rafmacalaba/data-use-mentions-extended huggingface dataset:
13 The Cambodian and Sri Lankan governments had different strategies to improve working conditions in the apparel sector. Sri Lanka, as a part of its Five-Year Strategy, designed international and local campaigns to improve the image of the apparel sector and working conditions. The 2006 international image-building campaign was called _Garments without Guilt_ , and the 2008 local campaign was called _Abhimani_ (“pride”). Despite these efforts, working conditions are still far from ideal. As mentioned earlier, labor costs in Sri Lanka are lower than in China and India. Besides low wages, other issues have been problematic in parts of the apparel sector, particularly in smaller firms. These issues include the lack of appointment letters, long working hours, high work intensity, and, in particular, denial of the right of association and collective bargaining (as many firms are reluctant to recognize trade unions) (Staritz and Frederick 2011b).

Cambodia has a good record of labor compliance because of the _Better Factories Cambodia_ program that began in 1999. Through this program, compliance with international labor standards was directly linked to the apparel export quotas that Cambodia received from the United States. In the 2004 Foreign Investment Advisory Service survey of the 15 largest U.S. and European Union (EU) buyers of Cambodian apparel, Cambodia was rated the highest on “level of labor standards” and “protecting the rights of workers to organize unions” among Asian apparel-exporting countries, including Bangladesh, China, Thailand, and Vietnam (Staritz and Frederick 2011a). The MFA phase-out coincided with the expiration of the U.S. quotas in 2004, which eliminated the incentive motive of _Better Factories Cambodia_ (Staritz and Frederick 2011a). Nevertheless, the Cambodian government encouraged the apparel sector to continue with the program to maintain its reputation for compliance with good labor standards in order to remain attractive to the foreign investors.
Mexico's significant engagement in processing has traditionally been tied to Mexico's close economic relationship with the United States. Over the past ten years, however, Mexico's trade has been diversifying away from the United States. Figure 2, for example, shows a declining trend in the U.S. share of both Mexico's imports and exports. While over 80% of Mexico's non-petroleum exports continue to go to the United States, non-petroleum imports from the United States fell from over 70% at the beginning of the decade to less than 50%. The crisis, however, does not seem to have affected the share of Mexico's trade with the United States other than possibly to stabilize the values. This is important for our analysis because the lack of intercountry substitution as a result of the crisis suggests that trade with the United States is a reasonable proxy for total Mexican trade.<sup>1</sup> The _change_ in trade flows varied significantly across industries during the crisis. Using Harmonized System 6-digit industries, Figure 3 shows the distribution of the change in imports and exports between April 2008 and January 2009. The average change (difference in log values) in Mexican exports to the United States was about 42%. The standard deviation was nearly 1.14. The mean change in Mexican imports from the United States was smaller—about a 17% drop—but the standard deviation was larger - just over 1.18. We take advantage of the differences in the changes in trade across industries to analyze how the trade shock begins as an industry-specific shock and then ripples through the labor market.

In even classical trade models, prices are fixed in small countries, which implies that demand shocks would affect quantities more than prices. We calculated the coefficient of variation across both unit values and quantities for all available products across the 24 months in 2008 and 2009 traded between the United States and Mexico, combining changes along the

> 1 In the aftermath of the crisis
# **VI. Channels of change in the distribution of regional intersectoral productivity gaps**

To further identify potential channels behind the distribution of regional intersectoral productivity gaps, we examine the change in this distribution and the relationship between the change and the pattern of structural transformation in Ghana since the early 2000s. The analysis here examines the intersectoral (informal-formal) productivity gap in industry and in services.

As discussed in Section III.C, the intersectoral productivity gap averaged across the five richest regions is three times larger than that for the five poorest regions. Refining further, the intersectoral productivity gap in industry averaged across the five richest regions (180.8 thousand cedis per worker) is also roughly three times larger than that for the five poorest regions (66.8 thousand cedis per worker) (Figure A2). On the other hand, the intersectoral productivity gap in services averaged across the five richest regions (55 thousand cedis per worker) is about 1.6 times larger than that for the five poorest regions (32.8 thousand cedis per worker). These numbers suggest that the difference in the intersectoral productivity gap between the richest and the poorest regions of Ghana is driven by the corresponding difference in industry.

