rafmacalaba/data-use-mentions-extended huggingface dataset:
Complementing this aggregate decomposition analysis, we provide a detailed decomposition of the mean gender gap, identifying the contribution of each observable covariate towards the endowment and structure effects. In contrast with the available microeconomic evidence, the detailed decomposition documents, within a partial-equilibrium framework, the relative quantitative importance of each factor in explaining the mean gender differential. This in turn facilitates further analysis to identify the causes of differences in key factors contributing to the gender gap so that the emerging insights could inform the design of policy interventions addressing the gender gap at its roots.<sup>11</sup> The second contribution of our study relates to the application of the decomposition methodology to distributional statistics beyond the mean through the use of recentered influence function (RIF) regressions. Since key contributors towards the gender gap might differ across farmer subpopulations of varying productivity levels, the RIF decomposition is a useful tool for tracing out the heterogeneity in constraints faced by farmers with different gender and productivity profiles, and thus, tailoring better targeted policies that are underlined by analyses that move beyond the “average” male vs. female farmer. Towards this end, we carry out (i) the aggregate decomposition of the gender gap at each decile of the agricultural productivity distribution, and (ii) the detailed decomposition of the gender gap at the 10<sup>th</sup> , 50<sup>th</sup> , and 90<sup>th</sup> percentiles. The paper also discusses the changes in the shares of endowment and structure effects as part of the aggregate decomposition, and the variations in the contributions of key factors towards the endowment and structure effects at selected percentiles.

Finally, the multi-topic and national-representative nature of our household survey data represents the third contribution to the literature on the gender gap in sub-Saharan Africa. The availability of geo-referenced household and agricultural plot locations also allows us to create synergies with geographic information system (GIS) data for the purpose of incorporating relevant geospatial variables into the modelling efforts.

There
through Video.” _The World Bank Economic Review_ , Volume 29, Issue suppl_1, 1 January 2015, Pages S226–S237 Berg, G and Zia, B (2017), “Harnessing Emotional Connections to Improve Financial Decisions: Evaluating the Impact of Financial Education in Mainstream Media”, _Journal of the European Economic Association_ 15(5): 1025–1055.

Berge, L. I., Bjorvatn, K., & Tungodden, B. (2011). Human and financial capital for microenterprise development: Evidence from a field and lab experiment. _NHH Dept. of Economics Discussion Paper_ , (1).

Bertrand, M., Karlan, D., Mullainathan, S., Shafir, E., & Zinman, J. (2010). What's advertising content worth? Evidence from a consumer credit marketing field experiment. _The Quarterly Journal of Economics_ , _125_ (1), 263‐306.

Bruhn, M., Legovini, A., & Zia, B. (2012). Financial Literacy for High School Students and Their Parents: Evidence from Brazil. _World Bank Working Paper_ .

Bruhn, M., Ibarra, G. L., & McKenzie, D. (2013). _Why is voluntary financial education so unpopular? Experimental evidence from Mexico_ (No. 6439). The World Bank.

Bruhn, M., & Zia, B. (2011). Stimulating managerial capital in emerging markets: The impact of business and financial literacy for young entrepreneurs. _World Bank Policy Research Working Paper Series, Vol_ .

Cardoso, A. R., & Verner, D. (2006). School drop‐out and push‐out factors in Brazil: The role of early parenthood, child labor, and poverty.

Cialdini, R. B., Trost, M. R., & Newsom, J. T. (1995). Preference for consistency: The development of a valid measure and the discovery of surprising behavioral implications
**5 Estimation Strategy**

In order to calculate the rawh.1 1<sup>s.tiiindex</sup><sup>**G**for each3-digitindustryandyearwe</sup> regate the plant-level employment data to the 3-digit industry and state level in each year weighting **by** sampling weights. To obtain the **EG** index for each 3-digit industry and year using Equation **1,** we make use also of a Herfindahl index for each industry and year calculated using plant-level data on market shares." Table 2 shows that the mean of the **EG** index rose from **1980** to **1990** and then declined in **1999.** Thus Indian manufacturing ;1.-. i.-'t1 during the 1980s and dispersed during the 1990s. Appendix Tables B.2 and B.3 present the ten most and least - 1.-. n.-.t1 industries in **1980** and the evolution of their **EG** indices over the next **19** years.

