.003|-0.084|
||(0.103)|(0.102)|(0.059)|(0.069)|
|Number of observations|272|272|272|272|
|R-squared|0.422|0.648|0.791|0.865|
|Year dummies|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

25
**Appendix I: Sources and quality of data and explanations regarding development of component indices**

_Political Freedom:_ Haq (1995). This index measures four factors that reflect an indlividual's ability to exercise political freedom: a country's political process, statutory freedoms, an individuals ability to exercise freedom of expression, and degree to which discrimination is tolerated.

_Political Stability, Judicial Efficiency, Red Tape:_ Mauro (1995). These indices, developed by Business International Corporation (BIC), are unavoidably subjective. These indices were developed with an investor's perspective in mind. This perspective may lead to different assessments of, say, judicial efficiency than an index with a less pecuniary perspective. These indices, which we have not been updated, are based on older data than the other indices -- 1980-83.

_Corruption:_ Transparency International/Gottingen University (1996). This index, frequently updated, is based on survey results from several risk assessment consulting groups, the Global Competitiveness Report, and the Institute for Management Development.

_Human Development Index:_ Human Development Report (1996). This index, published by the United Nations Development Program measures life expectancy, adult literacy, enrollments in primary, secondary and tertiary education institutions, and per capita GDP in purchasing power parity terms.

_Gini Coefficients:_ Deininger and Squire (1996). These estimates of income equality are based on household surveys which are presented by the authors as both a substantial improvement over previous work in both data quality and coverage.

_Outward Orientation:_ World Bank (1996a). This index uses an initial assessment of outward orientation made for 1980-83 and modifies it according to the speed of integration index calculated for 1990-93. The factors composing the index are: a population-adjusted trade ratio, country credit rating by Institutional Investor, Foreign Direct Investment as a share of GDP (in PPP terms), and the share of manufacturing that is exported.

_Central Bank Independence:_ Cukierman, Webb and Neyapti (1992
Policy Research Working Paper 8788

# **Abstract**

The formal private sector has a key role to play in fostering growth and reducing unemployment in South Africa— strengthening its performance is therefore critical. This paper looks at firm behaviour, firm entry and exit, job outcomes, and productivity dynamics using firm-level administrative data for South Africa. It is the first paper to benchmark employment and productivity dynamics against various comparator countries for which similar analysis has been undertaken. The paper finds that South Africa has an aged private sector with low firm dynamism and characterized by large firms that hold a large share of employment and revenue, although they are not as productive as micro firms and pay lower wages on average. The paper also finds that job creation is concentrated predominantly in incumbent firms, which are old and large, and job creation from entry and exit is negligible. The static and dynamic productivity decompositions raise a concern that although productive efficiency is gained, it is at least in part at the expense of labor. Large firms are not exploiting economies of scale, and particularly unproductive large firms may drive the weak performance of the private sector. Relatively high wages in South Africa could be partly explained by the inefficient use of labor and negative correlation between productivity and size. Likewise, these larger firms could be responsible for the negative direct impact on jobs of firms raising productivity.

This paper is a product of the Social Protection and Jobs 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/research. The authors may be contacted at raterido@worldbank.org, Ayanda.Hlatshwayo@treasury.gov.za, Duncan.Pieterse@treasury.gov.za, and Andre.Steenkamp@treasury.gov.za.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage
Table 12: The impact of PAL on parental investment

|_Outcome =_|(1)<br>Index of parental<br>activities[0-5]|(2)<br>Principal<br>Component|
|---|---|---|
|In-kind|0.459*<br>(0.266)|0.309*<br>(0.183)|
|Cash|0.343|0.238|
||(0.319)|(0.219)|
|State FE|YES|YES|
|Year FE|YES|YES|
|Grade FE|YES|YES|
|Pre-program controls|YES|YES|
|Observations|283|283|
|Outcome mean in control group|3.19|-0.02|
|**_Effect size:_ In-kind - Cash**|0.12|0.07|
|**_H 0 :_ In-kind = Cash (p-value)**|0.68|0.71|
|**_H 0 :_ In-kind = Cash (Randomization Inference p-**<br>**value)**|0.78|0.81|

Notes: *** p<0.01, ** p<0.05, * p<0.1 (1) Standard errors are clustered at the village level.

(2) Pre-program controls include gender, age, and indicators for whether the household head is married, whether the house has running water, and whether the closest school offers morning shift.

(3) Results based on the Enlace de Contexto for all years between 2008-13 (4) The _Index of parental activities_ equals the number of activities where parents are involved among the following: 1) helping with homework; 2) explaining topics that were not clear from the lecture; 3) inviting them to review class material that was not
On the surface, it seems that the HR has no effect when coupled with an SSR, as this rule already saves most of the transitory windfalls in the short run. However, the HR prevents the government from increasing consumption over time as the financial returns on the invested assets start to improve the fiscal budget.<sup>26</sup>

# **III. General Baseline Calibration**

In this section, we describe how to calibrate the LTGM-NR to a generic oil exporter for a “business-as-usual” baseline simulation. The discussion below intends to be comprehensive and provide a detailed description of the calibration. The general reader that is more interested in how the LTGM-NR works in practice can jump ahead to the Angolan exercise in section IV.

