rafmacalaba/data-use-mentions-extended huggingface dataset:
controlling for this trend leaves no variation that can be exploited to identify these coefficients.<sup>8</sup> Our main industry-level female intensity variable ( _FL_ ) is computed as the average share of female workers in each sector in a sample of some 20 developed and developing countries. We favor this measure because it is most representative of the female intensity of sectors in an average country. However, a drawback of our main measure is that it does not include non-manufacturing sectors. To check robustness of our results, we implement an alternative strategy, of constructing a measure of _FL_ based on data for a single country – the U.S.. We thus use the Labor Force Statistics database of the U.S. Bureau of Labor Statistics (BLS). Using data from the Current Population Survey, the BLS publishes “Women in the Labor Force: A Databook” on an annual basis since 2005. It contains information on total employment and the female share of employment in each industry covered by the Census. The data are available at the 4-digit U.S. Census 2007 classification (262 distinct sectors, including both manufacturing and non-manufacturing). In order to construct the share of female workers in total employment in sector _i_ , _FLi_ , we take the mean of this value across the years for which the data on the female share of employment are available (2004-2009). The resulting sample includes 78 manufacturing and 15 non-manufacturing sectors. Appendix Table A1 reports the values of _FLi_ for the top and bottom 5 sectors according to U.S. data. These sectors are similar to what we find in our baseline measure: the least female-intensive sectors tend to be in heavy machinery, while the most female-intensive sectors in textiles and apparel. Appendix Table A1 presents the basic summary statistics of the U.S.-based _FL_ measure, breaking up the manufacturing and the non-manufacturing sectors.

While the U.S.-based alternative _FL_ measure has the advantage of extending the set of sectors to agriculture
_Source:_ World Bank compilation.

Table B.2. Definitions used for household descriptive variables from country surveys

|**Country**|**Land ownership (0 if none, 1 if any)**|**Education**|
|---|---|---|
|Ethiopia|Ownership of land plots|Literacy|
|Lesotho|Ownership or cultivation of land|Level: primary complete|
|Malawi|Land area cultivated|Can read and write|
|Mauritania|Hectares owned|Literacy|
|Mozambique|Number of plots|Can read and write|
|Niger|Land area cultivated|Level: any|
|Nigeria|Land area cultivated|Level: primary complete|
|Zambia|Land area cultivated|Level: primary complete|
|Zimbabwe|Ownership of any arable land|Level: primary complete|

# _Source:_ World Bank.

_Note:_ Country-specific definitions for land ownership and education are based on survey information.

40
to FINCA agent network representatives, hardly any agents quit the business in the first few years of developing the network, precisely because those agents were picked based on ongoing business relationships with branch officers.  We acknowledge, however, that 14% of our sample is comprised of agents that are missing operational data for some or all of the last five months covered by our study. We cannot say with certainty whether they quit or took a break from agent work, which sometimes happened due to liquidity issues, or whether data were missing for some other reason.

> 17 About 10 percent of our transactions data come from agents located outside Kinshasa. We were unable to create reliable measures of income and commercial/financial development for those locations, many of which were larger than a municipality, and thus we have excluded them from the regressions that include municipal-level variables that follow.

9
was imposed against country A in _t_  1 and not against country B until _t_ ). Third, the stock series can also be affected through differential timing in the _removal_ of a previously‐imposed TTB over the same HS‐06 product. For example, if the TTB on trading partner A is removed in _t_  1 but the TTB on trading partner B is not removed until _t_ , this differential timing in the removal will affect series (2). However, there will be no change in series (1) until _all_ previously‐imposed TTBs affecting this product are removed.

# I conclude this section with a discussion of four remaining caveats to my approach.

First, some economies impose TTBs at a level of product disaggregation (e.g., HS‐08, HS‐10) that is finer than the HS‐06 level that is my focus. Nevertheless, examination at the HS‐06 level is desirable for our context, since HS‐06 is the finest level of disaggregation that is both comparable across countries and with available import value data over the 1990‐2009 period. While the application of measures using HS‐06 data will overstate the trade impact (in the level) for any economy that typically does not cover all sub‐products within an HS‐06 category, because my measures are defined consistently over 11
controlling for observable characteristics?

