rafmacalaba/data-use-mentions-extended huggingface dataset:
surveys in the LAC region use an additional reweighting procedure to construct individual weights. These weights essentially adjust for survey non-response and calibrate the sum of weights to match those from pre-crisis survey or census data. For surveys outside LAC, household weights are used with no comparable adjustment for individual weights. Therefore, comparisons of individual outcomes like labor market impacts between the RDD surveys in LAC and the recontact surveys in SSA should be interpreted with care.

As a robustness check, we attempt to make individual-level outcomes observed in the HFPS data more representative of the broader population by applying inverse probability weights estimated using the Global Monitoring Database (GMD). The GMD is a harmonized database of household surveys used for official poverty measurement. For each country, we use the most recent available year from the GMD to estimate inverse probability weights based on age, gender, living in an urban area, and level of education where available. Using this type of inverse probability weighting is well-established in the statistics and economics literature.<sup>7</sup> In some countries, the urban and/or education indicators are not available. In these cases, we rely on the set of available variables for weighting. (Table 3 shows the available weighting variables for each country.) We pool the HFPS and GMD data, and run a probit regression of an indicator to estimate the probability for being in the HFPS data on the available set of weighting variables from the GMD, weighted by the survey-based household weights. The inverse of the estimated probability is the weight. This gives greater weight to observations that appeared in the HFPS sample despite having a low predicted probability of being included in it. This procedure only corrects for selection bias due to the few observed variables used to estimate the probability. In Section 4.5, we examine robustness and find that this correction generally makes little difference between our main results based on HFPS household weights and our weighting system based on GMD data.

> 7See, for example, Horvitz and Thompson (1952
headcount index could be reduced by the same amount with a budget which is 159 million to 361 million soles smaller; the poverty gap could be reduced by the same amount with a budget which is between 288 million and 353 million soles smaller; while the P2 measure, finally, could be reduced by the same amount with a budget which is 309 to 397 million soles smaller. Clearly, substantial savings can be achieved with geographic targeting in Peru.

## _Non-parametric density estimates_

Figure 3. 1, finally, graphs the non-parametric (kernel) density estimates of the log of PCE _after_ hypothetical transfers totaling 2.2 billion soles have been made to households in the 1997 LSMS. I do not show the comparable picture for 1994 because it is very similar indeed. To avoid cluttering the picture, and because outcomes are so similar across indicators, I present density estimates for the FONCODES index and the no targeting scenario only. A vertical line which corresponds to the log of PCE at the poverty line is also included. Figure 3.1 clearly shows that the left tail of the distribution for the no targeting scenario has more mass than the corresponding distribution for the FONCODES index: that is, the fraction of people with very low levels of income would be higher had transfers to districts been based simply on population rather than on the FONCODES index. By contrast, more mass is concentrated around the middle of the distribution when transfers are made using the FONCODES index. The cumulative density functions of log PCE (not presented, but available from the author upon request) show that the distribution corresponding to the FONCODES index first-order stochastically dominates the no targeting distribution up to the poverty line (the two distributions intersect at about the poverty line).

# **5. Conclusion**

Geographic targeting of social programs has become increasingly important in Peru. Expenditures in basic health, primary education, nutrition programs, and basic justice conducted by the regular line ministries through the Basic Social Expenditure Improvement Plan have generally been directed towards the poorest departments
l POLICY RESEARCH WORKING PAPER 1796

# Summary findings

Narayan and Pritchett construct a measure of "social capital" in rural Tanzania, using data from the Tanzania Social Capital and Poverty Survey (SCPS), a large-scale survey that asked individuals about the extent and characteristics of their associational activity and their trust in various institutions and individuals.

They match this measure of social capital with data on household income in the same villages (both from the SCPS and from an earlier household survey, the Human Resources Development Survey). In doing so, they show that "social capital" is indeed both capital (in that it raises incomes) and social (in that household incomes depend on village, not just household, social capital).

The magnitude of social capital's effect on incomes is impressive: a one standard deviation increase in village social capital increases a household proxy for income by at least 20 to 30 percent.

