rafmacalaba/data-use-mentions-extended huggingface dataset:
A separate regression of state spending on roads and bridges finds that the estimate of the effect of elections on spending is highly insignificant. Putting together the two pictures of increases in road construction without corresponding increases in spending could suggest that the election year effect is driven by greater efficiency in government management.<sup>20</sup> Electoral effect on state roads for big versus small states: This section explores whether the electoral effect on roads is different across big and small states, as measured by the geographic area of the states. The underlying assumption is that the internal state roads network has greater importance for bigger states in order to link all centers of commercial significance within its boundaries, with each other and with the national highways. On the other hand, national highways are relatively more important for smaller states because they may suffice to link major nodes of communication within the state.

The five states with the largest area are, in order, Madhya Pradesh, Rajasthan, Maharashtra, Uttar Pradesh, and Andhra Pradesh. An indicator variable is employed for state size equalling 1 for the five largest states and 0 for the remaining nine smaller states. Table VIII reports the results for the differential electoral effect on state roads for the big versus the smaller states. The results show that state roads increase significantly and substantially in election years for the big states. The difference between the electoral effect for big and small states is significant at the 10 per cent level 20A concern is often raised that governments over-invest in new construction at the expense of maintaining existing assets. Therefore, it could be that in election years funds are diverted from road maintenance to new road construction. However, regressions on disaggregated data on current and capital spending on roads did not show a negative effect of elections on maintenance spending.

26
it is important to acknowledge that there are dimensions of property ownership on which the DHS data cannot provide answers. First, the DHS data only capture the incidence of men and women owning any land and/or housing, and do not provide information on the monetary value of these assets. Second, ownership itself is a complicated context in societies shaped by legal pluralism and informal claims to the property. As discussed in Schlager and Ostrom (1992), property rights can be described along a continuum, which ranges from authorized user to claimant, proprietor and ultimately owner. In this spirit, Kilic and Moylan (2016) distinguish between reported owners, economic owners, documented owners and holder of various bundles of rights. These differing ownership and use rights do not necessarily fall together (Slavchevska et al 2017; Doss et al 2020).<sup>6</sup> The DHS questions that were added to the 6<sup>th</sup> round, capture a concept closest to that of reported ownership but cannot distinguish between different forms of ownership or provide information about the security of ownership.<sup>7</sup> Moreover, even though the DHS are fairly standardized, regional differences in tenure systems may affect the interpretation of questions about individual ownership. Likewise, because the DHS data ask about land and housing in general and do not refer to specific assets (e.g. a specific parcel of land, or a

> 5 The DHSs for South Africa and Liberia collected data only on housing ownership, but the countries are still included in the analysis. We do not include data sets that collect data on women’s (but not men’s) property ownership, or vice versa. In countries where data on women’s and men’s property ownership are available from multiple DHS rounds, only the most recent survey is used. Data sets released after June 18, 2020 are not considered.

> 6 For example, in many parts of Africa, village
date was September 30, 2008. Some additional payments were made after the formal due date; MRFC reports that there is typically no additional loan repayment two months past the due date for agricultural loans. In the empirical analysis we obtain our dependent variables from the August 2008 survey data as well as administrative data from MRFC on loan take-up, amount borrowed, and repayment.

# **_Balance of baseline characteristics across treatment vs. control groups_**

To confirm that the randomization across treatments achieved balance in terms of pre-treatment characteristics, Table 2 presents the means of several baseline variables for the control group as reported prior to treatment, alongside the difference vis-à-vis the treatment group (mean in treatment group minus mean in control group). We also report statistical significance levels of the difference in treatment-control means. These tests are presented for both the full baseline sample and the loan recipient sample.

Overall, we find balance between the two groups in both the full baseline sample and the loan recipient sample. In the full baseline sample, the difference in means for the treatment and control groups is not significant for any of the 11 baseline variables. In the loan recipient sample, for 10 out of these 11 baseline variables, the difference in means between treatment and control groups is not statistically significantly different from zero at conventional levels, and so we cannot reject the hypothesis that the means are identical across treatment groups. For only one variable, the indicator for the study participant being male, is the difference statistically significant (at the 10% level): the fraction male in the treatment group is 6.6 percentage points lower than in the control group.<sup>16</sup>

# **3. A Simple Model of Borrower Behavior**

To study how dynamic incentives affect borrower behavior, Online Appendix D develops a simple model that incorporates both moral hazard and adverse selection. We provide here some

> where the follow-up survey was administered, but as we discuss below in Section 5.C. (see
30|1.73|
||Treated|0.492|0.503|0.522|0.546|0.429|0.514|
||Control|0.363|0.411|0.445|0.481|0.414|0.431|
|Women|Diff(p.p.)|0.129|0.092|0.077|0.065|0.015|0.083|
||Diff(%)|0.356|0.224|0.173|0.134|0.037|0.193|
||t-stat|7.74|6.16|3.68|2.52|1.09|2.74|

Source: Authors’ tabulations using SEDLAC (CEDLAS and the World Bank).