# Achieved samples

As is often the case with online surveys, the achieved sample for the Facebook survey differed from the population on several observable characteristics (see Table 5). Respondents to the Facebook survey were more likely than the general population to live in the West Bank, more likely to be male, more likely to report working in the past week, and much more likely to have completed secondary or higher education. The distribution of observable characteristics in the PPCS sample was more closely (although not perfectly) aligned with that of the population.

**Table 5: The achieved sample for the Facebook survey is farther than the PPCS sample from the population distribution**

|**Indicator**|**Facebook sample**<br>**(unweighted)**|**PPCS sample**<br>**(unweighted)**|**Population**|
|---|---|---|---|
|_Share who live in the West Bank_|69.4|56.4|62.0|
||(1.3)|(2.6)||
|_Share who are female_|24.5|51.8|49.4|
||(1.2)|(0.5)||

> 10 Based on publicly available <u>ACLED data for the West Bank and Gaza from May 6 to 21, 2021, accessed around</u> December 2022 and filtered to the following event and sub event types: Battles, Explosions/Remote violence, Riots, Violence against civilians, and Excessive force against protesters. See also Raleigh et al (2010). 11 See for example Mehvar et al (2021) and World Bank Group et al (2021).

12
this tends to reduce exports initially and to increase them subsequently. The share of educated labor in the population falls steadily over time, as a result of both insufficient public investment and high population growth.

The projections reveal also that, given the above assumptions about Ethiopia’s policies and international environment, prospects for reducing poverty by 2015 are mixed. The table shows the evolution of the headcount index obtained by linking the model to a household survey, using the methodology described earlier. In addition, the table shows the response of the poverty rate to a change in the growth rate of consumption per capita, using three alternative partial elasticities, as discussed 34
details in Appendix F and Blattman et al. (2015).

## **7.1 Approach to validation**

Of more than 4,000 endline surveys, we randomly selected 7.3% and validated answers to six survey-based measures with two-week recall periods. We chose four potentially sensitive behaviors—marijuana use, thievery, gambling, and homelessness. We also chose two everyday expenditures that we did not consider sensitive but could be subject to recall bias or other error—paying to watch television in a video club, and paying to charge a mobile phone.

We used intense qualitative work—in-depth participant observation, open-ended questioning, and efforts to build relationships and trust—to try to elicit more truthful answers. Over several days of trust-building and conversation, plus direct observation, we tried to elicit a direct admission or discussion of the behavior.

We selected and trained eight of the study’s most talented qualitative research staff as validators, all Liberians. In the ten days following the survey, a validator visited the respondent over four days, spending several hours each day in conversation and observation.

32
145-186.

Hamilton, L. C. (1991). "How Robust is Robust Regression?," Stata _Technical Bulletin,_ 2, 21-26.

International Monetary Fund. "International Financial Statistics," 1969 - 1992.

lyigun M. F. and A. L. Owen. (1999). "Income Inequality and Macroeconomic Fluctuations," unpublished manuscript.

Kashyap, A. K. and C. J. Stein. (1995). "The Impact of Monetary Policy on Bank Balance Sheets," _Carnegie-Rochester Conference_ Series.

Kashyap, A. K. and C. J. Stein. (2000). "What Do a Million Banks Have to Say About the Transmission of Monetary Policy?," _American Economic Review,_ forthcoming.

King, R. G. and R. Levine. (1993a). "Finance and Growth: Schumpeter Might Be Right," _Quarterly Journal of Economics,_ August, 108, 715-37.

King, R. G. and R. Levine. (1993b). "Finance, entrepreneurship, and growth," _Journal of Monetary Economics,_ 32, 513-42.

Kiyotaki, N. and J. Moore. (1997). "Credit Cycles," _Journal of Political Economy,_ 105, 2, 211-48.

La Porta, R., F. Lopez-de-Silanes, A. Shleifer and R. W. Vishny. (1997). "Legal Determinants of External Finance," _Journal of Finance,_ 52, 1131-1150.

La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. W. Vishny. (1998). "Law and Finance," _Journal of Political Economy,_ December, 106(6), 1113-55.