Our main specification modifies that used **by** Rosenthal and Strange (2001) based on two important considerations. First, the spatial distribution of manufacturing industry is a slow moving process. This is bound to be the case although both the raw **-** 1.-.fn.-:.-jij index **G** as well as the **EG** index are expected to respond fairly quickly to changes in their determinants. That is because these indices change not only due to entry/ exit of plants, but also due to changes in employment **by** existing plants. However, it does take time for existing plants to change location or for new plants to emerge in response to changes in policies or industry characteristics. Hence, we allow all our independent variables to affect 1-ii.
on the largest firms? This is where I believe time series analysis and structural modeling can play their most important role provided that _lots_ of data is collected on such firms. If we have daily production data on the same firm for a decent period of time pre-intervention, it is more credible to believe that we can approximate well its production function than is the typical case of only a couple of periods of data on many firms. Detailed data on a lot of different dimensions of firm behavior also allows for construction of a spatial and temporal causal chain, and testing of falsifiable predictions of competing theories.

16
This study combines a large set of near real-time indicators with the World Bank’s highfrequency phone surveys to assess the ability of the indicators to predict changes in a variety of proxies for household economic well-being. In addition to Google Mobility data, Google search trends, night-time lights, vegetation and pollution data are used to develop predictive models of trends in a variety of outcomes measured in household surveys. First, the study examines the ability of these real-time indicators to predict variation in the severity of the initial shock, as measured by the prevalence of self-reported decreases in income and job losses since the start of the crisis, recorded in the first survey wave that each country fielded between April and August 2020. Second, the study examines the ability of the real-time-indicators to predict variation within regions – typically states or provinces – over time as the impact of the crisis evolved.

While the use of real-time data is relatively new in economic research, several studies provide insight into best practices for predictive modeling and how this data can contribute to poverty research. Many real-time measures, such as Google mobility, night lights from satellites, nitrogen dioxide (NO2 ), and Google search of certain keywords, have the potential to predict traditional well-being measures. Google mobility has been recently used to predict industrial production growth (Sampi & Jooste, 2020) and economic growth (Putra & Arini, 2020) during the pandemic. The Google mobility index shows how visits to places are changing over time in each geographic region. The index is measured for six categories of places, including workplaces, residential, grocery and pharmacy, transit stations, retail and recreation, as well as parks. The data are not representative, however, as they are only collected from smartphone users that have location services turned on.

