rafmacalaba/data-use-mentions-extended huggingface dataset:
# 2. Data and descriptive evidence

## a. Data and measurement

Our worker-level dataset covers 47 countries at different development levels (Table A4 in the Appendix). Most of the country coverage comes from the OECD’s Programme for the International Assessment of Adult Competencies – PIAAC (2019). During three rounds of the study (2011-2012, 2014-2015, and 2017-2018), data were collected in 37 countries. The sample sizes amount to a few thousand 16-65 years old individuals. We complement PIAAC with the Skills Towards Employment and Productivity – STEP (World Bank, 2017) survey data from nine low- and middle-income countries. The STEP data were collected in 2012-2014 among urban residents aged 15-64 and covered a few thousand respondents in each country. We also use the “skill use at work” module of the third wave of the China Urban Labour Survey (CULS, 2017), which directly implemented the STEP questionnaire and ensured comparability with other countries in our sample. The survey collected data from six large cities in China (Guangzhou, Shanghai, Fuzhou, Shenyang, Xian, and Wuhan) and covered about 15,000 individuals.

Following Lewandowski et al. (2022), we create a worker-level task content measure of occupations across countries in the spirit of Acemoglu and Autor (2011). As the STEP surveys are urban surveys, for comparability we omit farmers and skilled agricultural workers (ISCO 6 from the sample in all countries. For methodological details, see Lewandowski et al. (2022). We calculate the worker-level routine task intensity according to the following formula: where, rcog, nranalytical, nrpersonal are routine cognitive, non-routine cognitive analytical, and non-routine cognitive personal task levels. Table A1 in Appendix A enlists survey items used to construct these task measures. Particular task measures and RTI are standardized using their mean and standard deviation in the United States.

We use hourly wages in US dollars, adjusted for purchasing power parity, with a 99% winsorization. Wage data are available for 38 of the
office to find out who owns a piece of land in your community, how likely is it that you could get this information?’ _Fraudulent registration (ordinary and rich)_ tabulates the share of respondents who think it is very likely for an ordinary or a rich person, respectively, to ‘...pay a bribe or use personal connections to get away with registering land that does not belong to them’ _Women’s rights_ is the share who ‘agree or strongly agree’ with the statement ‘Women should have the same rights as men to own and inherit land’. All data are based on the Afrobarometer Survey Round 7.

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the supplementary evidence from the Sargan test of the over-identifying restrictions. Robust standard errors in parentheses; asterisks indicate statistical significance at the ***1, **5, and *10 percent level. Data for the transition economies refer only from 1997 onwards. All equations include time period and group-specific dummies.

The next step, therefore, is to assess the role of financial development and financial frictions. Financial development is examined by adding indicators of financial depth into a growth equation and exploring their impact on the underlying foreign savings-growth link among the EU-specific country groups we have highlighted throughout this paper. The assumption is that only after a certain threshold of financial depth is reached are foreign savings good for growth. More precisely, once such a threshold has been exceeded, EU-specific foreign savings for the EU10 and EU candidate countries should no longer be important. If such thresholds do not exist, however, it might then be the case that financial frictions are more important. In other words, it may be institutional development what makes the effective use of foreign savings possible. Again, if thresholds in indicators of financial frictions are included, then this should 18
electricity services, we assign a value for _S0_ in eight missing industrial sectors following intuitive criteria.<sup>15</sup> Table 7 presents some summary statistics on these two subsamples of firms.