We cannot examine the change in the distribution of regional intersectoral productivity gaps over time in industry and services because the previous rounds of establishment censuses in Ghana only surveyed industrial establishments. As a second-best option, we examine the change in the distribution of regional intersectoral productivity gaps only in industry, by comparing statistics from the National Industrial Census (NIC) 2003 to those from IBES 2014, a period of roughly a decade.<sup>21</sup> In doing so, we can only obtain a partial picture of the change in the distribution of regional intersectoral productivity gaps in the nonfarm sector. To allow for a comparison of statistics between 2003 and 2014, we redefine some key variables. Specifically, an informal enterprise is redefined as an enterprise that is not registered with RGD, labor productivity
is to establish a tight link of high-growth experience with initial plant productivity.<sup>5</sup> Ethiopian manufacturing makes an interesting laboratory for studying HGFs because of the phenomenal services-driven double-digit growth that the country experienced in the last decade or so. While Ethiopia’s real GDP grew at 10.9 percent per annum over 2004-14, the share of manufacturing stagnated at 4 percent of GDP (Moller, 2014).<sup>6</sup> Studying HGFs in Ethiopian manufacturing is thus likely to be informative on the possible challenges in boosting the growth of this sector in an environment that has rather been centrally planned, thereby undermining the performance of private firms. Evaluating the correlates of HGFs that relate not only to firm capabilities but also to external factors relating to spatial location and industry concentration offers interesting insights for improving the potential of private sector driven growth.

This paper makes the following key contributions: _First_ , it presents evidence on the incidence and persistence of HGFs in the context of a low-income country in Africa. About 7 percent of total establishments during 1999-2009 experienced high-growth, per the OECD definition, peaking at 15 percent

> 1 Diao et al. (2016) is perhaps one of the few studies that isolate the performance of informal high performing firms in an African country. Using Tanzania’s nationally representative survey of micro, small and medium sized enterprises the authors find that most of the labor productivity growth in the country is attributed to a relatively small subset of firms. Page and Söderbom (2015) use these detailed longitudinal data from Ethiopia to analyze firm entry and exit and patterns of net employment growth across firms of differing size. They find that exit rates were significantly higher for small, young firms. Half of the firms starting with 10 employees (or fewer) were gone after 3 years; and after 8 years two thirds of the firms starting small had disappeared. However smaller firms that survived often grew.

> 2 Some studies go a step further to provide guidance on the forms
8 sampling clusters themselves, which correspond roughly to villages in rural areas, were the same as those randomly selected for use in the 1993 Human Resource Development Survey (HRDS), hence the SCPS and HRDS data can be matched village by village<sup>5</sup> . The total usable SCPS sample is 1376 households located in 87 clusters<sup>6</sup> .

_Social capital module of SCPS._ The survey's social capital component queried a household respondent about three dimensions of social capital: first, their membership in groups, second, the characteristics of those groups in which the households were members, third, the individuals values and attitudes, particularly their definition, and expressed level, of trust in various groups, and their perception of social cohesion. In this work we describe the groups only briefly, with a fuller description of the groups, their activities, and the results of qualitative information from interviews and participatory data collection methods in a companion paper (Narayan, 1997).

The first set of questions was simply the number of groups in which an individual was a member. The average number of groups per person was 1.5 and table 1 lists the most prevalent groups, individuals' responses as to their 'most important' group and the groups they would join if they could join only one group. Most groups are Christian churches, Mosques, the village burial society, women's groups, and the political party. The more purely economic ' Also, rather than carry out new randomization, the households in the sample were the same as those sampled in the 1994/95 Agricultural Survey, with the addition of up to five nonagricultural households randomly selected within the cluster.

' The survey was implemented in two parts, a social capital module and a household module devoted primarily to measuring household expenditures, but unfortunately the second part was only administered in every other cluster so only 53 clusters have SCPS expenditure data.
.g., Rosenthal and Strange, 2001; Ellison _et al._ , 2010) has used until now (yet, see Storeygard, 2016, for an approach using time-varying proxies for transport costs).

