rafmacalaba/data-use-mentions-extended huggingface dataset:
market share of global manufactured exports and imports from about 20 and 35 percent in 1991/92 to 44 and 46 percent in 2007/08 respectively.

Section B explains the methodology and the data. In Section C, the shares of total imports from the rest of the world and their decomposition are analyzed separately for the selected industrial and developing countries. Section D focuses on exports from developing countries to industrial and other developing countries, and extends the analysis to 3-digit ISIC sectors. Very rapid growth of China, especially during the 2000s distorts most of the conclusions and analyses in the previous sections. The impact of China is discussed throughout the text in but given its importance, Section E summarizes its impact on world trade. Conclusions are presented in Section F.

Our results show the following: World demand growth was very different during the 1990s and the 2000s. Demand growth has accelerated during the 2000s. While market share changes explain most of the growth during the 1990s, its contribution is relatively smaller during the 2000s. Even without demand growth, continued liberal trade regimes could generate 3-5 percent p.a. manufacturing trade growth caused by market share changes.

Imports from developing countries have grown much faster both in industrial and developing country markets. Despite the rapid growth, their share in the absorption of the sample countries is still very low and can expand substantially even if demand growth is much lower in the near future. However, most of this share increase is driven by China. Furthermore, China is among the few countries that have reduced their share of imports from other developing countries. Future manufacturing trade growth will depend on the evolution of trade patterns of China as much as other economic developments.

# **B. Methodology and Data**

The total gross absorption (demand) in each country is estimated as gross production in manufacturing, minus exports plus imports, for the beginning and end years. Gross production is taken from UNIDO database and checked against other sources for consistency.<sup>4</sup
.

- Maurer-Fazio M., Connelly R. (2017). “How Do Caregiving Responsibilities Shape the Time Use of Women and Men in Rural China.?” In: Connelly R., Kongar E. (eds) _Gender and Time Use in a Global Context_ . Palgrave Macmillan, New York.

- Connelly, Rachel, and Margaret Maurer-Fazio (2016). Left Behind, at Risk, and Vulnerable Elders in Rural China. _Chinese Economic Review,_ 37, pp. 140–153.

- Montenegro, C., and C. Pagés. (2004). “Who Benefits from Labor Market Regulations? Chile 1960-1998.” In J.J. Heckman and C. Pagés (eds.) _Law and Employment: Lessons from Latin America and the Caribbean_ . University of Chicago Press

- Muravyev, A., D. Schäfer, and O. Talavera. (2009). “Entrepreneurs’ Gender and Financial Constraints: Evidence from International Data.” _Journal of Comparative Economics_ , 37(2), pp. 270–286.

- Nicodemo, C., and R. Waldmann. (2009). “Child-Care and Participation in the Labor Market for Married Women in Mediterranean Countries.” IZA Discussion Paper Series 3983, Institute for the Study of Labor, Bonn.

Oakley (1985). _The Sociology of Housework_ . Cambridge: Basil Blackwell.

25
Winter, 2017).

The impact of urbanization on social infrastructure is also found to be positive and statistically significant. The result is in keeping with existing literature which suggests that infrastructure facilities like education, health, and housing are important for the urban population, and thus, the supply of such facilities rises with urbanization (Biehl, 1989). It is not surprising to observe that more urbanized states, such as Kerala, Karnataka, Tamil Nadu, and Maharashtra, perform well in terms of their social infrastructure indices. The impact of population density is found to be positive and significant, implying that higher population density increases demand for social infrastructure leading to higher development. These findings are in line with previous findings on infrastructure (Glover, 1976, Frederiksen, 1981; Randolph et al.,1999).

25
breastfeeding up to six months (2019)|62.6%|
|Children weighed at birth (2019)|51.9%|
|Delayed bathing (>24 hours after birth) (2019)|80%|
|Under-five received all vaccination (2017)|86%|
|Care-seeking for fever (2019)|55.6%|
|Contraceptive prevalence rate (2019)|70.3%|
|4+antenatal care visits by any provider (2019)|36.9%|
|Institutional deliveries (2019)|53%|
|Skilled birth attendance (2019)|59%|
|Postnatal health check for the mother at least one (2019)|65%|
|Contraceptive prevalence rate among 15-49 age women|62%|

