rafmacalaba/data-use-mentions-extended huggingface dataset:
these reasons, in her survey of the literature on gender and labor market outcomes, Bertrand (2011, p. 1544) states, “whether this body of psychological research will be more than just a decade-long fad and have a long-lasting impact on how labor economists think about gender differences will crucially depend on further demonstration of its economic significance in real markets.” When facing competition in doing real-world jobs, for instance, women have much more time to plan, receive feedback, and they may be thoroughly trained in the tasks, and for which they have strong confidence, then the apparent disadvantage of women in confronting competition may disappear (Lavy 2012).

In this paper, we use a worldwide sample of firms to study how female-headed firms fare relative to male-headed firms in productivity and its growth, and how the disparity is related to the underlying cultural, institutional, and business environments. Most of the existing studies focus on developed countries and on outcomes related to accounting and stock market performance, corporate financial structure, and risk taking. However, we know little about how female business leadership affects firms’ productivity levels and growth, and whether female business leadership interacts with the local cultural, institutional and business environment in shaping firm outcomes.

Understanding the role of female business leadership in productivity is important. After all, an economy’s long-term growth depends crucially on its productivity level and its sustainable growth (Syverson 2011). By focusing on productivity, we put our emphasis squarely on the key determinants of long-term prosperity. And by focusing on the role of female business leadership in a developing country, we focus on a key area of untapped potential for long-term growth.

We find a significant female leadership productivity disadvantage: conditional on a rich set of conventional controls, female-headed firms exhibited lower productivity level by around 15 percentage points, and labor productivity growth by around 2 percentage points, and these disadvantages are more pronounced in manufacturing than in services, with complete disappearance in service sectors. The disadvantage is also more pronounced in small
as job or life satisfaction. It will then show that in evaluating their position in the ladder, people use or are strongly influenced by variables other than their objective income ranking; for instance, they are influenced by their experience and their health status. These results are in line with what Ravallion and Lokshin (2002) find for the Russian Federation using the Russia Longitudinal Monitoring Survey and with the analysis of Cojocaru and Diagne (2015) for the second round of the Life in Transition Survey, which is part of our sample. Lastly, this section concludes by showing that the subjective ranking, just as other happiness variables, is influenced by individual’s relative income with respect to their reference group.

**Comparing subjective ranking to other well-being measures** The Life in Transition Survey includes three measures of well-being other than the subjective ranking in Cantril’s ladder. These are: (i) financial wellbeing ("Are you satisfied with your financial life?"), (ii) job satisfaction ("Are you satisfied with your job?"), and (iii) overall life satisfaction ("Are you satisfied with your life?"). Answers to these questions are measured in a five step scale. Figure 3 shows the distribution of answers for our measure of interest (aggregated to five steps for comparison) and the three other well-being measures. A bunching towards the upper middle can be observed in the measures of job and life satisfaction, while in the case of financial satisfaction the distribution is more spread out. The distribution of the subjective ranking does not appear to be an outlier when compared to these other measures: while the mean of the distribution is not exactly the same, in all cases there is a certain degree of concentration among specific values, which are typically not far from the middle of the scale.

15
tool data that are identified the same way in the diagnostic research tool data. These different simulations using the QAS levels, the level of a particular QAS dimension, and specific items, illustrate how these different approaches to using QAS data to inform action might perform.

We examine these questions at the district, school, and class levels for a variety of reasons. First, policies might operate on these different levels, depending on the design of the quality enhancement system and administration. Second, measurement error may be reduced when looking at aggregates, particularly if outcomes in a school, class, or district are highly correlated. We examine, for the outcomes that target a certain ranked (top or bottom) percentage, both 10% and 25% targeting. The narrowness or coarseness of targeting may affect consistency. These simulations do not incorporate covariates, or use the multivariate models, but rather describe the average, national consistency of the QAS system as a data source for quality enhancement action.

7
The inequality performance in SSA also raises questions regarding development theory, the application of which would predict a systematic increase in inequality in SSA over the past two decades <mark>(Spilimbergo et al. 1999; B</mark> eegle et al. 2016). Literature on the topic ( _inter alia_ Lewis 1955; Gollin et al. 2014) has noted that, even during the recent economic take-off, large productivity gaps between agriculture and nonagricultural sectors have persisted (MacMillan et al. 2014). According to the Kuznets curve theory (Kuznets 1955; Kanbur 2017), inequality first increases and then declines as a country develops. In SSA, the low initial level of development as well as persistent sectoral and spatial productivity gaps would predict an increase in income differentials during the early growth spurt. However, as mentioned, inequality figures from SSA fail to provide clear evidence for a Kuznets-type trajectory (Ravallion 2005). Also, the existing significant positive relationship between the level of GDP and inequality (Bhorat et al. 2016; Beegle et al. 2016) is in fact driven almost entirely by the Southern Cone.