# **3 Measurement and descriptive evidence**

Our analysis requires two key pieces of information: (i) a measure of geographic concentration of industries; and (ii) a measure of transport costs of industries. To control for unobserved factors that drive cross-sectional differences in geographic concentration and transport costs, we also require a panel dimension. We now discuss the data and the procedures that allow us to construct these key empirical measures. We also take a first descriptive look at the data.

## **.1 3 Geographic concentration**

We draw on Statistics Canada’s Annual Survey of Manufacturers (asm) Longitudinal Microdata file from 1990 to 2009. This file contains between 32,000 and 53,000 manufacturing plants per year, covering 257 naics 6-digit industries. For every plant, we have information about: its 6
Data Group (DECDG) also contributed historical data from before 1990, and recent survey data from Luxemburg Income Studies (LIS). Selected variables have been harmonized to the extent possible such that levels and trends in poverty and other key sociodemographic attributes can be reasonably compared across and within countries over time. The GMD’s harmonized microdata are currently used in Poverty and Inequality Platform (PIP), World Bank’s Multidimensional Poverty Measures (WB MPM), the Global Database of Shared Prosperity (GDSP), and Poverty and Shared Prosperity Reports.

63
Two of the potential mechanisms described above are consistent with the combination of reduced demand for informal credit and lower interest rates: a decrease in the administrative costs associated with lending due to the use of SHG credit to service informal debt, and increased competitive pressure in the context of a market in which lenders exercise market power.<sup>14</sup> Household data on use of loans at endline indicate that only 2.8% of SHG loans in treatment areas, and 2.4% in control areas, were used for debt service, casting doubt that this mechanism plays a major role, and leaving a reduction in market power as the most convincing explanation.

# _4.5 Downstream impacts: Assets, consumption and women’s empowerment_

While Jeevika had immediate and dramatic effects on credit markets in targeted villages, we see more limited short-term impacts on household asset possession, material well-being or various measures of women’s economic and social empowerment (Tables 4 and 5). We do see significant positive effects of the program on asset holdings among landless households (Table 4). Consumption assets, productive assets, and housing quality are all significantly affected within this sub-group.

The program’s effect on women’s empowerment is less clear. We see a positive impact on an index of collective action, but a negative impact on women’s role in decision-making within the household (both p<0.1). These two effects cancel each other out in the aggregated index for this family of outcomes, resulting in an estimated family-wise treatment effect of zero.

# _4.6 Robustness tests_

In the presence of a baseline difference in outcomes, the ANCOVA estimator is generally less biased than either the simple difference or difference-in-difference estimators, which tend to under- or over- correct for the baseline difference respectively (Frison and Pocock, 1992). We present these two alternative estimators as upper and lower bounds of the true effect in Tables A2 through A5. Treatment effects are similar in magnitude and
This was followed by a 2.2% drop in the seventh month and later by a monthly average of -3.8% between the tenth and sixteenth months. In general, export prices decreased more due to domestic than external lockdowns in the first 12 months, going by the magnitude of the coefficient and starting period of the effect. Nonetheless, the effect of external lockdowns on export prices was long-lived as shown by the 4% decline in the nineteenth month. The decrease in export prices due to external lockdowns may also signal that lower demand may have led to lower price setting by Indonesian exporters.

# **5 Conclusion**

This study sought to analyze how Indonesia’s international trade was affected by its own lockdown policies (domestic) and those of its trading partners (external) in response to COVID-19, differentiating between sea and airports as well as imports affected by non-tariff measures (NTMs). We employed survival analysis and event-study approaches on monthlyproduct-country export and import data ranging from May 2018 to August 2021.

Results from the survival analysis show that the failure rate of exports and imports increased during both domestic and external lockdowns, but sea exports had higher survival than air exports, while air had higher import survival rates. Imports subject to NTMs also had lower survival rates during both domestic and external lockdowns, especially those subject to port of entry restrictions and import approvals. Event-study results show that overall, imports were more negatively affected by lockdown policies than exports, which recovered during the sample period, unlike imports. The decline in imports was mainly attributed to lockdown policies by Indonesia’s trade partners, which affected all imports. The impact of external lockdowns was more pronounced and persistent for imports entering Indonesia by air, while imports by sea recovered relatively faster. We find no impact of Indonesia’s lockdown policies on imports, except for negatively affecting intermediate imports subject to non-tariff measures.

