rafmacalaba/data-use-mentions-extended huggingface dataset:
the probability of leasing technology from developed countries.

Another important source of eciency gains at the level of the rm is through an increase in management quality.<sup>23</sup> Bank (2017b) provides evidence on the average management quality of rms in Brazil relative to some peers (Mexico, Chile, Turkey and Argentina) and to higherincome OECD countries.<sup>24</sup> It shows that average management quality of Brazilian rms is lower than most comparator countries. Moreover, management quality dispersion of rms in Brazil is higher than in other countries, with a much larger share (18 percent) of rms being poorly-managed. This contrasts with 2 percent of rms in the U.S., 6 percent in China, and 11 percent in Mexico.

This evidence is consistent with the stylized facts previously discussed according to which Brazil has a larger productivity dispersion and a higher proportion of low-productivity rms than other emerging economies. The existence of a large segment of poorly-run rms suggests

> 22See Klenow and Li (2020) for evidence on the importance of young rms for innovation.

> 23See Bloom et al. (2013).

> 24There is available data for many countries, including Brazil, in the World Management Survey.

21
(such as education, gender, and social identity) and local economic conditions. A second key question explores whether participation in non-farm activities leads to improved household welfare, specifically focusing on consumption patterns and poverty reduction. The study also investigates the constraints faced by rural micro-enterprises, seeking to understand both the qualitative and quantitative measures of enterprise performance and the various barriers that might impede their growth. Finally, the research examines whether the historical predictors of rural non-farm employment identified in earlier periods (1999-2011) remain relevant in more recent years, utilizing contemporary data from 2015 to assess the stability and evolution of these relationships. This multi-faceted approach allows for a comprehensive understanding of both the historical patterns and contemporary dynamics of rural non-farm employment in India.

# **2 Data**

This analysis uses data from five distinct sources. The historical data on individual employment and household consumption is obtained from nationally representative employment surveys (focusing on the Rajasthan component) ; an alternate household and individual employment survey is used to gauge the more recent relationship between individual characteristics and rural non-farm employment. The data on micro-enterprise performance is gathered from an establishment level survey of micro-enterprises. Data on bank branches and bank credit are obtained from the Basic Statistical Returns (BSR) published annually by the Reserve Bank of India; and finally, data on annual district-level rainfall is obtained from the ICRISAT’s district-level database.

## **2.1 Employment-Unemployment Surveys**

Our primary analysis exploits rich data collected by various rounds of the National Sample Survey Organization’s (NSS) household surveys. These are nationally representative surveys conducted every 5 years, covering every state and district in India, with a sample size exceeding 80,000 households in each round. The data includes individual characteristics such as labor force participation, sector of employment, and employment type, in addition to gender, age and education levels. Household characteristics such as household location (urban or rural), landholdings, religion, caste and size are also included. Each household (individual)
these surveys for poverty analysis. Other researchers analyze the LSMS-ISA surveys for various topics such as agricultural input uses (Sheahan and Barrett, 2017) or temperature shocks and household consumption (Letta, Montalbano, and Tol, 2018).

> 10 We did not include the 2015/16 round for Nigeria for comparability issues (e.g., the total consumption aggregate for this round does not include health care expenditures and it is not adjusted using temporal and spatial price deflators).

13
Policy Research Working Paper 9944

# **Abstract**

Household surveys remain underutilized for understanding transport choices such as expenditure level and composition, the economic impacts of road accidents, and the economic and distributional impacts of environmental policies such as vehicle restrictions or fuel taxes. This paper reviews more than 30 Living Standards Measurement Study surveys conducted after 2010, non-Living Standards Measurement Study surveys, and two World Bank harmonized household survey databases, to compile and categorize an extensive list of transport-related questions. The paper discusses current limitations in using Living Standards Measurement Study household surveys. Most of the transport-related questions in the Living Standards Measurement Study survey collection are not harmonized across years and countries. Consistent and more detailed data on road accidents and the type and use of vehicles should be added to help design and evaluate road safety and climate policies. A standard set of guidelines and sample questions to be integrated into future household surveys is therefore provided.

