rafmacalaba/data-use-mentions-extended huggingface dataset:
has its own row and there is one Links table for each year from 2007 to 2020. The Links table for 2019 has about 1.9 billion ownership links.<sup>25</sup> Additional cleaning to remove duplicates as well as to identify the most up-to-date information for each pair of entities was implemented.

- **Financials:** the financial module contains both the consolidated and unconsolidated balance sheets, income statements, and employment data. The information is presented in a long format where a row corresponds to the data for one entity for one year. Examples of variables in this table include the value of assets, debt, revenues, profits, stock turnover, and an estimation of some financial data for the upcoming year, among others. However, working with the financial module from the vintage files presented several limitations as documented by (Kalemli-Ozcan, Sorensen, Villegas-Sanchez, Volosovych, & Yesiltas, 2015). To overcome this and to work with the most up-to-date financial information, the global database used the financial module through the interface access.

Additional tables containing sector, industry classification, and contact information are also transferred with the main tables. The Links and Entities tables form the Ownership module of the ORBIS data, while the financial module consists of the financial data and the information in the additional tables. For the construction of the Global BOS database, we combine information from the vintage files as well as the online interface. For the identification of SOEs and the creation of ownership structures, we use ORBIS’s

> 22 The following files are available in the raw disks: Entities, Links per year, Industry classifications, Identifiers, Contact info, Additional company information, Legal information, Controlling shareholders, Basic shareholders, Headquarters, Overviews, Cash flow, BvD ID changes, Banks’ financials, Insurances’ financials, Industry's financials, and Key financials.

> 23 ORBIS is not a single database but rather a collection of separate modules and partitions of data that required further processing to
opposed to seeing the government toppled even if they were not necessarily supporting the Houthi forces. These factors are also potentially important when investigating changes in subjective questions on welfare, particularly in dimensions aside from access to food and basic services.

# **4. Data**

We use the 2014 Household Budget Survey (HBS) to analyze whether changes in subjective welfare metrics were similar to the nearly universal worsening of traditional welfare metrics following the capture of Sana’a in 2014. The survey was conducted by the Central Statistical Organization of the Republic of Yemen, and was in progress as Houthi forces captured the capital. Given the capture was accomplished with relatively little violence, the survey was able to continue uninterrupted across the entire country. The survey was conducted over the entire calendar year with roughly equal numbers of households interviewed every month. The total sample size was 9391 households. But only 8772 households had precise information on the dates of the interviews, which is needed to identify whether the interviews were conducted prior to or following the capture of Sana’a.

In Sana’a, there were 314 households surveyed in September through December for which the survey was not completed before the capture of the city; and there were 1905 households where the entire survey was completed before the capture of the city. Importantly, given the sampling design, the survey can identify how households were affected by the capture. The survey was representative at the governorate/capital city level. Additionally, respondents were randomized over space and time, and there were no observable differences in difficult-to-adjust characteristics immediately after the capture of the capital, which might have happened if the onset of the conflict affected the sampling of the survey (Tandon 2019).

The 2014 HBS was a multi-purpose household survey that captured standard information on household consumption, employment, education, health, etc. However, the survey also included a thorough module on subjective welfare. For each of 13 dimen10
of elementary education at first and of secondary education years later. This seems consistent with the trajectory of the Portuguese learning indicator, which fell sharply up to 2005, showed a slight improvement until 2011 and stagnation afterwards, but does not seem to have had the same influence on the math learning indicator. Thus, it is not clear whether the variation in enrollments was able to counterbalance the positive effects expected from the improvement in teachers' schooling.

