rafmacalaba/data-use-mentions-extended huggingface dataset:
When it comes to census data, how old is too old? Or, put another way, at what age do census-based predictions become less accurate than current geospatial predictions? It is difficult to know, but Newhouse et al. (2022) offer one small piece of evidence on this point. When evaluated against Mexican 2015 small area poverty estimated based on the intercensus, 2010 census-based estimates are more accurate than 2015 estimates based on geospatial indicators (correlation of 0.91 vs 0.86). However, this is only representative of one context, and regional patterns of poverty in Mexico may have been more static during this time than in other contexts.

# d. Prediction accuracy is very sensitive to the training data

Many of the papers discussed above predict poverty rates or average asset indices estimated at the village level. Since these are often derived from surveys with a limited number of observations per village, this raises the issue of noise in the dependent variable. In fact, correlations between predicted values and census-based estimates depend critically on the extent of noise in the training data, which will reduce measured accuracy. For example, Engstrom et al. (2022) considered how the accuracy of predictions depends on the size and nature of the sample used to estimate average per capita consumption at the GN division level. That analysis correlated interpretable geospatial features with predicted per capita consumption imputed into a census. Model R<sup>2</sup> fell from 0.61 when using the mean over all census households, to 0.55 when using the mean over thirty households, and further to 0.40 when taking the mean over 8 households per enumeration area. Differences in the extent of noise present in the training data, as well as the reference evaluation measure, will not necessarily affect the ranking of different types of models within the same context. But it explains much of the wide variation in R<sup>2</sup> observed in Table 1 across different studies, which underscores the
distributing across the cells of the ‘USE’ matrix<sup>3</sup> the total value of domestically produced goods and services consumed by the productive sectors (intermediate consumption), using technology coefficients from the GTAP<sup>4</sup> 2007 SAM for Bangladesh. The use of 2007 GTAP coefficients means ignoring possible structural change between 2007 and 2012 in the Bangladesh economy.

All elasticity parameters are taken from Timilsina and Shrestha (2007). As it is difficult to empirically estimate the elasticity of substitution across commodities for individual countries, using values from the literature is the norm. Data on government transfers to BPDB were obtained from the Medium Term Macroeconomic Policy Statement of the Ministry of Finance, Government of Bangladesh, while quantity forecasts for natural gas and LNG were obtained from Petrobangla (2012), with natural gas price data for 2015 for Bangladesh coming from Petrobangla and LNG price data for India from Sen (2015).

# **4. Results and Discussion**

In this section, we first discuss results from the CGE model simulations of electricity subsidy removal followed by results from simulations in which the price of natural gas is increased to its opportunity cost. Under each set of results, aggregate level results, such as impacts on GDP, total sectoral outputs, total household demand, total exports, total imports, total investment, disposable income, the trade balance, and government income are discussed first; this is followed by a discussion of sectoral and commodity level impacts.

# **4.1 Impacts of electricity subsidy removal**

## **4.1.1 Aggregate economic impacts**

Elimination of the electricity subsidy and recycling the associated budgetary savings to the economy would be beneficial because this would end distortions caused by the electricity subsidy,

> 3 The ‘USE’ matrix in a SAM refers to inputs of different goods and services for the production of various goods and services.

> 4 GTAP is a global network of researchers who conduct quantitative analysis of international economic
constituencies, and minimizing the number of idle parcels. But land is “subject to multiple, overlapping claims and ongoing debate over these claims’ legitimacy and their implications for land use” (Berry, 2001). Consequently, disputes are common over both who has a right to a particular plot, and even who has the right to allocate that plot. Without a central registry, there are frequent instances, especially in urban and peri-urban areas, where land is rented, sold or allocated land multiple times to different people. Of course, such practices can create an array of conflicts, disputes and ownership insecurity, as for example shown in Kuntu-Mensah (2006), which have resulted in frequent litigation (Aryeetey & Udry, 2010). As noted by Jones-Casey & Knox (2011), the Ghanaian courts were clogged with 35,000 land disputes in 2006.

