rafmacalaba/data-use-mentions-extended huggingface dataset:
“good development” increases resilience, especially if it reduces poverty and improves social safety nets.

The comparison between Sweden and Malawi illustrates that our results are country‐specific: some factors are more important in some countries that in others. For instance, a low exposure is even more important in countries with a weak social protection system. Or a good post‐disaster support delivery mechanism matters only where the government has resources to distribute after a shock. This variability supports the choice of using a model to assess resilience, instead of a weighted average of sub‐indicators, such as most other indicators for vulnerability or resilience, in which the weights are global and cannot be adjusted to local circumstances.

# 4.3. Comparison with other indicators

Several other indicators measure resilience and risk at the country and sub‐national level, work which our analysis has built on. Importantly, these indicators have different boundaries: some of them (e.g., InfoRM) include fragility and conflicts. Others – such as ND‐GAIN – try to measure the ability to adapt to climate change, and thus include long‐term trends that may not materialize through natural hazards and disasters (e.g., increasing water scarcity). As a result, these indicators cannot be directly compared among each other or with the indicator proposed in this paper. However, all these indicators tend to measure similar socioeconomic “capacities,” namely the ability to avoid, resist and absorb shocks, and are thus closely related.

In this sub‐section, we review existing indicators and compare our results on resilience and risk with three well‐known country‐level indicators, ND‐GAIN, InfoRM, and WRI, for which data are available online.

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# **Higher firing costs are associated with smaller employment loss and higher earnings loss**

<!-- Start of picture text --> East Asia (omitted)<br>Europe ***<br>Latin America ***<br>Middle East **<br>Low middle income (omitted)<br>Middle middle income **<br>Upper middle income<br>Small manufacturing sector ...<br>Large manufacturing sector<br>Less dependent (omitted)<br>**<br>More dependent **<br>Fixed or Peg (omitted)<br>Managed float<br>Full float<br>Low difficulty of redundancy<br>High difficulty of redundancy<br>Low redundancy cost<br>High redundancy cost *<br>Firm survey<br>Household survey (omitted)<br>Change in employment growth -4 -2 0 2 4 6<br>Change in earnings growth<br>Note: Bars indicate coefficients from fourteen separate cross-sectional regressions<br>of changes in employment and earnings growth on change in GDP growth, data<br>source, and each country characteristic. Stars indicate statistical significance at the<br>10 , 5, and 1 percent level. There are 35 or 36 observations in the employment<br>regressions and 30 or 31 in the earnings regressions.<br>Region<br>Income<br>e<br>Structur<br>Exports<br>rate<br>Exchange<br>Regulation<br>Data source<br><!-- End of picture text --> _Countries with larger export sectors, and less stringent regulation better maintained previous trends in earnings without incurring large employment costs._ Figure 9 shows how earnings and employment are associated with each set of country characteristics after accounting for the declines in countries’ GDP growth.<sup>22</sup> The
of 2019. The COV-BPS

> 3See Appendix A for more details on these questions.

> 4The answers to each question in the management module take values in ascending order starting with 1 for no, none, or never (see Appendix A). We compute the mean and the standard deviation of each indicator in each country (with sampling weights), and use these moments to convert each measure into a z-score. Our management score is the simple average of the four z-scores within a firm.

> 5The FAT sample is a representative sample drawn from the most recent and comprehensive establishment census available in each country (see ( _17_ )). The COV-BPS sub-sample was similarly randomly selected to remain nationally representative for Senegal and Vietnam, and representative at the state level in the case of Cear ́a-Brazil. We provide a description of the sampling strategy in Appendix A. Data collection was conducted by the same agencies that implemented the FAT data in each country. Unless we note otherwise, we do not weight our calculations with sampling weights.

6
24 Global Financial Development Database goes back some 50 years (to 1960), although some of the indicators (e.g., the only recently defined financial stability indicators) go back only to the 1990s. The focus of much of the analysis presented here is on the more recent period, and in particular on crisis period (since 2008) compared with the pre-crisis period.