This is as great an impact as an equivalent increase in nonfarming assets, or a tripling of the level of education.

Data from the two surveys make it possible to identify some of the proximate channels through which social capital affects incomes: better publicly provided services, more community activity, greater use of modern agricultural inputs, and greater use of credit in agriculture.

- This paper a joint product of Social Development, and Poverty and Human Resources, Development Research Groupis part of a larger effort in the Bank to understand the social determinants of sustainable development. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Sheila Fallon, room N8-030, telephone 202-473-8009, fax 202-522-1153, Internet address sfallon(ciworldbank.org. July 1997. (41 pages)

_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
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# **3.7 Macro-economic Background**

Since the start of the adjustment program in 1986, a growing debate has emerged as to the efficiency of these programs to bring about sustainable growth, and as to their short and long term impact on poverty and basic needs fulfillment. It is much less clear what the impact has been on the poor and the social sector in particular. Initial claims of a strong negative impact were based on inadequate empirical evidence and a too limited conceptual approach. This section presents some macro-economic indicators to evaluate their change over the period 1985 to 1993.

**Table 3.1: Nominal and Real GDP Growth, 1985-92**

||**1985**|**1986**|**1987**|**1988**|**1989**|**1990**|**1991**|**1992**|**Average**|
|---|---|---|---|---|---|---|---|---|---|
|Nominal GDP at market prices<br>(N billion)|72.4|73.1|108.9|145.2|224.8|260.8|324.0|549.8|244.3|
|Real GDP at market prices<br>(N billion)|108.3|110.3|108.9|119.3|128.4|138.4|145.9|150.0|128.8|
|Real Growth rates|9.8|1.8|-1.3|9.6|7.6|7.8|5.4|2.8|3.8|
|(in percent)||||||||||
|Real
Fifth, since men generally hold leadership positions in trade unions, women may have less say here, let alone the impetus to start their own unions.<sup>21</sup> Sixth — and this point extends to all sectors — there is an “erroneous perception that women's income is supplementary rather than central to households’ well-being. Gender stereotypes perpetuated through social norms assign women to unpaid social reproduction roles and functions, such as primary caregivers in the family and as those responsible for household chores, leaving women with very little time to improve their skills or seek new work opportunities.”<sup>22</sup> Indeed, wage gaps between men and women persist even in rich countries.

_The Near Invisible Nature of Trade in Creative Output_ Although the creative sector is increasingly becoming recognized, it is not yet fully understood. In many countries this is the case.<sup>23</sup> Consider the following points: In 2012, Wasant Bharath, the Government of the Republic of Trinidad and Tobago’s Minister of Trade, Industry, and Investment remarked: “The creative industries sector's economics, operations and commercial potential is not fully understood in many countries around the world.” This is from a minister from a nation where a “‘study on the local music industry has shown that it is estimated to generate TT $169 million annually, [about USD$26.5 million] employing some 5,600 persons. [And where at] a national level, [the] Carnival remains by far the largest generator of revenues within the music/entertainment sector, [contributing] TT $154 million [about USD$24.2 million] and employing over 3,500 persons.’”<sup>24</sup> In <u>Creative Industries and Economic Evolution Jason Potts goes on to conclude: while</u> creative industries play an important role in the innovation process, this contribution is both under-appreciated and
4<br>CHINA 1,632,546 11.5 7.5 -3.1<br>BRAZIL-RUSSIA-INDIA 588,617 4.1 21.1 1.0<br>TURKEY 618,949 4.4 30.6 7.9<br>REST OF WORLD 3,596,671 25.3 13.7 -9.8<br>Total 14,198,908 100 10.7 -6.3<br>CHN<br>250 250<br>200 200<br>150 150<br>100 100<br>Trade to GDP (%), 9799 Trade to GDP (%), 0709<br>50 50<br>0 0<br><!-- End of picture text -->

* Compound average growth rate

Source: COMTRADE indication of the dependence of domestic producers on foreign demand and of domestic consumers and producers on foreign supply. There is a concave relationship between trade openness and per capita income: countries tend to trade more as incomes rise, but at a decreasing rate.