Economists increasingly use night lights detected by satellites as a proxy for economic indicators (Chen & Nordhaus, 2011; Gibson et al., 2021; Henderson et al., 2012; Ishizawa et al., 2017; Mellander et al.,
|Bulman et al., 2017|Yes (Table<br>A1.2 and<br>Text)|What is the relative importance of country vs. project<br>characteristics in driving aid effectiveness at the WBG and<br>ADB?|Country-level characteristics explain only 10-25% of project success, indicating an<br>important role for project-specific factors in understanding project outcomes. Shorter<br>project duration, the presence of additional financing and the track record of the project<br>manager in delivering successful projects are highly significantly correlated with project<br>outcomes. Findings do not significantlydiffer between the two institutions.|
|---|---|---|---|
|Buntaine and Parks,<br>2013|Yes (Table<br>A1.2 and<br>Text)|Does the influence of previously identified potential predictors<br>of successful outcomes in environmentally focused assistance<br>projects carry over to environmentally focused World Bank<br>projects’ outcome ratings?|<br>Three predictor variables had a robust relationship with outcome ratings: the<br>government effectiveness index and the IEG rating for quality of supervision showed a<br>strong and positive relationship; a project focus on preventing climate change or<br>protecting biodiversity (“global outcomes”) had a negative relationship|
|Caselli and Presbitero,<br>2021|<br>Yes (Table<br>A1.2 and<br>Text)|How predictable and effective are aid inflows to fragile states?|<br>Using a large data set of aid projects funded by seven large donors over 190-2012, the<br>authors find that a project implemented in a fragile state is about 8 percentage points<br>less likely to be successful than a similar project financed in another developing country.|
|Chauvet et al., 2010|Yes (Table<br>A1.2
natural capital share of total wealth below its human capital share, while this share increased in Niger. A large part of this was due to countries replacing their depleting reserves with new discoveries, plus if prices rise enough the value of those depleting reserves also rises even as their stock might decrease, reflected in increased natural capital wealth. The boom did not translate in a significant improvement of the asset portfolio in resource-rich Africa. Despite resource-rich countries benefited from additional income from their abundant natural capital, they did not translate into significant higher investment in produced or human capital compared to non-resource-rich African countries. Therefore, the concentration of wealth in resource-rich Africa remain as twice as much as in non-resource-rich African countries (Figure 25).

Figure 25. Sub-Saharan Africa wealth composition, 2004 vs 2014.

<!-- Start of picture text --> 16 100%<br>14 90%<br>80%<br>12<br>70%<br>10<br>60%<br>8 50%<br>6 40%<br>30%<br>4<br>20%<br>2<br>10%<br>0 0%<br>2004 2014 2004 2014 2004 2014 2004 2014<br>nonrr nonrr rr rr nonrr nonrr rr rr<br>Natural capital Produced capital Human capital Natural capital Produced capital Human capital<br>Thousand constant 2018 US$<br><!-- End of picture text --> _Note: the left panel shows the average composition of wealth per capita of resource-rich and non-resource rich Sub-Saharan Africa in thousand US dollars. The right panel shows the component of wealth share of total wealth, excluding net foreign assets. Source: World Bank staff estimates based on the Changing Wealth of Nations 2021._ When we limit this to just nonrenewable natural assets, seven countries had the largest share (more than 50%) of total wealth in assets including oil, natural gas, coal
agriculture) wage gaps represent important drivers of inequality, this set‐up allows to explain changes in distributions.

For the goods markets, equilibrium for domestically produced goods sold domestically is assumed through market clearing prices. And the small country assumption is assumed for export and import prices and thus they are exogenous, i.e. export levels do not influence international prices and import demand does not influence (CIF) import prices. For the factor markets, wages equate demand and supply of the various segments (agriculture and non‐agriculture) of labor markets with the assumption of full employment, i.e. a vertical labor supply. Capital supply is assumed to be fixed and mobile across sectors and a market clearing rental rate is calculated by the model. Finally, a sector‐specific factor, representing natural resources, is employed exclusively in production in the oil and gas sector; in the current version its supply is sensitive to the international price of oil, therefore its reduction triggers a reduction of the supply of the natural resource and some contraction of the output of the oil sector.

The version of the CGE model used here has a 2011 base year and relies on the Social Accounting Matrix for the Russian Federation and on bilateral trade flows from the Global Trade Analysis Project (GTAP) 9 database to calibrate initial parameters.

The scenario of the oil shock is implemented by reducing the exogenous international price of oil by 50 percent and a new equilibrium is calculated.<sup>7</sup> The results of the model, in terms of changes between the initial equilibrium and the oil shock one of: (i) prices (for food and non‐food items), (ii) wages (for the four labor market segments), (iii) unskilled labor migration from agriculture to non‐agricultural activities, and (iv) per capita consumption are passed to the household survey data and, in the microsimulation, a new hypothetical global income distribution is generated for the oil shock equilibrium.