Figure 1 - Percentage of graduates of secondary education with adequate levels of learning in math and Portuguese and Enrollment in secondary education

|11.6<br>45.4<br>5.4|17.9<br>39.7<br>6.4|11.9<br>27.6<br>7.8|11.6<br>25.8<br>8.4|12.8<br>26.9<br>9.1|10.9<br>22.6<br>9.0|9.8<br>24.5<br>8.4|11.0<br>28.9<br>8.3|10.3<br>29.2<br>8.4|9.3<br>27.2<br>8.3|7.3<br>27.5<br>8.1|
|---|---|---|---|---|---|---|---|---|---|---|
|1995|1997|1999|2001|2003|2005|2007|2009|2011|2013|2015|
||||Math|Portu|guese|Enrol|lment (mil|lion)|||

_Note._ Minimum score on the SAEB scale that characterize
# _3.2. The net financial return to the use of authentic inputs_

The ratio of crop output and input prices ultimately determines whether yield gains translate into increased profits for farmers. We now turn to examine the variation in relative prices across time to assess the profitability of input use for the average farmer.<sup>9</sup> We examine the agricultural inputs for which we were able to identify yields from controlled experimental studies undertaken in Uganda (mainly fertilizer and improved seeds) for crops that capture a high share of agricultural income in Uganda: maize, beans, matooke and coffee (see Table 2.2).

Although input prices are stable over time, crop prices are volatile. There has been some, but relatively modest, variation in the cost of inputs in Uganda over the period 2005/06‐2013/14, as shown in Figure 3.4.4.<sup>10</sup> However, there has been considerable variation in crop prices. This is particularly relevant for the adoption of inputs, as fluctuations in wholesale prices are often passed on to producers who then receive a smaller market share, particularly at the farm‐gate when they sell their crops (Fafchamps and Hill 2008). Figure 3.4 details the volatility in the real wholesale price of maize and matooke from 2000 to 2012 using data on wholesale prices collected by the Uganda Bureau of Statistics (UBOS) in 8 markets as part of price data collection for the consumer price index (CPI). The graphs show the distribution of prices using data only from months in which farmers typically sell (namely November to February and June to August, as reported in the UNPS survey). The price distribution is analyzed separately for each of the four regions.

**Figure 3.4. Distribution of real crop** **<u>prices</u>**

<!-- Start of picture text -->
1. Maize crop prices (2000 ‐
# Table 3: **Summary Statistics** – Respondent and Loan Characteristics

This table reports summary statistics for all accounts in the sample frame. _Debt Relief_ is the total amount of debt relief granted to a household. _Total Debt_ is the amount of total self reported debt at the time of the survey and prior to the program. _Bank Debt_ includes loans from commercial and cooperative banks, _Informal Debt_ includes loans from friends and family, shopkeepers, traders, and moneylenders. _Investment_ in agricultural inputs includes spending on irrigation, nonorganic fertilizer, pesticides and ploughing, gross and per acre, and is averaged over two pre-program seasons. _Productivity_ is measured as total revenue from all agricultural activities, gross and per acre, averaged over two pre-program seasons. Robust standard errors, in brackets, are clustered by bank and district. +p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01 _Panel A: Treatment_

||N|Mean|Median|StDev|Min|Max|ˆ_τRD_|
|---|---|---|---|---|---|---|---|
|Log debt relief, Rs|2,897|9.96|10.08|1.02|2.4|13.53||
|Treated [_≤_2 ha]|1,716|10.44|10.51|0.82|3.09|13.53|1.383***|
|Control [_>_2 ha]|1,181|9.284|9.386|0.87|2.4|11.94|(0.07)|

_Panel B.1: Debt_

||N|Mean|Median|StDev|Min|Max|ˆ_τRD_|
|---|---|---|---
- 22 -

between EPL (often using the OECD’s EPL index) and labor market outcomes, using crosssectional data over time. Interestingly, most of the earlier studies found a negative effect on employment and, in some cases, unemployment (e.g., Scarpetta 1996; Nickell 1997; Nickell and Layard 1999; Elmeskov, Martin, and Scarpetta 1998). Some more recent studies using this approach have found no significant employment impact (e.g., Baccaro and Rei 2005; Bassanini and Duvall 2006).