> 4 An OLS regression (using cross-country data from WDI) of trade/GDP on the log of per capita income (PPP current $), its squared value, cost of exporting (using a sub-indicator from Doing Business 2008-2010) and population, gives a predicted trade share of 65 percent. The actual trade share of less than 32 percent is therefore extremely low.

5
simply apply the contract terms in the table to calculate what average payouts would have been in past seasons, if the contract had been available (see Giné et al., 2007, for details). Historical daily rainfall data is available from 1970-2006 for the Andhra Pradesh contracts, and from 19652003 for the Gujarat contracts. These data are not available for three Andhra Pradesh stations, where payouts are based on automated rain gauges, or for Anand in Gujarat.

Calculated expected payouts range from 33% to 57% of premiums, with an average of 46%. Consistent with the generally higher price of financial services in developing countries, these levels are below those of U.S. auto and homeowner insurance contracts, where the payout ratios average 65-75%.<sup>5</sup> Giné et al., (2007) also show that the distribution of insurance returns on ICICI Lombard rainfall insurance contracts is highly skewed. Policies produce a positive return in only 11% of phases. The maximum return, observed in about 1% of phases, is 900%.

In Gujarat, sufficient rain fell in 2006 and 2007 that no payout was triggered. In Andhra Pradesh, every policy paid out at least once between 2004 and 2006. Some payouts were quite modest (Rs. 40 in 2006 for the Atmakur policy), while others were large (Rs. 1,796 in 2004 near Narayanpet). Using administrative data for all policies sold by BASIX in Andhra Pradesh from 2003 to 2009, Giné et al. (forthcoming) find an average ratio of total insurance payouts to total premiums of 138%. The difference between this figure and our historical estimated return may reflect unusual shocks such as the severe drought of 2009, or structural changes such as greater monsoon volatility (B.N. Goswami et al., 2003). Given the limited history of existing rainfall data and the skewness of the insurance return distribution, however, statistical tests of structural change are not likely to be powerful.
**Table 3: Summary statistics for the sample of large states (continued)**

|**Performance**|**Indicator**|**Number of**<br>**observations**|**Mean**|**Standard**<br>**deviation**|
|---|---|---|---|---|
||Share of manufacturing (share of working‐age population)|512|0.06|0.05|
||Share of other industries (share of working‐age population)|512|0.04|0.03|
||Share of services (share of working‐age population)|512|0.13|0.06|
|Economic structure|Share of medium‐size firms (share of firms)|512|0.0055|0.0033|
||Share of large firms (share of firms)|512|0.0008|0.0011|
||Diversification index (index)|512|16.0|6.0|
||Mineral production capacity (million metric tons per year)|512|58.0|482.0|
|Elt tt|Share of self‐employed (share of working‐age population)|512|0.40|0.13|
|mpoymen srucure|Share of wage workers (share of working‐age population)|512|0.27|0.11|
||Primary education (share of working‐age population)|512|0.17|0.07|
|Human capital|Secondary education (share of working‐age population)|512|0.04|0.03|
||Tertiary education (share of working‐age population)|512|0.01|0.01|
||Access to finance (share of total
most countries. This non-randomness can be partly controlled for by allowing fixed effects.

For this set of firm-level data we generate the necessary variables to estimate equation (13). We assume that flow variables (such as investment and depreciation) during period t are decided upon at the beginning of period t. Since accounting data only provides end-of-period data, we use end-of-period t-1 figures to construct variables at the beginning of period t.

To test for a difference in financing constraints between firms of different size, we split our sample according to firm size. As measure of firm size we use net sales, reported in US dollars for comparability across countries. We construct a small size dummy, 20
execution that covers more than half of the total municipalities of Brazil and represents over 40% of the country’s population. Our procurement dataset allows users to see information on specific tenders, such as the number, reserve price, and description of items being sold, the number of participants in competitive tenders, and the identity of participants and winning parties. On the budget execution side, the data includes information on each commitment, verification (an important step in the budget spending when buyers recognize that a good or service was delivered), and payments, again allowing users to see the identity of payees and the amount and dates of each step of the budget execution. In particular, we can compute the time to pay a particular transaction by using the time elapsed between the verification and payment stages.