The database and this paper highlight the multidimensional nature of financial systems. Focusing on only one characteristic– say, financial depth, or financial stability – is too narrow and misses important characteristics of financial systems. And, focusing only on financial institutions, or just on banks, is too narrow and misses important components of the overall financial system.

This paper illustrates that financial sectors come in different shapes and sizes, and they differ widely in terms of their performance. The paper also emphasizes a need for humility, and for further research. Despite the remarkable progress in gathering data and intelligence on financial systems around the world in recent years, researchers and practitioners still do not have precise measures of financial systems. The data that are being made publicly available together with this paper should help country officials, researchers, and others better measure and benchmark financial systems.
Finally, buyer firms may foster productivity in their suppliers through increased training of the workforce, quality control, inventory management, technical assistance, or product development (see e.g., Amiti and Cameron, 2007; Javorcik, 2004; Blalock and Gertler, 2007). We estimate a variant of Eq. (2) where the diversity index given by Eq. (3) is calculated separately for suppliers and non-suppliers (column (1)) or for buyers and nonbuyers (column (2)). We identify suppliers and buyers based on the 1986 Chilean inputoutput matrix.<sup>19</sup> The estimates in Table 8 show significantly negative MAR externalities and no effects of competition nor average firm size. Regarding diversity, columns (1) and (2) show that the diversity of non-suppliers and the diversity of non-buyers are the most important types of sectoral diversity for TFP growth. These findings go against our priors from the agglomeration literature and the findings in Amiti and Cameron (2007). While our input-output linkages measures are imperfect - because they are based on a national input-output table rather than regional input-output tables - our findings suggest that the sectoral diversity unrelated to supplier and clients is what matters most for TFP growth. Such agglomeration externalities could happen through the exchange of ideas and workers through labor pooling, knowledge spillovers, or the availability of financing.

Evaluating the impact of agglomeration indices in 1992 on long-run TFP growth between 1992 and 2004 across Chilean sector- _comuna_ cells deals away with reverse causality problems, but the 1992-2004 period may be too long to identify certain types of

> 19 The implicit assumption is that input-output relations hold across locations. For any 3-digit sector _j_ we calculate the share that each 3-digit sector represents in its intermediate input usage, we rank those shares in ascending order and compute the cumulative sum of shares. „Suppliers‟ are the 3-digit sectors whose cumulative sum of shares in total intermediate inputs is closest (from above) to 90%. „Non-suppliers‟ are all
Table 1: Coverage Ratio of Non-Tari¤ Measures by Importing Countries

|Importing Country|Percent|Importing Country|Percent|
|---|---|---|---|
|ARE|99.22|JPN|67.08|
|ARG<br>|51.38|KAZ<br>|62.80|
|AUS<br>|79.32|KOR<br>|94.39|
|BGD|98.20|LKA|89.87|
|BOL|90.11|MAR|37.30|
|BRA|88.27|MEX|49.14|
|CAN|56.54|MYS|72.48|
|CHE|60.62|NGA|99.00|
|CHL|57.43|NZL|86.21|
|CHN|71.86|PAK|56.47|
|CIV|94.78|PER|89.13|
|CMR|71.90|PHL|99.01|
|COL|97.25|PRY|82.37|
|CRI|59.45|RUS|69.53|
|CUB|98.03|SAU|38.77|
|DZA|64.74|SEN|98.36|
|ECU|75.31|SGP|84.98|
|ETH|97.87|THA|90.05|
|EUN|60.98|TUN|46.83|
|GHA|97.07|TUR|86.34|
|GTM|86.63|URY|73.84|
|HKG|84.33|USA|85.44|
|HND|96.19|VEN|92.22|
|IDN|78.95|VNM|84.25|
|IND|79.19|||

Data Source: UNCTAD TRAINS. Coverage Ratio measures the percentage
Gasoline Juba Malakal 95.3% 31.2% 32.7% Source: Authors’ calculations based on WFP data.

