rafmacalaba/data-use-mentions-extended huggingface dataset:
almost unlimited degrees of freedom to _p_ -hack their way to his or her desired results.

# **5.2 Coefficient Ordering**

While the specification charts summarize a large amount of information in a compact form, it can be difficult to detect the extent to which the diamonds indicating EO product form a pattern or are patternless. To dig further into coefficient ordering, we present a series of figures which represent the order (ranking) of coefficients within a country, across models, by EO source using bumpline functions (Naqvi, 2024). These are presented for total seasonal rainfall in Figure 12 and mean daily temperature in Figure 13. Results for days without rain and GDD are functionally similar and so we present figures for those variables in the Appendix. Within each country panel, the x-axis (each column) presents a different outcome variable (value or quantity) and a different econometric specification (weather only, weather with FE, and weather with fixed effects and inputs). The y-axis (each row) represents the ranking of the coefficient size for each EO source within each regression. They are ordered from one to six, with one representing the largest coefficient and six representing the smallest coefficient. These results give greater insight to the changes in ordinality across specifications and across countries.

We first consider the findings from total seasonal rainfall (see Figure 12). Going country by country: in Ethiopia, we see that in the weather only specification, CPC and TAMSAT have large coefficients while ERA5 has the smallest. This ordering changes once FE are included, with ERA5 producing the largest coefficients and TAMSAT and CPC producing the smallest. In Malawi, the same trend does not hold. Rather, there appear to be trends based on the dependent variable, with ARC2 and TAMSAT producing large coefficients when the value of total farm production is the dependent variable and small coefficients when the dependent variable is quantity of maize yield. Thus, what drives heterogeneity in results in Ethiopia differs in Malawi. Results in Niger appear
. For Kenya, World Bank (2018a)_ _Note: The data for Ethiopia corresponds to 2010, for Ghana to 2012, for South Africa to 2010, for Tanzania to 2011, and for Kenya to 2015._

# 2.3. Direct taxes

Revenue from direct taxes (when measured relative to the economy’s size) is higher in Uganda than in Ethiopia, but lower than in Tanzania, Kenya, Ghana and South Africa (Figure 3b). Direct taxes are mostly levied on individual income and corporate income. PAYE is the main direct tax revenue source, accounting for twice as much as corporate tax revenue (Table 1). Uganda’s revenue from corporate income taxes (as a share of GDP) is notably lower than in all other countries appearing in Figure 2, attributable to costly exemptions and multiple deductions that lower the effective tax rate.

The personal income tax, pay as you earn (PAYE), is collected on a monthly basis and exempts individuals making less than 235,000 Ugandan shillings (UGX) per month,<sup>11</sup> employees of the armed forces and judicial services, pension incomes and allowances of members of parliament. Rates range from 10 to 30 10 For instance, cigarettes are charged an excise duty of UGX 55,000 per 1,000 sticks of locally manufactured soft caps (or UGX 80,000 per 1,000 sticks of hinge rids) and a VAT of 18 percent; whereas beer is charged an excise duty of 60 percent of the ex-factory price per liter (or UGX 1,680, if the latter is higher), and also a VAT of 18 percent (see URA, 2017 for a full list of tax rates).

> 11 About 10 percent of Uganda’s per capita GDP.

9
Third, the level of aggregation within occupational categories may introduce a bias. For example, in cases where the ISCO-08 code is only available at the 2-digit level (as it is the case in the analysis throughout this paper), we need to make assumptions about the distribution of workers across all the 4-digit occupations nested within a 2-digit one.<sup>22</sup> This paper makes the assumption that such nested distribution is identical to that of the U.S.<sup>23</sup> Table 9 analyzes—for the group of countries with 4-digit ISCO-08 data— the share of employment classified as green if one were to assume that such countries’ highest level of disaggregation is 1, 2, 3 or 4-digits. It shows that higher levels of aggregation lead to different estimates of the share of green jobs, in total and by type. At the same time, they lead to different country rankings. For example, Peru and Panama have 22 and 18 percent of jobs in green occupations according to the 4- digit classification, respectively, but such shares are 22 and 24 percent according to the 1-digit classification. To overcome this limitation, a potential extension of our methodology is to use the occupational structure of “similar” countries (instead of that of the U.S.) with 4-digit ISCO08 data to impute the green employment shares at the 2-digit level for countries without more granular occupational data.

> 22 Even though some countries in SEDLAC report the ISCO08 occupations at the 4-digit level, we decided to use the more aggregate 2-digit level since this paper is focused on cross-country comparisons. Thereby, using 4-digit classifications for some countries and 2-digit for others would introduce noise. However, the mapping of green occupations at the ISCO08 4-digit level that this paper develop is available for analyses at that level of disaggregation.