Levine, R. (1997). "Financial Development and Economic Growth: Views and Agenda," _Journal of Economic Literature,_ June, 35(2), 688-726.

Levine, R. and S. Zervos. (1998). "
different strata may be matched. To avoid this, rankings need to be rescaled within strata. If possible, the sensitivity of the method to the rescaling should be assessed, for example, by recalculating the decomposition with subsamples of the original data.

This section has outlined the practical issues of applying the ASN methodology to the multidimensional headcount H. We now turn to two different empirical applications to illustrate the performance of the methodology, which highlight the importance of the issues described.

# **4 to cross section data: The case Application repeated of Colombia**

We apply the proposed methodology in the context of the decline in multidimensional poverty observed in Colombia between 2008 and 2012. The Colombian case is interesting for a couple of reasons. Being a middle-income developing country, a large share of Colombians is still deprived in the dimensions considered for cross country calculations of MPI measures. On the other hand, the pace of decline has varied over the and multidimensional declined years. Monetary poverty sharply from 2003 to 2008; while over the last five years, the decline has been less sizable though steady.

## **4.1 Description of the data, the MPI and trends**

We use data from the Colombian Quality of Life Survey for the years 2008, 2010 and 2013. This is the survey that the National Administrative Department of Statistics ( _Departamento Administrativo Nacional de Estad ́ıstica_ , DANE) uses for multidimensional poverty calculations. The survey included around 50,000 households in 2008; while the sample size increased by about 5 percent and 38 percent in the next two rounds, respectively. We were able to replicate the official poverty measures published by DANE closely. For a detailed summary, see Angulo (2010).

The multidimensional poverty index for Colombia includes 15 dimensions grouped into five broad categories: education, childhood and youth, labor, health and standard of living<sup>7</sup> . Each of the categories has a weight of 0.2, which is distributed evenly

> 7This
A final point worth mentioning is the productivity growth of the very small firms in Vietnam. The census of establishments in Vietnam covers firms with fewer than 10 employees. Productivity growth in these very small Vietnamese firms was around 9 percent per year between 2000 and 2017; the corresponding growth rate in Ethiopia is imprecisely estimated at zero percent per year. We do not have an explanation for this difference. The firms with fewer than 10 employees in the Vietnamese enterprise survey are all registered; we do not know the registration status of the small-scale industries in Ethiopia but in 2014, more than 85 percent of these firms had a license. In any case, given the prevalence of small manufacturing establishments in Africa, this difference seems worth investigating.

The evidence discussed so far suggests that employment growth in Africa’s small and informal manufacturing firms is considerably more rapid than employment growth in manufacturing firms which employ 10 or more workers. However, labor productivity growth in the larger formal firms is reasonably strong. This mixture of outcomes is not all bad news. Productivity growth in formal sector firms is indicative of an advancing technology frontier. The rapid growth in small manufacturing firms is indicative of entrepreneurial spirit; indeed, anyone who has spent time in an African country understands the incredible ingenuity of African entrepreneurs. Nevertheless, if African governments want to expand manufacturing exports and employment opportunities for those who prefer formal wage work, they will need to grow their formal manufacturing sectors. How might this happen? In the next section, we consider some of the opportunities.

# **Opportunities**

_African Continental Free Trade Area_ The African Continental Free Trade Area was founded as a free trade area in 2018 with 54 of the 55 African Union nations as signatories (the exception being Eritrea). To date, 36 states have ratified the agreement, and trade under the agreement officially commenced at the start of 2021.<sup>4</sup> Its key functions include progressively eliminating tariffs on intra-African trade (with alternate timelines for implementation based on
, controlling for firm, sector, and year characteristics through fixed effects—if other sector-specific regulations had been introduced, their effect will not be confounded because it is controlled by the fixed effects. The regulatory variables are coded as dummies where 1s imply a change in regulation as defined above. Appendix A.2 presents a compilation table and a summary of important ICT regulatory changes with reference to dates and justifications of the legal changes. For interpretation purposes, the regulations identified are matched to the OECD competition checklist to assess whether they affect the number of suppliers, the ability of suppliers to compete, the incentives of suppliers to compete, or the choices and information available to customers.

