rafmacalaba/data-use-mentions-extended huggingface dataset:
costs would be higher for students in grade 12 than in grades 4 or 8. Estimated impacts of the partial reopening are smaller when including households with grade 12 children in the sample (Table A3). Given expected differences in school-related costs, net childcare burden, and child agricultural labor by student grade, smaller increases in parent work hours for a grade 12 student returning to school relative to a younger student suggest childcare burdens are the main mechanism, rather than schoolrelated costs and child agricultural labor.

# **5 Discussion**

How do the labor supply effects of the positive shock of partial school reopenings compare to the initial labor supply reductions at the onset of the pandemic in Kenya? Using recall data on respondents’ labor supply in February 2020, we find that labor participation across adults ages 18-64 in Kenya fell from 76% before the pandemic to 59% in May-July.Average working hours in the last 7 days also fell, from 23.9 hours in February to 16.9 hours in May-July.<sup>17</sup> For respondents ages 18-64 in our analysis sample households, average weekly hours fell from 30.4 in February to 19.3 in May-July. Adult work hours in the last 7 days increased by 3.6 after the partial reopening, corresponding to 32.4% of the pandemic reduction in work hours in this sample.

The partial reopening only affected a subset of children older than those with the greatest childcare needs, whereas initial closures affected all school-age children. Reducing labor supply is also likely easier than increasing it, reflected in the lack of significant impacts of the partial reopening outside of household agriculture hours. Considering impacts of the partial school reopening should thus provide a conservative estimate of the contribution of school closures to initial pandemic labor participation decreases.

Increasing work hours for adults in households with school-age children (66.4% of households) during the school closure period by our estimate of the amount they increased due to the partial reopening reduces the drop in average
# **1. Introduction**

Pakistan has faced various shocks in recent years, including the COVID-19 pandemic, natural disasters, and economic shocks. These shocks are expected to profoundly impact household welfare and poverty rates in the country. Yet, the latest official poverty rates are available for fiscal year 2019 (FY19),<sup>1</sup> the year of the last Household Income and Integrated Survey (HIES). Due to resource constraints and the time it takes to collect and process household survey data, surveys are only implemented every few (2-5) years, undermining data availability. This presents challenges in providing real-time welfare (namely poverty, vulnerability, inequality) analysis and informing the design of policies to mitigate the impact of shocks like economic crises (e.g., price shocks) or natural disasters (such as floods) on people’s well-being and their risk to fall into or stay in poverty. To address this issue, policy makers rely on "nowcasting" and "forecasting"<sup>2</sup> poverty rates using aggregate measures of national welfare to estimate poverty incidence levels. This approach is based on the fundamental principle that the incidence of poverty and aggregate measures of national welfare, like GDP per capita, are intertwined.

Our approach explicitly models change in the welfare distribution by using information from sectoral growth and household-specific inflation rates, producing a more nuanced picture of the incidence of poverty and the distributional changes experienced in the country. We model the evolution of labor and non-labor incomes. First, we account for the differential growth trends and concentration of labor across the economy by modeling labor income using information on the growth rates of 11 different sectors. This is key since there are significant variations in sectors' contribution to overall growth, their labor intensity, and the welfare level of individuals employed in different sectors. Second, we include specific conditions to mimic the evolution of non-labor income transfers, with a particular focus on the changes in the real value of
set using the community profiles from the 1996 and 2011 population censuses run by Statistics South Africa. In their original format, the community profiles (at the sub-place level) provide aggregated category counts for each variable in the census. South Africa is divided into 9 provinces (equivalent to states), 266 municipalities and over 21,000 communities (subplaces). Each community has a population of 2,000 individuals on average, though there is

> 14During the period of analysis, the emergence of China and the resulting increase in global demand for natural resources is perhaps the clearest empirical counterpart to an exogenous shock to global demand (see, e.g., Autor et al, 2013).

12
Figure 3.7: _Excessive market regulation hurts job creation_ <!-- Start of picture text --> 5 est<br>lva<br>cze<br>hun bgr<br>svk<br>ukr ltu<br>0 pol<br>hrv rom<br>svn<br>-5<br>3 4 5 6<br>Market regulation index<br>sv emp. controlled for gdppc<br><!-- End of picture text --> 35
term of the program in which the student is enrolled, and dummies indicating the study area. DS stands for discontinuity sample and equals the cut-off points for the Economic Need Factor (ENF) where the loan <u>amount increases by additional 10% of tuition.</u> The results from the econometric analysis based on the survey data are presented in Table 6. For other factors than the treatment effect, the coefficient on student’s family income is very small and insignificant. The student’s age has a positive effect on the probability of having a job on the side. A one year older student has a 2.3%-point higher probability of work. Students in Economics and Administration (Area II) and Engineering and Natural Sciences (Area III) less frequently take part-time jobs.

