rafmacalaba/data-use-mentions-extended huggingface dataset:
education and full health. Prior to the COVID-19 pandemic, a child born in a typical country in Sub-Saharan Africa could expect to achieve just 40 percent of her potential human capital, relative to a benchmark of complete education and full health. The data is collected from the World Bank (2018, 2020d). Another indicator in this dimension is the global health security index, an index that takes values from 0 to 100, that captures the prevention of the emergence or release of pathogens, early detection and reporting for epidemics, rapid response to and mitigation of the spread of an epidemic, sufficient and robust health systems to treat the sick and protect health workers, compliance with international norms, and risk environment and vulnerability to biological threats. The data is taken from NTI and Johns Hopkins University. Finally, we gathered information on health indicators such as hospital beds (per 1,000 people), physicians (per 1,000 people) and nurses (per 1,000 people). These indicators are gathered from WDI.

_Macroeconomic Space_ . The variables of macroeconomic space provide information on whether countries had some extent of macroeconomic resilience prior to the pandemic. These variables aim to capture whether the countries had fiscal space, monetary policy space, less risky debt profiles and external savings. We specifically examine the impact of having (liquidity or policy) space on growth amid the pandemic. Fiscal space is proxied by the general government primary balance and the general government gross debt —and both variables are expressed as a percentage of GDP. Monetary policy space is indirectly measured by the rate of annual CPI inflation. External savings are proxied by the current account balance. Finally, the risk profile of public debt is captured by including the composition of public debt by creditor. The regression analysis includes public and publicly guaranteed external debt owed to official creditors (bilateral and multilateral lenders), and to private creditors (bonds, commercial banks, and other private creditors). The data is collected from the IMF’s World Economic Outlook
||Mean|Mean|Mean|Mean|Year|Source|
|---|---|---|---|---|---|---|
|Rural|23.8|22.2|22.3|25.0|2016|UNHS|
|Urban|9.0|9.6|10.2|9.6|2016|UNHS|

# 6.2. Empirical evaluation of the longitudinal estimates

Panel surveys produce net change and gross change estimates (Section 2.2.2). Gross change offers valuable insights into net change, so it is important that the cross-sectional estimates achieved by transition matrices are consistent with the estimates obtained with the overall UNPS 2015/16 sample.

Table 6.5 shows the dynamics of employment across the 2009/ 2015 and 2013/2015 panels. In particular, the first three columns report the employment status in 2015 with respect to the status in 2009. Only individuals present in both 2009 and 2015 are represented in the table. The last three columns show the same statistics for those individuals in 2015 who also responded to the survey in 2013.

Focusing on the 2009/2015 estimates, we note that all estimators produce upward estimates with respect to the estimates presented in table 6.2. This result suggests that the individuals who left across the years – characterizing most of the attrition – were largely unemployed. The three estimators indicated that the panel comprised 78.6%, 77.6%, and 81.4% employed persons. The refresh operation rebalanced the panel. Focusing on the cross-sectional estimates given by the 2013/2015 panel, 77.4% of individuals were employed according to the unweighted estimator, 76.7% for the current UNPS estimator, and 80.8% for the calibrated GWSM base weight estimator.

We can state that the last estimator produces more stable and consistent estimates when considering different sub-samples, i.
publications of Customs Notices and Reports at the Australian Customs website, as well as on-site inspection of Customs Documents at the Australian Customs Office in Canberra. Information about Australia’s antidumping process is available as an online public document named “Australia’s AntiDumping and Countervailing Administration”, which can be accessed via the Customs website. For a discussion of Australia’s antidumping process see also Moulis and Gay (2005).

In addition to the standard data described in section 2, the Australian spreadsheets contain additional data, which are described in the table below.

**Table 3.2.1 : Additional Australian Data Included in AD-AUS-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-A**|**US-Master :**||
|**U.**|**P_DUTY(MIN)**|The minimum preliminary anti-dumping duty imposed|
|**V.**|**P_DUTY(MAX)**|The maximum preliminary anti-dumping duty imposed|
|**W.**|**F_DUTY (MIN)**|The minimum final anti-dumping duty imposed|
|**X.**|**F_DUTY (MAX)**|The maximum final anti-dumping duty imposed|
|**Y.**|**INIT_GP_ACDN**|The government publication (Australian Customs Dumping Notice)<br>containing the initiation of the case|
|**Z.**|**P_GP_ACDN**|The government publication (Australian Customs Dumping Notice)<br>containing the preliminary findings of the case|
|**AA.**|**F_GP_ACDN**|The government publication (Australian Customs Dumping Notice)<br>containing the final findings of the case|
|**AB.**|**GP_ADD_ACDN
# **6. Conclusion**

This paper documents the degree to which women in developing countries are excluded from the formal financial system and gender difference in the use of formal and informal financial services. We use individual-level data from the Global Financial Inclusion Indicators (Global Findex) database to show that there exists a persistent gender gap, and that even after controlling for a host of individual characteristics including income, education, employment status and age, gender remains significantly related to the use of financial services. Moreover, the results show that gender is related to measures of financial inclusion not only directly but also indirectly, through gender differences in income, education, and employment status.

