rafmacalaba/data-use-mentions-extended huggingface dataset:
# **2.4 Quantities Sold and Expenditures**

Data on expenditures are obtained in two ways. First, we use the total nominal monthly sales for each store constructed from the retail scanner data. In particular, we construct a measure for quantities using real sales, which is defined as the nominal sales divided by the store price index. Second, we use the Nielsen Consumer Panel Dataset.

## **2.4.1 Nielsen Consumer Panel**

The Nielsen Consumer Panel Dataset represents a longitudinal panel of approximately 40,000-60,000 US households who continually provide information to Nielsen about their households and what products they buy as well as when and where they make purchases. Panelists use in-home scanners to record all their purchases, from any outlet, intended for personal, in-home use. Products include all Nielsen-tracked categories of food and non-food items, across all retail outlets in the US Nielsen samples all states and major markets. Panelists are geographically dispersed and demographically balanced. Each panelist is assigned a projection factor, which enables purchases to be projectable to the entire US.

For each period, we calculate the total expenditures of each household on SNAP-eligible and SNAP-ineligible products. We classify goods as SNAP-eligible based on guidelines published by the FNS. The average SNAP-eligible-goods-expenditure share across households is close to 60%. The advantage of using this measure as opposed to the nominal sales for each store is that demographic information for the household can be observed instead of county-level demographics for the store, such that we can use information on household income and household size to classify households as SNAP-eligible.<sup>25</sup> We also construct several measures of shopping intensities among households, following Stroebel and Vavra (2019). For each good purchased, the household records whether the good is purchased with coupons and if it is on sale. The barcode of the good is also scanned so the brand of the good is observed. We use three measures of shopping intensity: (1) the share of expenditures using coupons
information for the three months preceding the date of the survey. Our unit of observation is the household and we obtained information on monthly income and expenditure over time for each household. The CPHS database also includes qualitative information on household borrowing, savings, and asset ownership. The CPHS provides these data at a wave frequency (four months); however, different households are surveyed in different months within a wave. _MGNREGA_ We obtained monthly data on the numbers of households that worked and those that demanded work under the MGNREGA for all the districts in Kerala, Tamil Nadu and Karnataka from the MGNREGA Public Data Portal maintained by the Ministry of Rural Development. MGNREGA is a demand-driven wage employment program. It provides at least 100 days of guaranteed employment in a financial year to every household residing in a rural area and covers all adult members of rural households who volunteer to do unskilled manual work. A surplus of households that demand work over those who work under MGNREGA reflects excess labor supply in rural labor markets. Declining excess demand for MGNREGA employment from households indicates a tightening of labor market conditions. We later examine the impact of the Kerala floods on the difference between the number of households that worked and number of households that demanded work under MGNREGA.

# _Flood Relief_

We collected district-wise flood relief data from a scanned copy of a report issued by the Disaster Management (A) Department of the Government of Kerala, obtained from the official web portal of the Government of Kerala.<sup>18</sup> It contains the allotment of state disaster response funds sanctioned for 18 items - including agricultural crop loss, ex gratia, food and clothing, and repair of damaged houses - to district collectors in all 14 districts of Kerala. We normalize the relief amounts using district-wise population data from the Census. Every district in Kerala received some amount of assistance and the amount varied from around 250

> 18The report is G.O. (Rt) No. 460/2018/DMD, issued by the Disaster Management (A)
Policy Research Working Paper 1394

# **Abstract**

This paper quantifies the impact of effective corporate tax rates on aggregate total factor productivity. Using Chilean manufacturing data, the paper documents a large dispersion in the effective tax rate faced by firms and a mass of firms facing a 0 percent tax rate. These empirical patterns are incorporated into a standard monopolistic competition model with corporate tax rates. The paper’s quantitative findings show that the TFP gains between the economy implied by the Chilean tax code of 1998–2007 and a hypothetical economy without effective corporate tax rate inefficiencies are between 4 and 11 percent. The paper considers counterfactual policies in which all firms face the same tax rate and finds a monotonically decreasing relationship between the level of the tax rate and total factor productivity.

