rafmacalaba/data-use-mentions-extended huggingface dataset:
in international migration, with an average of 2.2 international migrants each. Remittances from international migrants are an important income source, comprising an average of 11 percent of household total income. Although the number of internal migrants is higher than the number of international migrants, remittances from internal migrants represent a smaller share of household total income—1.7 percent.

Different types of expenditures have different periodicity, and this was taken into account when gathering expenditure information on the survey. Separate sections of the survey form were designed for annual expenditures (household durable goods, housing investments, farm machinery, taxes, health, education, etc.) and monthly and weekly expenditures (utilities, consumption expenditures in markets, butcher shops, from traveling vendors, etc.). For intermittent expenditures (e.g., at butcher shops, tortillerias, markets, etc.), households were asked whether or not they spent money on a given good at some time in 2002, and if so how often, where, and how much each time. Consumption of home-produced goods (e.g., maize) was calculated as output minus sales minus intermediate use (e.g., use of maize as animal feed).

Expenditure data from the survey were aggregated into three consumption categories, four types of investment, and one “other” (miscellaneous) expenditure category (Table 1). The consumption categories include food, except for that purchased in supermarkets; consumer durables (furniture, appliances, etc.); and expenditures in supermarkets. Expenditures in supermarkets were isolated from expenditures on other 20% in 2000, INEGI, Population Census 2000.

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governments and organized groups are quick to publicly hail the programs, which then reinforces perception of good governance and strong outcomes. Tax compliance then improves because a broader number of citizens come to believe that they have a voice in the political system and also because public services improve.

Thus, we also hypothesize that the network effect from individual participants to the rest of the municipalities will be greater when individuals participate as members of groups. One reason is that larger groups are more likely to have a greater impact on policy decisions because the leaders representing larger groups carry more weight in decision-making processes. The leaders then report back to their group about their positive impact, which then spills-over beyond the group members as these citizens take their ideas back to their broader communities. In addition, collective participation through community groups decreases the cost and time burden on the broader population, which then has the effect of overcoming basic collective action problems associated with participation.

Several studies provide evidence for the “collective-input” pathway connecting participation to civil society development and public services. For instance, Hordjik (2006) and Touchton and Wampler (2014) demonstrate that PB is associated with an increase in the number of civil society organizations operating in areas where PB has been implemented. In a similar vein, the literature suggests PB strengthens the provision of public services. For instance, a World Bank study (2008) assessing PB in Brazil finds a strong, positive association between PB and access to water services. Several studies in Brazil also find that PB is associated with higher spending in health care–related areas and lower levels of infant mortality (Gonçalves 2014; Touchton and Wampler 2014). Furthermore, Touchton et al. (2017) connect the presence of Brazil’s policy management councils in areas related to health care with systematically lower infant mortality levels.

We evaluate the potential impact of individual and collective inputs by using data from participatory budgeting, where participants have the right to select infrastructure projects, social programs, or some combination of the two. We
converted into output, while TFP incorporates adjustments in other production factors (e.g., capital stock and intermediate inputs) as well as technology, innovation, management practices, and other ways of augmenting firm output with the same input endowment (Syverson 2011). Although TFP presents a more complete view of productivity, this analysis relies on LP given the lack of reliable data measuring capital or investment, which is a key prerequisite for TFP estimation. As no data on firm-level prices or quantities are available for Zambia, this paper calculates LP in revenue terms rather than in terms of physical quantity. LP calculated in revenue terms reflects not only firm physical efficiency in production but also prices that reflect product quality and marketing in addition to input costs (Cusolito and Maloney 2018). This analysis is unfortunately unable to capture these price-related dimensions and thus cannot definitively conclude that phenomena such as productivity dispersion are the result of allocative efficiency (or lack thereof), quality, or markups. The specific analytical implications of this limitation are further discussed in the subsequent discussion of the results.

Labor productivity is defined as value added per worker for the purposes of this paper. Value added is defined as sales less non-labor cost of sales. The number of workers is estimated by dividing firms’ total wages bills by average annual wages at the ISIC A*38 level (calculated from the PAYE database). Value added is deflated in line with sectoral deflators obtained from ZamStat, which are roughly in line with the ISIC A*38 level of aggregation.

