rafmacalaba/data-use-mentions-extended huggingface dataset:
related analyses in the range of 0.8 and 0.9 (Abadzi, 2011; Piper, 2010). Abadzi (2011) compares various measures on EGRA and refers to the tight correlation between reading fluency and comprehension, consistent with the literature (Piper, 2010; Vagh, 2009; Gove, 2009; Kim et al., 2010). Our own analysis corroborates this. Across 81 country-year observations, we find a correlation of 0.86 (see Figure 1.0).

Figure 1.0: Correlation between Reading Comprehension and Oral Reading Fluency <!-- Start of picture text --> Pearson Correlation Coefficient: .8547<br>NIC<br>MKD<br>TJK IDNIDN<br>AFG KGZKGZ HND<br>IDNKGZKGZ PHL<br>LBR<br>MKD<br>JOR LAOTZARWA TJK PNG<br>GMB SDNRWATUVRWAMMRAFGJOR PNG<br>GMB VUTMMRBDIIRQBGDHTILBREGYEGYETH BDI BGDTONTLSAFGPNGLBRTONKENGUYKIR KEN<br>ZMBGHAMLIGHAMWINGASLEHTINGAGMBMLICODYEMLBRHTILBRCODMARNGASENBGD RWA<br>MWI UGA TLS<br>0 25 50 75<br>ORF score<br>100<br>75<br>50<br>Reading %<br>25<br>0<br><!-- End of picture text --> Despite the viability of both measures, the oral reading fluency sub-task does not have as sound a conceptual link to RSATs and ISATs as comprehension. Moreover, fluency and words per minute used in ORF scores are likely to be more susceptible to differences in implementation and language. Reading comprehension, on the other hand, is not as tightly timed and is less sensitive to language and implementation details. Moreover, it has the most plausibly direct and substantive proficiency link to RSATs and ISATs. For example, reading comprehension on EGRA maps directly onto one of the levels in Levels 1-8 on SACMEQ, the RSAT for East and Southern Africa. Analysis and work done by ACER for the UIS Reporting Scale – a global effort to compare underlying proficiencies across assessments – focuses on
Figure 1: Number of TAs with and without enforceable provisions in government procurement <!-- Start of picture text --> With gov. procurement Without gov. procurement<br>195819601962196419661968197019721974197619781980198219841986198819901992199419961998200020022004200620082010201220142016<br>20 200<br>15 150<br>10 100<br>5 50 Cumulative number of TAs<br>N. of TAs that enter into force<br>0 0<br><!-- End of picture text --> Note: Authors’ calculations on the basis of the Deep Trade Agreements data from Mattoo et al. (2020). We exclude “Partial Scope Agreements”.

Against this seemingly contradicting policy landscape (with unilateral discriminatory measures being adopted together with liberalizing ones), this paper aims to assess the importance of trade barriers in government procurement and identify the impact of trade agreements on cross-border flows. We employ data from the Trade in Value-Added (TiVA) database of the OECD on 62 countries between 1995 and 2015. Our preferred definition of purchases by the public sector sums the “General Government expenditures” and the “Public Administration”, “Health” and “Education” output columns of the inter-country input-output (ICIO) tables. Bilateral trade values are distinguished by goods and services. Descriptive trends in the data show that the public sector spends considerably more on services than on goods compared to the private sector. What is more, the import share of expenditure in government procurement relative to the one in the rest of the economy is particularly low in services, although important heterogeneity emerges across countries.

To move beyond descriptive evidence and estimate trade barriers in government procurement, we apply a canonical gravity framework relating bilateral cross-border procurement flows to different variables proxying for trade costs (or their inverse) and multilateral resistance terms. We also apply the same gravity framework to bilateral trade – – in the rest of the economy what we refer to as the private sector or market which we use as a benchmark as well as a way to observe possible spillovers of procurement-specific
Appendix Table A 1 shows the actual post-lasso model coefficients for the random sample results. In general, areas that are more urban on average are associated with higher female LFP, as mean vegetation intensity is negatively associated with participation, while the correlation is positive for the number of residential roads and street crossings. Consistent with this, areas that have been urbanized more recently, as indicated by the mean year of switching to impervious surfaces, have lower participation rates all else equal. Furthermore, smaller municipalities (by area), many of which are in Southern Mexico, are correlated with lower female labor force participation all else equal.