One standard worry with firm surveys is the potential non-response bias, as some firms may not respond to specific questions. Overall, non-response is more frequent among small and service firms, and this is also the case when looking at missing data on generator ownership. This is a standard observation in firm surveys, the main reasons including lack of time and/or information by the respondant—a situation more likely to occur in small firms. Because of the way generator ownership is distributed among firm size categories, this may affect our estimates, even if these non-responses are not driven by strategic considerations at the firm-level. While we have no systematic way to address this problem, results not shown here to save space indicate that most of our conclusions below are robust to excluding small firms and all service activities respectively.<sup>16</sup> The results from estimating equation (8) are shown in Table 8. All specifications include country, industry and year fixed effects, and standard errors are clustered at the countryindustry level. We introduce in columns 1 and 2 the number of power outages and its square. Column 1 reports the results with only fixed effects, while column 2 introduces firm-level controls (age of firm, location, whether it exports or not, whether it has foreign capital or not, and size). To the extent that more power outages are likely to lead to more generator ownership, we expect the coefficient for the number of outages to be positive, and this is indeed what we obtain. The square value of the number of outages should be negative to match the concavity stressed in the theoretical model, and indeed we find that its coefficients are negative and significant in both columns. The threshold values indicated below mean that

> 15 A ―core‖ set of sectors is systematically included
br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>Private Consumption<br>Investment<br>40<br>Export<br>30<br>20<br>10<br>0<br>China US Euro Area<br><!-- End of picture text -->

- **D. Spillovers to EMDE output growth from decline in Euro Area output growth**

<!-- Start of picture text --> Percentage points<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>-3.0<br>1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text -->

- **F. Spillovers to EMDE investment growth from decline in Euro Area output growth**

<!-- Start of picture text --> Percentage points<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>-6.0<br>1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text --> Sources: Consensus Economics, World Bank estimates, World Input‐Output Database. A. Five‐year ahead Consensus Forecasts as of the latest available month in the year denoted.

C.‐F. Cumulative impulse response of weighted average EMDE output growth (C.D.) or investment growth (E.F.) at 1‐8 quarters to a 1 percentage point decline in
extract from a group of variables a few orthogonal linear combinations of the variables that capture the common information most successfully. The first principal component of the asset and housing condition indicators is the linear index that captures the largest amount of information, so this study uses that as the measure of wealth. Filmer and Pritchett (2001) demonstrate the construction and internal validity of this index as a measure of wealth.

The assets included in the construction of the index include ownership of mortar, bed, table, chair, fan, air conditioner, radio, CD or cassette player, television, sewing machine, stove, refrigerator, washer, bicycle, motorbike or vehicle, drum, sofa, coffee table, cupboard, lantern, desk, clock, iron, computer, satellite dish, solar, generator and cellphone. The housing condition included in the construction of the index include the material (permanent or not) of which the dwelling, outer walls, roof and floor are made of; the source of lighting (electricity or otherwise) in the house; the source of water (pipe or otherwise); the kind of toilet facility (water closet or otherwise); number of rooms in the house; and number of rooms per capita. Following Filmer and Pritchett (2001) and Dzanku (2015) households within the top 60% of the distribution of the wealth index are classified as non-poor, and those within the bottom 40% are classified as poor. Using the same logic, households with the bottom 16% are classified as ultra-poor.<sup>7</sup>

# **Data and Sample Selection**

The study uses the two-wave Malawi Integrated Household Panel Survey (IHPS) data collected by the collected by the National Statistical Office of Malawi (NSO) with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA)

> 7 In order to test the sensitivity of the estimates to the benchmarks used for the asset poverty classifications, we also classified farmers into quintiles
the selected baseline variables. We use the resulting coefficients to estimate the probability that each woman in our sample responds to the SMS survey. We weight each woman’s responses to the SMS survey by her estimated inverse probability of responding. Throughout our analysis of the SMS data, we show results using both weighted and unweighted responses.

# **5 Results**

Our primary estimates of interest are intent to treat effects for a range of prespecified outcomes that shed light on how effective the program is at promoting women’s economic inclusion.

## **5.1 Program Impacts on Skills**

Given the nature of the program we study, we first examine program impacts on skills to determine whether women are implementing the practices taught in the entrepreneurship modules, and for how long. Columns (1) and (2) of Table 1 show that the program has initial impacts of 28%–38% on our two indices of “hard skills”. The first is an index of business tracking, which combines a number of questions about record keeping and inventory management. The second is an index of price management that combines questions about price negotiations with suppliers, research on competitor pricing, and efforts to attract customers through promotions. Effects survive multiple inference corrections at midline but decline in size and significance at endline. We also examine changes in work hours and find large increases at midline and smaller increases at endline that do not survive multiple inference corrections.