Given the lack of clear land rights, it is not surprising that investment in housing provision and mortgage markets has been inhibited. In 2010 for example when the housing stock deficit stood at 1,200,000 houses, only 199,000 units of houses were built (Afrane et al., 2016). In the wider international context, tenure insecurity particularly discourages investments by multinational companies in Ghana and thus the national economy forgoes potential positive effects from additional job creation and technology transfer (Barthel et al, 2011).

Amid this complexity, however, a cultivator’s rights over her growing crops, on the other hand, are quite secure. Plots are virtually never lost while under cultivation; the 2018 wave of the ISSERNorthwestern-Yale Ghana Panel Survey (GPRL, 2019) reveals only two instances among 5,366 plots in which disputed tenure caused interference with cultivation. Historian Ivor Wilks (1993) summarizes the principle as “the cultivated farm is my property, the land is the stool’s”. This feature of the property rights system creates an incentive for farmers to use (or appear to be using) their land. Goldstein &
Hamilton (2001). Using nationwide consumption surveys between 1888 and 1994 in the United States, Costa (2001) finds that the CPI underestimated increases in the true cost of living until 1919, but consistently overestimated them thereafter. Hamilton (2001), using a different set of data, the Panel Study of Income Dynamics between 1974 and 1991 in the United States, finds a similar trend. In particular, he estimates the annual CPI upward bias at approximately three percentage points between 1974 and 1981 and slightly less than one percentage points between 1981 and 1991. The results of Costa (2001) and Hamilton (2001) are also broadly consistent with the findings of Boskin et al (1996, 1998), who calculate the contribution of each source of CPI bias and conclude that between 1979 and 1995 the United States CPI exhibited an upward bias of 1.1 percentage points per year.

Since then the Engel curve method has been applied to various other countries. In two papers referring to Canada, Beatty and Larsen (2005) and Brzozowski (2006) find that the Canadian CPI overstated changes in the cost of living among specific demographic groups. Similarly, Barrett and Brzozowski (2010), in a study on the Australian CPI, show there was cumulative upward bias of 34 percent between 1975 and 2003, although the magnitude of the bias varies by household type and time period considered. In New Zealand, Gibson and Scobie (2010) argue that the country’s CPI overestimated inflation by an amount similar to the bias in the United States. In contrast, Larsen (2007), following the same method but using data on Norway in the 1990s, concludes that the CPI understated the increase in the cost of living.

More recently, the application of the Engel curve method has been extended to developing and transitional economies in Asia and Latin America. De Carvalho Filho and Chamon (2012) argue that post-reform growth in real household incomes in Brazil and Mexico was underestimated because the CPI overstated the increase in the cost
if the stock market liberalization occurs before or during the lifetime of the spell.

In Subsection 5.3 and Online Appendix D, we also use alternative measures of stock market liberalization. First, we construct a dummy variable based on the dates related to the first signs of liberalization that can sometimes precede the official liberalization dates. The source of these alternative dates for stock market liberalization events is Bekaert, Harvey, and Lundblad (2005). Second, we introduce two versions of a continuous variable capturing the stock market liberalization intensity at the industry-country level. Both versions are based on the proportion of domestic equities the foreign investors can acquire. We compute them from the data on the market capitalization of firms included in the IFC Investable index and IFC Global index available in the Emerging Stock Market Factbooks of International Finance Corporation.

