rafmacalaba/data-use-mentions-extended huggingface dataset:
characteristics. Our empirical analysis highlights the trade-off in terms of undercoverage. Extensive information campaigns, ongoing registration, and flexible budgeting can help to reduce this trade-off. Although this strategy should prove more effective at reducing undercoverage of eligible households, in the absence of eligibility criteria that are strongly associated with the underlying program objectives (in our case, poverty alleviation), it may also result in large leakage of benefits to nonpoor households. Avoiding an adverse trade-off between decreasing undercoverage and increasing leakage requires clear and effective eligibility criteria that strongly correlate with poverty and rigorously implemented.

As always, there are remaining concerns regarding the separate identification of benefit and cost impacts because some unobserved cost determinants may correlate with the determinants of benefits. Our identification of the effects of benefits relies on variation due to program rules and pre-program schooling. For future work, our estimation suggests the usefulness of studies of participation in environments with experimental variation in benefits. Another area in which other data sources would be welcome is the formation of expectations; we assume rational expectations as a simple way to close the model, but we cannot exclude the possibility of systematic biases in the perception of the later stages. Another issue that we do not explore in this paper is the possibility of correlated errors in estimating the determinants of participation at the different stages. Addressing this possibility would require a move away from a simple recursive estimation approach.

In summary, both the model developed in this paper and the empirical results indicate the need to view program participation as an outcome of a disaggregated participation process, especially where the program design allows for a strong element of self-selection. Such an approach reinforces the need to consider the costs and benefits facing households at each stage and to design targeting and eligibility strategies accordingly. An empirical identification of the factors that determine outcomes at each of the participation stages also requires sufficiently disaggregated data on outcomes at these stages and 23
>[0.375]<br>6.97|19,814|ears 2005, 2011, and <br> is described at the t<br>endent variables in c<br>gories exactly describe<br>ples include married w<br>mate is from a uniqu|
|ms on Husband|Husband’s<br>Years of<br>Schooling|(3)<br>0.869<br>(0.475)<br>[0.067]<br>6.94|17,998|ealth Survey (in y<br>ependent variable <br>olumn 1. The dep<br>ps; the other cate<br>re, ∆_Izy_. All sam<br> for age. Each esti|
|xposure to Refor|Husband’s<br>Age|(2)<br>-0.017<br>(0.976)<br>[0.986]<br>5.70|18,174|Demographic and H<br> instruction. The d<br>women is shown in c<br>led manual job grou<br>ned intensity measu<br>rends, and a cubic <br>brackets.|
|f Efect of Wife’s E|Wife’s Years<br>of Schooling<br>[ First Stage<br> Married Only ]|(1)<br>0.097<br>(0.041)<br>[0.020]<br>5.70|18,174|a from the Ethiopian <br> MTI = mother tongue <br> schooling for married <br>sional, clerical, and skil<br>mented with the combi<br>ts, zone-specifc linear t<br>es are shown in square|
|Table C.20: National Estimates o|-|Add
or cultural norms slanting the intra-household bargaining, or both. Let us briefly look at some empirical evidence: Charmes (in Blackden, 2006) reviews empirical evidence from time-use surveys in four Sub-Saharan African countries (Benin, Madagascar, Mauritius and South Africa) along with data from the Ghana Living Standards Survey. These surveys are interesting since they (i) are large scale (ranging from 7,743 individuals in Madagascar to 25,664 individuals in Ghana), (ii) have nationally representative samples, and (iii) collect information about many different kinds of time use, both ―formal‖ (i.e. included in the System of National Accounts, or SNA) and ―informal‖ (i.e. non-SNA work). The overall results confirm that women spend 3-5 times as much time as men on domestic activities. Specifically for firewood collection the picture is less clear, as can be seen in Table 1, with women spending four times as much time as men in Benin (16 vs. 4 minutes per day) while in Madagascar men spend almost twice as much time as women (13 vs. 7 minutes per day). The picture is similar, although less extreme, if we look at only those collecting. This implies that both participation and intensity in participation show the same gender pattern in a country, that participation seems to be the dominating factor and, most importantly, that we can refute the received wisdom that it is _always_ mostly women and girls that are involved in fuelwood collection<sup>8</sup> . However, as can be seen in Table 2, when it comes to time spent preparing meals, women completely dominate the men – and this is consistent across all five countries. A direct implication of this could be to focus on energy solutions for cooking, rather than on the availability of fuels, if the time use of women is a high priority. Charmes also looks at time spent fetching water. Here the pattern of time use is
about 2 ppts per year. The LTGM also addresses the first concern by allowing for steady capital accumulation based on prevailing rates of investment as a share of GDP in each country.