This puzzle induced scholars like Jerven (2015), to question the veracity of numbers and accuracy of measures used. On one side, many have questioned the extent to which the GDP growth has been driven by structural transformation of African economies. They maintain that much of the recent performance seems to be due to temporary boosts: advantageous external context and making up of lost ground after a long period of economic decline. In other words, describing a growth model mainly due to fortuitous and potentially temporary favorable external conditions (Rodrik 2016a; Bhorat et al. 2017; Diao et al. 2018), they underline that the traditional engines behind rapid GDP growth, structural change and industrialization operated at less than full power, implicitly resizing the extent of the African miracle, especially from a qualitative viewpoint.

All of these explanations have several shortcomings. First, they do not take into adequate account the fact
3  Structural transformation in Japan: employment in manufacturing increased during 1920-1973, followed by a slow decline as the services sector expanded**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>Services<br>70%<br>60%<br>50%<br>40% Ag, forest&fisheries<br>30%<br>Min&Construction<br>20%<br>10% Manufacturing<br>0%<br>1920 1935 1950 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006<br><!-- End of picture text --> Source: Historical Statistics of Japan, shares of employment by sector. Detailed data by subsectors is available at http://www.stat.go.jp/english/data/chouki/index.htm 21
vessels that called in PR in 2019 satisfied these conditions in the LLI data. (See Panel C. in Table 1). A comparison of Panel C with Panels A and B shows that the composition of freight supply that is available on U.S.-PR routes is quite different than it is in the broader Caribbean, or even among PR routes as a whole. With the exception of just two vessels, the LLI data indicate that the JA fleet serving PR in 2019 consisted entirely of container ships and barges. Container ships are responsible for the vast majority of port calls and available DWT. The tankers, bulk, liquid and general cargo vessels that play such a large role in PR and Caribbean shipping are almost absent from the JA fleet.

While the LLI data are useful for comparing shipping in the broader Caribbean, they miss a large volume of barge traffic serving U.S.-PR routes. We redo the same calculations using publicly available data from NBIC (2024), reporting the results in Panel D of Table 1. These data better track barge traffic, resulting in a much larger share of barge traffic in Panel D than in Panel C. Although there are differences between the LLI and NBIC data, the dearth of tankers, bulk, liquid and general cargo vessels is common across the two tables.<sup>20</sup> The near absence of such vessels serving the JA market motivates our subsequent exercises involving the gravity model of trade.

# **5 Theory**

We motivate our empirical work with an adaptation of Krugman and Venables (1996). The model’s prediction for trade in final products is conventional: buyand ownership. We screen first for U.S.-flagged ships, and then check by hand to verify that the firms that own U.S.-flagged ships are located in the U.S. The JA also requires domestic crewing. Our data lack comprehensive data on crews. We assume that the ships arriving in
of subdistricts in Jordan, the characteristics of those 60 and older from the 2004 population census (who have

> 5. Data from the JLMPS 2010 indicate that 94.1 percent of Jordanians between the ages of 25 and 35 who went to basic schools within Jordan were enrolled in public schools run by the Ministry of Education or some other Ministry of the Jordanian government or by the UN Relief and Works Agency for Palestinian Refugees in the Near East (UNRWA), which provides basic schooling to Palestinian refugees in Jordan. For purposes of the analysis, UNRWA schools are considered public schools. The proportion of secondary school students in 2010 enrolled in public schools is 94.7 percent.

9
clusters in Ethiopia could be natural clusters or those supported by government policy. The latter have involved policy interventions to promote activity for firms in particular industries in specific locations (Ali 2012).

The effect of industrial diversity (represented by the Herfindahl index) is negative but statistically insignificant in model (8). Given that most Ethiopian cities (with the exception of Addis Ababa and possibly Dire Dawa) are relatively small, it is no surprise that firms are yet to benefit from economic spillovers related to industrial diversity.