There have been changes in price patterns over time. For example, in the case of sorghum and wheat flour, the price gaps shifted from negative to positive. While initially prices for these two products tended to be higher in Juba than in the rest of the state capitals, as time passed, prices became relatively lower in Juba. This is likely related to a shift in production and trade structures for those products as a consequence of the imposition of trade restrictions with Sudan. Although trade data by product and origin is scarce and incomplete, evidence suggests that imports of both wheat flour and sorghum increased substantially in recent years, and the origins of these flows are Uganda (for wheat flour) and the USA (for sorghum). In both cases, these imported products enter the country through Juba, making it reasonable for their prices being lower in that point of entry. For example, the prices of sorghum in Malakal during 2009 and 2010 were lower than in Juba, but these prices increased rapidly and became volatile after the independence referendum (January 2011) remaining higher than in Juba for the following two years. The increases in prices are likely related to transportation costs, multiple check points, roadblocks and payments of several formal and informal taxes.<sup>12</sup> How did the border closure of August 2011 affect spatial price differences across the country? Are there identifiable differences for states in the north? We look at the effect of the border closure on price differences and its differential impact on northern states by product. Results are presented in Table 2.<sup>13</sup> The absolute values of price differences of a given product in a given city with respect to Juba are greater after the border closure, suggesting that the trade restrictions increased segmentation. For the cases of groundnuts, maize, sorghum and wheat flour, these differences are statistically
term or long-term is far from a settled point in studies from low- and middle-income countries (See Akyeampong et al., 2023).

There is growing evidence of the benefits of quality pre-primary education as a costeffective catalyst for improvements in learning outcomes and long-term economic benefits later in life. Compared to other educational policies to improve foundational learning, global experts propose that investing in quality pre-primary education can help address learning inequities and reduce deficits by the time children enter primary school (Global Education Evidence Advisory Panel, 2020). This is confirmed by results from international large-scale assessment studies, which have shown that enrollment in pre-primary education results in higher average learning outcomes in reading, mathematics and science (Mullis et al., 2020; Mullis et al., 2023). Yet, even when investing in improving access to and quality of early childhood education can be seen as smart policy, most of the studies that have investigated this linkage come from high-income countries (Holla et al., 2021). Fortunately, more and more evidence on this linkage is being generated in low- and middle-income contexts to support the investment in quality early childhood education (Augsburg et al., 2022; Hasan et al., 2019; Nakajima et al., 2021).

Hence, this study provides a window into one of the less explored and more important policy aspects of young children’s education: what goes on in the early childhood education classroom in a lower-middle-income setting such as Pakistan – specifically in South Punjab – and how early childhood settings are associated with children’s cognitive and socioemotional development. It attempts to fill a knowledge gap by describing what relevant skills pre-primary aged children can do and the quality of classrooms in which they study. We also report on how children’s skills and classroom quality vary by student, teacher, and school characteristics and assess the correlations between these variables to understand the role that quality may play in supporting young children’s acquisition of school readiness skills. We use a large, representative
survey, only the time from the last job for the nonworkers and job tenure for the workers were collected. Consequently, only changes from previous employment to the nonwork states can be identified. Moreover, as we assumed that the current worker moved from unemployment, we only have transitions from unemployment to work. Due to this data limitation, other extensions such as on-the-job search cannot be implemented. Even when the model does not have job-to-job transitions, there are some workers who found a job within the 12 months considered, and we have simulated transitions from employment to PT or FT, but they have frequencies of less than 1 percent. Table 6 shows that the model does not predict many transitions from the labor market to nonparticipation. Besides that, the model is capturing the labor market dynamics decently for the Roma population.

**Finally, the simulated wages fit the data closely (table 7).**

24
content, and speed according to students’ needs and feedback. As labor mobility is lower than the mobility of goods, it is costly to send service providers to distant customers.

# **VI Robustness**

> 33 See https://www.newyorker.com/magazine/2018/07/23/how-e-commerce-is-transforming-rural-china 34 Additional results on the relations between food consumption and whether the household has young children and those on the breastfeeding and e-commerce z-scores are available upon request. Forthcoming in another research paper. 35 https://www.pwccn.com/en/retail-and-consumer/publications/global-consumer-insights-survey-2018-china-report.pdf

> 36 Due to data limitation, we cannot examine the use of online education or health services, such as interactive online learning program for children and health monitoring apps, which may have a spillover effect over time for consumers to consume more of not just online consumption goods but also get exposed to the kind of goods and services that can potentially enhance human capital.