> 23 As mentioned in Section 2, employment shares are based on U.S. estimates, which are obtained from the 2018 Occupational Employment and Wage Statistics (OEWS)
Corporation is included (FY1990 figure) then sector remuneration becomes $681.8 million 6 If operating receipts from Singapore Broadcasting Corporation are included (1985 figure) then sector growth rate becomes 9.7% 7 If operating receipts from Singapore Broadcasting Corporation are included (1985 figure) then sector growth rate becomes 9.4% _Note_ : The creative content sector includes the following industries: IT development; publishing; motion picture production, distribution & allied services; radio & television services, theatrical producers, entertainment. IT development has been included in both the KIBS sector and the creative content sector _Sources_ : Singapore Department of Statistics (DOS). (Various years). _Economic Surveys Series: Business Services and Real Estate_ . Singapore: DOS. Singapore Department of Statistics (DOS). (Various years) _Economic Surveys Series: Community, Social & Personal Services._ Singapore: DOS. Data obtained from DOS 18
, Oka 2010b, and Polaski 2006). Shea et al. (2010) and Brown et al. (2016) find that the improvements in working conditions and factory compliance come through engagements with multiple stakeholders as part of the program in Cambodia. Bair (2017) show similar results for Nicaragua. This stand of literature relates most closely to the three additional research questions of this paper.

Two papers examine transparency through public disclosure and compliance in Cambodia, which relate most closely to the primary research question of this paper. First, using firm‐level data from Cambodia, Ang et al. (2012) find that the end of public disclosure in 2006 adversely affected compliance. The authors also use linear probability modeling to test if compliance in different compliance categories is correlated with a binary indicator during the period of public disclosure. The authors also test for differences in compliance performance of firms that sell to reputationally sensitive buyers. Their results suggest that the threat of public disclosure of noncompliance induces compliance, even in factories lacking reputation sensitive buyers. In the post‐public disclosure period, all groups of factories maintained a significant record of compliance, but the propensity for improvement fell.

Second, Robertson (2017) exploit a policy reversal in Cambodia when in 2014 Better Factories Cambodia announced the return to public disclosure to study changes in compliance trends before and after the policy change. The paper evaluates the change in compliance in Cambodian garment factories using different empirical approaches: estimating endogenous trend breaks in compliance, probit regressions, and survival analysis. Overall, the results found in Robertson (2017) are inconclusive. Compliance was generally falling prior to the change. Controlling for year effects in probit regressions, the author finds 5
may have important measurement error, leading to attenuation bias in the estimated coecients. This should be considered for the quantitative interpretation of the results below.

Data on nominal and real eective exchange rate at monthly and annual frequencies come from the International Monetary Fund 2008, and data on the development of foreign exchange derivatives in dierent countries, measured by the ratio of average daily turnover to total GDP and total exports come from the Bank of International Settlements (1998, 2001, 2004, and 2007) Triennial Survey of Central Banks. Since this survey is conducted only every three years, the average value across available years is considered as representative country value. Data on a country's degree of nancial development come from Beck et al. (2000) (2006 revision).

The correlation between the annual growth of dierent international good prices and the growth of the nominal exchange rates of the countries in our sample was computed as described in section 2, using the data detailed above. This procedure yields a matrix of estimated correlations where each row corresponds to a 4-digit SITC good, each column corresponds to a country, and each entry is the estimated time-series correlation between the growth rate of the price of the good described in the row and the country described in the column. The distribution of the estimated correlations is shown in Figure 2. It shows that while the correlations cover a broad set of values, the mass of the distribution is shifted to the negative side. There are 57484 correlations in the database across 129 countries and 749 commodities with available data. Since our sample covers at most 17 observations, most of the estimated correlations are not statistically signicant, in fact only 22 and 32 percent of the estimated correlations are signicantly dierent from zero at the 10 and 20 percent level, respectively. This introduces an additional source of noise that should make even more dicult nding any signicant result.

The 20 country-good pairs with the most positive and negative correlations are reported in
_Figure 5 Age effects in the household saving rate, by categories of households_ <!-- Start of picture text --> Household saving rate, by Household saving rate, urban vs.<br>consumption quintile rural<br>0.35 0.3<br>0.3<br>0.25 0.2<br>0.2<br>0.1<br>0.15<br>0.1<br>0<br>0.05<br>2528313437404346495255586164<br>0 -0.1<br>-0.05 2528313437404346495255586164<br>-0.1 -0.2<br>Age of the household head Age of the household head<br>Q1 Q2 Q3 Q4 Q5 urban rural<br>Age increments (rel. to age 25) Age increments (rel. to age 25)<br><!-- End of picture text --> In keeping with saving rate patterns, patterns in household wealth accumulation are similar across consumption quintiles 2 through 5 (Figure 6, upper panels). Residential wealth increases at similar rates. Financial wealth stagnates early on, then picks up after age 45, and levels of debt decline steadily. Patterns differ somewhat for the first consumption quintile: residential wealth growth is markedly slower, even expressed in terms of their lower consumption levels. As we describe below, this appears to correlate strongly with the urban/rural divide.