# _Employment outcomes_

The analysis shows that, conditional on being employed, individuals in El Salvador and Guatemala who live in households that receive remittances are more likely to have informal employment. Salvadorans and Guatemalans were 11 percent and 14 percent more likely, respectively, to be informal if they received remittances (Table 5). The analysis suggests that there is no statistically significant relationship between remittances and informality in Honduras. Looking at informality separately by gender reveals that the effect of remittances is larger for men than for women in El Salvador (15.7 percent and 6.5 percent in 2014). This was also the case in Guatemala in 2006, but the effect was about the same size for men and women in 2014 (13.7 percent and 14.6 percent, respectively).

**Table 5 Informality rates by gender, propensity score matching results**

|||<br>El Sa|<br>lvador|<br>Guat|<br>emala|<br>Hon|<br>duras|
|---|---|---|
The result was shorter waiting times but more deaths as a result of the choice of measure. The authors note that the issue here was not intent, but the extent to which one target consumed managerial attention, to the detriment of all else; as they note, it “seems unlikely that hospitals deliberately set out to decrease survival rates. What is more likely is that in response to competitive pressures on costs, hospitals cut services that affected [heart-attack] mortality rates, which were unobserved, in order to increase other activities which buyers could better observe” (Propper at al. 2008).

More recently, in October 2019 the Global Health Security Index sought to assess which countries were “most prepared” for a pandemic, using a model that gave the highest ranking to the United States and the United Kingdom, largely on the basis of these countries’ venerable medical expertise and technical infrastructure, factors which are readily measurable.<sup>22</sup> Alas, the model did not fare so well when an actual pandemic arrived soon thereafter: a subsequent analysis, published in the _Lancet_ on the basis of pandemic data from 177 countries between January 2020 - September 2022, found that “[p]andemic-preparedness indices ... were not meaningfully associated with standardised infection rates or IFRs [infection/fatality ratios]. Measures of trust in the government and interpersonal trust, as well as less government corruption, had larger, statistically significant associations with lower standardised infection rates” (Bollyky et al et al 2022, p. 1).

Needless to say, variables such as ‘trust’ and ‘government corruption’ are (a) hard to measure, (b) hard to incorporate into a single theory anticipating or informing a response to a pandemic, and (c) map awkwardly onto any corresponding policy instrument. For present purposes, the 22 See https://www.statista.com/chart/19790/index-scores-by-level-of-preparation-to-respond-to-an-epidemic/ 12
.378<sup>_∗∗∗_</sup>|-0.529<sup>_∗∗∗_</sup>|-0.649<sup>_∗∗∗_</sup>|
||(0.030)|(0.030)|(0.029)|(0.029)|
|Controls|Yes|Yes|Yes|Yes|
|N|2112|2045|1993|1975|

Note: Standard errors are clustered at the household level. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001

# **7. Impact on Household Balance Sheet Items**

Examining household balance sheet items, such as borrowing and saving, contributes to a better understanding of how households responded to the disaster. In this section, we use qualitative information on household borrowing and saving, from the CPHS, to estimate linear probability models of the following specification: 28
Table 1: Summary statistics, estimation sample, 1995-2013

||1995-2013|1995-2004|2005-2013|
|---|---|---|---|
|TFPR (log)|4.496<br>(0.889)|4.586<br>(0.882)|4.812<br>(0.883)|
|Employment|169.452|218.992|147.329|
||(381.65)|(468.696)|(333.21)|
|Revenues (in 000)|56.145<br>(283.240)|44.482<br>(199.495)|61.354<br>(313.353)|
|Export participation|0.667|0.650|0.675|
||(0.471)|(0.477)|(0.468)|
|Share of foreign owned|0.179<br>(0.383)|0.159<br>(0.366)|0.188<br>(0.391)|
|N (obs.)|135189|41420|92754|
|N (frms)|18355|13712|15674|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. The table reports means and standard deviations (in parentheses) for the estimation sample of manufacturing firms with more than 10 employees over the period 1995-2013 (except 2000-2002). A firm is foreign owned if foreign investors hold at least 50% of capital. Monetary variables are in 2010 prices. TFPR is estimated using the method in Levinsohn and Petrin (2003).

21
collar workers as regards the risk of becoming unemployed, but more similar to white collar workers as far as the probability of leaving the labor force is concerned.