·· 21
# **4.2 Access to public support**

Despite efforts of South Asian governments to support firms and their respective workers, access to support programs is very limited, similar to what has been observed in other poor countries (Cirera et al., 2020). Analysis of the BPS data suggests that relative to other regions, South Asian firms are less likely to access firm support programs. Across the sample, only 11% of firms in SAR are likely to have access to public programs, much lower when compared to 25% in EAP and 59% in high-income countries (Figure 7, left panel a). Unreported estimates suggest that in all South Asian countries except Sri Lanka, less than 11% of firms are likely to have received public support. Nearly a third of the firms have access in Sri Lanka compared to 0.7% and 3.7% in Afghanistan and Nepal, respectively. Lack of awareness explains the gaps in access, especially for firms in South Asia and Sub-Saharan Africa (Figure 7, panel b).

Figure 7: South Asian firms’ access to public support.

<!-- Start of picture text --> Regional/by income group comparison Comparison by firm-size<br>60 59 50<br>40<br>40 30<br>30 28<br>25<br>21<br>25 20 19<br>20 18 11 13<br>10 8<br>11<br>9<br>0<br>0 Micro (0-4) Small (5-19) Medium (20-99) Large (100+)<br>SSA SAR Non-high income EAP High income SAR Rest of the world<br>(excl. SAR)<br>Panel (a): Average probabilities of receiving public support<br>60<br>56<br>48<br>43 44<br>41<br>40<br>26<br>22
Tunisia’s export-promotion agency, Gourdon et al. (2011) use a combination of data from a survey and from national sources (the customs agency and national statistics institute). Yet another alternative is to include questions on program participation and details in ongoing surveys. This also requires close collaboration between the evaluator and the local institution implementing the survey.

# **6. Looking ahead: Challenges facing IE of trade assistance**

In this section, we consider three key challenges that credible IE of trade interventions must address.

## **6.1 External validity and cost**

One concern with impact evaluations is that their external validity is an act of faith. When a program is found to be effective (or ineffective), how do we know that the result would carry over to similar programs run in different environments?

> 39 See Freund and Pierola (2011) and Lederman, Rodriguez-Clare, and Xu (2011) for uses of such data in a non-IE context.

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(3.2-5.4)||
|Sierra Leone|25.5|40.6|31.0|399|
||(21.6-29.5)|(36.4-44.8)|(26.7-35.4)||
|Tanzania|25.4|39.1|27.4|180|
||(20.1-30.7)|(32.4-45.9)|(21.8-33.1)||
|Togo|0.0|0.0|0.0|390|
|Uganda|-<br>1.0|-<br>4.7|-<br>1.3|544|
||(-0.1-2.1)|(2.2-7.2)|(0.1-2.6)||

**Note** : Column (1) reports the estimated probability of care readiness that meets minimum quality standards for child, neonatal & maternity care (diarrhea, pneumonia, post-partum hemorrhage, and neonatal asphyxia). Column 2 reports the estimated probability of care readiness that meets minimum quality standards, assuming essential drug(s) (oral rehydration salts or rehydration therapy for diarrhea; antibiotics for pneumonia; and oxytocin for postpartum hemorrhage) for treating the conditions are available. No essential drugs data were collected for neonatal asphyxia. Column 3 reports the estimated probability of care readiness that meets minimum quality standards, assuming no more than 5% absenteeism. All individual country statistics calculated using country-specific sampling weights. 95% confidence intervals in parenthesis. Number of facilities reported in column 4.

61
the Ministry of Trade or its equivalent), as bureaucrats lose their power to collect license fees and pre-screen, monitor, or supervise businesses. This can result in significant government-internal forces to block reform efforts.

Yet while a disciplined government may be able to deal effectively with internal conflict, some of the issues captured by the DB studies transcend borders and are thus beyond the full control of individual governments. The ‘Trading Across Borders’ indicator can only be fully addressed if governments from neighboring countries cooperate. To the extent that borders are politically sensitive as they relate to a country’s core interests like sovereignty, national security, and other foreign policy matters, this indicator may be difficult for governments to address if their neighbors do not share an interest in improving the conditions for cross-border trade. The feasibility of reform is thus a crucial factor to consider for governments that aim to improve their DB rankings.