This paper is a product of the Finance, Competitiveness and Innovation 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 fpatinopena@worldbank.org and mdiner@wharton.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 Bank or the governments they represent._ Produced by the Research Support Team
_65_ _Social Capital, Household Welfare and Poverty in Indonesia_ significant contribution to household welfare, over and above that stemming from human capital and other household assets. The use of household-level data to make this case and to quantify the impact of social capital is novel in the literature on social capital. To our knowledge, this is also the first study which quantifies the effects of different dimensions of social capital and of different types of associations.

Our findings support a policy by donors and governments to invest in social capital-either directly or by creating an enviromnent friendly to the emergence of local associations. Our findings also indicate that investments in local social capital deserve to be part of poverty alleviation programs since the returns to investment in social capital are larger for the poor than for others. Lastly, our findings provide indications of the type of associations which are likely to impart the largest benefits.

Future research on other countries will have to confirm the findings of the Indonesia case study. Similar work to that reported in this paper is currently ongoing for Bolivia and Burkina Faso (World Bank, 1998). Further work on estimating directly the structural equations which portray the effects of social capital on access to credit or other inputs, on smoothening income fluctuations and on group decision making would further add to the case for treating social capital as genuine "capital" in the household's production function.
Figure 1: Data‐intensities using US Census software expenditures over labor by sector (2010) <!-- Start of picture text --> Non-Capitalized Software Expenditures over Labour (ISIC Rev 3.1)<br>2.7<br>2.2<br>1.8<br>1.6<br>1.5<br>1.4<br>1.2<br>0.8<br>0.7<br>0.5<br>0.3 0.3 0.3 0.3 0.3<br>Source: US Labor Statistics and US Census.<br>Figure 2: Data policy index by country and type (2019)<br>Data policy restrictiveness index for digital innovation<br>CHN VNM IDN THA MMR KHM KOR MYS SGP TWN PHL MNG LAO JPN HKG<br>CA CBDF DP IL INF IPR<br>Post & TelecomEducationInsuranceOther transportComputerFinanceCom. equipmentChemicalsCoke & PetroleumFood productsMotor vehiclesRenting machineryOther businessMedical & OpticsRecreation<br>3<br>2<br>1<br>(D/L) Data-intensity US Census & BLS<br>0<br>1<br>.8<br>.6<br>.4<br>Restrictiveness index (0-1)<br>.2<br>0<br><!-- End of picture text --> Source: US Labor Statistics and US Census.

Figure 2: Data policy index by country and type (2019) Source: Authors’ using Ferracane and van der Marel (2018). Note: Latest year taken for 2019. Abbreviations in each column are consistent with Table 1 which provides the type of restriction falling into each category. Intellectual Property Rights (IPR); Cross‐border data flows (CBDF); Domestic use and processing of data (DP); Intermediate liability (IL); Content access (CA); Infrastructure &
how political cycles could affect economic outcomes. The literature tends to find weak effects in developed countries, with the US being a notable exception (see Alesina and Rosenthal, 1995 and Alesina et al., 1997). The effects are stronger in more immature democracies. For example, Russia’s regional public expenditure rises shortly before regional elections (Akhmedov and Zhuravskaya, 2004). This lends support to the argument that the strengths of a democracy – such as press freedom, checks and balances, or accountability – can help mitigate opportunistic political cycles (Akhmedov and Zhuravskaya, 2004 and Aidt et al., 2011).

The remainder of the paper is as follows. Section 2 presents the data. Section 3 shows the main results. Section 4 presents several robustness checks. Section 5 concludes.