The methodology for decomposing labor productivity growth is drawn from Olley and Pakes (1996), Melitz and Polanec (2015) and Patiño Peña and Ferro (Forthcoming). Labor productivity is estimated and decomposed at the ISIC 1-digit level given the low number of observations per sector at the ISIC 2-digit 5
While there is a long tradition in the literature documenting that political rent seeking can distort the capital allocation process by government-owned banks, economists know little about how it affects the credit supply in an economy without public banks. This paper aims to fill this gap using loan-level data containing all commercial loans in Mexico, which has a fully privatized banking sector. We include a rich set of fixed-effects to absorb any factor driven by firm-specific credit demand, banking relationship and time-varying industry and bank fundamentals. Relying on changes in the committee chairmanship to identify variation in political connection, this study finds compelling evidence that loan terms offered to politically connected firms are substantially better than those available to the counterparts. At the same time, political loans exhibit much worse performance _ex-post_ . Such political lending is mainly offered by large and domestic banks, concentrated in politically connected firms with stronger links to the politician (same industry or municipality) and in the final year of the chairman's term. Banks receive significantly more government borrowings with better credit quality as the reward for offering political rents. We also find that the excess credit supply leads to a large and significant increase in firm’s employment, assets and total liabilities.

The results provide support for the view that banks use lending to politically connected firms to exchange favors with politicians, and confirm the importance of rent-seeking in influencing the lending decisions of financial intermediaries, in this case private banks. The study also provides an insight on the long-standing debate on the long-run benefit of privatization. Privatization advocates often assume that it will necessarily eliminate rent-seeking behavior through competition and crowding out political interventions. However, we still find evidence of political rent in bank lending of a privatized economy. A better way to overcome the root problem of rent seeking may be stricter regulation in terms of increased reporting requirements, scrutiny of illegal transactions, and closer supervision.

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# Table A.9: Child Birth and Number of Children

||Full sample<br>(1)|Rural<br>(2)|Urban<br>(3)|
|---|---|---|---|
|**Panel A: Gave birth**||||
|Fraction of female leaders|-0.021|-0.017|-0.036|
||(0.016)|(0.018)|(0.028)|
|Observations|235,036|166,801|68,235|
|Outcome mean|0.092|0.099|0.076|
|**Panel B: Number of children**||||
|Fraction of female leaders|-0.054|-0.033|-0.113|
||(0.038)|(0.039)|(0.075)|
|Observations|235,036|166,801|68,235|
|Outcome mean|2.394|2.505|2.124|
|State fxed efects|x|x|x|
|District characteristics|x|x|x|
|Individual characteristics|x|x|x|

_Notes:_ Data are from the retrospective woman-year panel data based on the 2015-16 National Family Health Survey (NFHS) of India. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. The dependent variables are an indicator for giving birth in a given year in Panel A and the number of children born up to a given year in Panel B. Column (1) presents estimates for the full sample of women, while columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the victory margin
**_<u>________________________________________________________</u>_**

# **2.2.1 Standards and Technical Regulations**

# _<u>World Bank Technical Barriers to Trade (TBT) Database</u>_

The Bank conducted a survey of 690 manufacturing firms in 17 developing countries for the period 2001-02 to obtain information on relevant standards, government regulations, and technical barriers to trade (TBT) confronting exporters wanting to expand output, exports, or enter developed country markets. The country and sectoral coverage is relevant to allow comparisons. The information will include data on:

- Production costs, including fixed costs;

- Output measures, including domestic sales, exports;

- Measures for assessing market structure;

- Major obstacle facing firms;

- Assessment by firm managers of the cost compliance with TBTs.

Firm managers carried out assessments of the costs of compliance with TBTs in 4 activity types (1) investing in equipment and plant, (2) investing in product re-design, (3) hiring labor for production, (4) hiring labor for product testing. The release date is June 7, 2004.

# _<mark>World Bank: Global Economic Prospects –</mark> Wilson, Mann, Otsuki 2004_

This database by Wilson, Mann, Otsuki, includes four variables, and is constructed for 75 global countries from survey data across countries published in year 2000. Each measure is defined as an arithmetic average of multiple inputs from several data sources that are normalized to be a mean of one. A raw series (which is an observation representing a country) of survey inputs is indexed to the maximum of all the countries’ value for the raw series (e.g. global best practice). The maximum is used as a benchmark since this easily indicates how far a country’s performance is from the best practice country whose indexed value is 1.0. The data set builds on Kaufmann, Kraay and Zoido-Lobaton (2002) (KKZ) and
(as in the previous experiment, the policy leads to a higher incidence of unemployment among the unskilled workers). With regard to distributional effects, the density of formal-sector productivity shifts to the right, reflecting the upward shift in the reservation productivity, while in the informalsector, it shifts to the left.