26
them relatively worse off when prices rise.

The aim of this paper is to quantify how these sometimes conflicting forces will play out in India over the next thirty years. Viewing global warming essentially as a change in the prices of land, labor, and food, we investigate the effects on household-level welfare using a simple comparative statics framework. Our analysis takes a step beyond conventional partial equilibrium comparative statics, though, by considering the welfare impact of these three future price changes _jointly_ . The first part of the paper estimates the climate sensitivity of land and labor productivity in India. With these estimates and forecasts of future food prices changes in hand, the second part of the paper predicts the welfare consequences of climate change for households at each point along the initial welfare distribution.

Aside from its sheer size and importance as an agricultural producer, India provides an

> 1Hertel et al. (2010) study the poverty impacts of climate-induced agricultural productivity change in 15 developing countries using computable general equilibrium (CGE) modeling. Our approach is distinct in its reliance on household data, which allows us to draw out more fine-grained and country-specific distributional implications.

1
many factors can be controlled for which are similar in both parts of the country, e.g., a common language, similar education systems and a shared cultural and political history after the Second World War and prior to the separation. As a consequence, an East-West comparison has a methodological advantage as compared to cross-country studies. Thus, it is possible to observe how taxpayers adapt to a new legal environment that provides signals to them how they should behave and how others behave regarding taxation. As the Iron Curtain fell, former GDR citizens became exposed to the West German system including the social welfare state, the tax system and the whole set of formal and informal rules. The question remains how fast individuals may change their social norms. In the focus countries, people gradually internalized norms and values they were forced to respect for years before the reunification. Feld et al. (2008) were interested in exploring these social learning effects on tax morale over time. The unification process provides for a unique opportunity to study this research question. East German citizens faced different legal systems before and after unification. While deterrence was generally high in the old GDR, black markets and the shadow economy were ubiquitous. Thus, each GDR citizen could observe that their fellow citizens engaged in the shadow economy. After unification, East German taxpayers were confronted with new laws and new deterrence levels which have only been slowly increased across time. East Germans could also realize the tax morale of the West German taxpayers and adjust their behavior accordingly. So far, the literature on tax compliance has failed to explore social learning, herding behavior or convergence processes in general. This paper addresses this shortcoming by exploring the unification process in Germany using three World Values Survey/European Values Survey waves between 1990 and 1999. German unification is interesting for the analysis of tax morale over time and the exploration of a social learning process. As mentioned, many factors can be controlled (e.g., a common language, similar education systems and a shared
24 investor ability to act collectively to sanction rulers who attempt this. They argue that the ability of the ruling party to survive leadership transitions indicates that party members can undertake collective action independent of the party leader. Members of ruling parties who can act collectively are more likely to invest than in the case of ruling parties where the ruler bars collective action by party members. Consistent with this, private investment is substantially higher in non-democracies with ruling parties that are older than the ruler‟s years in office.

Though Gehlbach and Keefer (2010) do not examine this, the effect of party age less leader years in office should be attenuated among democracies. In non-democracies, options for collective action outside the ruling party are scarce. The variable _ruling party age – years in office_ therefore distinguishes non-democracies in which collective action is possible from those where it is not. The distinction among countries that exhibit competitive elections is much weaker. In these countries, even if the ruling party is not institutionalized, other parties may be; citizens still have the possibility of acting collectively to pursue their political interests.

Following Gehlbach and Keefer (2010), the analysis below uses variables from the Database of Political Institutions to test the prediction that _ruling party age – years in office_ is significantly associated with development outcomes in non-democracies, but not in democracies. The variable _gov1age_ in the DPI captures ruling party age – the age of the largest government party. The variable _yrsoffc_ is the number of years that the country‟s executive has been in office. The DPI offers ample evidence that countries with competitive elections offer ample alternatives to ruling party organization: the average age of the second and third largest government and the largest opposition parties is 20.6 years in countries with competitive elections; it is only 2.5 in countries lacking competitive elections, a difference of
150 million people worldwide are prevented from falling into extreme poverty thanks to social protection.<sup>32</sup> This is a sizeable dent in global poverty which counts 1.4 billion people (at $1.25 a day). Taking the pre-transfer poverty headcount as the base, the average reduction in poverty across developing countries is about 45 percent. This impressive global impact differs across countries: it is largest in Eastern Europe and Central Asia and smallest in Sub-Saharan Africa, where only 1 percent of the population moves out of poverty thanks to social protection transfers.