Urban and rural households exhibit the same rate of growth in residential wealth, but their financial wealth evolves differently (Figure 6, lower panels). Urban households improve their situation steadily by increasing their financial wealth and reducing their debt throughout the life cycle. Rural households see a deterioration of their financial wealth and reduce their debt at a slower rate. Several explanations are consistent with these patterns. Rural households may be exposed to more frequent or damaging shocks early in the life cycle than the urban households, depleting
Policy Research Working Paper 9240

# **Abstract**

There is a crisis of demand brewing around the globe as social distancing becomes the norm to counter the COVID-19 outbreak. So, which parts of the economy are most in the line of fire? Looking at jobs that can be done at home or that require a high degree of face-to-face interactions with consumers can capture complementary but distinct mechanisms to assess this vulnerability. This paper uses data on 900 job titles from the Occupational Information Network (O*NET) database for the United States to demonstrate that there is substantial heterogeneity in vulnerability across industries, income groups, and gender. First, industries vary in whether they emphasize face-to-face interactions and home-based work and the two do not always go hand-in-hand. Second, occupations that are less amenable to home-based work are largely concentrated among the lower wage deciles. Third, a larger share of women’s employment is accounted for by occupations that are intensive in face-to-face interactions.

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 bavdiu@worldbank.org and gnayyar@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
for school size, in terms of number of students, are considered in columns (3) to (5). The “ _DD_ , Results _−_ Based Financing" shows the _RBF_ impacts. The “ _DD_ , Teaching Assistance” refers to the effects of introducing the technical assistance component and “ _DD_ , Results _−_ Based Financing, re” shows _DD_ estimates reformulating the RBF mechanism. The baseline difference at the border is _β_<sup>_pre_</sup> . Standard errors are clustered by municipality and shown in parenthesis.

on curriculum coverage is even stronger for teachers in 9<sup>_th_</sup> grade. In addition to the 8 _._ 1 _pp_ increase during TA periods, teachers were 11 _._ 6 _pp_ more likely to cover 80% of the curriculum after RBF reformulation. However, reinforcing this argument, an opposite movement is observed in column (5). It shows a 3 _._ 6 _pp_ decrease in the percentage of 5<sup>_th_</sup> grade teachers reporting covering less than 40% of the curriculum during the TA periods, and 2 _._ 7 _pp_ less likely among 9<sup>_th_</sup> grade teachers after RBF was reformulated. In the same way that textbook quality plays a prominent role in explaining the gains in performance for students in 5<sup>_th_</sup> grade, greater curriculum coverage by teachers seems to be an important driver for learning improvement in 9<sup>_th_</sup> grade.

Figure 6 complements Table 5 by showing that _Ceará_ is distributing textbooks where they are scarcer. Using school principal questionnaires from SAEB and plotting the _DD_ coefficients from Eq. (2), Figure 6 shows no change in the provision of pedagogical materials by the municipal council (blue bars), but significant decreases in the incidence of schools reporting a lack of text36
58. In general, housing and real estate property represent an important share of household wealth in total household net worth.<sup>38</sup> Wealth effects from the real estate market and the recovery of the equity market made households feel richer, and hence, consume more. Financial innovations allowed households to refinance their mortgages, and capitalize their wealth gains in the housing markets.

59. Consumption, as well as household debt, increased rapidly during this period. Figure 38 shows that in 2001, U.S. household debt as a percentage of household disposable income was 100 percent. By 2007, it had increased to 132 percent. This 32 percentage point increase in household debt was mainly used to support household consumption. The boom was not only limited to the housing market. The U.S. stock market also increased by more than 50 percent from June 2003 to October 2007.<sup>39</sup> Figure 39 shows the evolution of households‘ net worth relative to disposable personal income. Disposable personal income has been constantly increasing over 1986-2009. However, net worth fell from 1999-2002 to rise again from 20022006. Hence, during 2002-2006 households saw their wealth increase as a consequence of soaring housing and equity markets.

