rafmacalaba/data-use-mentions-extended huggingface dataset:
For each country, we first deflate the official poverty lines to the annual average of monthly consumer price indices of 2011 and then convert them to PPP using the 2011 ICP update.<sup>26</sup> Table 2 presents population-weighted averages of extreme and moderate urban official poverty lines for all countries in LAC in 2011, and Figure 2 shows the density function of all subnational official poverty lines. Extreme poverty lines fall within a limited range between $2 and $4.4 per person per day, while moderate poverty lines are more disperse, ranging from $4 to $12.2 per person per day. This implies that the moderate poverty lines of some countries are lower in PPP terms than the extreme poverty line of other countries. For instance, the population-weighted average extreme poverty line of Paraguay ($4.4 a day) is slightly higher than the population-weighted average moderate poverty lines of Nicaragua, Ecuador, and Brazil.

The relatively large dispersion of the moderate poverty lines is also evident in Figure 3, which presents the cumulative distribution function (CDF) of the total number of official extreme and moderate poverty lines at the subnational level. The figure shows that the rank of the subnational regions based on the extreme official poverty lines is different from the rank based on moderate official poverty lines, suggesting a large variation of the Orshansky coefficients in LAC. Additionally, the slope of the CDF based on extreme poverty lines is steeper than the one based on moderate poverty lines, which suggest that there is more heterogeneity in the normative cost of living when non-food items are added to the overall household consumption bundle.

Additional key messages emerge from Figure 3. First, there is a substantial overlap between subnational poverty lines – both extreme and moderate – across countries in LAC. Taking Brazil as an example, Leste-Urban Area has the lowest moderate poverty line ($3.5 per person per day) in LAC, which is considerably lower than the moderate poverty line of Region Pampeana in Argentina ($4
of this conditional distribution provides a natural estimate of the precision of this aggregate governance measure for each country.

We illustrate our approach with reference to three fundamental aspects of governance: rule of law, government effectiveness, and graft. We group 31 indicators constructed in 1997 and/or 1998 into three clusters corresponding to these three concepts of governance, and compute aggregate indicators spanning 166, 156 and 155 countries respectively. In our companion paper documenting the governance database, we construct similar indices for several other aspects of governance.

Although the unobserved components methodology we use is quite standard, we find its application to the construction of composite govemance indicators interesting.' One of our major findings is that the aggregate governance indicators we construct are rather imprecise, despite the high correlations observed between various sources of governance data. In particular, a 90% confidence interval around the point estimate of governance for a typical country spans almost the entire interquartile range of the distribution of estimated govemance. This implies that although it is possible to robustly identify twenty or so countries with the best and worst govemance in the world, it is much more difficult to identify statistically significant differences in govemance among the majority of countries.

Our results are based on three key assumptions: (1) that the measurement errors in individual indicators of govemance are uncorrelated across indicators; (2) that the relationship between unobserved govemance and observed indicators is linear, and (3) that the distribution of unobserved governance across countries is normal. Relaxing the first assumption is difficult to do in practice, simply because without this assumption we cannot determine whether the correlation of observed scores across indicators is merely due to correlated perception errors or whether it reflects the common concept of governance being measured. However, under the likely altemative that perception errors are correlated across sources, the measures of precision we report will be biased downwards. As a result, the standard errors we report should be interpreted as a lower bound on the precision of aggregate govemance indicators. We consider the 1Unobserved components models were pioneered
Policy Research Working Paper 6220

# **Abstract**

Firms have various ways to cope with external risks. This paper analyzes the risk coping behavior that entails the smoothing of inputs (labor, raw materials, or capital). The theoretical framework shows that, if they face adjustment costs, firms prefer to smooth their inputs, especially if they expect a demand shock to be temporary. However, credit constrained firms will be adversely affected by the presence of liquidity constraints, and this will create a welfare loss due to incomplete smoothing. The authors estimate this behavior using a panel of Cambodian firms at the time of the 2008 global economic crisis. The survey shows that these firms were hard hit by the economic crisis between 2008 and 2009, with an average fall in demand (sales) of 30 percent. Based on the theoretical framework, the analysis can estimate the responsiveness of labor, capital, and raw materials input demand to demand shocks. It finds that firms try to smooth in particular if they believe the shock is temporary; in fact non-credit constrained firms reduce their inputs much less than firms that were credit constrained when the demand shock is expected to be temporary. The paper estimates that the welfare loss from incomplete smoothing due to credit constraints is many multiples of the adjustment costs of the firms that were not credit constrained. This has important policy implications about the role of financial sector development and regulations beyond the capital market. This micro analysis also has macro implications: if all firms expect a shock to be permanent, their combined limited smoothing of inputs will indeed make the shock more likely to be permanent.