Osiptel and Indecopi enforcement data capture the number of investigations of anticompetitive behavior that resulted in a fine or a corrective measure in each year and sector. Additionally, for telecommunications, the data set contains the fines by company name, which are then mapped to a corresponding ISIC code. Each resolution that found infringe14
||||1|
|Denmark|1|1||||
|Egypt,Arab Rep.|1|1||||
|India|2|1|||1|
|Indonesia|1|1||||
|Italy|1|1||||
|Norway|1|||1||
|United Kingdom|3||1|1|1|
|United States|14|4|4|3|3|
|Total|26|9|5|5|7|

The papers rely on a wide range of data, including survey and administrative data. A significant share of surveys are nationally representative, some focus on low-income populations. Some studies focus on a specific subsample – due to methodological demands (e.g., children with younger siblings only). <mark>Table AX <table 1?></mark> presents the details for each study.

# 3. Scope of the measures

## Maternal employment

There was a broad spectrum of how maternal employment was operationalized. 6 studies used exclusively a binary variable indicating whether the mother is employed and 3 studies differentiate between parttime and full-time employment with full-time usually defined as more than 35 hours per week ~~.~~ 13
years in India, and 0.6 years in Indonesia. Gender gap in Indonesia is much smaller, consistent with a large literature showing that girls in Indonesia do not face any signicant disadvantages compared to the boys.

# (4) Evidence on Conditional Variance

Estimates of equations (1) (conditional mean) and (2) (conditional variance) for our full estimation samples (1950-1990 birth cohorts) are reported in Table 2. The estimates for the mobility equation (1) are in odd columns and those for the conditional variance equation (2) are in even columns. The evidence is consistent across the three countries: conditional variance of children's schooling is a negative function of father's education. Estimates from the mobility 10
in reality, a significant share of firms use transport services on their own account which are not captured in the aggregate statistics.

# **4.2 Transport models**

Transport models are built on theories that try to explain and predict the behavior of actors in the transportation system. A transport model typically uses the classical fourstep modeling framework: trip generation, trip distribution, modal split, and traffic assignment (Ortúzar & Willumsen, 2011). As such, this model is suited to assess the impacts of transport policies on transport choices of shippers. Each of the steps might be modeled with different approaches, such as regression and input/output models for trip generation, gravity models for trip distribution, discrete choice and elasticity-based models for modal split, and stochastic and general equilibrium models for traffic assignment. Given their focus, transport models are usually used to analyze the impact of GHG mitigation measures on transport systems. They cover transport costs and shippers’ behavior in utilizing transportation network; this includes the value-to-weight ratio of goods, transported as well as mode and route choices of shippers. The following sections review existing types of transport models.

## **Value-to-weight ratio models**

A study conducted by Ong and Sou (2015) established the relationship between import and export price indexes and the value-to-weight ratio of commodities traded globally using an autoregressive moving average (AMA) model. The model was calibrated using data obtained from the U.S. Census Bureau’s foreign trade division. Martínez et al. (2015) and (ITF/OECD, 2018) introduced a value-to-weight conversion model using a number of socio-economic indicators like GDP, GDP per capita, trade agreement, contiguity, transport time, and distance. The model is based on a Poisson regression model. It is calibrated using values and weights data for 25 commodities obtained from ECLAC (Economic Commission on Latin America and Caribbean) and the Eurostat database on exports. This model has developed to take into account the impact of transport costs for different commodities
> (j)<br><!-- End of picture text --> <!-- Start of picture text --> (k)<br><!-- End of picture text --> <!-- Start of picture text --> (l)<br><!-- End of picture text -->

# **3.2 Structural transformation**

**Human capital is not equally distributed across sectors and space** . Across multiple countries, highly educated workers are less likely to work in agriculture and live in rural areas (Gollin et al., 2014; Young, 2013), and more likely to work in skill-intensive services such as education, finance, public administration and health (Herrendorf and Schoellman, 2017).<sup>4</sup> Figure VIII visualizes this pattern by plotting average years of schooling across four broadly defined sectors against GDP per capita. High-skill services is the most schooling intensive sectors, followed by low-skill services and manufacturing, and then agriculture. Interestingly, these cross-sectors differences in schooling are largest in poor countries and tend to diminish with development.