There has been some skepticism about whether cross-country regressions using aggregate data can accurately identify the impacts of EPL (and, indeed, other labor market institutions). In recent years, a number of researchers have examined the effect of a regulatory change in a single country using panel data (household or firm) and time-series models. A collection of these studies were compiled for a number of Latin American and Caribbean countries in Heckman and Pagés (2004). The results were not conclusive, with some studies identifying a negative employment effect of job security rules (e.g., Kugler 2004 for Colombia; Saavedra and Torero 2004 for Peru; Mondino and Montoya 2004 for Argentina) while others (Paes de Barros and Corseuil 2004 for Brazil and Downes, Mamingi, and Antoine 2004 for three Caribbean countries) found no significant effect. Using a similar approach, Petrin and Sivadsadan (2006) found that changes in EPL in Chile had no significant impact on employment.

Outside of Latin America, there has been very little analysis of the employment impact of EPL in developing countries. The major exception is India where a number of researchers have exploited the state-level variation in labor regulations (e.g., Besley and Burgess 2004; Gupta, Hasan, and Kumar 2009; Ahsan and Pagés 2009). In general, these studies have shown that states with more protective regulations have significantly lower (formal) employment that states with more flexible rules (World Bank 2010, 2012). To
interest here is not to identify the causal effect of having a female or widowed female head but rather to identify differences in conditional means between different types of households. The issue I am addressing is whether FHH and in particular, widowed FFH are poorer than other households, and not whether the fact of being a FHH or becoming a widow caused them to be poorer. From a policy point of view this is the relevant question, for example with respect to targeting on the basis of observed characteristics.

The regressions given in Table 3 focus on the determinants of log household consumption per capita for four different samples. In columns (1) and (2) the samples are restricted to FHH and MHH, respectively. The models prove to be quite different. FHH headed

> 12  Unfortunately, the ELIM survey did not collect information on ethnicity.  I am thus unable to test whether these factors vary across different ethnic groups, although much of the variance due to ethnicity will be captured by the geographic fixed effects since different ethnic groups tend to be concentrated in different areas.

11
WPS3747 full liberalization excluding Indonesia (FLIBX); finally, in the third scenario, we analyze the combined impact of full liberalization including Indonesia (Full-Lib).

Unilateral liberalization, whereby Indonesia cuts all the duties facing imports from the rest of the world, has some impact on “nominal” GDP<sup>7</sup> and generates an increase in both exports and imports. At the sector level, there is some reallocation of factors out of the textile and wearing apparel sector and towards the paper printing, transport equipment and metal products industries. Although the removal of imports tariff hurts the sectors that benefited from higher relative protection levels, the total impact on household income is positive, as can be seen from Table 4. Per capita income increases by 0.6 percent at the national level, a number which is consistent with the increase in “nominal” GDP.

Results suggest that unilateral liberalization would generate an increase in the average per capita household income of 0.6 percent and that the impact on the distribution of income would be negative but small. As a result of the average per capita income increase, poverty decreases modestly. The poverty headcount decreases by 1.0 percent at the national level (from 18.3 to 18.1 percent) and the impact appears higher in the urban areas. Higher order poverty indicators vary by the same magnitude, a result that indicates that the poorest of the poor also benefit from the unilateral liberalization, despite with the slight worsening in the distribution of income. Overall, these changes translate into a total of 375,000 people escaping poverty.

As a result of full trade liberalization in the rest of the world, the Indonesian economy faces decreasing import and export prices but an improvement in overall terms of trade: while the export price index decreases by 0.6 percent, the import price index decreases by 2.3 percent.

> 7 “Nominal” GDP is computed with respect to the Consumer Price Index (CPI). Since the CPI
Figure C.2. Probability of Women’s Marriage Before Age 18 Post the Gujarat Riots of 2002: Borusyak et al. (2021) Estimation <!-- Start of picture text --> Marriage Year<br>Data Source: NFHS-4<br>.1<br>.05<br>0<br>Diff-in-diff Coefficients<br>-.05<br>-.1<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> _Note_ : This figure plots the difference-in-differences estimates from specification 2 using NFHS-IV. The outcome variable is an indicator of women’s marriage before age 18. The control states include all other states of India. The control states only include the bordering states of Gujarat, which are Maharashtra, Rajasthan and Madhya Pradesh. Standard errors were clustered at the state-year level 36
# Measuring other indicators of well-being

To track welfare in countries without household consumption surveys, one can resort to using indirect measures, either non-monetary indicators or non-traditional surveys.