# _The
|**COUNTRY**|Antidum<br>**(1)**|ping'<br>|Counter<br>(|vailing<br>2)|Safeguar<br>(3)|ds|
|---|---|---|---|---|---|---|
||1989-94|1995-98|1989-94|1995-98|1989-94|1995-98|
|**Pakistani_**|||||||
|Paraguay||.<br>|||||
|**Poru**<br>|**0.05**|<br>3.50%||.|||
|Philippines|0.06%|0.51%|||||
|**Seegal=**|||||||
|**Singapore**||0.02%|||||
|**South Africa**||0.76%°|||||
|**Sn Lanka**|||||||
|**Thailand**|0.05%|0.05%|||||
|**Tunisia.**||__<br>|||||
|Turkey|**0.45%**|<br>0.22%|_||||
|Uganda|||||||
|Uruguay|||||||
|Venezuela<br>|0.03%|0.12%|_|1|||
|Zambia||.|||||
|Zimbabwe|||||||

Explanation:

* Investigations based on WTO database. Frequency ratio relative to total tariff lines

** Number of Products, based on TPR's and WTO notifications

x =
well as the weak state capacity in the vast majority of countries in the developing world. National and subnational states generally do not provide tax data at the individual or firm level. There are some public opinion surveys that link specific tax behaviors to public opinion (Alm 2012), but these studies are conducted in wealthier democratic countries and do not provide leverage on explaining tax compliance in the developing world (e.g., nonOECD countries). Wealthier countries tend to have more capable states, less corruption, and higher government legitimacy than those in the rest of the world. We lack basic knowledge of tax compliance in countries where corruption is high, state capacity is fragmented, and governments must contend with low legitimacy.

Researchers have turned to survey data and laboratory experiments to better understand what factors condition citizens’ willingness to pay taxes without excessive coercion or high investment in state capacity because observational research has been so difficult to successfully carry out. James Alm has been at the forefront of research that uses laboratory experiments to better understand citizens’ levels of tax compliance. In an early contribution to this area, Alm and his co-authors report “experimental results provide strong evidence that individuals are more likely to respond positively - and so to increase their tax compliance - when faced with a public sector expenditure program that they select themselves and that they know enjoys widespread approval. In contrast, compliance suffers when individuals have no control over the use of their tax payments, when their taxes pay for public goods that are unpopular, and when they do not know the level of support for the government program” (Alm et al. 1993: 201). This experimental finding is in line with our expectation that the direct engagement of citizens in policy making venues is associated with a change in the policy environment, which then leads to changes in tax compliance.

Torgler conducted a field experiment among ordinary citizens in Costa Rica to side-step the criticism that too many lab experiments rely on college students. Torgler found evidence that participants were influenced by the
covering research territory that is more difficult to probe with household survey data. This is demonstrated in the themes identified above. many of which are issues that would not have emerged from the quantitative analysis alone. There is also a useful overlap, though, in the research domains of the PPAs and **1he** household surveys. Where the PPA research themes and VLSS data overlapped, findings from the quantitative surveys and the participatory research usually coincided closely - with a few notable exceptions. Where the findings from the PPAs and the VLSS do apparently contradict each other, there are clearly understood reasons for the difference in results. As such, the differences are not undermining for either the participatory or quantitative research. This opportunity to triangulate the findings from the PPAs with tbie quantitative data and the general consistency of findings gave both sets of information more credibility, but this reinforcement of key messages is doubtless more significant for 18
date of measurement of consumption and thus an investigation of the short-term impacts of typhoons occurring during the six month interval prior to the collection of household expenditure; and (ii) the possibility of investigating the medium-term impacts of typhons, i.e. the impacts of typhoons occurring in the first six months of the calendar year on the consumption of households in the second semester (bearing in mind that this may be biased due to attrition of households between survey rounds).