As for other financial variables, our proxies for the level of domestic banking and stock market development in the liberalizing countries are the ratio of private credit to GDP and the ratio of stock market capitalization to GDP, respectively. Both variables are from the database by Beck, Demirg ̈u ̧c–Kunt, and Levine (2000). The industry measure of dependence on external finance at the ISIC 3-digit level is from Braun (2003). This measure goes back to the seminal paper by Rajan and Zingales (1998) and captures the share of capital expenditures that cannot be financed by internally generated funds. It is computed as ratio of the capital expenditures minus cash flow from operations to the capital expenditures for the median publicly listed company in a given US industry, using the financial data on US firms from Compustat.<sup>11</sup>

# **3.2 Distance to comparative advantage**

Distance to comparative advantage captures how far is a given product from the comparative advantage of a given country. Building upon the notion of “product chain of comparative advantage” within the Heckscher-Ohlin theoretical framework (see Online Appendix A2 as well as Jones 1956-57; Bhagwati 1972; Deardorff 1979
||[0.001]|
|Currency overvaluation (t-1)|||0.000|0.075***|
||||[0.000]|[0.0119]|
|Currency mismatch (t-1)|||-0.000|-0.000|
||||[0.000]|[0.000]|
|FDI (t-1)|||-0.001|-0.013|
||||[0.016]|[0.016]|
|Constant|-1.710**|-2.119***|-1.764***|-1.503***|
||[0.746]|[0.158]|[0.295]|[0.290]|
|No. of observations|1,634|2,085|1,325|1,271|
|No. of countries<br>Estimated on annual data 1985-2018 e<br>*** p<0.01, ** p<0.05, * p<0.1|103<br>xcluding 2009. St|92<br>andard errors in b|88<br>rackets.|88|

48
technological change can be safely assumed to reinforce each other, in developing countries both factors probably operate in opposite directions and empirical evidence is crucial to determine which tends to prevail. This paper provides new evidence on that question, by estimating the net effect of the use of imported inputs, exports and FDI on the relative demand for skilled workers, using recently collected manufacturing plant-level data from Brazil and China, two developing countries which have experienced signi fi cant increases in their degree of international integration.

Previous empirical work on the U.S. and other OECD countries has generally found skilledbiased technological change to be more important than trade as the main factor underlying the aforementioned labor demand shifts. Thus, only a small share of the observed changes in the demand for skilled labor can be attributed to a reallocation of production towards skill-intensive — sectors as the trade hypothesis would predict. Rather, it appears that most of the shift away from unskilled labor has taken place within narrowly de fi ned industries, a result that is consistent with the hypothesis of skill-biased technical change. Moreover, studies using industry-level and plant-level data have found a direct link between the relative demand for skilled workers and investments in the adoption of computers or in research and development (R&D).<sup>3</sup> However, these studies have been less successful in uncovering the hypothesized effects of exports, imports and foreign investment fl ows on the demand for skilled labor.<sup>4</sup> The evidence for developing countries is more scarce. In Mexico, the relative wages of skilled workers increased dramatically after the country liberalized its trade regime and relaxed restrictions on FDI in the early 1980s. Feenstra and Hanson (1997) show that the states and

> 3 See Berman et al. (1994), Doms et al. (1997), Dunne et al. (1997), Autor et al. (1998), Machin and Van Reenen (1998), and Haskel and Heden (1999).

> 4
many times, and produces a distribution of “fake” treatment effects under the null hypothesis. The p-value is simply the proportion of cases in which the “fake” treatment effect estimated is at least as large in absolute value as the estimate we seek to test. The method is attractive in part because it relies on fewer modeling assumptions encountered in a regression framework.

# **4. Data and Descriptive Statistics**

# _4.1. Data Sources_

During the tree planting, 630 individuals were enrolled into the study and equally distributed among a treatment and a control group. In September to October 2017, right after the PES contracts were signed with farmers, we administered a baseline household survey in the villages of the respondents. The survey instrument included modules capturing households’ sociodemographic characteristics, asset ownership, and food consumption. Table 1 presents the baseline characteristics of our participants (and their households) in both the treatment and control group, as well as the tests to what extent the distributions of these characteristics in the two groups are similar. The table indicates that the treatment and control groups are fairly balanced with respect to most characteristics. In particular, there is no statistically significant difference between treatment and control groups in terms of baseline values of food consumption expenditures and household dietary diversity scores – two of our key variables of interest. There are, however, some imbalances with respect to other potentially important variables such 13
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- Ministère de la Communications et des Technologies de l’Information et de la Communication. 2008.