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GBIF data increase.

In this paper, we draw on the previous work to explore some implications for 30x30 in a sample of 10 countries in Latin America, Africa and the Asia-Pacific region. We focus on individual countries to highlight the role of conservation stewardship in local settings. Our country analyses begin with current protected areas, which exhibit great cross-country variation in territorial coverage and representation of species taxa. We consider both terrestrial and marine cases, focusing on species that are endemic to each country. Drawing on our GBIF spatial database for nearly 600,000 species, we overlay species occurrence maps on the most recent protected-area maps from the World Database of Protected Areas. This enables identification of endemic species whose coverage by existing protected areas is either non-existent or extremely sparse.

Our approach departs from many previous exercises by giving equal weight to all vertebrates, invertebrates, plants, and other species whose occurrence regions are mapped in our database. We use a spatially-efficient algorithm to identify a hierarchy of priorities for localities where new

> 1 Pimm, S. L., Jenkins, C. N., Abell, R., Brooks, T. M., Gittleman, J. L., Joppa, L. N., ... & Sexton, J. O. 2014. The biodiversity of species and their rates of extinction, distribution, and protection. Science, 344(6187), 1246752.

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PovcalNet continues to rely on such data.<sup>10</sup> To obtain a consistent number of observations per country-year, we fit a parametric Lorenz curve to the quantile groups and build a data set of percentile groups throughout.<sup>11</sup> The Appendix includes a robustness check which uses the raw quantile data without fitting a parametric Lorenz curve.

Each percentile group is population-weighted in the analysis, so every person is assigned the average consumption expenditure of her percentile in the country-year distribution. Welfare is measured using per capita consumption expenditure (or income), expressed in 2011 PPPadjusted USD (see methodological details in the Appendix). Because we compare consumption levels across countries it is important to account for differences in purchasing power across countries using purchasing power parity (PPP) exchange rates. For the same reason, welfare aggregates could also be adjusted for spatial price differences within countries, as recently discussed by Ferreira et al. (2016) in the context of estimating global poverty. In the baseline results, we follow the approach adopted in PovcalNet (Chen and Ravallion, 2010) and adjust for spatial price differences within China and Indonesia (see Appendix), but not in other countries in the region.<sup>12</sup> In the Appendix, we present robustness checks with further withincountry spatial price adjustments between 2002 and 2012, as well as using the older set of 2005 PPP exchange rates.

> 10 The number of quantiles per country-year ranges from 9 to 200, with an average of 71 across all years (and 36 on average in 1988).

> 11 We fit a log-normal Lorenz curve to the quantiles and use it to generate a distribution of 10,000 points. We use the ‘ungroup’ Stata routine (Abdelkrim and Duclos, 2007) which implements the Shorrocks and Wan (2008) method. As described in more detail in Lakner et al. (2014), this is very similar to the approach used by PovcalNet in the measurement of
C shows the average realized policy regret and the percentage of times the best arm is identified, both after wave 1 (where the experimenter would have chosen arm T1B) and after wave 2. Comparing the numbers for wave 1 with Panel A shows that the distribution of the simulated draws replicates the theoretical posteriors, as expected. The numbers for wave 2 show realized gains that more than halve the predicted policy regret of wave 1 and increase the share of optimal decisions by 10%. In the actual IVR experiment, had we conducted these calculations between waves, we would have likely concluded that the low monetary cost of sending the IVR calls to the second half of the sample would have more than justified the gains in certainty about the optimal choice. The actual IVR experiment performed even better than these simulations predict. A better prior for our parameters, for example based on pilot data, is likely to generate more reliable answers to research design questions.