# **2 Data**

Our analysis draws on two main datasets. The first covers election polls and outcomes for 207 elections across 50 countries worldwide over the period 1980-2020. Only elections with available polling data are included. On average, each country has four elections. There are 149 parliamentary and 58 presidential elections. The list of countries and the number of elections in each are country by year presented in Figure 1 and Table A1. The polling data originate from multiple polling agencies for each country. Official election results are available from multiple sources. For each election, voting intentions by political party (i.e. polls) are gathered, alongside election outcomes<sup>5</sup> . It is important to consider the incumbent changes at the political level rather than for individual since term limits could create me- party politicians, chanical turnover of individuals. Countries for which polling data are unavailable are not included in our dataset. In total, the dataset has over 13,333 polling observations, which are aggregated into 2,552 election-month polling observations for the incumbent party. Hence, on average, each election has about 12 election-month polling observations.

In addition to voter intentions, we include data
**MONGOLIAS INFORMAL SECTOR** -

**Notes**

**56**

1 9.For simplicity, some categories were grouped together. For example, the Annual Labor Force Balance distinguishes between those who are watching children under three years old, those watching children greater than three years old, those caring for old people, and people who are not working because of a temporary illness. These were all grouped together in the informal sector surveys. Some categories in the Annual Labor Force Balance (e.g., "released from prison," and "because of the peculiarity of spouse's work") were omitted from the informal sector surveys because they consistently represent small fractions of the not-employed. However, survey respondent who selected the "other" category wrote a qualitative response, and whenever the explanation matched one of the omitted categories, the response was recoded. So, responses of "other" on the informal sector surveys mirror very closely the category of "without any reason" on the Annual Labor Force Balance.

# 20.The statistics quoted in this paragraph exclude moonlighters.

21 .Among the subset who report to the _duureglsoum_ that they are not employed, 20% to 30% say that the reason they provide is "other" which ultimately gets placed into the category "without any reason." It should also be noted here that 60-75% of the informals who consider themselves not-employed have never been registered at the ERO. This is appropriate, since they are clearly economically engaged, but it serves as further evidence that participants in Mongolia's informal sector are largely omitted from both employment and unemployment statistics.

22.References to the household survey refer to the months of October and November, 1996. "Formal Income" is defined to include wages and salaries, pensions and benefits, rental income, loan income, and aid. "Informal Income" is defined to include income from selling agricultural products and raw materials, income from selling non-agricultural individually made products, income
10  ** p<.05  *** p<.01

_Notes_ . This table reports estimates of the relationship between schooling, skills, and labor force participation for men and women in Pakistan. All the columns report mean regressions estimates. “Working” is an indicator variable equal to 1 if the respondent is currently employed. The cognitive skills index is the mean of the Urdu, Mathematics, and English scores computed using Item Response Theory with a two-parameter logistic (2PL) model. The socioemotional (SEM) skills index is computed using principal-component factor analysis. Column (1) includes all men in the sample. Among them, one has missing information for years of schooling. Column (5) includes all women in the sample. For columns (2), (3), (6), and (7), the sample includes men/women who answered the direct version of the questionnaire. Columns (4) and (8) exclude any currently enrolled individuals. All regressions include age and district fixed effects. Standard errors are in parentheses (clustered at the village level).

47
Figure B1: Distribution of Total Hours of Consulting (All Firms) <!-- Start of picture text --> 0 50 100 150 200 250<br>Total hours of technical assistance<br>40<br>190 hours<br>30<br>20<br>Number of firms<br>10<br>0<br><!-- End of picture text -->

**Source** : Program administrative data. Firms with no hours of consulting recorded are shown as receiving zero hours..

# **C Data and Measurement Details**

Our primary outcomes use annual data on exports from 2010 to 2020 provided by the National Directorate of Taxes and Customs (DIAN) and supplied to us by the Colombian National Planning Department (DNP). Our outcomes are defined as follows:

1. **Extensive margin: Export at all in the past year** : This is a binary variable, defined as one if the firm exports directly at all in the year, and zero otherwise.