We also explore the degree to which economy-wide legal discrimination against women and gender norms can help explain this gender gap. As a result of differential treatment under the law or by custom, women may have less ability than men to own, manage, control, or inherit assets and property, which in turn might affect women’s access to and demand for financial services. Using data from the World Bank’s Women, Business and the Law database, our analysis shows that in countries where women face legal discrimination in the ability to work, head a household, choose where to live, or inherit property or are required by law to obey their husband, women are less likely than men to own an account and to save and borrow. We also consider gender norms as quantified by the Organisation for Economic Co-operation and Development’s Gender, Institutions and Development Database, such as the level of violence against women and the incidence of early marriage for women. The results confirm that gender norms are also significantly related to women’s use of financial services.

The relatively low use of financial products by women may increase their vulnerability to income shocks and reduce their ability to invest, save, and plan for the future. Improving 26
# **Estimating Road Freight Transport Costs in Eastern Europe and Central Asia**

# **Using Large Shipping Data**

Atsushi Iimi<sup>¶†</sup> Eastern & Southern Africa, Transport The World Bank Key words: Road freight rates, shipping costs, regional integration.

JEL classification: R41; C21; O52.

> ¶ Corresponding author.

> † This paper is a background paper of the Shrinking Economic Distance study.
is the variation in infrastructure development. However, only a few studies have examined the underlying factors responsible for the differential development of infrastructure across the Indian states. Using the panel data from 18 Indian states between 2004 and 2020, this study empirically analyzes the factors influencing infrastructure development across the states. The results show that although the inter-state difference in infrastructure levels has been narrowing over time, it is still high in all three types of infrastructure considered in the study – physical, social, and financial-. Kerela, Himachal Pradesh, and Karnataka have performed well in all three infrastructure indices, while Bihar, Uttar Pradesh, and Jharkhand have been lagging behind other states.

The determinants for physical, social, and financial infrastructure development are different. While economic factors are relatively more influential for physical and financial infrastructure, fiscal and demographic factors are more relevant for social and financial infrastructure. There is also complementarity between these three types of infrastructures. An increase in physical infrastructural assets is found to promote both social and financial infrastructures. Financial development also positively impacts both physical and social infrastructure development, as the availability of credit and financial instruments at a reasonable cost is important for public and private infrastructure investment.

The level of economic development is the primary driver for the endowment of all three types of infrastructures as it contributes to infrastructure development through both demand and supply pulls. Similarly, an increase in investment leads to infrastructure development both directly and indirectly. The latter occurs through its multiplier effect on the economy. Fiscal factors such as government expenditure (capital and social expenditure) and debt have differential effects on the different types of infrastructures. Contrary to expectations, an increase in debt seemed to promote infrastructure growth for all three kinds of infrastructure.

The study also confirms that demographic factors such as urbanization, population density, and proportion of SC/ST population have affected infrastructure development. Intuitively, a rise in urbanization increases the demand for physical and social, leading to an increase in the supply of such provisions. As expected, the higher
covering the entire period to the end of August 2019. A total of 18,224 unique panel observations are available for analysis, excluding the baseline data collected before the phone-based panel began.

Attrition is one potential concern using panel data of this type. To ensure that non-take-up in the first round (and attrition in subsequent rounds) did not seriously affect the required sample size for survey representativeness, households that refused to participate were replaced with other households drawn from the same sample cluster. However, any systematic difference in the household characteristics due to refusal to participate could lead to bias if the replacement households were different on average (with respect to observable characteristics) from the household that refused. Among the random sample of 1,500 households originally drawn from the baseline, about 25% refused to participate in the first round (i.e. initial take-up in the first phone round totaled 1,122 randomly sampled households, and 381 randomly selected replacement households to make up for those that refused or could not be contacted). Attrition rates (or nonresponse rates) have tended to be low and stable across rounds of the L2CU panel survey, ranging from 1 to 6 percent, and 72 percent of households that completed the first round completed all 12 used in the analysis reported here,<sup>6</sup> comprising 85 percent of the total interviews completed. These results are particularly encouraging if compared to similar high-frequency surveys. Other examples World Bank high-frequency surveys in Africa have resulted in similar rates of attrition, or higher (Demombynes et al. 2013; Croke et al. 2012. A similar study in Tajikistan (Listening to Tajikistan) that began in 2015 met with similarly high rates of compliance.