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<!-- Start of picture text --> 250 MMR<br>200<br>150<br>CHN<br>LAO<br>100 CHN<br>MMR<br>KHM<br>IDN THA CHN<br>MNG MMR<br>MYS 50<br>MYS PHL THA IDN MNG MNG<br>PHL LA O KHM<br>LAO 0 THA PHL MYI DNS<br>-20 -15 -10 -5 0 5 KHM10 15<br>-50<br>Change in employment share (% of total) between 2000-2007 and 2008-<br>2016<br>Agriculture Industry Services<br>Source: World Development Indicators<br>2000-2007 and 2008-2016<br>worker (constant 2010$ USD, between<br>Percentage change of sectoral output per<br><!-- End of picture text -->

# **4. Misallocation across firms within a narrowly-defined industry**

Productivity analyses based on resource misallocation provide a new framework for policy makers and researchers. More conventional discussions about productivity center on technology and require a different set of policy prescriptions. For example, policies geared toward increasing technological diffusion and innovation include measures that promote research and development (R&D) and the creation of new ideas. These policies are usually complemented with those that incentivize learning, upgrading of skills, and investment in education. However, a complimentary approach focuses on the benefits of reallocation of inputs from less to more productive firms as an important component of aggregate productivity growth. Policies associated with allocative efficiencies emphasize correcting or reducing market distortions (see Box 1 for an example of power sector reform that disproportionately benefits firms and sectors with heavier electricity use).

What is resource misallocation in a narrowly-defined industry? In an economy with low levels of distortion, productive firms will have access to more resources—namely labor and capital— compared to less productive ones, leading to an increase in the overall productivity of
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36% and 100%. In terms of assets of nonbank financial institutions, the coverage of our sample is between 47% and 90%. Almost 42% of these institutions experienced distress after July 1997, i.e., were either closed, merged, recapitalized or had their operations temporarily suspended. By July 1999, 13% of all institutions in existence in July 1997 had been closed. This illustrates the systemic proportions of the East Asian financial crisis.

We document various financial data (balance sheet and income data for end- 1996) for distressed, non-distressed and closed financial institutions and analyze their ownership structures to explore whether these characteristics help explain distress and closure. Using the end-1996 financial data, we find that traditional, CAMEL-type variables-loan loss reserves to capital, loan growth, net interest income to total income, return on assets, and loans to borrowings-predict subsequent distress and closure well. Ownership data also help predict financial distress and closure. Foreign portfolio ownership decreases the probability of financial distress and none of the foreigncontrolled institutions was closed, while privately owned institutions were more likely distressed. "Connections"-with industrial groups or influential families-increases the probability of distress, suggesting that supervisors had granted selective prior forbearance from prudential regulations. Connections also make closure more, not less likely, suggesting that the closure processes themselves were transparent. Larger-sized intermediaries were more likely to become distressed, but less likely to be closed, suggesting a "TBTF" policy. The significant forbearance of already weak financial 2
sector as in the non-state sector<sup>10</sup> . Both the average capital and labor productivity are much higher in the non-state sector -- in 1997, nearly 100 percent and 60 percent more respectively. Over time, the average productivity of labor stagnates in the public sector, while it rises in the non-state sector (by about 50 percent over the four years), resulting in a greater divergence over time.

Second, the average productivity of capital has declined in both the state and non-state sector from 1994-97, with the fall in the state sector especially large. It is interesting to observe that in spite of (or perhaps because of) the decline in the efficiency of capital, there has been a fast rise in capital intensity in both the state and non-state sectors, which nearly double during the four years." Surprisingly, the capital to labor ratios in the two sectors is about equal.12 1' Since 1996, an increasing number of workers called "Xiagang worker" have been made redundant while remaining on the state firms' wage bill, typically receiving minimum wage and some social services. By the end of 1997, some estimated about 11.5 million (some millions are from industry). However, in China Statistics Yearbook, the Xiagang workers remain included in the formal labor statistics. This introduces some bias in our analysis, but there are no reliable state-level estimates on the actual number of Xiagang workers that we can rely on to remove this bias at this stage.

" Similar findings were obtained by Enos (1984) when he examined five small rural industries: iron and steel, chemical fertilizers, farm machinery, cement, and energy (coal and electricity).