> 30 Welfare is a measure of well-being, and is defined either as per capita income or consumption

31 For example, it is assumed that if a person stops receiving a pension, he or she will try to survive on the remaining income after the pension is taken away. In practice that person can seek assistance from relatives and community that will partly compensate this loss of income, so will not be as poor as assuming no replacement. 32 This is based on assumption that 56 countries in ASPIRE are broadly representative of their regional peers currently not in the ASPIRE data set. If there is some correlation between the presence of data on social protection and real effort in providing social transfers, then extrapolating from surveys to countries without surveys will provide an overestimate of the number of people affected by social protection in the world. At the same time, since most surveys do not entirely capture all social protection programs in countries (for reasons of data collection costs), the ASPIRE itself may be underestimating the poverty impact of social protection in countries it covers. Thus there are forces pulling in opposite directions which tend to cancel each other out, giving us some confidenc at least for the range of estimates we produce, but it is difficult to know the net effect precisely.

12
GHS), households with functional difficulties have a higher share who own assets. However, this result does not hold for Dominican Republic and the Philippines in Appendix 8 when the analysis is done at the individual level and for South Africa, inconsistent results are reached across datasets (GHS and Census).

For cell phone ownership, households with any difficulty have a significantly lower rate of ownership except in Papua New Guinea.

Finally, in six countries where transportation expenditures are available, households with functional difficulties are not found to have a higher share of transportation expenditures out of total expenditures.

32
the first four pillars—deals with the enabling environment for business. Pillar 1 recognizes the importance of institutional quality for sustainable and strong economic growth. In this study it is proxied by the government effectiveness indicator (WGI). Pillars 2 and 3 refer to extensive and efficient infrastructure and ICT adoption; these are proxied by the use of energy and the Internet (WDI) in the baseline model. The extended version also uses the quality of roads and diffusion of broadband (WEF), respectively; the WEF infrastructure index; an infrastructure index, built as the average of energy consumption and Internet adoption, weighted equally. Pillar 4 stresses the relevance of macroeconomic stability to business development and thus growth.

> 4 Read more about the WEF methodology at reports.weforum.org.

7
Policy Research Working Paper 5980

# **Abstract**

The ability of consumers to make informed financial decisions improves their chances of having sound personal finance. This paper uses a panel dataset from Russia, where consumer loans grew at an astounding rate—from about US$10 billion in 2003 to over US$170 billion in 2008—to examine the importance of financial literacy and its relationship with behavior. The survey asked questions on financial literacy, consumer borrowing (formal and informal), and spending behavior. The paper studies the consequences of greater financial literacy on the use of financial products and financial planning. Even though consumer borrowing rose rapidly in Russia, only 41 percent of the survey respondents understood how interest compounding worked and only 46 percent could answer a simple question about inflation. Financial literacy is positively related to participation in financial markets and negatively related to the use of informal sources of borrowing. Individuals with higher rates of financial literacy are significantly more likely to report having more unspent income at the end of the month and higher spending capacity. The relationship between financial literacy and the availability of unspent income is more evident during the financial crisis, suggesting that better financial literacy may better equip individuals to deal with macroeconomic shocks.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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 author may be contacted at lklapper@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
Hajj, M. and U. Panizza (2009), “Religion and Education Gender Gap: Are Muslims Different?” _Economics of Education Review_ , 28: 337-344.

Hallward-Driemeier, Mary, Tazeen Hasan and Anca Bogdana Rusu1 (2013), “Women’s Legal Rights over 50 Years: What Is the Impact of Reform?” Working Paper No. 6617, World Bank, USA.

Hausmann, R., L. D. Tyson, and S. Zahidi (2006), **The Global Gender Gap Report 2006** , World Economic Forum, Geneva, Switzerland.

Hellerstein, J., D. Neumark, and K. R. Troske. 2002. Market Forces and Sex Discrimination. Journal of Human Resources, 37(2): 353-380 Heyman, F., H. Svaleryd and J. Vlachos. 2013. Competition, Takeovers and Gender Discrimination. ILR Review, 66(2): 409-432.

Irwin, Douglas and Marko Terviö (2002), “Does Trade Raise Income?: Evidence from the Twentieth Century,” _Journal of International Economics_ **,** 58(1): 1–18.

<mark>Islam, Asif, Muzi, Silvia & Amin, Mohammad (2019) Unequal Laws and the Disempowerment of Women in the Labour Market: Evidence from Firm-Level Data, The Journal of Development Studies, 55:5, 822-844.</mark> Islam, Asif (2013), “Is There a Gender Bias in Crime Against Firms in Developing Economies?” _Women’s Studies International Forum_ , 37: 1-15.

Joekes, S. (1995), “Trade-Related Employment for Women in Industry and Services in Developing Countries,” Occasional Paper 5, Geneva: United Nations Research Institute for Social Development.