In order to produce trajectories of adjustment, we calibrate the LTGM to each of the 19 countries in our MENA sample. For the 10 resource rich MENA countries (Algeria, Bahrain, the Islamic Republic of Iran, Iraq, Kuwait, Libya, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) we used the LTGM Natural Resource extension (LTGM-NR; Loayza et al. (2022)) rather than the standard LTGM since growth analysis using standard neoclassical models can be misleading. The LTGM-NR incorporates a resource sector (which does not use labor), and so takes into account the differential growth dynamics as well as reduced sensitivity of output to employment rates.<sup>15</sup> The details of the calibration are in Appendix Tables A2 and A3. In short, we calibrate key parameters like the labor share _β_ , the capital depreciation rate _δ_ , and the initial capital-to-output ratio based on Penn World Tables 10 data (with some exceptions). Future investment rates are based on IMF forecasts from the World Economic Outlook (WEO) until 2028 (their final year), which converge linearly to the historical average for each country by 2050. Total Factor Productivity (TFP) growth rates are chosen to replicate WEO GDP growth forecasts to 2028 (given other growth drivers) which then converge linearly to a cross-country average by 2050.<sup>16</sup> Future human capital growth comes from the LTGM-Human Capital Extension, with a few exceptions, which uses the same definition of human capital as the World Bank Human Capital Index (Kraay (2018)).<sup>17</sup> We assume that females and males have identical human capital, which is a reasonable assumption in most countries, but we relax this assumption in the next section.

Our analysis focuses on the _difference_ between the
Figure 2: COVID-19 demand shock: comparison with supply shock and uneven impact by region of birth Panel a. Supply and demand by occupation Panel b. Expected demand shock given occupation profile <!-- Start of picture text --> (by ISCO 3-digit) (CBO 2006, Rio-Chanona et al 2020)<br>Waiter/bartender Building frame worker 0<br>-2<br>-4<br>-6<br>Shop salesperson -8<br>-10<br>-12<br>-14<br>-16<br>-18<br>Administrative secretary<br>Heavy truck/bus driver Transport/storage woEngineSoftware & aCl er akeri n er/helg technician p p developererPersonal care worker<br>-80 -60 -40 -20 0 20<br>Demand shock (%)<br>1<br>.8<br>.6<br>.4<br>Demand shock (%)<br>Share of income-risk jobs<br>.2<br>0<br><!-- End of picture text --> Source: EU-LFS 2018 database, following estimates of Rio-Chanona et al (2020) and CBO (2006). Note: The sectoral demand estimates by the US Congressional Budget Office (2006) are based on Hong Kong’s experience with the past influenza pandemic “SARS” between 2002 and 2004. The CBO provides two scenarios and, following Rio Chanona et al. (2020), we use the severe scenario which describes a pandemic that is similar in size to the 1918-1919 Spanish flu outbreak. The CBO lists demand side estimates for broad industry categories, which we mapped to the NACE sectors.

35
**Institutions** The ES database contains both perception as well as quantitative information regarding the costs of doing business experienced by firms for a large number of countries. The perception indicators are often used to examine the effects of the business environment, but present a number of possible limitations. The main concern is that perception data are subject to measurement errors since they are likely to be affected by observed and unobserved firm and country specific factors. For example, they might simply reflect “individual differences in the degree of optimism or pessimism of the managers” and/or experience or performance of the establishment (Aterido et al., 2011). In view of this, we prefer quantitative measures, where managers are asked to specify the specific costs incurred when doing business, over perception data for our analysis.<sup>23</sup>

We construct an index of the quality of institutions based on the objective measures from the ES.