**Business Environment** : The analysis above indicates that a 1 percent increase in taxes leads to a 1.24 percent fall in firms’ employment growth. Since the regression controls for firm-specific controls (such as size, ownership, industry and location), this suggests that increases in taxes on manufacturing firms might have a deleterious effect on employment growth in the future. Currently, Ethiopia ranks 109 out of 189 economies with regard to paying taxes according to the 2014 Doing Business Survey (World Bank 2014).<sup>12</sup> and there has not been a significant overhaul of tax policy since 2004. The average tax burden on manufacturing enterprises has also remained broadly unchanged in the last decade or so, and increased somewhat after the 2004 reform. While Ethiopia uses fiscal instruments such as tax holidays, etc. to attract investment in particular locations and industries (KPMG 2012-2013), it is unclear that this policy is being used effectively. Tax policy ought to ensure a level playing field and in many cases can be misused in that special tax breaks are meted out to large, influential corporations (Gauthier and Gersovitz 1997). Indeed, there is research (see Tybout 2000 for an overview) to indicate that the tax burden falls disproportionately on medium-sized firms in developing countries – small firms often stay informal to avoid taxes and large firms are influential enough to obtain special treatment. Given the country context, it is often medium-sized firms that employ labor, both skilled and unskilled,
covariate shocks. For example, 80 percent of Argentines would turn to family, neighbors or friends if they became unemployed but only 40 percent would turn to this group in periods of wide-spread unemployment (World Bank 2001).<sup>24</sup> The large fraction of workers delinked from formal institutions and the relative frequency of large covariate shocks has led the region to experiment with three mechanisms which vary in goals and which depend to differing degrees on existing institutions for targeting and the quality of administrative capacity.<sup>25</sup> Workfare programs such as _Trabajar_ in Argentina self target by putting wages below market thereby being attractive only to those without other options. The program was less successful in Colombia where legal barriers proscribe subminimum wages payments; instead greater effort was put into conditional transfer programs that relied on well established targeting systems and training programs. These tend to benefit the young and informal sector workers. Conditional cash transfer programs tweaked with counter cyclical triggering devices can help families smooth crises while defusing somewhat concerns about _asitencialismo_ , but they require existing household poverty mappings for targeting which can be administratively complex and are less well suited to target those affected by employment shocks. Training programs for, particularly youth, combine cash infusion with human capital accumulation.

# **Future Work**<sup>**26**</sup>

- **<u>Nature of unemployment shocks.</u>** A mapping of the probability, duration, and

- demographic distribution of employment shocks is, in theory, necessary to know whether systems should emphasize self insurance, as LAC has done, versus pooling risk through true UI as ECA has done.

- **<u>Quantification of effects on labor supply and remuneration.</u>** As a complement, more

- research is needed on the magnitude of the moral hazard issues in terms of reduced search and hence higher unemployment. The most common areas of UI research in the US and European literature is
**Table 9: Reports of secondary activities by recall period, 1992/92 – 2009/10**<sup>**a**</sup>

||Survey<br>IHS 1992/93<br>|UNHS 2005/06|UNPS 2009/10<sup>16</sup>|
|---|---|---|---|
|Short Recall (7day recall)|25.3<sup>~~b~~</sup>|17.4|21.0|
|Long recall (12 month)|30.7|36.7|33.7|
|Difference|5.4***|19.3***|12.7***|

**Source:** Authors calculations from the IHS 1992/93, UNHS 2005/06, UNPS 2009/10. Note: a) ages 15 – 65, unweighted b) Based on 30 day recall period, c) *** indicates significant at 0.1% level of significance (LOS).

The symmetry of the questions across recall period in the Uganda National Household Panel survey (UNPS), 2009/10 allows a comparison of which secondary jobs are missed in the 7 day recall. Missing jobs were mainly wage work (see Figure 1), both farm and non-farm. In both cases, the missing activity in the 7 day recall could be daily labor, meaning that both demand and supply can vary by week, month or season. Not surprisingly, family farm work (reported either as own account or contributing family member) was also under reported in the 7-day recall.

**Figure 1: Comparison of reported secondary jobs by activity type: 7 day versus 12 month recall, 2009/10**

<!-- Start of picture text --> 12 month recall 7 day recall<br>Family worker-farm 317 533
# **VI. Discussion and Conclusion**

The analyses in this paper highlight several key results. We summarize each of these below and consider their implications for project design and implementation.

First, the vast majority (86%) of the 432 playgroups established under the project remained open three years after development aid ended. This is particularly high given evidence from comparable settings. For instance, evidence from 657 community-driven development microinfrastructure projects built between 1999 and 2007 in Indonesia suggest that only 72% were fully functioning when revisited between 2006 and 2008 (Kulongoski 2009). In particular, project playgroups coped with the cessation of donor funding by introducing and in some cases increasing student fees. Both our survey data and qualitative interview data show that project playgroups that closed had struggled to find the financial and human resources needed to continue operation.