Two forces may affect log wage inequality in opposite ways: a lower relative price of formalsector goods entails a lower wage rate in the bargaining negotiation, but the rightward changes in the distribution of types and productivities push the average formal-sector wage up. The first effect dominates so mean log wages in the formal sector decrease.

Despite the higher informal-sector good price, the mean wage in the informal sector drops as well as a result of the downward shift in productivities, so the overall impact on the log-wage gap is quite small. Both wage distributions shift to the left slightly (see Figures 38-43). Overall wage inequality is not affected significantly.

Table 11 summarizes the results of the simulated experiments.

# **5 Concluding Remarks**

In this paper I build a search and matching model to understand the impact of labor market policies and social assistance programs on steady state unemployment, informal sector size and wage distribution in a small open economy with search frictions and idiosyncratic productivity shocks. I solve the model numerically and estimate the structural parameters using Colombian household-level data. The model accounts for the division of the labor force among unemployment, informal and formal sector employment, selected moments of the earning distribution, and formal-sector employment duration. I simulate the model and perform a set of policy experiments consistent with the policy reforms implemented in this developing economy in the last two decades.

My contributions to the literature are on two fronts: within the literature of reforms, I provide a new perspective on the impact of the reforms using a structural model; within the search and matching frictions literature with an informal sector, I generalize previous settings by building the informal sector as a ‘disadvantaged’ sector of a dualistic labor market (consistent with empirical
heterogeneity in impacts by subsidy eligibility status (Column 4 in Table 4), we see that, on average, the impact of the credit intervention on toilet uptake was most significant for households which were classified as ineligible for the government subsidy. By 2017, subsidy ineligible households residing in sites where micro-credit for sanitation was made available were 20 percentage points (pp) more likely to own a toilet compared to their counterparts in control areas. This is similar in magnitude to the impact on sanitation loan uptake that we observed in column 2 for this sub-category (22pp). In contrast, for the subsidy eligible group the significant impact on sanitation loan uptake of 18pp observed in column 2 does not translate into a similarly significant impact on toilet uptake. With a p-value equal to 0.126, the coefficient estimate of 8pp is nonetheless close to being significant at conventional statistical levels.

Following ACGMS 2019, in Table 5 we construct a loan-to-new toilet conversion rate, by regressing interviewer-observed toilet ownership on sanitation loan uptake, instrumenting the loan uptake indicator with the treatment assignment. Provided that the treatment improved toilet uptake only through sanitation loan uptake - which we believe to be credible in this context - this estimate essentially tells us the rate at which sanitation loans provided as part of the intervention translated into _new_ toilets constructed _because of the intervention_ . Interestingly, there is significant heterogeneity in the magnitude of the loan conversion rate by whether or not the household is officially eligible for a subsidy. For the subsidy ineligible group the estimated conversion rate is high at 86%, whereas for the subsidy eligible group it is estimated at only 45%.

It is essential to keep in mind that the loan-to-new toilet conversion rate indicates the extent to which the sanitation loans translated into _new_ toilets in the treatment communities. That is, toilets that would not have been constructed in the absence of those loans. To interpret this statistic, it is therefore helpful to consider trends in toilet uptake in control areas. Using toilet ownership status and date of toilet construction reported during
acquire food for the household (such as buying 5 kilograms of rice), which is entered in the diary of the person acquiring the food. So the personal diary is a not an individual’s record of food consumption. Rather, it records the food brought into the household by each member even if for several members to consume (as well as food consumed outside the household). This intensive supervision of the personal diary sample would be impractical for most surveys but these investments were made in order to establish a benchmark for analytic comparisons.

Each of the eight designs varies how food expenditures (including value of home production and consumption) are collected along the lines specified in Table 1. Non-food items are divided into two groups based on frequency of purchase. Frequently purchased items (charcoal, firewood, kerosene/paraffin, matches, candles, lighters, laundry soap, toilet soap, cigarettes, tobacco, cell phone and internet, and transport) were collected by 14-day recall for modules 1-5 and in the 14-day diary for modules 6-8. Non-frequent non-food items (utilities, durables, clothing, health, education, contributions, and other; housing is excluded) are collected by recall identically across all modules at the end of the interview (and at the end of the two-week period for the diaries) and over the identical one or 12-month reference period, depending on the item in question. Any cross-module differences in measured nonfrequent non-food consumption we take as due to spillovers from the different amount of memory training or conditioning of respondents that may be induced by the different food consumption modules.