Juhn, Chinhui, Gergely Ujhelyi, Carolina Villegas-Sanchez (2014), “Men, Women, and Machines: How Trade Impacts Gender Inequality,” _Journal of Development Economics,_ 106: 179-193.

Kabeer, Naila and
been consistently above a threshold for three years; or, if a country exceeds the threshold by X percent; or a combination. The advantages of either system are that they provide a clear early warning of a likely change, but of course they also introduce a lag in reclassification. Such a system already exists in the operational guidelines, based on a three-year period.

Other options have been proposed to manage exchange rate volatility. One is to use longer averaging periods. At some point prior to 1983, for example, the Atlas method used a seven-year average. But a three-year period was felt to offer the best compromise between sensitivity to change and smoothing (World Bank, 1983). This still appears to be the case, and overall there does not seem to be a compelling reason to change current practice.

It should also be noted that GNI per capita estimates can be affected by revisions in the estimate of both GNI and the total resident population, caused, for example, by new data from economic and population surveys and censuses, or other sources.

# **_5.3 Market exchange rates versus Purchasing Power Parity (PPP) conversion factors_**

Another well-known issue concerns the conceptual underpinning of the use of market exchange rates as conversion factors. What one US dollar buys in the United States does not necessarily correspond to the amount of goods and services that one US dollar would buy in another country, when converted to that country’s national currency. The implication is that, while the use of exchange rates may be useful for some purposes—such as measuring countries’ relative spending power on the world market—they are not the most appropriate choice for the international comparison of income; they do not fully adjust for the differences in price levels between countries and therefore do not provide a measure of the relative sizes of the volume of goods and services they produce. Furthermore, exchange rate based conversions are likely to result in a systematic downward bias in the measurement of the GNI or GDP of lower
where _FTcokeCSY_ , _j_ , _baseyr_ is the quantity of coke consumed by _j_ in million tons in the base year 2010. _FTliquidfuelCSY_ is the sum of the quantity of gasoline, kerosene, diesel and fuel oil consumed (given in CSY 2012), and _FTotherpetroleumCSY_ is the sum of the quantity of lubricant, bitumen, naphta, etc. consumed (given in the LBL’s China Energy Databook).

is the quantity share of liquids consumed in consumption of total refined petroleum products for industry _j_ . Our model distinguishes between the Gas Mining sector and the Gas Utilities (or Gas Products) sector; most industries purchase only from Gas Products, while a few purchase from Gas Mining for transformation and combustion – Chemicals, Electricity and Gas Products. For all industries _j_ other than Gas Products the consumption coefficient is the _quantity_ of natural gas purchased by industry _j_ divided by the sum of the _values_ of natural gas and gas products purchased by industry _j_ in the base year: In contrast, the energy consumption coefficient for the Gas Products industry is divided by only the value in the Gas Products cell, excluding the Gas Mining cell: We now move on from calculating energy consumption to calculating energy combustion. The above consumption coefficients refer to the purchases of the different fuels. Some of these oil and gas inputs are not combusted but converted to other products such as fertilizer or bitumen. In the Refining sector part of the crude input is combusted but most are converted to liquid fuels or other petroleum products; the un- _ref_ loss_  _ref_ loss_ combusted portion is represented by the “refining loss” coefficient,<sup></sup> _j_ where (1<sup></sup> _j_ ) is the _ref_ loss_ fraction of un-combusted crude input. (For industries other than j=Refining,<sup></sup> _j_ is simply 1, reflecting that 100% of the crude input is combusted.) In the Gas Products (Utilities) industry, gas is purchased from the
this number by the proportion of teachers absent from class. If 10 teachers are supposed to teach 5 hours and 27 minutes per day, yet 4 teachers are absent from either the school or the classroom at any one time, then the scheduled teaching time is reduced to 3 hours and 16 minutes.

Moreover, even when in the classroom, teachers may not necessarily be teaching. We address this by using the results from the classroom observation carried out as a part of the survey – an observation schedule based on recording a minute-by-minute snapshot of what the teacher was doing, for a randomly selected fourth-grade mathematics or language class.<sup>9</sup> The percentage of the lesson lost to non-teaching activities varied from 18 percent in Nigeria, the country with the lowest classroom absence rate, to 3 percent in Uganda, the country

> 9 This approach likely provides an upper bound on the time devoted to teaching. In the two pilot countries, Tanzania and Senegal, data on teacher behavior were collected while observing the teacher both inside and from outside the classroom. We find that time spent teaching when in class is about 30 percent lower in Senegal and about 10 percent lower in Tanzania if the enumerator stands outside the classroom rather than inside.

9