## _Multidimensional poverty index (MPI)_

Many countries have developed “multidimensional poverty indexes” (MPI) to complement existing monetary poverty statistics and provide a more comprehensive perspective on poverty. Expenditure measures may not accurately assess the cost of some goods and services not obtained through markets, thus prices do not accurately reflect their value. Examples of such goods and services include a clean environment, a secure community, public education and health. Measuring multidimensional poverty can be viable for tracking well-being were consumption data are missing.

## <u>Method</u>

The method for calculating MPI, briefly described below, is based on Alkire and Foster (2011). The Commission on Global Poverty, led by Sir Tony Atkinson, recommended the method to measure global non-monetary poverty.

The Alkire and Foster method (AF) consists of two steps. The first is to create a deprivation profile by comparing individuals or households to the respective deprivation cut-off point for selected indicators. The second step is to apply weights (which sum up to “1” or to 100 percent) to each of the indicators, and to sum them for each person to establish a deprivation score of weighted percentage of deprivations. Individuals are then identified as multidimensionally poor if the weighted sum of their deprivations is higher or equal to the poverty cut-off point (United Nations Development Program, 2019).

## <u>Data requirements and pitfalls</u>

Constructing MPIs is data-intensive and requires information on each deprivation for all households. Therefore, information should be based on micro-level data and should come from one source. This is challenging because it is not easy to find microdata with information on all dimensions of non-monetary poverty. Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS) collect comprehensive data on the health status of women and children but have limited
# Table D: The coverage of the SDG labor market indicators by type of survey

|**Target**|**Indicator**|**LSMS‐ISA**|**LSMS**|**LFS**|**DHS**|
|---|---|---|---|---|---|
|**Goal 1. End poverty in all its forms everywhere**||||||
|1.1 By 2030, eradicate extreme poverty for all people everywhere,<br>currently measured as people living on less than $1.25 a day|1.1.1 Proportion of population below the international<br>poverty line, by sex, age, employment status and<br>geographical location (urban/rural)|88%|100%|0%|0%|
|**Goal 2. End hunger, achieve food security and improved nutrition an**|**d promote sustainable agriculture**|||||
|2.3 By 2030, double the agricultural productivity and incomes of<br>small‐scale food producers, in particular women, indigenous peoples,<br>family farmers, pastoralists and fishers, including through secure and<br>equal access to land, other productive resources and inputs,<br>knowledge, financial services, markets and opportunities for value<br>addition and non‐farm employment|2.3.1 Volume of production per labour unit by classes of<br>farming/pastoral/forestry enterprise size|100%|100%|0%|0%|
||2.3.2 Average income of small‐scale food producers, by<br>sex and indigenous status|100%|100%|0%|0%|
|**Goal 5. Achieve gender equality and empower all women and girls
and bridging social capital with economic growth.

In addressing these two problems, and by using a large world dataset, this paper obtains two main surprising results. First, proxies for bonding and bridging social capital appear to describe two sides of the same latent variable, rather than two distinct latent variables. This would imply that bonding and bridging cannot be conceived as two distinct dimensions of social capital but should instead be seen as two opposite sides of a single dimension of social capital, which is a broader concept possibly including other dimensions. This result prompts us to call the bonding/bridging dimension the _locus of socializing_ , in order to emphasize that it is where people mostly enjoy social relationships, whether within their kinship and existing ties, or across new social ties.

2
1996, the ENIGH is also representative for the states of Mexico, Campeche, Coahuila, Guanajuato, Hidalgo, Jalisco, Oaxaca and Tabasco.