This paper is a product of the Infrastructure Chief Economist Office. 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 author may be contacted at mlebrand@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
Second, because the decision on the FAIS allocation by the central government is based on information provided by the latest available population census available, the use of the lagged value of the FAIS circumvents any mechanical correlation between the contemporaneous welfare measure applied and the FAIS. This decision guarantees that, at any period, the outcome measure relies on information from the census at year , while the FAIS is defined using information from the previous census or count, at _t_ − 1. tt However, this specification still suffers from endogeneity because of the presence of simultaneity bias. FAIS allocations are targeted to improve welfare. If the FAIS is sufficiently flexible to respond to local welfare shocks, one will observe more resources being allocated during periods of lower welfare. Not accounting for this simultaneity may result in a downward bias in the estimates, 1. One may even observe a negative effect of the FAIS on welfare that is only explained by the opportune response of the FAIS to local welfare shocks, but not ββ by an actual negative effect of the resources allocated through the FAIS, which may be difficult to explain.<sup>15</sup> To circumvent such a simultaneity bias, the analysis relies on an instrumental variable strategy that exploits the centralized component of the FAIS allocation across municipalities, which should be exogenous to local welfare shocks. The actual allocation of the FAIS is defined in two steps. First, the federal government suggests a formula-based allocation that considers welfare information derived from the latest available population census. The second step is defined by a set of factors that operates at the local and state levels and that may respond to contemporaneous local welfare. The analytical instrument is therefore the predicted FAIS allocation that would have been forthcoming if the allocation had followed only the lines of the federal formula. This allows the analysis to exploit the variations in the FAIS that are not endogenous to local welfare.<sup>16</sup> By design, the formula-based FAIS allocation is highly correlated with the actual FAIS allocation. The firststage
the ACASI module to suit this study population’s needs, considering their illiteracy and minority languages. Questions were recorded on electronic tablets in multiple ethnic languages and participants were taught during survey administration how to select answers by pressing the appropriate color-coded symbols (e.g. green star = yes). A test module on non-sensitive questions included at the beginning of the GBV module confirmed that the participants had learned it correctly. Participants listened to the questions with headphones and selected their own answers, ensuring confidentiality. Studies that have compared the likelihood of revealing victimization experience suggest that the likelihood of reporting GBV is either higher or at the same level with ACASI, as with traditional face-to-face direct questioning, commonly used in demographic and health surveys (Cullen 2020; Dervisevic et al. 2020).

# 5. Empirical Approach

The analytical approach was registered as a pre-analysis plan with 3ie RIDIE (study ID: RIDIESTUDY-ID-5e61e44365ed9). This paper focuses only on the GBV outcomes, and we note deviations from the pre-analysis plan when they occur.

We measure the impact of the RMG program on women and their households through intent-to-treat (ITT) and treatment on the treated (ToT) models. ITT allows us to estimate the population-level effectiveness of the intervention when participants may not comply perfectly with the intervention activities, while ToT identifies the results on those who engaged in the intervention and shows the efficacy that can be achieved if participants comply. We estimate the ITT effect on GBV through the following estimating equation: where is the value of the outcome of interest at endline, β β is a dummy that takes the value of 1 for the treatment group (RMG) as assigned at baseline, and ii1 contains a set of baseline characteristics ii0 yy tt (age, marital status (1/0) ever went to school, ethnicity dummies, household size, winsorized household ii0 XX monthly income and baseline level outcome , when available (as for labor outcomes confirming participation). ii0 yy In the case of perfect compliance with treatment assignment, we can
|**Maximum per capita revenue after**<br>**transfer (IDR)**|9,943,591|4,956,495|2,660,132|5,114,642|
|---|---|---|---|---|
|**Minimum per capita revenue after**<br>**transfer (IDR)**|3,004,333|2,882,973|712,676|2,464,611|
|**Max to min ratio of per capita**<br>**revenue after transfer**|3.31|1.72|3.73|2.08|
|**Coef. of variation**|0.380|0.206|0.499|0.263|
|**Weighted coef. of variation**|0.307|0.185|0.489|0.248|
|**Relative mean deviation**|0.128|0.090|0.220|0.117|
|**Gini Index**|0.171|0.108|0.260|0.135|
|**Weighted Gini Index**|0.139|0.098|0.258|0.130|

Notes:

*) Excluding non-receivers, inequality measures calculated for after transfer per capita revenue

18
sale of second-hand goods in stores; and ISIC 525 Retail trade not in stores; ISIC 526 Repair of personal and household goods; ISIC 55 Hotels and restaurants.

> 18 The firm-level analysis applies both to Russia’s eight greater regions (Northwest, Central, South, Volga, Ural, Siberia, Far East, and North Caucasus), and to its 70 smaller regional districts. The data allow examination only of the number of exporting firms and their operational revenue in a single year, 2012; unfortunately, export and import data are not available in the database used.