**Sources:** DHS2017<sup>12</sup> ; MICS 2019<sup>9</sup> ; The World Bank 2017<sup>13</sup>

# **1.3 Context for the study**

With considerable investments and noticeable improvements, Bangladesh currently stands at a crucial phase in its RMNCH-N landscape. In order to accelerate achievement of improved RMNCH-N outcomes, global evidence suggests the need for smart and strategic health sector investments and interventions.<sup>1</sup> Further, global evidence also shows that the path to achieving the top RMNCH-N milestones (e.g. SDGs) is more complex and difficult for countries, compared to the earlier milestones (e.g. millennium development goals).<sup>1</sup> Consequently, for Bangladesh, strategic decision-making and investments are necessary.<sup>14</sup> It is already known that investments targeting appropriate RMNCH-N utilization would lead to improved RMNCH-N outcomes.<sup>15,16</sup> However, the success of such investments largely depends on how efficiently and timely (i.e. immediate to short-term) the service utilization determinants are addressed.
their validity as selection variables in Table A4. Moreover, their inclusion in equation 2.3 has little effect on results for donor aid share and most of the recipient-level variables. The coefficient for aid dependence declines somewhat when HIPC indicators are included, as the HIPC countries tend to receive more aid than most others.

Equation 2.4 adds the percentage of aid for each donor-recipient pair that is in the form of direct budget support (DBS), as reported in the PDMS. Budget support by definition uses country PFM systems. When this variable is included, the regression in effect is testing for relationships between the regressors and use of PFM systems for project aid and other non-DBS aid. Coefficients that were significant in equation 1.1 remain significant (although only at the .10 level for Control of Corruption) in equation 1.4, but are reduced in magnitude.

If using country systems strengthens them while bypassing them weakens them (OECD, 2011; 2008a), then quality of country systems is endogenous. We address this potential problem in several ways. First, any beneficial effects on quality from using PFM systems will be on the PFM systems themselves: budget execution, financial reporting, and auditing procedures. Our system quality indicators - Control of Corruption, supplemented by Civil Liberties – are defined far more broadly, and are likely affected only with a lag. Second, we lag the system quality indicators by a year in our tests. Third, we test for the presence of heterogeneous effects among donors that should be present – or absent - if coefficients on Control of Corruption are biased upward to a significant degree by reverse causation.

Our basic identification strategy is premised on differences among donors. First, we distinguish donors by size. The impact of a percentage point increase in a donor‟s use of country systems should vary with a donor‟s aid share. Each $1 increase should have the same impact, so 1 percentage point will imply a larger impact for larger donors. A sufficiently small
what he saw as the “silence about poverty” in the US. This is, however, hard to reconcile with Figure 1; the rising attention to poverty seen in the _Viewer_ after 1960 is hardly suggestive of a silence about poverty (though an even louder voice may have been needed).

Few of these anecdotal observations from the historical literature are borne out by Figure 1. While there is no doubt that the late 19th century was significant in the emergence of scholarly research on poverty, there is little convincing sign of heightened public awareness around that time. (There is at most a small positive blip around 1900 that might reflect, but it can hardly be deemed significant, as similar blips occurred at other times).

However, the changes in public awareness of poverty in both Poverty Enlightenments were undoubtedly grounded on less popular, more scholarly, writings over many decades. Other factors were no doubt also at work. One can speculate that fears of revolution may have led to efforts in popular writings to justify poverty and inequality in the late 18th and early 19th centuries—efforts that Landauer (1959) argued helped stimulate the development of socialism. The spread of literacy may well have helped in generating a more empathic literature by the time of the Second Poverty Enlightenment.

It must also be realized that the time of the First Poverty Enlightenment lacked the body of theories and data that we take for granted today in trying to understand poverty and link that understanding to public action. Economics was still in its infancy. Systematic sample surveys of household livings standards were unknown. Nor was there much sign yet of the theories and movements that could claim to represent the interests of poor people. All that had changed by the Second Poverty Enlightenment, such that authors like Harrington and Galbraith could formulate accessible arguments grounded on a body of theory and data, the latter stemming from sample surveys and much analytic work in measuring living standards and setting poverty lines.

While tracking words over time cannot resolve all these historical issues, some further clues
�A|1|Normalization|
|Steady State demand level|�Zy|0:25|Normalization|
|Inventory adjustment cost quarters I,II,III|�<sup>I</sup>|0:0204|Model calibration|
|Inventory adjustment cost quarter IV|�<sup>II</sup>|0:046|Model calibration|
|Yield volatility|�A|0:098|Stand Dev At|
|Demand volatility|�Z|0:018|Stand Dev Zt|
|Public signal A st dev|�<sup>�pu</sup><br>A|0:06|USDA Yield forecast|
|Public signal Z st dev|�<sup>�pu</sup><br>Z|0:048|USDA Demand forecast|

3.2.3 Step 3 30
Our baseline specifications use a panel data structure, that is, we examine the identical firms before and after the policy intervention (Basel III implementation). We restrict our data set of firms to SMEs only, that is, to firms with fewer than 250 employees (and split the estimations for small and medium-size firms in some cases). If SMEs confront challenges in accessing finance that are different from those confronted by large firms, focusing on SMEs only could help sharpen the identification of Basel III effects from cross-country data. In addition to the panel, we form the data set of firms with repeated cross-sections. Although the drawback is that we cannot track the individual firms over time using this data set, we can examine a richer sample of countries and firms. Therefore, we trade off cleaner identification for greater statistical power between the panel and repeated cross-section estimations to gain complementary perspectives.