**Figure 38: Household Debt as a Percentage of Personal Disposable Income**

<!-- Start of picture text --> 130%<br>120%<br>110%<br>100%<br>90%<br>80%<br>Sources: Bureau of Economic Analysis Data (2009), Federal Reserve, Flow of Funds Account (2009).<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text -->

> 38 Data from the Flow of Funds Accounts of the United States of the US Federal Reserve indicate that the share of real estate in total net worth for
important factor as to whether events are likely to be affected by recall decay. Bradburn et al. (2004) identify three factors that determine salience: “(1) the unusualness of the event, (2) the economic and social cost or benefits of the event, and (3) the continuing consequences of the event” (p. 64). Loftus and Marburger (1983) assess the improvement in accuracy of retrospective accounts when surveys use a highly salient landmark event (e.g. the eruption of Mt. St. Helens) to mark the beginning of the reference period. In their study on migration, Smith and Thomas (2003) find that migration events that are of greater salience to the respondent are less likely to be affected by recall decay. Relating specifically to agriculture, Judge and Schechter (2009) test the distribution of the first significant digit of various data to detect abnormalities. Using Benford’s Law, they find evidence of the importance of salience in data quality from an agricultural survey in Paraguay, specifically that more accurate information is recorded for crops that represent a larger share of household income.

Second, recall decay can affect the details of events reported by respondents. Much of the research in this area relates to consumption experiments, which focus on the reliability of reports of consumption or expenditure on food and non-food consumer goods. One exception is Coleman (1983) who addresses recall decay in agriculture through comparisons of the mean number of reported labor hours for each day during the week prior to the interview. Using a dataset of 129 households in 12 villages in Benue State, Nigeria from 1979/1980, he takes as a reference point the mean number of hours reported for the day before the survey. With each additional day from the reference point, he finds a consistent and significant over-reporting of labor hours, ranging from 17.5 percent on the day before the reference day to 61.6 percent on the first day of the recall period. His finding with regard
authorized broker-dealers registered at the exchange and buy and sell orders meet via an automated trading system. In contrast to fragmented markets in the United States and Europe, the BVC is the sole authorized trading venue for Colombian stocks, which means that we can observe the universe of transaction of Colombian stocks and avoid biases from incomplete reporting (e.g., from multiple trading venues such as multilateral trading facilities, dark pools, or from rival exchanges). Also, at the time of each addition/deletion, none of the stocks in our sample had dual listings, nor were they traded through global depositary receipts. Thus, all the transactions around rebalancing events, and hence, any change in ownership for stocks in our sample are reported in the BVC trading records.

symbols and announcement dates for each episode are given in Appendix Table OA1.

8We apply the ratio for the MSCI Emerging Markets Index also to the MSCI EM Latin America Index and the MSCI EM Colombia Index.

5
international migrants. _Journal of Development Economics 95_ (1), 42–57.

- Gross, D. M. and N. Schmitt (2003). The role of cultural clustering in attracting new immigrants. _Journal of Regional Science 43_ (2), 295–318.

- Hatton, T. (2005, November). Explaining trends in UK immigration. _Journal of Population Economics 18_ (4), 719–740.

- Mattoo, A., N. Rocha, and M. Ruta (2020). _Handbook of deep trade agreements_ . World Bank Publications.

- Mayda, A. (2010, September). International migration: a panel data analysis of the determinants of bilateral flows. _Journal of Population Economics 23_ (4), 1249–1274.

- McFadden, D. (1973). Conditional logit analysis of qualitative choice behaviour. In P. Zarembka (Ed.), _Frontiers in Econometrics_ , pp. 105–142. New York, NY, USA: Academic Press New York.

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. Importantly, the Family 500+ benefit is designed to be on top of any other existing social assistance benefit received by the household (such as family benefits, housing allowance, other social assistance, or payments from the alimony fund) and will not influence the eligibility to these aforementioned programs. Eligibility into the program for farming households, will depend on the amount of land owned by the family and the standardized income per hectare announced by the Polish Central Statistical Office.

In order to simulate the distributional impacts of the Family 500+ program, we classify household members into families using the program eligibility rules.<sup>17</sup> As described earlier for the analysis presented above, sampling weights from the HBS were calibrated as in Myck & Najsztub (2015) in order to account for differences on the number of children between the HBS and the census. Moreover, since the survey reports income at the household level, in order to obtain family per

> 17 A family includes the parents of the children, the spouse of the parent, caregiver of the child, and children up to 25 who reside with the parents and who are economically dependent on the parents. A family may also include children older than 25 provided that these are disabled and due to their disability are eligible for caregiver allowance. Children will not be considered as a part of the family if the child is married, or if the child is older than 18 and already has a child of his/her own.

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