The relationship between other indicators of urban density and female labor force participation is more complex, however. Areas that are more heterogeneous in terms of vegetation intensity, such as the suburbs, are other things equal associated with higher labor force participation. The relationship between night-time lights and female LFP is another example. The median value of nighttime lights is positively associated with female LFP while the mean value is negatively associated, although the latter is not statistically significant. This nonetheless suggesting that AGEBs with a large number of very bright pixels, perhaps due to the presence of highways, have lower female labor force participation rates.

Some of the infrastructure variables are more easily interpretable, as well, but these are rarely significant. The number of schools is significantly positively correlated with higher male LFP but does not show up for any of the other three indicators.

Appendix Table A2 decomposes model R<sup>2</sup> across different categories of variables. The latter shows that the determinants of male and female LFP differ significantly. For example, the geographic size of the AGEB and municipality, nighttime light luminosity, the mean of the vegetation index, the number of different points of interest recorded in Open StreetMap, and the year that pixels changed to being an impervious surface are important predictors of female LFP. Municipal area is positively associated with female LFP, which makes sense in the Mexican context because many
(*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Intergenerational Mobility Association(IGMA) is the slope of Conditional Expectation Function (CEF). Here, ∆ _IGMAy_ = _θ_ 7 + 2 _θ_ 11 _Eiy_<sup>_p× Inpres_, where</sup><sup>_E_</sup> _iy_<sup>_p_represents</sup> father’s years of schooling for _y_ = 0 _,_ 6 _,_ 9 _,_ 12 _,_ 16. Absolute Mobility is measured by expected schooling conditional on father’s schooling. Here, ∆ _ESy_ = _θ_ 4 _× Inpres_ + _θ_ 7 _× Inpres × Eiy_<sup>_p_+</sup> _θ_ 11 _× Inpres ×_ ( _Eiy_<sup>_p_)2, where</sup><sup>_E_</sup> _iy_<sup>_p_represents father’s years of schooling for</sup><sup>_y_=0</sup><sup>_,_6</sup><sup>_,_9</sup><sup>_,_12</sup><sup>_,_16.</sup> The IGMA and ES values are based on coefficients reported in Table 2. The variable Inpres intensity measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. The Normalized IGMA is the IGMA value relative to IGMA of children for father’s with average years of education (5.57 years), comparison cohort (born between 1957 to 1962), and zero Inpres intensity for the full sample (combined sample of sons and daughters). Normalized IGMA values are reported in percentage. 56 Data sources: Indonesia’s full count census 2000 and Duflo (2001).
Some studies have also analyzed the Taxpayer Opinion Survey (TOS), data collected in the United States in 1987 and providing a broad set of taxpayers‘ opinions and evaluations of aspects as the tax system, the Internal Revenue Service, tax evasion, cheating on taxes etc. The TOS offers the possibility to separately analyze two determinants of tax evasion, overstating of deduction or expenses and underreporting income, as dependent variables. Furthermore, it allows for the possibility of developing different tax morale variables to check the robustness of the results. Unfortunately, the TOS has not been used by many researchers (see, e.g., Smith, 1992; Sheffrin and Triest, 1992). Even if the data set is quite old, the huge amount of questions and the fact that not many papers have used the data set, makes it attractive for newer research projects (see, e.g., Forest and Sheffrin, 2002; Torgler, Demir, Macintyre and Schaffner, 2008; Torgler, Schaffner, and Macintyre, 2010). Furthermore, the TOS has not been conducted post 1990.