2. **Number of Distinct Products Exported in the past year** : The number of different product categories exported in the past year, using the 6-digit product classification in the harmonized system for the Andean Community. This is coded as zero for firms that do not export, and is winsorized at the 99th percentile.

3. **Number of Different Countries Exported to in the past year** . The number of different countries the firm exported to in the past year, coded as zero for firms that do not export, and winsorized at the 99th percentile.

4. **Number of Distinct Product-Country Combinations Exported in the past year** : This counts the number of product-country combinations a firm exported to 50
distribution, though households with per-capita incomes above $20 per day (in 2017 PPP) were much less affected than the rest (World Bank 2022).

Within countries, evidence suggests the brunt of the burden from the COVID-19 labor market shock was borne by women, less educated, and urban workers (Bundervoet et al. 2022; Kugler, Viollaz, et al. 2021). The disproportionate impact on these population groups has also been corroborated in a number of countries that continued standard survey data collection, including T ̈urkiye (Aldan et al. 2021), South Africa (K ̈ohler et al. 2023), Brazil (World Bank 2022a), Vietnam (Dang et al. 2023), and India (Deshpande 2020). Similar patterns are also observed in advanced economies. For instance, harmonized data from the EU Statistics on Income and Living Conditions (EU-SILC) show that in Q2 of 2020 the risk of temporary layoffs or reduced hours declines with household income. Chetty et al. (2020), based on data from the United States, find that employment losses between January and April 2020 decreased monotonically with income, from 37 percent in the bottom wage quartile to 14 percent in the top wage quartile.

These differential impacts across population groups are in part because the ability of workers to continue jobs remotely is higher among better-educated workers, as a result of the digital divide and the cognitive nature of work for high-skill occupations. In the case of developing countries, some studies have analyzed task characteristics across occupations to estimate the share of jobs that can be done at home. Dingel and Neiman (2020) find the share to be increasing with country income levels, with fewer than 25 percent of jobs in Mexico and T ̈urkiye being amenable to working from home. World Bank (2020) uses data from India to estimate that less than 10 percent of jobs are amenable to working from home at the 70<sup>th</sup> and lower percentiles of the earnings distribution
likely to indicate relative income uncertainty. Table 6 reports the regression results for the full sample. The top of each column identifies the country, the number of observations, the mean of the dependent variable and the R<sup>2</sup> , which falls within the typical range for cross section regressions based on household surveys.

By and large, the effects of the standard determinants do not contain major divergences from the typical pattern found for developing countries. The estimated age profile is quite flat, with an econometrically significant but quite small non-linear effect. The positive age effect for older households may reflect memory of past deprivation [Bernheim (1991,1994)], leading to greater precautionary savings of individuals remembering the immediate postwar period. Compared with the marked U-curve agesavings relation obtained by Gregory, Mokhtari and Schrettl (1999), the age schedule is less pronounced for these three transition economies, perhaps reflecting, the much sharper decline in life expectancy for middle-aged Russians since the early 1990s.

> 7 With some exceptions, the literature on savings in developing countries tends to find a positive income elasticity [See for instance Mikesell and Zinser (1973), Giovanini (1983), Mason (1988), Gersovitz (1988), Collins (1991) and Deaton (1990,1995), among others].
As mentioned above, the model has two main emissions matrices, which track all emissions in the economy. The second emissions matrix tracks emissions in all economy activities (except deforestation, which is dealt with in the transition matrix described above), where emissions are associated to each productive sector and final demand, and can be of two broad types (sources): emissions associated to fuel use and emissions associated to the level of activity of each sector (like fugitive emissions in mining, or CH4 emissions in livestock for example). All emissions are accounted by the original GHG gases, and transformed to CO2 equivalents using the Global Potential Warming for 100 years (GPW-100) coefficients from the IPCC Fifth Assessment Report –SAR (IPCC, 2014). These emissions are in fixed coefficients in either fuel use or the level of sector activity.