To take non-take-up and attrition into account, the participating sample is reweighted by developing a model using observable and relatively time-invariant characteristics from the baseline to predict the probability of dropping out for each household. Responses are then weighted to account not only for the sampling design but are also reweighted with respect to baseline
Define _DWORK_ =1 when the student has a job on the side and 0 otherwise. The proposed econometric strategy is to estimate a PROBIT-model of the form Table 5A and 5B present the results for the SOFES database. For Bachelor-students (Table 5A) mildly positive but mostly insignificant treatment effects of student loans are found around DS=0.08 and DS=0.15, while a negative impact is found at DS±0.30. In the latter case, an effect is found in the order of magnitude of – 0.1, implying a 10%-point drop in the probability of work for students with a higher credit. This result survives the introduction of controls. For Master-students (Table 5B), we typically find a positive impact from treatment on the probability of part-time employment, especially around DS=0.08.

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operating costs, and sales and other income. The reference period for the data was the 2013 calendar year. See GSS (2015) and GSS (2016a) for further information on the design and implementation of the IBES.

We measure labor productivity at the enterprise level, as the enterprise’s value-added per worker (in thousand cedis). In the IBES, the GSS defines a formal enterprise as one that is registered with the Registrar-General’s Department (RGD) and professionally maintains its financial accounting records (GSS 2015). We adopt the same definition in this study.

Our main analysis focuses on nonfarm establishments, toward examining the effects of the regional disparity in intersectoral misallocation on national nonfarm productivity. For some ancillary results that connect the regional disparity in intersectoral misallocation to national aggregate productivity, we estimate productivity based on the small sample of farm enterprises available in the IBES.<sup>8</sup> From here on in, we refer to nonfarm establishments as enterprises unless otherwise specified; in addition, we frequently refer to labor productivity as simply productivity, and employment in enterprises as simply employment.

B. _Informal and formal enterprises and employment by region and sector_ Figure 6 plots the distribution of enterprises by formality status and region. The number of informal enterprises exceeds that of formal enterprises in each region.<sup>9</sup> Richer regions have more informal and formal enterprises than poorer regions.<sup>10</sup> Figure 7 plots the distribution of employment by formality status and region. Except in Greater Accra, the number of informal workers exceeds that of formal workers in each region.<sup>11</sup> Richer regions have greater numbers of informal and formal workers than poorer regions. As the information in Figures 6 and 7 collectively show, while Greater Accra dominates other regions in terms of the numbers of enterprises and workers (both informal and formal), its dominance is stronger with respect to workers than enterprises.

C. _Estimates of regional intersectoral
.336|

Note: This table reports district fixed-effect RDD-DD regressions of individual-level outcomes. The additional control variables include education levels of individuals (dummy variables), education levels (dummy variables), household size, the proportion of children in households, the proportion of older members in households.

Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

73
rate of high skill migrants and the growth rate of low skill migrants in each source-destination corridor.

A clear positive relationship is evident both for the cross section and for the growth rates: countries that sent relatively many high skilled migrants to one country also sent relatively many low-skilled migrants to the same country. Similarly, when a country increases the number of high skill migrants it sends to a recipient country, the number of low skilled migrants to the same country also increases. To investigate further, we look for Granger causality in these datasets from low skill migrants to high skill migrants and vice versa. Granger causality measures causality in a statistical sense: variable _x_ is said to Granger cause y if the history of _x_ predicts the current value of _y,_ conditional on the history of _y_ . We regress the level of high skill migrants on the lag of high skill migrants and the lag of low skill migrants, all measured in logarithms, along with year and destination country fixed effects. In the OECD data, there is strong evidence of Granger causality from high skilled to low skilled migration (coefficient of 0.118), and strong evidence of Granger causality from low to high skilled (coefficient of 0.129), both significant at the 1 percent level. For the U.S. data, it is also the case that the number of high-skilled migrants in one period helps predict the number of low-skilled migrants 10 years later (coefficient of 0.128), with significance at the 5 percent level.

Therefore it does not appear to be the case that the rise in skilled migration is coming at the expense of less-skilled migrants – instead countries with a high number of skilled migrants also have a high number of less-skilled migrants in both the cross-section and over time.

# **Question 5: What Makes Brain Drain More Likely?**

High-skill individuals often stand to multiply their income by emigrating out of lowincome countries. Yet in most developing countries, the majority of skilled individuals do not emigrate. Recent
both men and women globally?