The list of countries included in our analysis, together with the years of pre-treatment and posttreatment Enterprise Surveys, is available in table A.1 in the appendix. Overall, our sample contains 32 countries—29 countries with a panel structure of firms and three additional countries with repeated cross-sections. In our sample, 12 countries implemented Basel III, and among those,, nine countries have a panel structure.

Our panel data set contains 4,475 firms from 29 EMDEs—nine of these countries introduced the Basel III framework, and we consider them as the treated group, while the remaining 20 countries comprise the control group, which had not implemented the Basel III at the time when the posttreatment Enterprise Survey was conducted (therefore, the number of observations for the panel is 4,475 x 2 = 8,950). Our repeated cross-sectional data set contains 33,449 observations.

Our dependent variable, _access to finance as a constraint_ , is a categorical variable that represents the firm’s overall perception of its access to finance and reasonably aggregates different elements of the constraints in accessing finance (lending rate, loan volume, maturity
been gazetted.

As a result of the NNPC’s action, the price of kerosene for household use has been frozen nominally at ₦50 (US$0.25) per liter, providing enormous financial incentives for commercial malpractice. It is rare to find kerosene on the market selling at the official price of ₦50 a liter. There have been rumors of five different channels for abusing the kerosene subsidy: (1) black markets, (2) diesel fuel adulteration, (3) diversion to the aviation fuel sector, (4) smuggling to neighboring countries, and (5) presenting it as having just been imported and claiming the subsidy reimbursement for the second time ( _All Africa_ 2011). A forensic audit into the affairs of the NNPC released in February 2015 reports that actual retail prices of kerosene were in the range ₦120–140 per liter (PwC 2015). The official price of gasoline is also not enforced, although the price differences are smaller than for kerosene. Payment arrears to oil marketers have historically caused fuel shortages, pushing up the actual prices paid. In December 2015, in the face of continuing gasoline shortages, the government agreed to pay ₦407 billion (US$2.1 billion) of overdue reimbursement payments to oil marketers.

The National Bureau of Statistics began surveying the actual prices paid by households in June 2014, and makes the data available by state. The national average surveyed prices are shown against the official price in Figure 20. The results are indicative of varying degrees of gasoline shortages, with not a single state reporting the official price between April and August 2015, and again in November 2015, when the highest surveyed price was recorded—₦187.5 (US$0.94) per liter, or more than double the official price and much higher than the price that would have prevailed in a deregulated market.
22 The second way we show that the social capital effect is a village, and not only a household, effect is to match the HRDS data on expenditure per household and other household and village characteristics with the SCPS data on social capital at the village level. The households surveyed to estimate expenditures are in the same villages but are not (except for possible coincidental repeats) the same households used to measure social capital. Column 2 of table 4 shows the results of regressing household incomes from the HRDS on social capital from the SCPS (and the other household and cluster variables calculated from the HRDS) using instrumental variables estimation. The estimated impact is still large and statistically significant in both the cluster and household level regressions. That is, the social capital of the households interviewed in the SCPS has an impact on the incomes of _other_ households in their village (surveyed two years previously) as well as on their _own_ incomes'9. This is like finding that one household's land or asset ownership is important not only for their own but also for their neighbor's output. It is hard to overstate the importance and uniqueness of this result, as this implies that at least some significant fraction of associational life creates capital that is locally social.

Moreover, these results provide a powerful second argument against a causation from income to greater social capital. If individuals with higher incomes have greater social capital because social capital is a luxury (or even normal) good then one would expect the results would only appear when linking a given household's income to that same household's social

**19** This similarity is all the more remarkable given the very low correlations of cluster level averages across the two surveys for most of the variables. The Spearman correlation coefficient is .42 for expenditures per person, .12 for average education, .13 for assets, .33 for median distance to market.
carried out between 2014 and 2019 or later; recent price data are collected between 2018 and 2019 or later; recent census data are collected between 2009 and 2019 or later.

Once we have established that data has been collected recently, we assess whether the data is publicly accessible. For each data category, we classify microdata accessibility into 4 groups as follows:

1. **No coverage:** if no representative microdata was recently collected.

2. **No openness:** if representative microdata was recently collected but the data or a link to the data is not available on the website.