13
Policy Research Working Paper 6149

# **Abstract**

While there are many positive societal implications of increased female labor force opportunities, some theoretical models and empirical evidence suggest that working can increase a woman’s risk of suffering domestic violence. Using a dataset collected in peri-urban Dhaka, this analysis documents a positive correlation between work and domestic violence. This correlation is only present among women with less education or who were younger at first marriage. These results are consistent with a theoretical model in which a woman with low bargaining power can face increased risk of domestic violence upon entering the labor force as a husband seeks to counteract her increased bargaining power. By contrast, husbands of women who have higher baseline bargaining power cannot resort to domestic violence since their wives have the ability to leave violent marriages. These findings are inconsistent with the models of assortative matching in the marriage market, expressive violence, work in response to economic shocks, or underreporting of domestic violence. The results on age at marriage are also inconsistent with the implications of a reverse causality model in which women enter the labor force to escape violent situations at home, although the results on education are consistent with that story.

This paper is a product of the Human Development and Public Services 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 rmheath@uw.edu.

_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
second sample is at the individual level (children) and the dependent variable is the school enrollment of children age 5 through 15. The size of this sample is 17,696.

# **3.2. Construction of women’s relative decision-making power (θw)**

There are eight questions in the PSLM survey regarding women’s decision making about their education, employment, use of birth control, having more children, and household food, clothing, medical treatment, and recreation expenditures for married women age 15 through 49. The answers to these questions are broadly categorized as ―woman decides alone,‖ ―household head or husband decides alone,‖ ―household head or husband and woman decide jointly,‖ and ―other family members decide.‖ A woman is considered to have decision-making power on a particular issue if she jointly or by herself makes the decision (equal to 1), since on at least some issues, such as birth control or number of children, women do not have to have the sole decisionmaking power. However, I have also done the calculations by assigning ―jointly making the decision‖ a weight of 0.5 or 0, and the results are very similar and available upon request. Similar scales are also applied to the influential male household members. The results on distribution of women’s decision-making power are quite striking (see Table 1). For decisions on women’s own education, only 18% participated in this decision-making process (alone or in consultation with a male household member), but 86% of men in households participated in this decision (made the decision alone or in consultation with the woman). The results are very similar for decisions on women’s employment. Women have more to say about expenditures on food and clothing, but men still play the major decision-making role on those and other expenditures, particularly medical and recreation expenditures. For decisions on birth control and having more children, most men and women participated in the decision-making
TABLE VI: Heterogeneous Income and Price Effects

||(1)|
|---|---|
|tc|0.0174***<br>(0.006)|
|tu|0.0182*<br>(0.011)|
|tc_×_ y<sup>_f_</sup>|-0.000272*<br>(0.000)|
|tu_×_ y<sup>_f_</sup>|-0.000281<br>(0.000)|
|tc<sup>2</sup>|0.0000488<br>(0.000)|
|tu<sup>2</sup>|-0.00118<br>(0.001)|
|tc_×_ tu|0.00000588<br>(0.001)|
|y<sup>_f_</sup>|0.00399<br>(0.004)|
|Observations|17432|
|R-squared|0.124|

_Note:_ Standard errors are clustered at the locality level and are presented in parentheses. * _p <_ 0 _._ 10, ** *** _p <_ 0 _._ 05, _p <_ 0 _._ 01. Dependent variable: _Enrollit_ . Sample: unbalanced panel of children aged 12-15 years in 1997-1999 from eligible (poor) households with two parents and one parent reports to be the head of the household. y<sup>_f_</sup> denotes father’s weekly per-capita income; tc (tu) denotes the weekly, offered per-capita CCT (UCT). We instrument for father’s per-capita income with average locality wage (excluding the wage of the father in question), and we instrument for _tc × y_<sup>_f_</sup> and _tu × y_<sup>_f_</sup> with the offered
recent application for measuring migration, see Tjaden (2021).</mark>

> 13 For additional information on the Development Data Partnership, see https://datapartnership.org/.

> 14 In a similar manner, establishing linkages between household and mobility surveys can help investigating with greater detail the potential transmission channels through which climate change can lead to changes in the labor market and subsequent cross-border migration (see Section 3.1 for a more detailed discussion on this issue).

7
# 1. **Introduction**

In recent years there has been a surge of interest in the consequences of governance and misgovernance for development. This has been accompanied by a proliferation of data measuring subjective perceptions of various aspects of governance. In this paper, we describe a new governance database containing over 300 such governance measures compiled from a variety of sources.<sup>1</sup> We provide a detailed description of each of these indicators and sources, and construct six aggregate indicators corresponding to six fundamental governance concepts. We conclude with new empirical evidence that governance matters, in the sense that there is a strong causal relationship from good governance to better development outcomes such as higher per capita incomes, lower infant mortality, and higher literacy.