**Returns to education are generally larger in sectors with many highly educated workers and urban areas** . Herrendorf and Schoellman (2017) document across 13 countries that an additional year of schooling is associated with a significantly larger increase in wages in outside of agriculture compared to agriculture. Figure IX makes use of the World Bank I2D2/GLD database to generalize this finding to a larger sample of country - on average, the return to schooling is 9% in non-agriculture and 7% in agriculture. One caveat for these comparisons is that many agricultural workers in poor countries are self-employed, and as such are not in-

4Moreover, when focusing on workers moving across sectors, the more educated agricultural workers are more likely to leave agriculture, while the less educated non-agricultural workers are more likely to move to agriculture (Young, 2013; Hamory
through an experiment and measure the effects on spending using the administrative data.

The remainder of the paper is structured as follows. Section 2 provides a theoretical framework to motivate our experimental design and develop our hypotheses. In Section 3, we summarize the setting, research design, and implementation of the experiment. In Section 5, we describe additional data sources and provide descriptive statistics. Section 6 reports the results, and Section 7 concludes.

# **2 Theoretical Framework and Hypotheses**

We use a standard model of intertemporal consumer choice to motivate our experimental design. Letting subscript _t_ denote the time period, we assume that the consumer faces an exogenous stream of nominal income _Yt_ and can have positive or negative holdings of an asset _At_ that pays an exogenous nominal interest rate _Rt_ . There are four types of consumption goods, which we can classify according to their durability and tradability: durable tradables (denoted _Xt_<sup>_T_),durable</sup> nontradables ( _Xt_<sup>_N_), nondurable tradables (</sup><sup>_C_</sup> _t_<sup>_T_), and nondurable nontradables (</sup><sup>_C_</sup> _t_<sup>_N_).We assume</sup> that durable goods depreciate at a rate of _δ_ , tradable goods (both durable and nondurable) have an exogenous price _Pt_<sup>_T_,andnontradablegoods(bothdurableandnondurable)haveprice</sup><sup>_P N_</sup> _t_<sup>.</sup> The consumer gets utility _U_ ( _Ct_<sup>_N, X_</sup> _t_<sup>_N, C_</sup> _t_<sup>_T, X_</sup> _t_<sup>_T_)fromagivencombinationofgoods,whichis</sup> concave in each of its arguments and has a discount factor _β_ .

The consumer’s optimization problem can thus be summarized as follows: subject to We denote the exogenously given
women select into paid employment in Ghana (Owoo et al. 2022), and the education and literacy of family members are strong predicators of women selecting into paid employment in Pakistan (Hussain, Rabbi, and Ali 2012). These findings are consistent with the results here and likely explain why there are not large gender gaps in the job quality of wage employment.

Figure 5. JQM among the Wage Employed, by Education, Location, and Age-Group <!-- Start of picture text --> 4.00 100%<br>3.50<br>80%<br>3.00<br>2.50 60%<br>2.00<br>1.50 40%<br>1.00<br>20%<br>0.50<br>0.00 0%<br>Male Female Primary Secondary Tertiary Rural Urban 16 - 24 25+<br>Sex Education Location Age group<br>Income Benefits Stability Working Conditions Share of Wage Employees<br>JQM<br>Share of Wage Employees<br><!-- End of picture text --> _Source:_ Estimates based on multiple labor force and household surveys (see annex table A1). _Note:_ The sample includes 37 countries with information for all dimensions. This figure shows the JQM by sex for the wage employed only and the share of the wage employed among all workers by sex. The sample for the education portion excludes two countries without information on education, and the sample for the location portion excludes nine countries without information on location.

The analysis of quality of the jobs among other disadvantaged groups of the population offers additional insights into disparities in labor markets across developing countries. Common disadvantaged groups are those less well educated, living in rural areas, and youth. In some countries, young people usually have unpaid jobs, and, even if young people are paid, they are less likely to have benefits and