Assuming that several reforms are feasible, governments may still want to prioritize some reforms over others, given limited public resources. Visibility and impact are the two factors that can help government in deciding on the reforms to embark on.

**Visibility** refers to the impact the DB ranking, and in particular reform efforts, will have on perceptions. The DBIs attract considerable attention in the media and countries that are most committed to reform are singled out in the annual DB reports. In 2010, Rwanda was the top reformer, and Rwanda remained a strong performer in 2011 – when Kazakhstan improved its DB ranking the most. To countries that are eager to improve their image as a business-friendly location the DB rankings can be an excellent publicity tool.

Governments may wish to improve their image for several reasons. For one, an improved DBI ranking may result in larger Foreign Direct Investment (FDI) inflows. FDI can have spill-over effects and increase the productivity of domestic industries and hence economic growth (Rodriguez-Clare, 1996; Markusen

> 2 At the same time, it could be argued that reducing tax rates constitutes a cost to
of picture text -->

<!-- Start of picture text -->
-0.5<br><!-- End of picture text -->

<!-- Start of picture text --> 0<br><!-- End of picture text --> <!-- Start of picture text --> 0.5<br><!-- End of picture text --> <!-- Start of picture text --> 1<br><!-- End of picture text --> <!-- Start of picture text -->
1.5<br><!-- End of picture text -->

Source: Authors’ calculations based on data from the INS.

Note: The “within” component stands for productivity growth due to capital accumulation, technology change or reduction of resources misallocation across firms, while the “structural change” component measures productivity growth due to labor flow from low to high-productivity sectors.

While structural change has made a small and positive contribution, the data show a large negative contribution of the within component to productivity growth in Guinea during the period 2006-15. From that perspective, the finding in Guinea is different from many developing countries, where the “within” component generally contributes positively to productivity growth. This highlights a lack of capital accumulation, lower technological adoption and higher resources misallocation within firms, weighing on productivity growth.<sup>8</sup>

> 8 Along the same vein, the global innovation index ranked Guinea 119th of 126 countries in 2018. More information on the index can be found at: <u>https://www.wipo.int/publications/fr/details.jsp?id=4330</u>

14
PC y RESEARCH WORKING PAPER 2098

# Summary findings

Gautam and Anderson revisit the widely disseminated results of a study (Bindlish and Evenson 1993, 1997) of the impact of the training and visit (T&V) system of management for public extension services in Kenya. T&V was introduced in Kenya by the World Bank and has since been supported through two successive projects. The impact of the projects continues to be the subject of much debate.

Gautam and Anderson's paper suggests the need for greater vigilance in empirical analysis, especially about the quality of data used to support Bank policy and the need to validate potentially influential findings.

Using household data from 1990, Bindlish and Evenson found the returns from extension to be very high. But Gautam and Anderson find that the returns estimated by Bindlish and Evenson suffer from data errors, and limitations imposed by cross-sectional data.

After correcting for several data processing and measurement errors, the authors show the results to be less robust than reported by Bindlish and Evenson and highly sensitive to regional effects. When region-specific effects are included, a positive return to extension cannot be established, using Bindlish and Evenson's data set and cross-sectional model specifications.

After testing the robustness of results using a number of tests, Gautam and Anderson could not definitively establish the factors underlying strong regional effects, largely because of the limitations imposed by the crosssectional framework. Household panel data methods would have allowed greater control for regional effects and would have yielded better insight into the impact of extension.

The impact on agricultural productivity in Kenya expected from T&V extension services is not discernible from the available data, and the impact may vary across districts. The hypothesis that T&V had no impact in Kenya between 1982 and 1990 cannot be rejected. The sample data fail to support a positive rate of return on the investment in T&V.

- - This paper a product of the Sector and Thematic Evaluation Division, Operations Evaluation Department is part of a larger exploration by the department of the effects