The survey experiment conducted was termed the Survey of Household Welfare and Labour in Tanzania (SHWALITA), and was implemented by a well-established data collection enterprise, Economic Development Initiatives (EDI). This survey was designed and fielded to study the implications of the alternative survey designs for consumption expenditure measures and labor market indicators (see Bardasi et al., 2010 for a description of the labor survey experiment). Here we focus on the component
Policy Research Working Paper 8789

# **Abstract**

This paper estimates private and social returns to investment in education in Turkey, using the 2017 Household Labor Force Survey and alternative methodologies. The analysis uses the 1997 education reform of increasing compulsory education by three years as an instrument. This results in a private rate of return on the order of 16 percent for higher education and a social return of 10 percent. Using the number of children younger than age 15 in the household as an exclusion restriction, the analysis finds that returns to education for females are higher than those for males. Contrary to many findings in other countries, private returns to those working in the public sector are higher than those in the private sector, and private returns to those who followed the vocational track in secondary education are higher than those in the general academic track. The paper discusses the policy implications of the findings.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at hpatrinos@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 paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
in subsequent F2F interviews indicates that discussing sensitive topics in private initially helps respondents open up to enumerators later, or that respondents wish to maintain consistency in their responses. Overall, our results suggest that introducing respondents to sensitive questions privately, even if on a limited scale, is both feasible given high comprehension, and effective, as it helps respondents ease into difficult topics.

We implemented this experiment during in-person surveys from February to March 2023, involving over 6,000 currently married women from rural-poor households in the Layyah district of Punjab, Pakistan. All surveys were conducted by female enumerators.<sup>2</sup> Respondents belong to poor households which were within a +/- 5 bandwidth of a multi-dimensional poverty score cutoff used to determine eligibility for Pakistan’s national unconditional cash transfers program. All respondents are under the age of 60 years and the average age is 48 years. The vast majority of these women (93%) are illiterate. We relied on questions from the standard IPV module of the Demographic and Health Surveys (DHS), and asked 7 questions on experience of physical violence, 4 on sexual violence and 4 related to injuries due to violence.

The literature so far has focused on comparing the measurement of IPV using ACASI with F2F interviews by randomizing respondents to _either_ answer questions using ACASI or F2F surveys (Park _et al._ , 2021; Cullen, 2023; Peterman _et al._ , 2024). Our experiment exploits within subject randomization instead. Park _et al._ (2021) also report that ACASI based questioning was poorly comprehended by respondents. We successfully addressed this concern by making the ACASI interface more accessible to our respondents (i.e., illiterate, rural-poor women). Moreover, existing studies have generally found significantly higher rates of ACASI-based IPV reporting in Malawi, Rwanda and Senegal but the results are not always consistently higher across all types of IPV, and no significant differences were found for any type of IPV in Liberia (Park _et al._
through three main conversion mechanisms within existing stock: (i) sublease of privatized or public rental units by individual private landlords; (ii) restitution to prewar owners or their heirs; and (iii) acquisition by landlords on the active secondary market that emerged from massive privatization.

# _Letting of Privatized or Public Rental Units_

Cash income-poor owners or tenants who let their own unit or individual rooms were the first source of private rental supply during transition and continue to play a significant role in the poorer countries. The higher the rental income relative to employment or retirement income, the more active are these subsistence landlords. A 1999 survey of the rental markets in Sofia and Budapest (Lowe 2003) showed that 60 percent of private landlords in Sofia rented out a dwelling originally intended for their own use, while only 45 percent did so in Budapest. The main reason cited for the difference was that cash income levels of landlords were lower in Sofia than in Budapest. Without detailed analysis one can conjecture that these figures could be representative of the studied countries, with Armenia, Russia, and Serbia having the highest share of subsistence landlords.

# _Restitution of Prewar Property_

It is hardly coincidental, though difficult to substantiate with data, that Poland and Lithuania are the two countries most advanced in the restitution process, featuring the largest formal private rental sectors. Conversion restrictions in both countries generally forced the restituted owners to continue renting to sitting tenants, rather than obtain vacant possession of their buildings.<sup>43</sup> Countries with slow (Romania) or no (Russia) restitution processes have smaller formal private rental sectors.<sup>44</sup>

# _Units Bought on Secondary Market for Renting_

Secondary market acquisitions have become an increasingly relevant source of private small rental supply since the mid-1990s, particularly in the more established markets of Poland and Lithuania, and to a lesser degree also in Romania and Russia. The initial movement of amateur micro-investors who accumulated portfolios by buying out neighbors gave way to a small private rental