This paper is a product of the Poverty Reduction and Economic Management Unit, East Asia and Pacific Region. 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://econ.worldbank.org. The authors may be contacted at
countries demonstrate a statistically significant relationship between household dietary diversity and child and adult nutritional status, and, more broadly, the access to and utilization of food (Hoddinott _et al._ , 2002; Ruel, 2003; Arimond and Ruel, 2004, Rose _et al._ , 2002).

Daily per capita calorie intake and protein intake are calculated by dividing weekly total household calories and protein intake by seven days and by the effective household size. The effective number of household members incorporates guests eating meals within the home.<sup>29</sup> Food quantities were converted to kilocalories and nutrients using the FAO Food Composition Tables for the Near East.<sup>30</sup> The food consumption score (FCS) is an indicator developed by the World Food Programme originally used in southern Africa, and now used in food security analyses throughout the world.<sup>31</sup> During the global food price crisis, the FCS was used in field assessments to gauge short-term changes in food security; a benefit of using the FCS is that it is comparable across countries.<sup>32</sup> It is constructed by taking the weighted sum of the frequencies with which households consume foods within eight food groups over the previous week.<sup>33</sup> The food groups are staples, pulses, vegetables, fruit, meat/fish, milk/dairy, sugar, and oil/fat.

# _3.2.2  Variable of interest and control variables_

Our coefficient of interest is ��, which measures the price elasticity of the dependent variable with respect to changes in the price of domestic wheat flour. (In other words, �� measures the percentage change in the food security measure from a one percent change in price.) We use the average price of domestic wheat flour since wheat is the primary staple of

> 29 Some studies use household size to calculate per capita amounts. In Afghanistan the custom of sharing meals with guests
To implement our cross country estimation, we<sup>collapse our data into two time</sup> periods and modify equation (1) to estimate where Zi,t_j includes _ACCOUNT, ENFORCE, CORRUPT,_<sup>and</sup><sup>_STRUCTURE._</sup> We measure Z2,t_j in the initial time period<sup>(1976-1980) and then measure subse-</sup> quent volatility and Xi,t over the period 1980<sup>to 1998.16 Thus, our cross-country data</sup> set gives us some advantages. It allows the inclusion<sup>of additional variables and gives us</sup> the ability to measure volatility over a much longer<sup>period of time. Moreover, the addi-</sup> tional variables in our cross-country data set also<sup>allow us to assess whether or not our</sup> main results are affected by endogeneity. Reverse<sup>causation may also be an issue in this</sup> 1<sup>5</sup> For a more detailed discussion of the definitions<sup>of these vaxiables, see also Demirguc-Kunt and</sup> Levine (1999).

'<sup>6</sup> Since we view these additional variables<sup>included in Z as country characteristics that are essen-</sup> tially fixed over the time period of the data,<sup>the exact timing of when we measure them should be</sup> inconsequental.

15
Policy Research Working Paper 10816

# **Abstract**

This study explores the factors shaping the aspirations of youths in Ethiopia, Malawi, and Nigeria, specifically focusing on their academic goals; science, technology, engineering, or mathematics career interests; and migration intentions. The study draws on a robust dataset comprising 2,725 youth respondents aged 15–25 years, collected through high-frequency phone surveys conducted as part of the World Bank’s Living Standards Measurement Study. Exploring the aspirations of youths in Sub-Saharan Africa is crucial, particularly given that by 2050, half of the region’s population is expected to be younger than 25 years. The findings highlight significant gender and age disparities across youths, with female youths aged 15–18 showing higher educational aspirations than their male peers. However, female youths’ aspirations decline and become lower than those of male youths when they reach 19–25 years old. Conversely, male youths aged 15–18 and 19–25 report higher career and migration aspirations than their female peers. Additionally, educational background emerges as a pivotal factor influencing aspirations. The analysis shows that youths without formal education degrees are less inclined to aspire to higher educational, career aspirations, or migration. Moreover, family and community role models, along with individual attitudes, may contribute to shaping the aspirations of youths in these three countries. In conclusion, building on the findings, the paper formulates a comprehensive set of policy recommendations. These recommendations aim to align the skills and aspirations of youths with the prevailing labor market opportunities, ultimately working toward the reduction of youth unemployment and underemployment rates across these countries.

This paper is a product of the Development Data Group, Development Economics. 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 vcosta@
with it, conditional on other observed characteristics. Both these factors suggest that the omission of controls may have biased demand upwards in arm C, although the point estimates remain very close.

