rafmacalaba/data-use-mentions-extended huggingface dataset:
employment in modern tradable services and non-textile and apparel manufacturing and a need for more increases in TFP across all sectors.

Underlying this cross-sectoral resource reallocation are patterns of enterprise dynamics that include a changing size and regional distribution of firms, and an increase in the number of firms and participation of workers in export processing zones (EPZs). Figure 3, based on manufacturing establishments, highlights: a shift between 1993 and 2007 in the number of establishments and employment shares from smaller firms (less than 40 employees) to medium and large firms (panel A); a shift in the number of establishments and employment shares from Central and Uwa regions to North-Central, North-West and Western regions (panel B); an increase in total employment in EPZs from 24 to over 100 thousand between 1983 and 2003 (panel C); and an increase in employment and export shares from roughly 4 and 8 percent in 1983 to over 9 and 30 percent by 2003.

# **IV. Empirical Approach for Productivity and Innovation Analyses**

# **Subjective and objective indicators affecting performance outcomes**

A commonly-used metric to assess the severity of business environment constraints has been the percentage of firms that subjectively report a given constraint as ‘major or severe’ to their business operations. Corruption, electricity and political instability are typically top bottlenecks affecting business operations as reported by enterprise managers across South Asian countries.<sup>4</sup> Figure 4, panel A illustrates the ranking of constraints as perceived by manufacturing firms between 2003 and 2010, ranked by top perceived 2010 constraints. Interestingly, while electricity has remained the top subjective constraint (for 42 percent of firms in 2003 and 36 percent in 2010), followed by tax rates (up to 29 from 19 percent), inadequately educated labor (up to 27 from 22 percent), access to financing (up to 25 from 20 percent), and tax administration (up to 22 from 13 percent); political instability is no longer in the top five, falling from
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# **8.  Effects of Tariffs and Transport Costs on Export Variety**

Table 4 presents least squares (LS) estimation linking export variety to all instruments and exogenous variables of the nonlinear 3SLS system presented in Table 2. This is similar but not identical to the first-stage estimation of the nonlinear system, which involves regressing the derivatives of each equation with respect to the parameters of the system on all the instruments and exogenous variables. For example, differentiating (21b) with respect to 1/(1 − σ n ) we obtain

> <u>12</u> ( scnt + s*nt ) ln λ cnt* , which is the export variety index for country c and sector n, times the average share of that industry.  In comparison, the regressions we present in Table 4 just use the export variety index ln λ cnt* as a dependent variable, which allows us to see the partial relationships between export variety and the trade cost variables.

The top part of Table 4 shows the effects of a one percentage point increase in the U.S. tariff on the export variety of the industry in the columns. Tariffs are constructed from detailed U.S. custom data by taking the ratio of duties paid over imports. They vary by industries, countries and years. We expect industry export variety to decrease with the own tariff of the industry, while there may exist some positive effects due to reallocation of resources among industries when there is a tariff increase in _other_ industries.

All industry export variety indexes are negatively correlated with own tariffs except for the textiles & garments and the electronics industry. For textiles & garments, MFA quotas are known to be more restrictive and binding than tariffs, which may explain the insignificant effect of tariffs on export variety. For the electronics industry, it could be the case that non-tariff barriers, transport costs and skilled labor endowments are more important in explaining expansion in export variety than tariffs. For the rest of the industries, the own tariff
Africa that meet our criteria are Burkina Faso, Chad, Ethiopia, Madagascar, Malawi, Nigeria, São Tomé and Príncipe, Senegal, Sierra Leone, Somalia, South Sudan, Sudan, and Zimbabwe, representing 45 percent of the population of SSA (516 million people) in a range of countries from different parts of the continent, large and small economies, different levels of economic development, and large and small labor forces. While the sample is not representative of the Sub-Saharan Africa region, it does reflect the region’s diversity.<sup>5</sup>

**The HFPS is a nationally representative sample of households, with two important caveats** . The HFPS includes weights that attempt to make the survey nationally representative of households. These weights are derived from nationally representative baseline surveys conducted prior to the crisis (Brubaker et al. 2021).  However, the sampling frame has two major limitations. First, the survey excludes the portion of the population that were unwilling or unable to participate, for example because of lack of access to phones or a stable phone network.<sup>6</sup> Second, because

> 2 https://www.worldbank.org/en/data/interactive/2020/11/11/covid-19-high-frequency-monitoring-dashboard

> 3 The HFPS sample was not stratified by poverty status, social assistance receipt, or any other variable.

> 4 As shown in Contreras-Gonzalez et al. (2021) and Kugler et al (2021), although rural households were affected by the pandemic (especially through increasing prices and inflation, as well as reduction in remittances), they were able to maintain a more stable farm income and cope with the crisis.