Financial friction is often thought to be an important source of misallocation, particularly in countries with poorly-functioning financial market. It can severely harm aggregate TFP by preventing the optimal adjustment of factor inputs. A recent study by Buera et al. (2011) has shown that a substantial portion of the variation in aggregate productivity across countries may be accounted for by resource misallocation due to imperfect financial markets. The ES dataset contains a number of questions in regard to access to finance. Specifically, the survey asked firms to indicate whether the establishment has ‘access to finance’ as well as the actual ‘cost of financing’.<sup>24</sup> We define the quality of the financial institution as the ease for establishments to get external financing. More specifically, we measure the financial friction as the percentage of establishments in the each region that do not have a line of credit or loan from a institution.

Another possible driver of misallocation is uneven implementation of regulations which allows unproductive firms to enter and survive easily. The ES dataset contains a large number of measures of
to inclusion in commercial lending? Finally, what are the implementation conditions needed to promote additionality and effective targeting of MSME finance operations?

To answer these questions, we use administrative loan-level data from the KUR program itself, which includes information on over 8 million debtors between 2015 and 2020. Using the program database as a sampling frame, we complement the administrative data with surveys of a representative sample of 1,402 KUR borrowers as well as qualitative interviews with separate samples of 100 KUR borrowers and 100 nonborrowers. In sum, these data sources provide a rich and novel set of insights into the profiles, perspectives, and experiences of KUR borrowers since 2015 and shed light on the ways in which the publicly supported scheme plays out in supporting MSMEs on the ground.

Our main and novel finding is that the subsidized credit provided through KUR enhances financial inclusion by reaching previously excluded micro and small businesses; however, it does not serve as a stepping- stone to unsubsidized commercial lending. Prior to receiving a subsidized KUR loan for the first time, only 11 percent of borrowers had ever received a commercial loan from a bank. Although successful at reaching first-time borrowers, the longer-term impacts on their financial inclusion are uncertain because KUR borrowers do not move into unsubsidized commercial lending. Approximately half of KUR borrowers went on to receive KUR loans multiple times within a 5-year period, and only 3 percent of firms who received KUR financing graduated to other forms of commercial lending. Until recent regulatory changes, firms had little incentive to choose commercial sources of financing over KUR’s below-market interest rates, and banks preferred to encourage repeat lending to minimize risk.

We also identify key design features of the KUR scheme which pose challenges for inclusivity and consumer protection and hold important lessons for the design of public MSME finance support schemes. First, we find that although the KUR program targets firms with insufficient collateral, in practice, 96% of KUR borrowers submit collateral for loans, with collateral worth an average of more than four times the amount of their loan
parents are only present for children related to the head of their new household (grandchildren and other relatives).

The external validity of this study might be questioned since it is difficult to argue that children being “adopted” into recomposed households in the years following the Genocide in Rwanda are representative of the general parent-child relationship. In addition, non-biological children seem to be discriminated against, according to Hamilton’s rule, where the children most distant from the household head are worse off. However, given the rising numbers of orphans from conflicts and from the HIV/AIDS epidemic in Africa and elsewhere, the analysis offers important policy lessons. In particular, the study demonstrates that placing orphans in households where they have relatives minimizes their educational losses and favors the intergenerational transmission of human capital.

> 16 Björklund, Lindahl and Plug (2004), using data from Sweden, find a positive and significant effect of the adoptive mother’s schooling, but with a smaller magnitude than in biological families. Sacerdote (2004) comes to a similar conclusion with a sample of Korean-American adoptees.