The coefficient estimates for the controls in column 2 cannot be interpreted causally, but it can be noted that beliefs about the safety status of the well strongly predict demand: well owners thinking that their well is safe had little to gain from buying a test, and indeed they were 12 percentage points less likely to purchase the test (p-value _<_ 0 _._ 01). The belief that the water was _unsafe_ also decreased the probability of purchase, although by less than half as much ( _β_<sup>ˆ</sup> = _−_ 0 _._ 049, p-value _<_ 0 _._ 01). This is overall consistent with the conceptual framework, where it was highlighted that a key factor for willingness to pay for the test is that its result will provide new information.

# **5.2 Test Results**

Although the purchase rate was low, the intervention generated a large increase in the number of tested wells in Sonargaon. Before looking at the responses to the information made available by the tests, it is useful to first describe such information. The test results are summarized in Table 3, which also includes the detailed figures on switching behavior that will be described later, and so the statistics are calculated for the 10,412 households (91.3% of the total) that could be tracked in the endline survey. Of these, 2,417 purchased tests during the intervention.

Overall, 19% (455/2417) of the tested wells which had been used for cooking and drinking at baseline had unsafe arsenic levels relative to the national standard of 50 ppb.<sup>16</sup> Recall that these results are _conditional on demand_ so that the randomization across treatments does not guarantee similar distributions across arms, even in large
coefficient of welfare loss = 0.32 and 0.33 Source: 2012 1-2-3 Survey, DRC Table 7 presents the welfare and equity impacts of price increases by poverty status and area of residence. The welfare impacts of the price changes are sizable for all analytical groups. Households in both rural and urban areas suffer aggregate welfare losses ranging from 68,207 to 170,638 FC in total household expenditure on food as a direct consequence of the introduction of the VAT in the case of an 8.0 percent price increase. They are even higher for a 12 percent price increase. These welfare losses are statistically different from zero and amount to 10 to 12 percent decrease in food expenditure. The ratio of welfare losses to total food expenditure varies across analytical groups but more so between urban and rural areas irrespective or poverty status.

In addition to the measure of the proportionate welfare loss, we compute the Gini coefficient for the welfare loss (actually the absolute value of the welfare loss). Notice that a strictly positive Gini coefficient shows progressivity in this setup because it implies that the welfare loss is concentrated at high levels of expenditure. We find a Gini coefficient of 0.32 and 0.33 for effective tax rates of 8.0 and 12.0 percent respectively. These results further support the idea that the VAT is progressive. They also highlight the increasingly progressive nature of the VAT as the statutory tax rate rises.

19
Table 6: Coffee price shock, borrowing, and assistance

||Received Credit|Total Credit|Received Assist|Total Assist|
|---|---|---|---|---|
|Land share of cofee|-0.240|-982.719**|0.068|-162.086|
|* LogPrice|(0.179)|(459.695)|(0.079)|(242.118)|
|_N_|9968|9968|9968|9933|
|_R_<sup>2</sup>|0.530|0.369|0.617|0.512|

Notes: This table is based on three rounds (2011, 2013, and 2015) of ESS survey. Standard errors are clustered at zone level. All regressions include household and zone-year fixed effects, and log rainfall. _Received Credit_ and _Received Assistance_ are dummy variables indicating whether the household has received loan or transfers from formal or informal sources. _Land share of coffee_ is the fraction of household land allocated to coffee in 2011. _LogPrice_ is the log of average coffee price over the four quarters before the survey month. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 Table 7: Credit, assistance and consumption smoothing

||Cre|dit|Assis|tance|
|---|---|---|---|---|
||Total AEC|Food AEC|Total AEC|Food AEC|
|Land share of cofee * LogPrice|0.817***<br>(0.197)|0.827***<br>(0.202)|0.821***<br>(0.212)|0.822***<br
growth and inequality**

The literature indicates that housing finance affects economic growth by reducing the cost of capital, increasing savings, increasing tax revenues, increasing investment in education, reducing vulnerability, and increasing financial deepening. By reducing the cost of capital, housing finance can spur economic growth. In fact, Dubel (2007) provides a model analyzing the relationship between housing finance and housing affordability and shows that well-functioning housing finance reduces loan interest rates. That in turn results in greater affordability of housing, with a potential positive impact on economic growth.

_By increasing savings, housing finance can spur growth._ According to Buckley (1996), many reasons explain why improving housing finance may lead to increased savings in the economy. First, the returns to housing will likely be positive. Second, housing provides the most secure collateral against market fluctuations and in immature markets can be easily expected to offer positive yields over the long run. Third, housing prices are less volatile than other asset prices. Fourth, the availability of housing improves labor mobility and therefore employment potential. Finally, the availability of affordable housing finance may lead to increased savings as potential homeowners save to make the required down payment and to maintain their assets. This saving will be an engine of investment, helping to stoke economic growth and development.