12 This reflects mixes of small and large firms in both the public and private sectors, with the first owning millions of small and medium size firms in addition to a few thousands towering firms, while the latter is comprised of small TVEs and large joint-ventures. It is evident that TVEs has attracted most
Table B.3: Effect of Years of Schooling on Labor Market Outcomes and Fertility Preference – Replication of Table 4: Anderson-Rubin Weak IV Robust Confidence Sets

||||Sector of Wor<br>|k<br>||
|---|---|---|---|---|---|
||Working|Skilled /<br>Professional|Service /<br>Sales|Agriculture /<br>Unskilled Manual|Ideal Number<br>of Children|
||(1)|(2)|(3)|(4)|(5)|
|||Coef|cient on<br><br>Years of|�<br>Schoolingizy||
|Weak IV Robust 95%<br>Confdence Set|-0.018, 0.382<br>[0.093]|0.018, 0.218<br>[0.005]|-0.074, 0.198<br>[0.228]|-0.178, 0.014<br>[0.107]|—<br>—|
|First Stage F-Statistic|6.06|6.63|6.63|6.63|—|
|N|13,909|13,755|13,755|13,755|13,789|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : IV = instrumental variables. The dependent variable is described at the top of each of the five columns. In columns 1–4 it is an indicator that equals 1 if true, and in column 5 it is the ideal number of children. Skilled/Professional jobs include professional, clerical, and skilled manual job groups; the other categories exactly describe the occupation groups included. Ideal number
**Table 2 Risk indices for water and sanitation derived from household survey responses. Risk levels are presented in ascending order of risk.**

|**Risk level**<br>**Description**|
|---|
|Risk associated with household source of water|
|0<br>Piped to premises|
|1<br>Public standpipe/tap or borehole within 100m|
|2<br>Public standpipe /tap more than >100m from house|
|3<br>Well more than 100mm from the house|
|4<br>well within 1-10m of a pit latrine|
|5<br>well within 11-40m of a pit latrine|
|6<br>Surface water source|
|Risk associated with household access to sanitation|
|0<br>Flush to sewer no blockages reported|
|1<br>Flush to sewer (reported blockages)|
|2<br>onsite improved + unshared + not broken/leaking|
|3<br>onsite improved + shared + not broken/leaking|
|4<br>onsite improved + unshared + broken/leaking|
|5<br>onsite improved + shared + broken/leaking|
|6<br>unimproved/ambiguous + shared + not broken|
|7<br>unimproved/ambiguous + unshared + not broken|
|8<br>unimproved/ambiguous + unshared + broken|
|9<br>open defecation|

# _Development of a cholera risk map_

To generate a high resolution map showing spatial variation in underlying cholera risk across Lusaka, a log-Gaussian Cox Process (LGCP) model was developed (Diggle et al. 2013). LGCP models are appropriate when the data in question can be considered the result of a spatial point process, denoting the location of events (in this instance, cholera cases), and the objective is to infer an underlying spatial surface (a map) denoting how the intensity of occurrence varies across the
bet on TransCentury. Business Daily. http://www.businessdailyafrica.com/corporate/Why-US-based-fund-placed-a-big-bet-onTransCentury/539550-3914274-yp255t/index.html, accessed June 9, 2017.

Kenya Insurance Regulatory Authority (November 2015). Guidelines to the Insurance Industry on Management of Investment.

http://www.ira.go.ke/attachments/article/221/Draft%20Investment%20Guidelines.pdf, accessed June 1, 2017.

KPMG and EAVCA (June 2015). Private Equity Industry Survey of East Africa for the period 2007 to 2014.

Business Daily (October 28, 2015). Huduma centres push Kenya up in ease of doing business index. http://www.businessdailyafrica.com/news/It-is-easier-to-do-business-in-Kenya-World-Bank/5395462933230-blmhcx/index.html, accessed June 11, 2017.

Njini, Felix (May 11, 2017). Kenya's Largest Bank Expects Rate-Cap Removal in Second Half. https://www.bloomberg.com/news/articles/2017-05-11/kenya-s-largest-bank-sees-rate-cap-beingremoved-in-second-half, accessed June 11, 2017.

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