The fact that many project playgroups would choose not to charge fees at the outset was not expected at the project design stage. There was no explicit instruction for or against this in the project appraisal document. Thus one implication that follows is that project design needs to consider local ability and willingness to pay when designing such interventions. One possibility would have been to allow project playgroups to pick their own fee levels and to build in a mechanism under the project whereby eligible beneficiary households receive direct subsidies to pay these fees.

Second, we show that project playgroups that increased their share of expenditures for teacher salaries during the project period were significantly more likely to be sustained after the project ended. We find evidence that project playgroups reallocated their resources towards teacher salaries by cutting down on some supplementary services (such as weekly food programs) and scaling up others (such as deworming). Motivated teachers are critical for delivering high-quality education. In order to ensure that projects such as this attract the most qualified talent, it is important not only that salaries be provided but that they do not distort local market conditions. As noted earlier, some teachers under the project received payments that far exceeded local norms.
Policy Research Working Paper 6293

# **Abstract**

Although transfer of agricultural land ownership through land reform had positive impacts on productivity, investment, and political empowerment in many cases, institutional arrangements in West Bengal—which made tenancy heritable and imposed a prohibition on subleasing—imply that early land reform benefits may not be sustained and gains from this policy remain well below potential. Data from a listing of 96,000 households in 200 villages, complemented by a detailed survey of 1,800 owner-cum tenants, point toward binding policy constraints and large contemporaneous inefficiency of share tenancy that is exacerbated by strong disincentives to investment. A conservative estimate puts the efficiency losses from such arrangements in any period at 25 percent.

This paper is a product of the Agriculture and Rural Development 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 kdeinigner@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
in Sub-Saharan Africa). In South Asia, all countries experienced a decline in the intergenerational share, with India registering the largest reduction. The evidence suggests that the evolution of intergenerational share does not depend systematically on the level or evolution of sibling correlation in a country. The share of intergenerational transmission declined in both Pakistan and Bangladesh even though their trajectories for sibling correlation were opposite (an increasing sibling correlation in Pakistan, and a declining one in Bangladesh).

# (7) Conclusions

We provide comparable estimates of intergenerational educational mobility for 53 developing countries using sibling correlation as an omnibus measure, and data from 230 waves of Demographic and Health Surveys. Sibling correlation is an omnibus measure because it captures all the observed and unobserved family and neighborhood factors shared by the siblings when growing up together. Sibling correlation is thus a much broader measure compared to the other widely used measures in the literature such as intergenerational regression coecient, intergenerational correlation (Pearson correlation), and intergenerational rank-rank slope. Another important advantage is that sibling correlation is less susceptible to the biases caused by coresidency restrictions in the surveys as missing older children who grew up many years ago in dierent family and neighborhood environment does not bias the estimates. The Demographic and Health Surveys provide high quality data on schooling of children and father (years of schooling, not categorical), and the data on father's schooling are not based on children's recall. To the best of our knowledge, this is the rst paper to provide estimates of sibling correlation in schooling for a large number of developing countries using high quality data standardized across countries.

The estimates suggest that sibling correlation in schooling in developing countries is much higher (average 0.59) than that in developed countries (average 0.41). We nd substantial spatial heterogeneity across regions, Latin America and Caribbean with the highest (0.65) and Europe and Central Asia with the lowest (0.48) estimates. Country level heterogeneity within a region is more pronounced.
Chong, 2004; Araujo et al., 2014; Moller and Wacker, 2015);<sup>9</sup>

- School enrollment and life expectancy (alternatively child mortality) are included as standard variables in most growth regressions, as they reflect the level of human capital and the potential to generate income (see Weil, 2007 and 2014 for the health angle); and

- Financial development measured by the ratio of credit over GDP (Ghura et al., 2002; Lopez, 2004; Singh and Huang, 2015).

The second group of control variables reflects characteristics which may be less easy to change through policies although potentially important for economic growth and income distribution:

> 8 POVCALNET data are either income or consumption, LIS data are disposable income. We still refer to “income” in our paper.

> 9 We considered alternative infrastructure indicators, such as access to electricity, improved water sources, or paved roads. Their coverage is, however, more limited. They are nevertheless highly correlated with our telecommunication connectivity variable (0.8 for instance in the case of improved water sources).

-8-