**Inter-sectoral differences** . There are significant differences between sectors of the economy in terms of job destruction over the 2001-2002 period (see Appendix Figure 17 and Appendix Table 19 for estimation results). The highest rates of job destruction occur in hotels and restaurants, the manufacture of wood products, forestry, and fishing. The lowest rates of job destruction occur in public administration, education, and health care, followed by heavy industry, energy, manufacturing of paper products, printing and publishing.

**Previous labor market experience.** Finally, everything else equal, employed persons with less than one year of total experience, as well as those who have been registered as unemployed in the past, have significantly lower chances of finding a job within the year.   Individuals with less than one year of work experience are also more likely to leave the labor force.

# **D.  JOB SEARCH METHODS OF THE UNEMPLOYED**

This section explores patterns of job search by unemployed jobseekers. Use is made of several years of data from the Latvian LFS in order to characterize the activities engaged in by unemployed individuals to find new jobs. Information on the nature of job search is of key relevance in designing policies which aid those who have lost their jobs in finding new work.

**Main Job Search Techniques** Between 1996 and 2000, the LFS provided information only on the main action taken by unemployed individuals to find a job.  As can be seen from Figure 20, the proportion of those who indicate registration at SES (State Employment Service) as their main method of job search has been increasing from about one-quarter in1996 to more than one-third in 1999.Thereafter, use of this method started to decrease, but the SES remains the most popular method of job search.  Private employment services play a minor role in job search activities of the
# II – Data

The primary data used for this study come from the Listening to the Citizens of Uzbekistan survey conducted by the World Bank together with the Development Strategy Center of Uzbekistan and with guidance from the State Statistics Committee for Uzbekistan and other government partners. The study collected information in four distinct modes:

- A comprehensive national survey conducted in-person with a representative sample of 4,010 households. Recipients of social protection benefits were oversampled using registration data maintained at the mahalla level, and sampling weights were adjusted for the inclusion of these household in the baseline results. Full data on household consumption, expenditure, income, remittances, and information on any current migrants were collected. A full module on well-being and views on local economic conditions were also included.

- Administrative data collected from mahalla officials in each of the selected PSUs of the national household survey. This included comprehensive data on all officially registered migrants, the demographic profile of migrants, the registered destination countries, local labor market information, social protection beneficiaries, and related data.

- A nationally representative panel survey conducted monthly over the phone with a randomly selected subsample of 1,503 households that participated in the baseline. The survey also collected comprehensive information on potential/intending/current and returning migrants.

- Qualitative data collected in key informant interviews and focus groups. These data include discussions regarding hurdles to migration and the administrative procedures surrounding migration decisions.

The primary sampling units (PSU) for the L2CU baseline survey were _mahalla_ s, the lowest-level administrative unit in Uzbekistan. A total of 200 PSUs were randomly selected proportionate to size by World Bank staff using a full official list of mahallas provided by the Mahalla Foundation of Uzbekistan. Descriptive statistics of the sample frame, selected sample, and other details are included in Appendix E.

An “omniscient” adult household member was interviewed for each household, preferably the individual with the most information about the household budget, and multiple people could contribute if the primary respondent did not have responses for questions about other household members (
strategic changes.

# 3.2.2. Firm-Level Characteristics

To evaluate how firm characteristics affect firm closure and growth during the pandemic, we include the following set of firm-level variables drawing on prior studies (Beck et al., 2005; Beck et al., 2008; Cheng et al., 2020; D'Souza et al., 2017; Liu et al., 2020; Ullah and Wei, 2017). We include firm size, firm age, three ownership variables, manager’s experiences and labor productivity. All these firm-level variables are obtained from the standard WBES implemented before the outbreak of the COVID-19 pandemic.

The indicator for small firms, _Sizable_ , is a dummy variable indicating a firm’s number of permanent employees being more than 20. Firm age, _Log Age_ , is the natural logarithm of firm age plus one. Ownership and group affiliation are measured in three ways. (1) State ownership, _SOEs_ , is a dummy variable that equals one for firms with government/state ownership, and zero otherwise. (2) Foreign ownership, _Foreign_ , is a dummy variable that equals one for firms with foreign ownership, and zero otherwise. (3) Subsidiary status, _Subsidiary,_ is a dummy variable that equals one if a firm is a subsidiary of another firm, and zero otherwise. Manager’s experience, _Log Experience_ , is the natural logarithm of a manager's years of experience by 2020. Labor productivity, _Log LP_ , is the Log natural logarithm of LP which equals to sales over the number of employees. Employees include both permanent workers (with a weight of one) and temporary workers (with a weight of 0.5).