The stereotypical roles of the male breadwinner and female caregiver are hypothesized to have been born between the mid-19th and mid-20th century in many countries as industrialization gave rise to the separation of home and work. In this model of the family men are responsible for economic provision through employment while women are responsible for home and family. However, Alesina, Giuliano and Nunn (2013) examine the historical origins of gender norms based on a theory proposed by Boserup (1970), and show that gender attitudes are more unequal among descendants of societies that practiced plow agriculture. Since plow agriculture was much more capital-intensive and required greater upper-body strength, this led to a greater gender-based division of labor. The authors find historical

> 3Beliefs about what others do are referred to as _descriptive_ norms and beliefs about what others approve of are referred to as _injunctive_ norms in social norms theory (Cialdini, Kallgren and Reno, 1991; Heinicke, Konig-Kersting and Schmidt, 2022). Here we are measuring the injunctive norm.

> 4The survey was administered on Facebook in July 2020 to a sample of Facebook general population users. 5 See World Values Survey (WVS) for global data on gender beliefs and values and Bursztyn et al. (2023 _a_ ) for analysis of data on the rights of women to work outside the home and support for women in leadership positions from 60 countries in a 2020 Gallup World Poll survey.

3
of aid is reduced when there are too many duplicating initiatives, especially at country and sector levels.” It commits a broad range of aid providers to “reduce the fragmentation of aid by improving the complementarity of donors’ efforts and the division of labor among donors, including through improved allocation of resources within sectors, within countries, and across countries.” OECD (2009) suggests that donors re-allocate some of their aid from “non-significant” to “significant” relationships (those where aid is relatively small from both the donor’s and recipient’s standpoints).

Some observers, however, warn about harmful effects on recipients from excessive donor harmonization. Rogerson (2005) and Frot and Santiso (2009) note that a low Herfindahl index is interpreted as an indicator of healthy competition among firms in the economics of industrial organization literature, and argue that high aid fragmentation (measured by one minus the Herfindahl index) may similarly reflect more competition among donors, to the benefit of recipients. The DAC itself (OECD, 2011, Ch. 4) in describing the costs of fragmentation also acknowledges that a recipient may benefit from having a diversified set of funding sources and from working with numerous donor agencies with different areas of expertise. “In addition to the policy autonomy that might be forfeited” from dependence on one key donor, ”the recipient would also likely face a riskier ODA environment” if subject to the foreign policy whims and changes in economic circumstances of a single large bilateral donor (Rowlands and Ketcheson, 2002). A donor agency evaluation found that government officials in some countries (including Benin and Malawi) favored a single assistance strategy guiding all donors’ activities in their countries, but other governments were not interested, “as they preferred to have options, which would be undermined if donors coordinated on a joint strategy” (IEG, 2011). In general, therefore, benefits to the recipient from this competition, diversity of ideas and more consistent flow of total funds should be balanced against the
the survey to the next between 1985 and 1998. We then combine this constructed panel with the pre-formed panel provided by the ASI from 1998-2004.

We construct our panel as follows. First we pair firms that appear in consecutive years. We search for exact duplicates in Open and Close values between one year and the next (e.g. we look for a match between the Close value in 1985 and the Open value in 1986) in one of the following six variables: stock of raw materials, fuels, and stores; stock of semi-finished goods; stock of finished goods; inventory; loans; and fixed capital. We identify all exact non-zero matches in Open and Close variables and count the number of digits in each match excluding trailing zeros; we then match every pair of firms that has at least six digits of exact matches.<sup>6</sup> In the case of conflicting firm matches, we take the pair that matches the largest number of digits over the six variables. On average, matched firms have 24 non-trailing-zero digits in common. We apply this technique from 1985-1994 and from 1996-1998.<sup>7</sup> Second, we attempt to link firms over 1995, a year for which firm-level data have not been released, and over other years in which individual firms may not have been sampled. In this step, we consider matches within state, 2-digit industry code, and permanent serial number. Though the ASI provides a permanent serial number for each firm, this number is not unique; however, excluding two periods in which permanent serial numbers appear to have changed, permanent serial numbers are equal across known matches more than 90% of the time. We therefore use the permanent serial number along with state and 2-digit industry codes to bridge gap years. We validate any observations matched in this manner by requiring that they match on at least five of the following six variables: year of initial production, district, ownership, 3-digit industry and growth in labor and fixed
was a possible candidate for a mechanism of those measured, given it showed significant treatment effects from the Interpersonal Initiative intervention, and indeed the indirect effect estimate was significant in this condition (IEII = 0.02, 95% CI [0.00, 0.04]) and not in the Personal Initiative condition (IEPI = 0.00, 95% CI [-0.01, 0.02]).

While it is not possible to infer the direction of causality among outcomes given their simultaneous measurement, Fig. 6 shows a pattern of positive treatment effects and correlations

> 14 We used the ‘mediation’ package in R. The indirect effect represents the amount of the treatment effect of the intervention on the economic outcome index that is accounted for by the mediator. Formally, it is the product of the treatment effect on the mediator (“a” path, see Table 2 and Table S9) and the correlation between the mediator and economic outcome index controlling for treatment condition (“b” path, see Table S9). All regressions control for covariates.

21