The baseline simulation runs from 2021 to 2050 (though the simulation horizon can be changed). “Flow” variables are usually calibrated to a 20-year average (2000-2019) to reduce year-to-year volatility—also, 2019 is before COVID-19— and slow-moving “stock” variables are calibrated to the most recent year available. Many of the parameters are common to the standard LTGM (indicated with a red cross on Table 2) and so are not discussed further here (see Loayza and Pennings 2018). However, other similar parameters need to be adjusted, as aggregate data sets like the Penn World Tables (PWT) do not include a resource sector. The user has the ability to override the default calibration if they have better data. If the data are missing, we suggest using an average within the same income group or region. Table 2 provides an overview of the calibration and data sources.

> 26 An equilibrium in the External-Balance submodel is defined as a collection of 12 endogenous trajectories 0 , R 0 D � plus 14 endogenous variables (that are specific to this submodel) tt ttii ttii ttjj tt tt tt tt, c tt ttii ttjj 0 tt , e � where with each endogenous variable specified as a function of the exogenous paths �YY , QQ ,
section uses tariffs to provide a first round of answers to each of our five major questions: Do some countries have lower tariffs than others? Within countries, which sectors receive the most tariff protection? How do tariffs change over time? Do countries set tariffs so as to discriminate among their trading partners? Finally, how liberalized are tariffs?

Much of our formal analysis in this section relies on cross-country and inter-temporal data comparisons where, for reasons of data quality, we do not attempt to be comprehensive. Instead, we focus on a sample of 31 economies listed in Table 1.<sup>6</sup> These major economies were not chosen randomly - they include the Group of 20 (G20) economies plus an additional set of developing countries each with 2013 populations of over 40 million. Collectively in 2013, these 31 economies represented 83 percent of the world’s population, 91 percent of GDP, 80 percent of imports, and 79 percent of exports. Figure 1 illustrates their geographic diversity.<sup>7</sup> We begin in Section 2.1 by introducing and describing _ad valorem_ import tariffs under the WTO. In the contemporary data, we find the tariffs that countries apply are relatively low on average. However, for many countries, these applied tariffs are substantially lower than the maximum tariffs allowed under the WTO, implying that trade is much less liberalized when viewed through the lens of the tariffs that countries are legally permitted to apply. There are also surprisingly large differences across countries in the average level of tariffs: high income countries have more liberal trade regimes than middle income countries which, in turn, have more liberal regimes than low income countries. Across the board, countries tend to charge higher tariffs in the agricultural sector, in manufacturing industries such as textiles, apparel, and footwear, and in final goods relative to intermediate inputs. Finally, over the most recent twenty year period (1993-2013), we find a general downward trend in tariffs for many, but
8 The World Bank’s Enterprise Surveys is a firm-level survey, conducted in over 110 economies, that provides useful insights on firm perceptions and experiences. It is important to note that most of the MENA Enterprise Surveys do not have weights, and the survey statistics cannot be extrapolated to be representative at the country or sector level. The exceptions are Iraq 2011 and Yemen 2010 surveys, which do follow the Enterprise Surveys Global sampling methodology. Note also that the surveys in the MENA region were conducted in different years.

> 9 Institutional quality factors include weighted components of the size of government, legal system property rights, sound money, freedom to trade internationally, and regulation.

3
cover ≥ 80%. The indicator is 0 otherwise. Finally, we study the impact of financial development, proxied both by the ratio of private credit to GDP (PC) and the ratio of private bonds to GDP (PR_BOND).

For our baseline results, we employ multivariate regression analysis on pooled countryyear data because of some correlation between the variables. We drop very small economies from the sample (countries with GDP less than 5 billion US constant 2000 dollars) because they tend to have idiosyncratic insurance industries due, for example, to their offshore nature. We adopt Ordinary Least Square (OLS) regressions since most of our explanatory variables are arguably exogenous. All OLS regressions are estimated with and year-fixed effects to address 11
investment in human capital, households storing crops spend an additional $10 on education in the 30 days preceding the follow-up survey, which took place in July and August. Assuming that (i) warrantage started translating into additional education expenses when stored crops were sold at destorage in April-May, (ii) the gains averaged $10 per month between then and the time of survey, and (iii) gains in education expenses were exhausted by the time of the follow-up survey, we found a lower bound for gains in human capital investment of $30 per household. This conservative calculation indicates that gains in education investment solely approximate the costs of the program.

Using effectiveness estimates from the livestock value, we found even higher gains. We roughly estimated $351 in livestock value gains per household against $30.10 in costs, for a ratio of approximately 12/1. This is higher than the gains in production value and indicates that additional investments resulting from gains in production value yield positive returns within three months after destorage. Finally, assessing cost-effectiveness based on input purchases only, we found the gains to be slightly lower than the program costs ($23.60).

We should note that we did not account for non-monetary costs, such as psychological costs arising from uncertainty related to storing crops in a neighboring village and those attributable to anxiety related to price volatility while crops are stored and individuals are unable to sell their stored grains. In addition, implementers designed the program explicitly to emulate how to use warehouses at full capacity. The program consisted of _extending_ access to warrantage in existing warehouses for neighboring farmers who did not have prior access. Most importantly, the program costs therefore do not encompass the costs of introducing warrantage into new villages, which would involve building warehouses. Accounting for the building of warehouses would dramatically change the cost-effectiveness of such an intervention.