- Document de politique et de stratégie du secteur de Télécommunications, des TIC et de la Poste. Available at: http://www.atrpt.bj/reference.php

- Thisday (Nigeria). 24 June 2008. ―Glo Benin Signs up over 600,000 Subscribers in 2 Weeks.‖ Available at: http://www.cellular-news.com/story/31953.php

# Irrigation

FAO (Food and Agriculture Organization). 2004. Aquastata: Benin Country profile. Downloadable from: http://www.fao.org/nr/water/aquastat/main/index.stm

- Heidecke, Claudia. 2006. Development and evaluation of a regional water poverty index for Benin. IFRPI, Discussion Paper 145.

- Svendsen, Mark, Mandy Ewing, and Siwa Msangi. 2008. ―Watermarks: Indicators of Irrigation Sector Performance in Africa.‖ AICD Background Paper 4, Africa Region, World Bank, Washington, DC.

- You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. ―Torrents and Trickles: Irrigation Spending Needs in Africa.‖ AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

# Power

- Eberhard, Anton, Vivien Foster, Cecilia Briceño-Garmendia, Fatimata Ouedraogo, Daniel Camos, and Maria Shkaratan. 2008. ―Underpowered: The State of the Power Sector in Sub-Saharan Africa.‖ AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. ―Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.‖ Policy Research Working Paper 4913, World Bank, Washington, DC.

- Helio International 2009. Energy
# Policies to Support Businesses through the COVID-19 Shock: A Firm Level Perspective<sup>_∗_</sup>

Xavier Cirera (World Bank) Marcio Cruz (World Bank) Elwyn Davies (World Bank) Arti Grover (World Bank) Leonardo Iacovone (World Bank and Hertie School)<sup>_†_</sup> Jose Ernesto Lopez Cordova (World Bank) Denis Medvedev (World Bank) Franklin Okechukwu Maduko (Central European University and World Bank) Gaurav Nayyar (World Bank) Santiago Reyes Ortega (World Bank) Jesica Torres (World Bank) Originally published in the Policy Research Working Paper Series on _January, 2021._ This version is updated on _February, 2021._ To obtain the originally published version, please email prwp@worldbank.org.

JEL classification: D22, L20, L25, O10 Keywords: COVID-19, Firms, Policies, Uncertainty.

> _∗_ The authors thank Caroline Freund for her guidance and support of the Business Pulse Survey initiative. We gratefully acknowledge feedback and advice on the survey questionnaires from Nicholas Bloom, Francisco Campos, Ana Paula Cusolito, Mark Dutz, Caroline Freund, Jorge Rodriguez Meza, David McKenzie, Silvia Muzi, and Bilal Zia. Similarly, we are grateful to all the colleagues and counterparts institutions that have supported the data collection (a complete list of all colleagues, collaborators and institutions that have been crucial in providing support during data collection is provided in the online appendix). We would like to especially thank Edgar Avalos for his outstanding research support, and Tanay Balantrapu, Joao Belivaqua Basto, and Kyungmin Lee for their help through the data collection. We thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the Enterprise Survey data available. Financial support from USAID, the World Bank, and the IFC is gratefully acknowledged. The views expressed in this article are solely those of the authors and do not necessarily reflect those of the World Bank, its Executive Directors, or the
17 positive. The expression also states that the marginal change in welfare consists of two components: an income effect equal to Sj/1-G; and a distributional effect, _aW_ - (Sj/1-G). Since in the case under analysis Sj is always positive, 15the income effect is likewise positive. For the same reasons, in this case, the distributional effect is always negative, because aW < (Sj/1-G).