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. An internal conflict dummy variable ( I I ) for internal conflict country-year pairs - as defined using the AA∆L LLFFL LLFFppFFppAA UCDP-Prio Dataset is included in our analysis. Given the significant macroeconomic repercussions of FFppFFI IIL IIppF conflict, growth in countries that frequently experience conflict and social unrest may be more difficult to forecast with accuracy (Novta and Pugacheva, 2021). We also include a lagged dummy variable for economic booms which is a dummy variable if growth is greater than or equal to the previous 10-year median growth. Frankel (2011) found that official forecasters tend to be overly optimistic during (BBL mm) booms and busts, more so than when GDP is at its long-run trend. They overestimate the durability of booms and underestimate the transitory nature of busts (Frankel, 2011). Recessions are more difficult for 12
**Graph 5 ‐ Liabilities included in the BS**

<!-- Start of picture text --> 30<br>25<br>25<br>20<br>16<br>14<br>15<br>11 11<br>9<br>10 8<br>5<br>0<br>Sovereign debt Currency in Deposits of Legal claims Guarantees Pension Other<br>circulation commercial liabilities<br>banks<br><!-- End of picture text --> Source: Responses to SALM survey

# 5. Main issues related to the production of the sovereign balance sheet

The development of a sovereign balance sheet presents many challenges, ranging from the availability of data, the complexity of the legal structure and governance arrangements between the central government and other public sector entities (sub‐nationals, SOEs, SWF), theoretical considerations pertaining to the valuation methodologies (in particular for illiquid assets and liabilities), differences in accounting principles and standards across the public sector, to list but a few of them. This section examines the challenges reported by the respondents in the production of the sovereign balance sheet.

The respondents cited a number of issues and challenges in producing the government balance sheet. First, balance sheet items are often accounted for at the level of individual department or agency, therefore an efficient central budget management system is needed to consolidate the inputs into the aggregated balance sheet as well as agree on uniform accounting principles and consolidation procedures. Additional challenges concern data collection as well as valuation of some fixed assets for the first time (such as lands, roads, etc.), valuation of assets that have cultural value, contingent liabilities, intra‐group transactions and estimates of the present value of future revenue and expenditure. The issue of valuation of assets and liabilities is particularly complex for the production of the initial balance sheet (initial valuation). Finally, consolidating the balance sheet requires clear understanding of the economic transaction
Policy Research Working Paper 9765

# **Abstract**

A better understanding of how individual wealth and time use are linked—across paid, unpaid, and leisure activities —is important for targeting widespread gender inequalities in time allocation, as well as in accessing economic opportunities. The lack of reliable, individual-level data on asset ownership across different subpopulations, however, has limited discussions of these issues in the literature. Using a unique nationally representative survey from Cambodia, this paper shows that individual wealth, as measured through self-reported ownership of physical and financial assets, is significantly associated with time allocation to different activities. The role of asset ownership in time use is also stronger, particularly among women, vis-à-vis the competing proxies for socioeconomic status. Ownership of financial accounts, motorized vehicles, and mobile phones —all of which can improve access to networks, markets, and services—is associated with less time in unpaid work, and in some cases greater time in paid work, specifically among women in off-farm jobs. There are also distinct gender differences in how men and women shift their time away from leisure and childcare, highlighting the importance of social norms in choices over time use. The analysis highlights the utility of integrated, intra-household, individual-disaggregated data collection on asset ownership, time use, and employment in lower-income contexts.

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 tkilic@worldbank.org and gkoolwal@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
was earning on average 121.5 euros more than those with secondary education and 172.2 euros more that those with primary education. Only about 3.5 percent of workers in 2018 earned a wage around the minimum wage. (The decomposition of minimum wage workers can be found in Section III.)<sup>18</sup>

**Figure 7. Real median net wages (2015 EUR)**

**a. By sector (private and public)**

<!-- Start of picture text --> 500 392 409 426 432<br>400 342 343 350<br>264 271 282 291 289 302 313<br>300<br>200<br>100<br>0<br>2012 2013 2014 2015 2016 2017 2018<br>Private Public<br>2015 Euros<br><!-- End of picture text -->

> 17 Average and median wage are estimated using Kosovo LFS and wages are deflated using CPI with 2015 as a base year. Series for CPI are obtained from Kosovo Agency of Statistics estimates (http://ask.rks-gov.net/en/kosovo-agency-of- <u>statistics/add-news/harmonized-index-of-consumer-prices-hicp-april-2018</u> ). 18 The proportion of minimum wage workers is computed using the 2018 LFS as the proportion of workers with nonmissing wage who reported having a wage between 0 and 200 euros.