The survey design was stratified, multistage and clustered. The final sampling unit is the household and all the members within the household who were interviewed.<sup>4</sup> In each stage, the selection probability was proportional to the size of the sampling unit. Then, it is necessary to use the weighs<sup>5</sup> in order to get suitable estimators. The available information can be grouped in three categories:

- Income and consumption: the survey has monetary, no monetary and financial items.

- Individual characteristics: social and demographic, i.e., age, school attendance, level of

- schooling, position at work, economic sector, etc.

# * Household characteristics.

In addition, data from the Direccion General de Planeacion, Programacion y Presupuesto (DGPPyP, Ministry of Education) regarding educational government expenditures (Federal plus state) assigned to the different levels of schooling for each state is used in order to calculate the unit costs.

- 3The sample in a given year is independent from another.

4The sample size for 1996 is as follows: households 14,042 and individuals 64,3 59. The weights should be calculated according to the survey design and corresponds to the inverse ofthe probability inclusion. 2
||11.0|
|Price of a call to the United States|(US$/min)|**0.39**<br>**0.33**|0.48||0.67|
|Price of inter-Africa calls, mean (U|S$/min)|**0.32**|0.57||n.a.|

Notes: Ghana 2005 data together with benchmarks are taken from the AICD Database. Ghana 2009 is compiled from a variety of World Bank sources to give a sense of the progress made during the last five years. Source: Minges and others 2009. Derived from AICD national database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data — = data not available. n.a. = not applicable.

One of the key explanations for Ghana’s relatively high service coverage has been its relatively low tariffs. In 2005, the price of a standard basket of mobile services was $7.53 in Ghana compared with $11.12 in other comparable African countries. At that time, Ghana also had access to the SAT3 submarine cable through competing international gateways, which can be expected to bring down the costs of international voice and Internet services. The prices of dial-up Internet service and international phone calls in Ghana were significantly lower than in other countries in the peer group with access to a submarine cable. Due to intensifying competition both in the domestic mobile market and on the international gateway, prices have fallen significantly since 2005. The price of a mobile basket is now around $6 per month, and the price of a monthly Internet subscription has fallen substantially from $35 in 2005 to $12 in 2009. Competition in Ghana’s ICT market is set to intensify further as landing rights have been granted to two new submarine cables, and international bandwidth could increase to 2,040 bits per second.

# **Challenges*
especially in markets characterized by low competition among firms in which monopolistic employers can afford to pay male workers higher wages than their productivity would suggest. Trade‐induced competition, however, makes taste‐based discrimination more costly. Oostendorp (2009) studies the role of globalization on the gender wage gap for a large cross‐section of countries and finds that gender wage discrimination falls with more economic development and trade. Evidence for a positive role of trade liberalization in lowering the gender wage gap and increasing female labor demand via reduced discrimination has also been found for Uruguay (Ben Yahmed 2017) and Colombia (Ederington et al. 2009).

But trading could also re‐enforce female discrimination through at least three channels: (i) Trading can increase firm profits through better access to inputs and markets or higher productivity and allows firms to maintain their discriminatory practices (see, e.g., Bernard and Jensen 1999; Ben Yahmed 2017). (ii) Trade‐induced competition could also reduce women’s bargaining power in the labor market (Darity and Williams 1985, and Williams 1987), adding further downward pressure on their wages. Support for this theory has been found in Chinese Taipei, the Republic of Korea, India and China (Berik 2000, Berik et al. 2004, Menon and Van der Meulen Rodgers 2008, and Chen et al. 2012). (iii) A recent study suggests that exporting firms demand highly flexible employees (for working peculiar hours, taking late night phone calls and engaging in international travel arranged at short notice) and therefore discriminate against women as they are perceived to be less flexible. This theory is confirmed in a study on Norway using matched employer‐employee data where the gender wage gap is higher in exporting compared to non‐ exporting firms, but only for higher‐skilled jobs (Bøler et al. 2018). It is not clear if this channel also applies to a developing country context abundant in unskilled workers.

Our paper contributes to the strand of