In general, extracting only a single question from the WVS also reduces complexity problems inherent with the construction of an index, especially regarding the measurement procedure or a low correlation between the items. Even so, one should recognize that there are some good reasons to use a multi-item index instead of a single question to measure tax morale.<sup>2</sup> Tax morale is likely to be a multi-dimensional concept, which may require a multi-item measurement tool, as in psychometric studies. In this context, a single-item measure like ours has some disadvantages compared to a multiitem index (Lewis, 1982; Jackson and Milliron, 1986). For example, a single-item measure may have difficulty in capturing adequately the interrelated facets of tax morale, and may also be adversely affected by random errors in measurement. Further, a multiitem index has the advantage that errors should tend to average out, therefore producing a more reliable
# **Labor Market Participation and Employment Choice in Ghana: Do Individual Personality Traits and Gender Role Attitudes Matter?**

Akuffo Amankwah<sup>1</sup> , Pauline Castaing<sup>2</sup> , Nkechi S. Owoo<sup>3</sup> , Amparo Palacios-Lopez<sup>2</sup>

- 1 Corresponding Author – Living Standards Measurement Study, Development Data Group, World Bank, Washington DC, email – aamankwah@worldbank.org.

- 2 Living Standards Measurement Study, Development Data Group, World Bank, Washington DC.

- 3 Department of Economics, University of Ghana, nkechi.owoo@gmail.com

**Keywords:** employment outcomes, self-employment, gender role attitudes, personality traits, Ghana

**<mark>WB Thematic Areas</mark>** <mark>: Gender and Employment; Labor Market diagnostics</mark>
The attraction of FYM, from our perspective, is that it is noncontractible. Farmers rarely purchase FYM, but rather typically collect it as a by-product of their own livestock, load it onto carts or donkeys, transport it to the plot and then spread and incorporate it into the soil.<sup>7</sup> This process is extremely labor intensive and thus costly to monitor. The extent and quality of FYM application is also not easily veri fi ed or observed ex-post, since weather and other sources of exogenous uncertainty make it difficult to extract this information from realized output alone.

Direct evidence on the noncontractibility of FYM application can be found in the farm wage employment section of the PRHS, where agricultural jobs held over the past year are enumerated by task. A miniscule 0.08 percent of all agricultural wage labor days fall under the category ”collecting/spreading farmyard manure”.<sup>8</sup> By way of comparison, about 7 percent of labor days are classi fi ed as ”weeding”, another seemingly hard to monitor task, whereas two-thirds of all paid labor days in agriculture are devoted to harvesting various crops. Harvesting work is more readily contractible because productivity per hour is relatively easy to observe.<sup>9</sup> Noncontractibility in itself does not preclude an efficient level of investment. If the landlord is able to commit fully to retaining the tenant for the duration of the investment, a contract that makes the tenant the residual claimant to the returns on his investment would induce optimal investment. The landlord could then simply remove any surplus accruing to the tenant by appropriately increasing his rent. The classic holdup problem arises only when the investment is noncontractible and the landlord is unable to commit. The landlord stands to gain, in this case, by renegotiating the contract terms or contract renewal ex-post. The tenant, aware of this potential for opportunism on the part of the landlord, knows that he is unlikely to recoup all the fruits of his investment.
# **3. Methodology**

We estimate three types of welfare distributions for each country: (a) welfare distributions for 2019 and earlier, (b) a 2020 counterfactual welfare distribution (without the influence of the COVID-19 pandemic), and (c) a 2020 observed welfare distribution. In what follows, we outline the methods to estimate each distribution.

## **3.1 Estimating the welfare distribution for 2019 and before**

Survey data are available for 60 countries (covering 59% of the global population) in 2019. For the rest of the countries that do not have survey data, PIP extrapolates the latest distribution to 2019 assuming all households grow with the real per capita growth rate of Household Final Consumption Expenditure or GDP (see Prydz et al. 2019 for details). This assumes that the only information relevant for projecting older surveys to 2019 is national accounts, and that inequality has not changed since the last survey. Mahler et al. (forthcoming) show that both of these assumptions are relatively accurate.<sup>12</sup> This approach is possible for 168 countries with data available in PIP at some point in time.<sup>13</sup> For the countries that do not have any prior household surveys, we assume their 2019 distribution equals the 2019 distribution of the geographic region they belong to, using PIP’s regional definition. For instance, the 2019 distribution we use for Afghanistan is the populationweighted distribution of the rest of the countries in South Asia. This method is applied to 3% of the world population in 2019.