In order to organize our data collection and dissemination efforts, we require a working definition of governance itself. 2 We define governance broadly as the traditions and institutions by which authority in a country is exercised. This includes (1) the process by which governments, are selected, monitored and replaced, (2) the capacity of the govemment to effectively formulate and implement sound policies, and (3) the respect of citizens and the state for the institutions that govern economic and social interactions among them.

The focus of our data collection effort is a set of indicators which measure subjective perceptions regarding the quality of governance in different countries. Our data are drawn from two types of sources: polls of experts, which reflect country ratings produced by commercial risk rating agencies and other organizations, and cross-country surveys of residents carried out by international organizations and other nongovernmental organizations. We do not attempt to compile or present the wide array of available quantitative and descriptive data on cross-country differences in political and social institutions.<sup>3</sup> While these are certainly important determinants of the crossIPending appropriate authorization from original sources, we plan to have the database itself ready for dissemination on the Web late in 1999. 2There does not appear to be a single accepte *
43.3**|**52.5**|**39.3**|**42.0**|**18.7**|**4.0**|**46.7**|**49.3**|**0.9**|**19.3**|**79.8**|

Notes: P stands for primary school education. S stands for secondary school education. P-S stands for post-secondary school education. The number in each cell represents the proportion of workers with each level of education. Teachers include survey respondents who self-identified as a primary or secondary school teacher and a wage worker. Other wage workers include respondents who self-identified as a wage worker in other occupations; university faculty, special education teachers and other teachers were excluded from the group of other wage workers. Primary teachers include respondents who self-identified as a primary school teacher and a wage worker. Secondary teachers include respondents who self-identified as a secondary school teacher and a wage worker. The survey in Malawi did not differentiate the levels of teachers. Source: Surveys listed in Table 1.

35
Table 1. Estimates of Enterprise Population in the Suame Magazine Cluster by Sector

||Garages|Metalworking<br>enterprises|Others|Total|
|---|---|---|---|---|
|2000|4,958|807|2,204|7,969|
|2002|6,222|990|2,618|9,830|
|2003|7,847|1139|2,844|11,830|

_Notes_ . These estimates are taken from the database of the Suame branch of the Ghana National Association of Garages (GNAG). Estimates do not include ancillary trades such as restaurants and telecommunication shops. ―Others‖ include truck body builders, pot makers, sign writers, and some types of welders.

29
calculated from the high-resolution global grid developed by Deveny et al. (2009).  Raster resolution: .0025 decimal degrees. The Deveny data have been drawn from two sources: (1)  Foregone agricultural rents from Naidoo and Iwamura (2007), who integrate spatial information on crop productivity, livestock density, and prices to produce a global map of the gross economic rents from agricultural lands. (2) Spatially-explicit calculation by Kindermann et al. (2006) of income foregone from a one-time timber harvest when timber rents exceed agricultural rents.

17
Columns 5 and 6 show the results for the strategic sectors, i.e. the 11 sectors where Advanced Technology Products are found, and for other sectors separately. The larger coefficient in column (5) than in column (6) indicates that tariffs had a stronger effect on imports in strategic sectors compared non-strategic ones.

We next investigate whether US imports became more diversified across partners as they shifted away from China and whether overall imports grew more slowly in tariffed products, to look for evidence of potential reshoring, using the following specifications: In equations (3) and (4), the dependent variable ΔY is either the total import growth in a product or the change in diversification, as measured by the Herfindahl-Hirschman Index. In equation (4), we also focus on the group of tariffed products and examine whether import growth was slower or diversification greater in products China retreated from most swiftly. In this case, the independent variable is a dummy for the decrease in US market share of China between 2017 and 2022 being greater than the median of products subject to tariffs.

Table 2 shows the results for overall import growth. There is some evidence that aggregate import growth was slower in the tariffed products (column 1), but the coefficient is not statistically significant when the sample is split (columns 2 and 3) and economic effect on all trade is small. Total US import growth was 39 percent between 2017 and 2022 for the set of products in the regression (that had codes that existed in both periods). Overall import growth of the subset of tariffed goods was 38 percent as compared with 43 percent for non-tariffed goods, a 5 percentage point difference.<sup>3</sup> The -0.055 coefficient on all trade suggests a 2.4 (.055 * 43) percentage point difference in growth rates at the product level due to the tariff. This decline is small considering the 40 percent decline in US imports from China in the tariffed goods (Table 1)