Earlier studies on the impacts of typhoons on different dimensions of welfare (Anttila-Hughes and Hsiang, 2013) have been hampered by the fact that they only had access to the annual averaged FIES consumption data, which, for the reasons mentioned above, are likely to be attenuated.<sup>12</sup> The use of household-level PCE data varying across two semesters of a calendar year as well as across calendar years 2003, 2006, 2009, 2012, and 2015, and across different regions, requires taking into account of the temporal (within year and across years) and spatial variation in the cost of living in the Philippines.<sup>13</sup> For this reason, nominal consumption in each

> 12 This is why Anttila-Hughes and Hsiang (2013) downplay the impacts on consumption and focus on capital losses the year <u>following typhoon exposure, as it seems unlikely that capital can be replaced immediately following a storm.</u>

> 13 The Philippines consists of 17 regions, 81 provinces and about 40,000 barangays.

10
risky sex, since there is now a potential loss of cash associated with risky behaviors. This theory assumes rational decision-making in the sexual domain (Philipson and Posner, 1995). The idea that individuals make tradeoffs between price and the riskiness of sex is consistent with Gertler et al. (2005) who find Mexican sex workers charge higher prices for sex without condoms, and Robinson and Yeh (2011) who find that sex workers charge more for anal sex. This of course does not indicate that individuals are _perfectly_ rational in such decisions, but lends credence to the idea that people do respond to sex prices. However, this price effect may be muted by the fact that not all risky behaviors will result in a positive STI test, so the expected loss may be lower than the reward value. In the RESPECT trial, approximately ten percent of individuals tested positive at each timepoint, thus a person of average risk who mixes with average risk partners could have an expected loss of only one-tenth of the reward amount. For example, the RESPECT study‘s higher cash reward amount of $20 might yield only a $2 ―price‖ of risky sex during a four month period, which by itself could be a weak spur to behavioral change.

# **_Neoclassical Income Effects_**

To the extent that health is a normal good, the rewards may change behavior through income effects, particularly with increasing value of cumulative repeated rewards. However, in many CM applications, the reward amounts are sufficiently small as to preclude neoclassical income effects. In the RESPECT study, the rewards over one year can be as high as 25% of mean annual earnings, which is a substantial amount. For some lower income women this could indeed ameliorate immediate economic pressures to engage in transactional sex, although there is mixed evidence on the size and even sign of the income effect on risky sexual behaviors. For men in particular, it is often hypothesized that higher income will lead to more transactional sex, which over time would
6 column of Table 2) and the GM field trials in India have been boosting yields by as much as 60 percent. More-recent commercial planting data suggest yield per hectare gains in India of more than one-third from adopting GM cotton varieties, and higher net profits despite the GM seed costing three times as much as non-GM seed (Qaim et al. 2006, Bennett et al. 2006).<sup>5</sup> Three GM cotton adoption simulations are presented below. The first one aims to measure the market and welfare effects of adoption that had already taken place by 2001 in the United States, China, Australia, and South Africa. In China’s case it was only about halfway through its adoption process as of 2001, so only a 2.5 percent TFP shock is applied in this case. The simulation is a negative one, in the sense that we examine how the world would have been had that 5 percent shock (2.5 percent in China’s case) not taken place.

That first simulation is then compared with two other shocks: one in which all other countries except the rest of Sub-Saharan Africa adopt GM cotton (and China completes its adoption process), and the other in which Sub-Saharan Africa also adopts. The reason it is worth examining separately the impact of adoption by the rest of SubSaharan Africa is that the region has a history of very slow adoption of new agricultural technologies in the 1970s and 1980s, and during the 1990s its investments in agricultural R&D grew only 1 percent per year and spending actually fell in about half the countries for which data exist (Science Council 2005). To reiterate, the TFP shock in these latter two simulations is also 5 percent except for India and Sub-Saharan Africa (excluding South Africa) where it is 15 percent and for China where it is 2.5 percent. The potential

> 5 There are also benefits from insect-resistant Bt cotton in terms of improved health for farmers (see Hossain et al. 2004