The dataset we build is fully and publicly available on the Data Basis ( _Base dos Dados_ ) platform (Dahis et al., 2022).<sup>4</sup> The platform provides high-quality data at scale, with tools such as a curated search engine and an SQL-powered data lake where tables share a unified schema. The platform allows users to seamlessly query and merge hundreds of tables, across a variety of themes, directly on Google BigQuery.<sup>5</sup> All code used to generate our dataset is publicly available on GitHub.<sup>6</sup> We collect our data from State Audit Courts ( _Tribunais de Contas dos Estados_ , TCEs). These courts are independent institutions that supervise the public finances of the municipalities of their states. One important concern is the quality of the data – are municipalities providing accurate information on procurement and budget execution, or are they providing incomplete and selective information? We test the quality of our data by generating aggregates from our microdata and comparing them with information from the Brazilian Public Sector Accounting and Tax Information System ( _Sistema de Informações Contábeis e Fiscais do Setor Público Brasileiro_ , SICONFI), a dataset maintained by the National
experiment known as CVIP: Methodological Experiment on Measuring Cassava Production, Productivity, and Variety Identification. The CVIP fieldwork started in July 2015 and ended in August 2016. The data were collected on Android tablets, using a computer-assisted personal interviewing (CAPI) platform that was designed with the World Bank _Survey Solutions_ CAPI software.

The CVIP target universe included households cultivating cassava in five major cassava producing districts in northern, central, and southern Malawi, namely Nkhatabay, Nkhotakota, Lilongwe, Zomba and Mulanje.P9F<sup>10</sup> The selection of these five districts ensured representation from each agroecological zone and Agricultural Development Division (ADD), with the exception of Karonga ADD.P10F<sup>11</sup> Based on the last National Census of Agriculture and Livestock (NACAL) 2006/07, Nkhatabay and Nkhotakota were in fact the two districts with the highest percent of households involved in cassava cultivation, production and area under cultivation.

Following the district selection, the district agricultural development officers, the assistant agricultural development officers and the crop specialists in each of the five districts were consulted. The consultations aimed to obtain the APES-based cassava production estimates for the MoAIWD Extension Planning Areas (EPAs) within each district, such that cassava-producing EPAs were identified. Following the identification of these EPAs, the NSO cartography department identified the census enumeration areas (EAs) in each EPA.

Out of the universe of EAs in cassava-producing EPAs, 9 EAs were sampled in each district, with probability proportional to the EA-level count of households as captured in the 2008 Population and Housing Census (PHC). In each sampled EA, a household listing exercise was conducted in June 2015 to identify the universe of households cultivating cassava at that time. Out of that list, 28 households were sampled at random in each EA, and 7 sampled households were randomly assigned to 1 of 4 survey treatment that differed in the approach to data collection on production, as explained in section 3.1. Although the target was to have 1
|.00324289|
|Rainfall - 3o Princ. Component|.02245587|.00465159|.02228627|.00712442|
|Employment rate in 1991|.15742175|.14161712|.17703837|.2183038|
|crit. fn. test of overid. restrictions|----|----|3.819309|3.819309|
|Dependent Variable:<br>Delta ln Employment|Spatial GMM<br>Est.<br>(Cutoff = 1)|Spatial GMM<br>SE<br>(Cutoff = 1|Spatial GMM<br>Est.<br>(Cutoff = 2)|Spatial GMM<br>SE<br>(Cutoff = 2)|
|Intercept|-.91925794|.23347463|-.8959102|.25081733|
|ln Wage in 1991|.18708293|.02332669|.183212|.0248753|
|ln 5 to 15 over 15 to 55 yrs Ratio|<br>.19600923|.05612046|.19536939|.06081697|
|Proportion of Natives in 1991|.30085957|.07981306|.29273868|.09316006|
|Delta ln Market Potential|.30237946|.05999995|.30237827|.06949838|
|ln Teacher Qualification in 1991|-.008132|.00415266|-.0078909|.0047722|
|Homicides in 1991|-9.6619034|15.947038|-12.576632|24.664917|
|Employment in Farming in 1991|-.52499872|.29569033|-.53298384|.3420592|
|Bank Dummy|.00465381|.01249154|.00546396|.01298614|
|ln Population in 1991|-.02584312|.01101769|-.02346004|.01342835|
|Mean Temperature|-.006529|.00497929|-.00703587|.00608096|
|Total Rainfall|-.00004341|.0000258|-.00003918|.00003218|
|Rainfall - 1o Princ
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data whether to underreport or over-report labor costs depends on the relative cost of the income and payroll tax.