# 3.2.3. Country Characteristics

We use two country-level variables. The first is a country’s economic development proxied by _Log GDPPC_ , which is the natural logarithm of GDP per capita in constant 2010 US dollars. The second is the index of government COVID policy stringency ( _Policy stringency_
picking up the phone (13.9% of the baseline sample), phone numbers being out of service or belonging to someone else (10.1%), households having their mobile phones turned off (8.8%) and the respondents being busy or not available to participate in the survey (4%).<sup>18</sup> Non-response reasons were similar for previous rounds but at lower rates.<sup>19</sup> Section 4.4 discusses attrition in more detail and describes how it will be accounted for when estimating program impacts.

# _Primary outcomes_

The baseline and follow-up surveys collected information on multiple topics, including household demographics (number of household members by gender and age group, language abilities of household members, and children’s school attendance), household expenditures, diversity of foods consumed, strategies to cope with lack of resources (such as reduced food consumption, selling assets, or return of household members to their country of origin), main sources of income, debt levels and repayments, remittances, etc.

After baseline, but before gaining access to any of the follow-up data, we registered a pre-analysis plan with the <u>EGAP study registration platform, which, unlike the AEA trial registry</u> that is only open to randomizedcontrolled trials, allows the registration of quasi-experimental designs like ours. The primary outcomes were per capita household expenditures, share of school-aged children attending school regularly, and indices for the Food Consumption Score (FCS), Reduced Coping Strategies (RCS), and Livelihood Coping Strategies. The three indices are described in detail below. The components of the indices were included as secondary outcomes in the pre-analysis plan. In addition, we also analyzed impacts on total household expenditures, as we did not anticipate the potentially large spillover effects with respect to household composition.

# _a) Total and Per Capita Household Expenditure_

Households were asked about their monthly expenditures on the following categories: rent, utilities (electricity/heating
provincial level would help address intra-provincial inequity. In addition, provincial-level agencies could develop capacity to establish a viable contracting and performance evaluation arrangement with the providers of care. Provinces could be incentivized to explore alternative service purchasing and payment mechanisms, based on schemes already piloted abroad, to improve efficiency of service provision. Moreover, provincial governments could boost their capacity to monitor the use of public resources by replacing the existing cascading system of transfers (which moves resources through several levels of government before they are actually spent) with direct payments from the provincial treasury system (via a treasury single account that is already operational in many provinces).

Importantly, the central government will have to effectively monitor and evaluate the use of public resources, policy implementation and overall service delivery performance across provinces, holding the provincial governments to account. As an innovative measure, citizen score card surveys could become a useful tool to gather citizens’ feedback regarding their experience with public services (including their ability to utilize primary care and other services, the required fees and out-of-pocket payments, their ability to access relevant information and resolve complaints, and their satisfaction with services and with the performance of service providers, insurance schemes, local government agencies and others). Such a direct mechanism for obtaining citizens’ feedback would allow the central government to better assess policy implementation performance at the local level, particularly with respect to equity and quality in service delivery. (20) The independent assessment could effectively feed into a comprehensive performance management system and help strengthen accountability at the provincial and sub-provincial levels (across government agencies and providers) for the delivery of health care and other public services and their outcomes. The strong monitoring, evaluation and performance management system, internalizing and addressing citizens’ feedback, with respect to service delivery (outputs) and its outcomes as well as public resource allocation at the local level, will boost incentives (and hence allow for a greater autonomy) at the provincial and sub-provincial levels.

In fact, overall improvements in public sector governance reforms are likely to
0.5 percent of the baseline mean.

A few factors could explain this difference. Since there are fewer working-age adults in female-headed households (Table A.4a), these households have less of one of the critical inputs into agricultural production: household labor. In the absence of market frictions, households should be able to flexibly hire labor from outside the household to compensate. But this is not the case in the presence of market frictions, which appears to be the case for most agricultural households (La Fave and Thomas, 2016). For female-headed households, missing income from a working age adult may result in additional liquidity constraints and borrowing may be impossible due to underdeveloped credit markets. Indeed, we did not find any evidence on the use of credit.<sup>17</sup> Our qualitative data corroborate the access to capital channel. We found that the households that appeared to have benefited the most from improved roads were households that either increased productivity by hiring machinery for ploughing and processing crops and transporting them to market or that were able to switch from cropping paddy to trees like acacia or orange. Both of these changes required up-front payment, with returns on the investment following with a lag of a few months or in the

> 16 We are restricted in the types of effects we can analyze because we rely on data from the VARHS for household outcomes and are restricted both by the choice of variables in this data set and the small number of observations for some variables (such as agricultural inputs).

> 17 Results available upon request.

14