3. **Satisfactory openness:** if representative microdata was recently collected and the data (or link) is available on website but is restricted i.e., users need to submit a request and/or register to be granted access to the data.

4. **Excellent openness:** if representative microdata was recently collected and the data (or link) is publicly available on website in machine readable format for immediate download.

We differentiate between “satisfactory” and “excellent” openness because microdata openness is examined from the perspective of the data user. From this perspective “excellent openness” is ideal because there is no wait time for data users to access available data. However, “satisfactory openness” is acceptable because it is okay for data guardians to require registration, authorization, and clearance before releasing data to prevent unauthorized access. The best scenario being where following satisfactory registration, access to the data is granted automatically.

3.3 Implementation exercise of microdata classification in MENA The exercise of visiting the websites of the NSOs<sup>13</sup> and international organizations<sup>14</sup> to examine microdata accessibility was designed to be cost effective and easy to apply to countries in MENA and beyond. <mark>To prevent bias and ensure accuracy and replicability, the exercise is implemented in a 3-step process by a</mark> team
|Percent of faculty that are women|-0.000868<br>(0.000789)|-0.0655<br>(0.0965)|-0.00500<br>(0.00517)|
|---|---|---|---|
|_Other practices_||||
|Percent of governing body that belongs to current students||0.0112<br>(0.210)||
|Min. scores in HS GPA or national entry test is an admission<br>requirement|0.0902**<br>(0.0449)||0.761***<br>(0.288)|
|Student body, program, and HEI characteristics (indexes)|✓|✓|✓|
|Noise controls, and field-fixed effects|✓|✓|✓|
|Observations|1,214|1,201|1,206|
|R-squared|0.230|0.325|0.247|
|Mean of dependent variable|0.348|23.81|2.294|
|Average wage (USD 2019 PPP)|||300.6|
|AdjRsquared|0.214|0.303|0.232|

_Source_ : Own estimations using individual-level data from administrative sources and program-level data from the WBSCPS for Ecuador. _Notes_ : This table shows coefficients from the (second stage) OLS regressions of student outcomes on the quality determinants selected by LASSO in the first stage. The unit of observation is an individual. Outcomes (dependent variables) are the following labor market outcomes pertaining to the 12-month period following graduation: whether the student is formally employed at least one month (col. 1); percent of months she is formally employed (col. 2); and average monthly wage (col. 3). Average monthly wage is computed over the months worked; it is zero if
Policy Research Working Paper 10683

# **Abstract**

This paper quantifies the impact of drought on household consumption for five main agroecological zones in Africa, developing vulnerability (or damage) functions of the relationship between rainfall deficits and poverty. Damage functions are a key element in models that quantify the risk of extreme weather and the impacts of climate change. Although these functions are commonly estimated for storm or flood damages to buildings, they are less often available for income losses from droughts. The paper takes a regional approach to the analysis, developing standardized hazard definitions and methods for matching hazard and household data, allowing survey data from close to 100,000 households to be used in the analysis. The damage functions are used to quantify the impact of historical weather conditions on poverty for eight countries, highlighting the risk to poverty outcomes that weather variability causes. National poverty rates are 1–12 percent higher, depending on the country, under the worst weather conditions relative to the best conditions observed in the past 13 years. This amounts to an increase in the total poverty gap that ranges from US$4 million to US$2.4 billion (2011 purchasing power parity).

This paper is a product of the Poverty and Equity 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 rhill@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>
year that damages will be zero and a 97% probability that damages will be below 10% of GDP. The isolated point at the top indicates that there is a 0.0003% probability that damages will be 91% of GDP or higher. The hurricane damages used in the simulations were drawn from the grey fitted line, because a functional relationship is much more computationally efficient than drawing damage events from the discrete data.

<!-- Start of picture text -->
1.00<br>0.90<br>0.80<br>0.70<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00<br>Probability range<br><!-- End of picture text -->

_Figure 6: Probability of hurricane damage in Jamaica: actual data (blue dots) and fitted equation (grey line). Source: (World Bank, 2018)._ Figure 7 presents the outcomes of these stochastic simulations under probabilistic hurricane shocks with insurance as the disaster risk financing strategy. The dark blue line represents the median result, and the 25<sup>th</sup> to 75<sup>th</sup> percentiles boundaries are the edge of the dark blue area, while the 10<sup>th</sup> to 90<sup>th</sup> percentiles boundaries are shown by the edge of the light blue area.

As with the deterministic scenarios, Figure 7 illustrates that GDP, potential GDP and consumption would be lower under a scenario with hurricanes than under a scenario without. While individual hurricane shocks are softened by insurance payouts, the opportunity cost