Tax evasion, however, is associated with uncertainty and incurs extra costs. For example, firms have to invest extra resources to make “double accounts”; under-the-counter transactions might hinder firms from fully utilizing public services such as legal and judicial systems, access to formal finance, et cetera. We assume the extra cost associated with tax evasion to be linearly dependent on the amount of profits underreported - with a coefficient , where , , denote the true sales/raw materials/labor costs while denote the reported sales/raw materials/labor costs to the tax office respectively. Therefore we assume that firms choose an optimal combination of sales, raw materials and labor costs reported to the tax office for retained profit maximization, taking into account the costs and benefits associated with tax evasion.

Since the tax authority conducts tax auditing with a budget constraint, only a small portion of the firms will be selected for tax audits. Firms know that the tax office faces a budget constraint and form a subjective (possibly firm-specific) perception of the efficiency of detecting underreporting by the tax office. Also they believe that the probability of detection increases with the extent of misreporting. More specifically, we assume that the subjective probabilities that the tax office will detect underreporting (or over-reporting) in sales, raw materials, and labor costs are respectively<sup>17</sup> : 17 The three probabilities are mutually uncorrelated but in reality we expect them to be correlated. Our specification also restricts raw materials to be over-reported. However, we’ve verified that it is possible to relax both of the restrictions and the same implied relationship between true and reported values still holds under suitable assumptions.

16
makers not only to understand the metric by which the benefits are measured, but also to make fully informed decisions. Unfortunately, cost data are reported far less systematically and less often than impact estimates. However, recent work has provided estimates across a range of studies (J‐PAL 2014). We use those estimates to demonstrate how our method – of translating learning gains into equivalent years of schooling and into increased potential earnings – could be extended to demonstrate the equivalent years of schooling (EYOS) per $100 and the benefit‐cost ratio of investment.

To convert test score gains into additional years of schooling or increased wages, we use data on the relative wages and the relative schooling of individuals with different levels of cognitive performance. A group of new, comparable international assessments measuring adult cognitive skills in low‐ and middle‐ income countries, the Skills Towards Employability and Productivity program (STEP), along with some other, stand‐alone surveys provide exactly that (Hanushek et al. 2015; Valerio, Sánchez Puerta, et al. 2016a; Díaz, Arias, and Tudela 2012; Aslam et al. 2011; Valerio, Sanchez‐Puerta, et al. 2016).<sup>5</sup> We find that across 5 STEP countries, one standard deviation gain in literacy skill is associated with between 4.7 and 6.8 additional years of schooling, depending on the estimation method. In other words, over the course of a business‐as‐usual school year, students learn between 0.15 and 0.21 standard deviation of literacy ability.<sup>6</sup> At the same time, one promising set of interventions – structured pedagogical improvements (or improving the quality of teaching with detailed guidance to instructors) – has an average effect size of 0.13 standard deviation, which means these interventions help students to learn what they would normally learn in between 0.6 and 0.9 years of business‐as‐usual schooling
held the jobs that were lost.

# 5.4 Projected Job Losses by Sector

In this sub-section we assess how well projections of job loss align across various methods and sources of data by sector of employment. Because neither survey (WBES and LFS) on which our job loss projections are based are representative at the sector level, we might not expect projections to be fully reliable at the sector level. However, since we must use sectors to link employers to labor force data, it is important to assess this

> 45 The HFPS does not contain variables permitting one to distinguish formal from informal jobs.

> 46 - Georgia LFS. https://www.geostat.ge/en/modules/categories/683/Employment <u>Unemployment</u> 47 The figures include all industrial sectors.

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