_By increasing tax revenues, housing finance contributes to the development of public infrastructure and ultimately to economic growth and reduction of inequality._ Hangen and Northrup (2010) and Econsult (2009) estimate that, in the United States, activity related to housing has a positive impact on revenues of states and other local governments. In the United States as well as in other countries, greater revenues to local government often translate into better infrastructure for the population and ultimately into reduced inequality. Moreover, using a large panel data set encompassing over 100 countries and over the period 4
C. FX volatility is the JPMorgan VXY Global index, a turnover-weighted index of the implied volatility of threemonth at-the-money options on 23 U.S. dollar currency pairs.

D. Data from the Financial Soundness Indicators Dataset (IMF).

E. Unweighted averages. Based on the deposit money bank assets to GDP (%) from the World Bank’s Financial Development and Structure Dataset.

F. Based on annual bank statements. “Before GFC” indicates 2008 or 2009 depending on data availability; “After GFC” indicates 2018 or latest data available.
triggered by initial climatic shocks and should be used to cover data needs for this type of empirical analysis which pertain to the first steps of the climate migration chain. For subsequent steps, _i.e._ , crossborder migration out of overcrowded urban areas triggered by pressures on urban labor markets generated by the initial internal flow, household survey data must be complemented by other data sources, such as labor mobility data from labor force surveys, bilateral migration flows, indirect estimates retrieved from population censuses, and also non-conventional data such as citizengenerated information. Understanding how such diverse sources can be consistently and thoroughly integrated with each other should be a priority area for future work, and one which would unleash an untapped potential for sound research on policy-relevant issues for which we currently have little to no evidence. Regarding the climate migration chain, for example, the few available studies (e.g., Marchiori et al., 2012) use country-level data based on direct and indirect estimates from various country-level sources to proxy rural-urban migration caused by climate-related events using the weather-induced increase in the share of urban population at the country level. Going micro by

23
Barrett (1996) also find that income variance is driven more by wage heterogeneity for males; however, they find that the opposite is true for women. Gottschalk and Danziger (2005) and Blundell et al. (2018) have similar findings. Our paper contributes to this literature by going a step further and decomposing income inequality into productivity and preferences, which is the more relevant decomposition for welfare analysis; our decomposition recovers preferences from hours worked net of substitution effects and recognizes that productivity may not be equivalent to the hourly wage if individuals exert differential effort per hour.

Second, while, to the best of our knowledge, this is the first paper that attempts to decompose income inequality into productivity heterogeneity vs. preference heterogeneity, there is a large body of work that investigates individuals’ _beliefs_ over the determinants of income inequality and how these beliefs relate to views on the merits of redistribution. Data from the World Values Survey shows Americans are about twice as likely 5
)|(1.170)|(1.156)|
|Year of schooling|0.020**|0.988***|0.971***|
||(0.008)|(0.156)|(0.160)|
|Party member|0.023|-0.013|1.214*|
||(0.048)|(0.696)|(0.683)|
|Membership in Congress or|-0.039|-2.204***|-3.929***|
|Consultative Conference|(0.050)|(0.765)|(0.664)|
|Prov, Sector, year|Yes|Yes|Yes|
|Observations|4,246|5,127|5,096|
|R-squared|0.027|||

_Data sources: National Surveys of Privately Owned Enterprises in China._ Notes: The coefficients of No. of IPRs and No. of Self-designed products are estimated by Tobit model. Standard errors are clustered at provincial level and reported in parentheses. Significance levels 0.1, 0.05 and 0.01 are noted by *, **, and ***, respectively.

43
Cambodia, are excluded.<sup>9</sup> In Latin America, food insecurity is surprisingly high (34 percent).

This may relate to the greater prevalence of urban poverty and of relatively poor net food consumers, although this is only a speculation.

# **Table 1. Regional Unweighted Means for the Two GWP Measures, Circa 2005, for Developing Countries Only (Percent)**

||**Food i**<br>|**nsecurity**<br>|
|---|---|---|
||**Mean**|**No. of obs.**|
|sub-Saharan Africa|58.3|27|
|South Asia*|31.2|5|
|East Asia*|24.0|6|
|Middle East & North Africa|26.5|2|
|Central America & Caribbean|34.7|9|
|South America|36.0|10|
|Transition<sup>a</sup>countries|29.1|23|
|OECD<sup>b</sup>|8.3|22|
|Low income<sup>c</sup>|48.6|49|
|Middle income<sup>c</sup>|29.6|28|
|Upper income<sup>c</sup>|11.0|34|
|Mean, total sample|31.7|433|

_Source_ : Data are from the GWP (Gallup 2011).

_Note_ : *Note that two outliers are excluded. Nepal is excluded from the South Asia results, and Cambodia is excluded from the East Asia results. In the case of Nepal, its food insecurity score is much lower than that of the other South Asian countries, whereas Cambodia’s is much higher. With the inclusion of these two outliers, the food insecurity scores for South