Table 6 shows the effect of a 1 percent increase in a particular income source on the Sen welfare index. The welfare effects vary greatly among the five sources of income. As might be expected, the largest improvements in welfare come from a exogenous increase in nonfarm income. A 1 percent increase in nonfarrn income causes the Sen welfare index to rise by 0.56 percent; this is almost three times the percentage increase in welfare that accompanies a rise in income from any other source. For example, a 1 percent increase in transfer income causes the Sen index to rise by only 0.19 percent.

The data in Table 6 show that the income and distributional effects of an exogenous change in source income are different. The 0.56 percent increase in the Sen welfare index resulting from an increase in nonfarm income includes a large positive income effect (0.908) and a large negative distribution effect (-0.345). For the reasons explained above, increases in all five of the income sources in Table 6 have a negative distributional effect on welfare. However, the largest negative distributional effect is recorded by a 1 percent increase in agricultural income. A 1 percent rise in agricultural income causes a negative distributional effect of -.460. This is not surprising, given the fact that agricultural income -as measured by the source Gini (Gj) - is distributed so unevenly. 16
100 years ago. He finds that children who were in utero and young at the time of the natural disasters are less likely to accumulate human and physical capital in adulthood (they have worse health and fewer assets). Most importantly, he finds evidence of an intergenerational transmission of the shock, indicating that children of mothers who have been exposed to natural disasters achieved fewer years of education. Caruso and Miller (2015) find similar results for the 1970 Ancash earthquake in Peru. Using the 1993 and 2007 Peruvian census data, their study reveals that both males and females affected by the shock in utero respectively complete 0.5 and 0.8 less years of education in adulthood as compared to those not affected by the shock. In addition, evidence of an intergenerational transmission of the shock was found with children of exposed mothers at birth completing 0.4 less years of education. Comparable findings emerge from Northeast Tanzania where Caruso (2015) found that children of women afected by the 1993 flood in the Tanga region before the age of 18 present lower Height-for-Age Z scores (HAZ) as compared to unexposed adjacent cohorts.</mark> <mark>The question of whether refugee crises are beneficial or detrimental to the socio-economic well-being of host communities is a topic that has attracted substantial interest only in recent years and the evidence</mark>

> 3 <mark>Peabody Picture Vocabulary Test (PPVT).</mark>

2
Pooled OLS: Between districts: Within districts only: where, _yid_ is the incidence of spending (or the number of applications or the percentage of applications funded) per thousand people in the ward, _Pid_ is the poverty headcount ratio at the ward level, and _X id_ and _Zid_ give a broader set of socio-economic and political controls. Equation (1) is estimated through pooled OLS, equation (2) is estimated at the district level using district averages, and equation (3) includes district-level fixed effects and hence is identified using only within-district variation across wards.

# **5. Targeting Performance at the National Level**

# _5.1. Funding (and Number of Projects) per Capita_

The typical analysis of targeting performance in Social Funds programs examines the incidence of spending among the poor relative to the population as a whole. To demonstrate that the targeting performance of TASAF II is not substantially different than those found in previous studies (World Bank 2002), Table 2 provides a first look at the incidence of TASAF II expenditures. The first two columns use the amount of money spent through TASAF II‟s NVF per person per ward as the dependent variable. When we regress this variable on the ward-level poverty headcount ratio, we see progressive targeting that is statistically precise but relatively muted in economic significance. The marginal effects indicate that a ward going from being universally non-poor to universally poor would see TASAF II spending per person increase by only $1.29 from a base of $2.63. In other words, a one standard deviation increase in the poverty rate of a ward (an approximately 20 percentage point increase in the headcount ratio) would cause an increase of only $0.25 per person or $5,000 total in a ward with average population. These results are typical of the broader literature on CDD that show mildly pro-poor benefitincidences. The results in the third and fourth columns, which analyze the number of TASAF II projects (rather than funding) per person,