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youth activity rates might suggest that there are too few part-time jobs on offer for young people. Part-time work, however, does not only indicate labor market flexibility. According to LFS 2002 data, 45 percent of part-time workers in Latvia would have preferred to work full time in their main job. Such acceptance of less-than-desired hours of work is one of the most common definitions of

> 2 According to enterprise survey on occupations, however, share of manual workers among employees declines slowly but steadily at the expense of the two other groups.

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increase in household income. The increase is most prominent for good quality protein. Although inelastic, the expenditure elasticity at the initial level of income is higher for total protein than total calorie intake. Household economies of scale appears to be significant for calorie and protein (in OLS estimates) and for vitamin A (in HFE estimates) intakes. In terms of agricultural seasonality, individual intakes of calorie, protein and good quality protein appear to be lowest in round 3.

**Vitamin C, D and Iron** : Table 5 presents the results of intrahousehold bargaining and individual allocation of vitamin C, D, iron, and haem iron. The adequacy ratio for all age-sex groups and pregnant and lactating women are significantly lower than that for adult males for each of these nutrients. Within each age-group, the adequacy ratio for males are significantly higher than that for females for each of these nutrients, while pregnant and lactating women have significantly lower adequacy ratio than adult males. For instance, based on fixed effect estimates adult females’ iron adequacy ratio are about 93% lower than adult males’, while the former’s haem iron share is 108% lower than that of adult males. A pregnant woman’s iron adequacy ration is 171% lower than that of an adult male, while her haem iron allocation (as a share of her total iron requirement) is about 178% lower than that for an adult male. Similarly, a female adolescent’s iron adequacy ration is about 18% lower than that of a male adolescent, while her haem iron share is about 25% lower than that of a male adolescent. The degree of sex-disparity in iron allocation is comparatively lower among children as a boy’s iron adequacy ration and haem iron share are about 9% higher than those of a girl. Among children, the magnitude of sex-disparity appears to be higher for vitamin C and D than iron.

Significant intrahousehold bargaining is observed for haem iron share for boys, male and female adolescents, and
where _Y_<sup>�</sup> _jst_ is estimated aggregate TFP (Φ<sup>�</sup><sup>_AGG_</sup> _jst_<sup>),averageTFP(</sup> Φ<sup>�</sup><sup>_U_</sup> _jst_<sup>),orreallocation(</sup> _R_<sup>�</sup> _jst_ ) for industry _j_ and state _s_ at time _t_ , _τ j,t−_ 1 and _τ_<sup>_I_</sup> _j,t−_ 1<sup>arefinalgoodstariffsandinputtariffs,</sup><sup>_Delicj,t−_1</sup> is a dummy variable equal to one if any products in an industry are delicensed, zero otherwise, _FDI j,t−_ 1 is a dummy variable equal to one if any products in an industry are FDI-liberalized, zero otherwise; and _α js_ and _αt_ are state-industry and year dummy variables, respectively. Since our firm data are annual, and policy changes occurred throughout the year, we lag all policy variables by one year. We employ a fixed-effects estimator to estimate Equation 2, and cluster all standard errors at the state-industry level. We use the balanced panel of state-industries in order to avoid confounding within-group effects with the entry and exit of certain industries in particular states, and we weight all observations using the total number of firms in each state-industry group over all years. This ensures that industries (and states) with large firm populations will receive higher weight in the analysis, and will make the results more representative of the all-India level.<sup>16</sup> Table 6 presents baseline results for 1986 to 2004.<sup>17</sup> Column (1) suggests that the trade liberalization is strongly correlated with aggregate productivity increases. The coefficient on final goods tariffs (-0.055) indicates that a 10 percentage point reduction in final goods