The distributions for the period between 1990 and 2019 are obtained similarly. In addition, adjacent surveys are interpolated for intervening years that do not have a survey estimate.

## **3.2 Estimating the counterfactual 2020 welfare distribution**

To isolate the impact of the pandemic on poverty and inequality, we construct a counterfactual 2020 welfare distribution that approximates what the income distribution in a country would have
is that efforts to encourage retirement at older ages for both men and women will likely have a positive impact on employment of spouses (Falkinger et al, 1996). Moreover, in rural China and both urban and rural areas of Indonesia, we find that a woman’s employment is more strongly correlated with spouse work status than men’s. In part, this reflects the youth of wives relative to their husbands and the likelihood that women will choose to retire at roughly the same time as their older husbands when there are (effectively) no gender differences in mandatory retirement and pension eligibility ages. In Korea, we observe a positive correlation

> 30Research using census data, which is unable to control for endogeneity of household composition, provides weak evidence that eldercare and childcare may explain a small share of women’s exits from the labor force (MaurerFazio et al, 2011). Giles et al (2006b) find that urban women are more likely to work if they have a college-age adult child, which may reflect a labor supply response the sharp increase in post-secondary tuitions after 1996.

21
0.34<br>(0.008)|
|“access to Safety-net” takes the value 1 if any member of the<br>household availed of safety-net programs in last 12 months and<br>0 otherwise|0.30<br>(0.005)|0.32<br>(0.007)|0.29<br>(0.008)|
|**Upazila Characteristics**||||
|Access to market index|0.38<br>(0.014)|0.40<br>(0.018)|0.36<br>(0.021)|
|Monsoon Coefficient of Variation (Based on Stations)|0.21<br>(0.002)|0.22<br>(0.002)|0.21<br>(0.002)|
|Monsoon Coefficient of Variation (Based on CRU 3.1)|0.18<br>(0.002)|0.18<br>(0.002)|0.18<br>(0.002)|
|“Flood prone Upazilas” are historically Flooded Upazilas of 1998|0.51<br>(0.027)|||

Full list of variables is in the annex Table A1 Household per capita monthly consumption expenditure measure is derived from the HIES 2010 survey as part of the poverty assessment. The consumption expenditure is deflated by rural Dhaka Division prices to account for regional price variations. We control for seasonal differences in consumption expenditure with three seasonal indicators; winter takes the value 1 for households surveyed in 5
# **1. Introduction**

Large firms define exports. There are well known examples, such as Nokia in Finland, Samsung in Korea, and Intel in Costa Rica, each of which accounts for around 20 percent of the country’s total exports. But patterns are not that different elsewhere. On average, the top firm alone holds almost 15 percent of total (non-oil) exports across 32 developing countries between 2006 and 2008. The top 1 percent of exporters accounts for 53 percent of exports on average during the same period. The remaining volume of trade is mainly concentrated in the next tier of large firms. Specifically, the top 5 percent of firms accounts for almost 80 percent of exports on average and the top 10 percent accounts for almost 90 percent. Using a novel firm-level panel, based on highly disaggregated customs data across all regions of the world, we demonstrate the importance of these “superstars" in defining trade patterns and uncover their origins.

The first contribution of this paper is revealing the importance of export superstars—the top 1 percent of firms—in defining comparative advantage. We examine the role of large firms in determining trade patterns in two ways. We begin by exploring their influence on the variation in the sectoral distribution of total exports and of the number of firms across countries. The distribution of exports by product group (HS 2-digit) is very different across countries, as would be expected given comparative advantage. For example, some countries export more footwear and others more fertilizer. Surprisingly, however, the distribution of the number of firms across sectors looks strikingly similar in all countries. In the example above, this implies that despite differences in the total values exported of footwear relative to fertilizers, the number of footwear exporters relative to fertilizer exporters is very similar across countries.