> 5 Given the differences in how and when COVID-19 affected economies across Africa, we were unable to identify a weighting strategy so that the sample could be representative of the region.

> 6 This may have been a small share since we focus on urban areas, which have quite high cell phone coverage
- Becker, G. S. and E. P. Lazear (2013). A market solution to immigration reform. _Wall Street Journal 2_ .

- Becker, G. S. and G. Nashat (1997). _The Economics of Life: From Baseball to Affirmative Action to Immigration, How Real-World Issues Affect our Everyday Life_ . McGraw Hill.

- Binswanger, J. and D. Schunk (2012). What is an adequate standard of living during retirement? _Journal of Pension Economics and Finance 11_ (2), 203–222.

- Bureau of Labor Statistics (2018). _Work Experience of the Population_ . US Department of Labor.

- Capps, R., J. Gelatt, J. Van Hook, and M. Fix (2018, 09). Commentary on “the number of undocumented immigrants in the united states: Estimates based on demographic modeling with data from 1990-2016”. _PLOS ONE 13_ (9), 1–10.

- Card, D. (1990). The impact of the mariel boatlift on the miami labor market. _Industrial and Labor Relations Review 43_ (2), 245–257.

- Chiswick, B. R. (1982). _The Impact of Immigration on the Level and Distribution of Economic Well-being_ . The Gateway: US Immigration Issues and Policies, American Enterprise Institute for Public Policy Research, Washington DC.

- Clemens, M. A. and M. Mendola (2020, August). Migration from Developing Countries: Selection, Income Elasticity, and Simpson’s Paradox. Working Papers 539, Center for Global Development.

- Clemens, M. A., C. E. Montenegro, and L. Pritchett (2019, 05). The Place Premium: Bounding the Price Equivalent of Migration Barriers. _The Review of Economics and Statistics 101_ (2), 201–213.

- DeVoretz, D. (2008). An auction model of canadian temporary immigration for the 21st century. _International Migration 46_ (1
Regardless of the level of aggregation or source of cost-shares, a practical problem is the appropriate correspondence between products and inputs on one side and tariff lines and levels on the other. For this study the basic source of _applied_ tariff data is that which the Egyptian government reports to the WTO. The latest such data for 2009 are available in the Integrated Data Base at the six-digit level. Tariff information adjusted for preferential trade agreements is also available from Bouet et al., although their data have not been updated since 2004; and it appears that trade-weighted average tariffs that account for preferences differ very little. In fact those lines where preferences and applied tariffs differ by more than 10 percent are small, fewer than 7 percent of tariff lines for products of any appreciable total import value. Therefore, the decision was made to use the updated tariff schedule for 2009, without preference adjustments, considering that such an adjustment in the case of Egypt would be highly unlikely to alter the results.<sup>3</sup> The reader should note that specific duties appear only to apply to tobacco products: there are 11 tariff lines with non-AV duties according to the WTO integrated data base. (For the Bouet et al. data base for 2004, these non-AV duties are converted to an ad valorem equivalent.) Also note that between the WTO for the years 2005 and 2006 use the 2002 HS codes, and 2007 and beyond use the 2007 HS codes. At least in the case of Egypt, the differences between the code years appears only apply to the telephony group (cell phones).

Returning to the topic of aggregation, within each of the 23 sectors in the I-O matrix available to this present study, there are, of course, likely to be some subsectors more protected than others; and so one would ideally like to see even more detail. The risk of a high level of aggregation is that it would hide large disparities between industries grouped under the same broad heading. For example, in the tariff
) The situation presented is, “Sarita is a woman whom you don’t know. She lives in a village with her husband who works and with her in-laws. She has two children who go to school. She recently came across a good work opportunity. The workplace is not too far from her home.” (2) The question asked is, “Do you think that it is okay for Sarita to take up the job? (Yes/ No).” The strength of social norms may also vary based on the woman’s circumstances, including her own characteristics, type of job, and spouse’s employment status. The second set of vignettes explores a few of these circumstances to examine the context dependency of norms for women’s LFP and approval for women’s participation. Varying the personal, job, and spouse’s circumstances, the vignette asks respondents whether they approve of and if they think others approve of the woman’s decision to work. This context dependency is important to understand from a policy perspective, as relaxing norms in those specific circumstances can help to promote women’s labor force participation. **<u>Figure B.1, Annex B</u>** summarizes the circumstances that were explored in the study.

Lastly, the study identifies relevant reference groups for respondents across two major aspects of their lives: Decisions about important events such as education, marriage, etc. and decisions regarding the main outcome of interest – paid work outside the home ( **<u>Box C.2, Annex C</u>** includes examples of these questions).