# **4. Simulation strategy**

The TERM-BR model was used to perform a series of stylized simulations aiming to illustrate the importance of sectoral technological changes for deforestation, under different scenarios. The first step is to build a baseline from 2015 to 2030, which was used as a reference for the policy shocks. The period 2015-2018 is the historical period, when the model reproduces the observed pattern of the Brazilian economy. The policy scenarios start in 2019, the results of interest being the difference between the baseline and the policy trajectory.

Three main regional broad scenarios were used: a total factor productivity shock (increase) in Brazil, in the Legal Amazon states, and in the South/Southeast Brazil. In the first two cases, the productivity shocks were applied to different broad sectors, one by one: agriculture, mining, services and manufacturing. All activities within those broad sectors received the same productivity shock. An annual 0.5% increase in productivity was considered in the simulations, for each region and sectoral scenario.

# **5. Results**

Table 2 depicts the cumulative impact of a permanent 0.5 percentage point increase in the displayed variables
# Figure 3: Risks to LIC debt sustainability

# (A) Private sector debt

<!-- Start of picture text --> Percent of GDP<br>Non-LIC EMDEs<br>50<br>LICs<br>40<br>30<br>20<br>10<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

## (B) Median export concentration

<!-- Start of picture text --> 0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Non-LIC EMDEs LICs<br>Export concentration index<br><!-- End of picture text -->

# (C) Growth

<!-- Start of picture text --> LICs Oil-exporting Metals- Non-resource-<br>LICs exporting LICs intensive LICs<br>2017 2018 2019 2020 2017 2018 2019 2020 2017 2018 2019 2020 2017 2018 2019 2020<br><!-- End of picture text -->

# (D) Global commodity demand growth

<!-- Start of picture text --> Percent<br>5 Baseline projection<br>2010-16<br>4<br>3<br>2<br>1<br>0<br>Metals Energy Agriculture<br><!-- End of picture text --> Source: BP Statistical Review, Haver Analytics, United Nations, United States Department of Agriculture, World Bank, World Bureau of Metals Statistics.

A. Domestic credit to the non-financial private sector provided by commercial banks and, if data are available, by other financial corporations. Median debt, based on 148 EMDEs and 29 LICs.

A.B. Non-LIC EMDEs= Emerging market and developing economies excluding LICs; LICs= Low-income countries.

B
priced fuels with subsidized fuels?|
||Perception of the oil sector|Is it considered opaque, corrupt, politically well-connected, or a<br>state within a state? Are there scandals to do with large leakages in<br>subsidy delivery? If so, raising prices could be difficult if the public<br>is angry about corruption; on the other hand, it might be possible to<br>persuade the public that higher prices get to the source of the<br>corruption and help stamp it out. If a large oil producer, is the<br>countryan EITI member?|
|Social<br>protection|Safety nets|Is there an up-to-date database of beneficiaries? Is there an<br>administrative system in place to deliver benefits? Does the<br>government have a national identity and smart card system for cash<br>transfer to the needy? Does the government have safety nets that<br>can be scaled up in terms of benefits, coverage, or both to<br>compensate the vulnerable for higher oil prices in a way that would<br>be consistent with medium- and long-term goals for social<br>protection? Or will deployment of sound social safety nets require<br>considerable preparatory work and development?|
||Delivery of essential social<br>services|What is the state of primary education, primary health, access to<br>safe water, access to sanitation? Is the track record of delivery such<br>that the public would consider credible the government’s promises<br>ofputtingthe savings from subsidyreduction to better uses?|
|Reform steps|Sector structure and regulation|<br>Is the market sufficiently large to become competitive over time,<br>making price deregulation a realistic goal? If there is market<br>concentration, what are the main bottlenecks to breaking down the<br>market power of the incumbents—import terminals, refineries,