2
States the correlation in the educational outcomes of siblings is 0.576. Dahan and Gaviria (2001) report that this correlation is substantially higher in a sample of 16 countries in Latin America, suggesting that using the educational status of a sibling as a proxy might work even better for developing countries. We also investigated the extent to which the variation in schooling attainment is primarily within or between households in the DHS surveys included in

> 11 Appendix 2 describes two datasets we have created for this project and which are freely accessible to analysts who wish to use them for their own work.  The first dataset includes mortality estimates for all subgroups at the level of each DHS survey that was carried out (that is, for each country and each survey).  The second dataset includes mortality estimates for all subgroups at the level of each country (that is, after we have averaged multiple countryand period-specific mortality rates).

8
To our knowledge this is the first paper that investigates the relationship between informal policies and measures of business success.

## • **Adoption of informal employee benefits**

**Small and medium enterprises in Myanmar provide their employees with ‘informal’ employee benefits.** The literature suggests that there are several different drivers at both the country and firm levels pushing businesses to adopt family-friendly policies. In a study set in India, Budhwar (2000), found evidence that age, size, ownership, type of industry, and union membership, are all positively related to the adoption of HRM policies. Other studies focus on either country level or firm-level characteristics. A country’s regulation and a firm’s compliance (UNICEF, 2019), the size of the firm, and the percentage of female employees (Poelmans, Chincilla and Cardona, 2003 and Ozturkler and Ozutku, 2009), are all positively related factors to the likelihood of a firm having family-friendly policies in place. Overall, a country’s legislation, the dynamics of the business environment, and the overall national culture, play an important role. At the firm level, the sector in which the business operates, the size of the firm, and the ratio of female employees, seem conducive to the adoption of better HRM practices. Evidence suggests that large organizations are better able to provide a broad base of work-family benefits and tend to adopt more policies pertaining to individual support and dependent-care benefits than smaller organizations (Bardoel, Tharenou and Moss, 2013). This is interesting because in our data gathered from micro and SMEs, we find that the majority of these enterprises are offering what we refer to as ‘informal HRM practices’, including provisions for food, housing, sick leave and maternity leave, among other things. We proceed to test whether the main characteristics, recognized by the literature to be conducive to the adoption of these practices, are also significant in the adoption of familyfriendly policies in the private sector in Myanmar.

#
from employment rates using closed-form expressions. Data availability is important because the above measures exclude many developing countries. For example, the sample size of the basic GEGI is double that of MGI (2015) and Devadas and Kim (2020), and 40% larger than Cuberes and Teignier (2016). The GEGIs are not intended to be the most accurate or comprehensive measure of gender employment gaps in countries with very good data, but rather to provide approximate estimates of GDP impacts in _every_ country, and the only estimates for many countries where the data used in other studies is missing.

The rest of this paper is organized as follows. Section 2 discusses the basic GEGI, Section 3 discusses the full GEGI, Section 4 explores differences between the basic and full GEGIs, Section 5 discusses the speed of adjustment of GDP toward the GEGI, Section 6 provides a quantitative comparison of the GEGIs with three other similar papers, Section 7 presents an application for Pacific Island countries and Section 8 concludes. Appendix Table 1 and an online spreadsheet [link] list the full and basic GEGIs for all countries, and the employment data from which they are constructed. Derivations are in Appendices 1-3. Appendix 4 provides adjusted GEGIs for eight countries with unbalanced male-female populations (Bahrain, Kuwait, Maldives, Nepal, Oman, Qatar, Saudi Arabia, and the UAE).

# **2. The basic Gender Employment Gap Index (GEGI)**

3 Goldman Sachs (2007) use a measure very similar to the basic GEGI to calculate GDP gains for 9 high income countries and the Euro area, though do not calculate it for a broader set of countries (and no developing countries). Unlike the GEGI, they claim that these gains are overstated due to “lower productivity, because it [higher employment] reduces the capital-to-labor ratio”, whereas the GEGIs are long run measures that include the effects of induced capital accumulation.

3