# SECTION 2: DATA COLLECTION

The quantitative survey instrument was administered to 2,000 Nepali men and women between May 29 and June 24, 2023. While individual respondents were selected using an in-field random sampling method, a set of criteria and targets guided the selection of provinces and households to ensure sample diversity in terms views about social norms and
hence also saw a strong subsequent decline. Falling behind the sampled countries with strong growth performance hence cannot be attributed to particularly unfavorable real exchange rate developments.<sup>18</sup> In terms of financial development (top right panel of Figure 8), Ghana started out at a very low level to begin with but did not manage to catch up to other aspirational peers. Especially the fastest-growing economies of Korea and Malaysia saw credit/GDP increasing about twice as fast as Ghana. In terms of infrastructure, the analysis provides a mixed picture. Ghana started out with a comparably low level of telephone lines, which also developed slower than in other countries thereafter. Similarly, coverage of mobile phones was low in the late 1990s, but Ghana managed to catch up at reasonable speed in the decade thereafter. In terms of roads, Ghana started at an average level in the late 1980s and saw the fastest increase among the sampled countries in the two decades thereafter.

> 17 Reliance on the 2010 data allows us to use the data set from the growth regression exercise above. Besides consistency, this also avoids missing data points in more recent years whereas these years are unlikely to substantially change the depicted pattern. Note that for data such as roads and cell phone subscriptions we had to use later starting points since no (or not enough) data were available in the 1970s.

> 18 We did not compare terms of trade changes, banking crises, and commodity price changes because those are cyclical ‘shock’ variables whereas all depicted variables are mainly ‘level’ variables. For example, a low _level_ of schooling in 1970 could suggest a catchup in education over the following decades, reflected in higher _changes_ of schooling. However, a banking crisis in the early 1970s or a positive terms of trade _change_ in the early 1970s is not indicative of any changes in banking crises or accelerated/decelerated (=2nd differences) terms of trade developments in the subsequent decades. Inflation is a special case because even though it
the rise in exports to Russia. Moreover, Central Asian countries have replenished their own budgets through an influx of labor, capital, and remittances. In addition, the countries increased the sale of their own oil and gas, the demand for which has hiked after the large drop in EU gas supplies from Russia. With the relocation of Russian business to Central Asian countries, the arguably temporary inflow of skilled labor from Russia, and a short-lived windfall reexport margin (ADB 2023), the total effects of Russia’s invasion of Ukraine on Central Asian business—and its dynamics over time—needs to be further studied using updated firm-level data and rigorous empirical methods.

> 9 <u>https://www.russia-briefing.com/news/ebrd-report-anti-russian-sanctions-are-boosting-central-asianeconomies.html/</u>

> 10 <u>https://www.adb.org/sites/default/files/publication/863591/ado-april-2023-special-topic.pdf</u>

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|
|Secondary|0.40|0.48|0.52|0.50|
|Tertiary|0.23|0.42|0.14|0.35|
|**Labour market**|||||
|Labour force|0.84|0.36|0.76|0.42|
|Employment|0.46|0.49|0.56|0.49|
|Informal|0.34|0.47|0.76|0.42|
|Weekly hours worked|44.4|14.3|47.8|15.3|
|Monthly wages|1,392.3|1,522.8|814.3|571.7|

_Note_ : Data comes from the Colombian Labour Market Survey (GEIH), Q1 2021. Monthly wages expressed in thousands of Colombian pesos.

In February 2021, immediately prior to our survey fieldwork, the Colombian government announced a mass regularisation for Venezuelan migrants currently living in the country, called _Estatuto Temporal de Protección para Migrantes Venezolanos_ (ETPV). This granted Venezuelans a temporary regular migratory status for a non-renewable 10-year period.<sup>6</sup> It also granted permit holders access to formal labour markets and public services

> 6 https://www.unhcr.org/news/press/2021/2/60214cf74/unhcr-iom-welcome-colombias-decision-regularizevenezuelan-refugees-migrants.html

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<!-- Start of picture text --> SKILLFINDER“—<br><!-- End of picture text --> <!-- Start of picture text --> @&) harambee— WORKFOR WORK —<br><!-- End of picture text -->

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canada You have access to a database of young entry-level candidates who have ae . been assessed by the Harambee Youth Employment Accelerator on a range of cognitive ("hard") and non-cognitive ("soft") skills.

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## Candidate Database

<!-- Start of picture text --> Numeracy: Concept Formation: Control:<br>@ TOP # MIDDLE # LOWER @ TOP # MIDDLE # LOWER @ TOP # MIDDLE # LOWER<br>Choose: a Communication: Flexibility: Grit:<br>. een @ TOP # MIDDLE # LOWER #@ TOP # MIDDLE # LOWER @ TOP # MIDDLE # LOWER<br>@ Alexandra<br>@ Atteridgeville<br>@ Auckland Park Search: en’...<br>ee eel ition Age Numeracy Communication ConceptFormation Flexibility Control Grit<br>1 C214 ‘Soweto
Mogadishu. The results from this subsample are in line with the main findings from the full sample of households (Table A.5). In rural areas, more drought‐exposed households are 36 percent more likely to be poor, experiencing a 15 percent reduction in consumption, slightly higher than the 26 and 15 percent, respectively, in the full sample. No significant effect was found in urban areas.

Third, the sample is restricted by iteratively removing from the analysis households from north‐eastern regions (Table 4, columns I and II), central regions (Table 4, columns III and IV), and south‐western regions (Table 4, columns V and VI). The main results are largely unchanged when removing north‐eastern regions, while a larger drought effect is found when removing south‐western households. In contrast, the results are weaker when excluding central regions, though point estimates are still in a similar range. This indicates that the drought effect was weaker in south‐west and particularly strong in central regions.

_Table 4: Regression results with restricted samples._ Rural, NE excluded Rural, Central excluded Rural, SW excluded

**_Sample_**

8
Bangladesh refugee camps showed that a multifaceted psycho-social intervention not only had positive impacts on the mental health of mothers but also on their children and improved child development outcomes more generally (Hussam et al., 2021).<sup>7</sup> An examination of educational outcomes using descriptive statistics (Mejia-Mantilla et al., 2019; Pape et al., 2018; Pape and Sharma, 2019) and studies controlling for observables (Wharton and Oyelere, 2011) shows that refugee and IDP children in low and middle-income

> 7This is corroborated by a larger literature on the intergenerational transmission of mental health (Gon ̧calves et al., 2016; Johnston et al., 2013) and the ability of interventions to reduce this transmission (Eyal and Burns, 2019).

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that wet season appear to have any significant effect on household per capita expenditures.

In a study building on these insights, Skoufias, Essama-Nssah, and Katayama (2011) use data from rural Indonesia and consider two rainfall-related shocks: (i) a delay in the onset of monsoon and (ii) a significant shortfall in the amount of rain in the 90-day post-monsoon period. Focusing on households with family farm businesses, they find that rice farm households located in areas experiencing low rainfall following the onset of the monsoon are negatively affected. A shortfall in the amount of rainfall in the post onset period is associated with a 14 percent reduction in the per capita expenditures of rice farmers. Rice farm households

> 24  There is a large literature on the extent to which short-term weather shocks in poor rural areas can have long-term effects on education, health, and nutrition, especially of children. For a recent review of these studies see Baez and Mason (2008).

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consistent reports of underspending on the demand side activities. There may be a number of origins of this problem. One is that software, in general, seems to receive less attention during supervisory missions than construction or other hardware. A second is that the ministries of health which are usually the lead agencies in Bank projects have little expertise in or commitment to demand generation activities and efforts to bring other agencies into the process have not always been successful.

Bank projects have made little use of the marketing skills of private sector agencies that have been central to the contraceptive social marketing activities supported by USAID.

# b. Acceptance of Family Planning and Contraceptive Prevalence

Measures of the utilization of services are often used as indicators of project effectiveness, but their limitations should be recognized. First, since Bank projects are directed at middle and long range objectives, their immediate impact may be more on system capacity than on the level of service utilization. Second, there are many factors other than project activities that influence the initial acceptance or continued use of services. Therefore, the simple statistics of acceptance are not sufficient evidence of project effectiveness. Third, the indicators themselves can be misleading. In Indonesia, for example, thanks in part to a special Bank mission report, it is now clear that contraceptive prevalence estimates based on service statistics may have been significantly overstated by BKKBN statistics. In Bangladesh there has been substantial controversy concerning service statistics related to condoms. Thus, service statistics, while part of a system of information on effectiveness are incomplete in themselves and should be supplemented both by detailed analysis and by the use of other measures of performance.

Measures of contraceptive prevalence have proven particularly useful for assessing program effectiveness. Measures of prevalence do not resolve the problems of lags between project inputs and effects or of complex causality, but they do provide an alternative measure of effect that does not depend on service delivery statistics. Moreover, contraceptive prevalence surveys have often been able to supplement information on current use with information about knowledge, accessibility and sources of supply
<!-- Start of picture text --> Figure 9. Linkages to manufacturing GVCs by industy: Non-oil resource<br>rich group<br>100<br>90<br>84<br>80<br>70 71<br>60<br>50 53 52<br>45 45 45<br>40<br>30<br>20 22<br>10<br>0<br>-10<br>Upstream Downstream Δ GVC 2015/1995<br>Δ Upstream 2015/1995 Δ Downstream 2015/1995 GVC linkage 2015<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

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