31
. For rural area, total private educational expenditures are examined by provinces, ranked by per capita income from poor to rich (Fig 7), and similar conclusion holds--that the prices faced by families in poorest rural provinces are high enough to create an obstacle to raising enrollment rates than those in the wealthy provinces. Infact, figure 8 shows that economic conditions prevent nearly 40 percent of children among the 7-14 age group, corresponding to primary and junior secondary education, from attending schools in the poorest province (quintile I) compared to 14 percent in the wealthiest province (quintile V). Moreover, regarding quality of schooling, one direct measure of the quality gap is the availability of textbooks and teaching materials in schools. The same data confirm that more than 35 percent of the children living in the poorest provinces reported either inadequate supplies of textbooks or other teaching materials as the main reason for not attending schools; corresponding figure for children in the richest province is 21 percent. In higher education, a simple regression with province-level data show that graduation rates are influenced by the
relatively lower ratio between noise and signal than the analysis of total income, which is affected by the lower quality of the information on nonlabor incomes. Labor income inequality is the most tractable variable for long periods of time. It is typically constructed using similar questions and recall periods. It also analyzes the same type of income (monetary income after taxes) and suffers from relatively lower underreporting issues than total income inequality across countries.<sup>21</sup> Monthly labor income is expressed as earnings per hour worked, which is a standard unit value traded in the labor market. Since the objective is to have the most accurate estimates of how the labor market assigns a price to certain skills either observable or unobservable (earnings structure), we limit our analysis to the sample of full-time workers (35+ hours per week) between 15 and 64 years of age. Depending on the country, this accounts for 75 percent to 85 percent of all workers, as has been traditionally done in other countries.<sup>22</sup> There are several advantages of focusing the analysis on the sample of full-time workers: (a) it reduces the dispersion of hourly earnings that could be caused by measurement error, (b) it allows those workers to be tracked who are strongly attached to the labor force and who are more similar both in observables and unobservables relative to part-time workers, and (c) it isolates the particularities of the demand- and supply-side forces that frame parttime jobs in the estimation of labor market prices, which is the main goal of this study. The main consequence of considering only full-time workers is that labor income inequality may be lower, and the trend may not be necessarily the same if we include part-time workers in the analysis. However, as mentioned above, we are interested in disentangling the trends in labor inequality derived from the inequality of the earnings structure rather than the labor supply decisions of workers.

Finally, the data series are spatially deflated between urban and rural areas
collection for the first SDI surveys in Latin America, East Asia/Pacific, and South Asia is currently underway.

Information available through health SDI surveys is not readily or reliably available through routinely collected administrative data even when they exist. In some countries with weak health systems, health management information systems (HMIS) capacity is severely lacking, if not absent altogether. SDI health survey data can fill the evidence gap in such scenarios. In particular, the health SDI surveys focus on three key areas: _(i) Providers’ knowledge:_ Are health care providers accurately diagnosing and treating common health conditions, adhering to clinical guidelines?

_(ii) Providers’ effort:_ Are health care providers present at their facilities during their shifts? Are there enough staff available to see all patients?

_(iii) Inputs:_ Do health facilities have the equipment (e.g., medicines, infrastructure, supplies) and resources they need to deliver high-quality care? Are providers receiving adequate on-the-job training?

> 6 Service Availability and Readiness Assessment (SARA)

> <u>https://www.who.int/healthinfo/systems/sara_methods/en/</u>

> 7 SPA Overview https://dhsprogram.com/What-We-Do/Survey-Types/SPA.cfm

> 8 Service Delivery Indicators https://www.sdindicators.org/

3
Table 1: Summary of Infrastructure data

|Infrastructure|Country|Year|Source|
|---|---|---|---|
||Nigeria<br>Nigeria|1991<br>2009<br>around 2013|Jedwab and Storeygard (2020)<br>Foster and Briceno-Garmendia (2010)<br>Ali et al. (2015)|
|Roads|Cameroon|2009|Foster and Briceno-Garmendia (2010)|
|||2018|Road authorities|
||Chad|2009<br>2018|Foster and Briceno-Garmendia (2010)<br>Road authorities|
|Electricity|All|varyacross countries|Nighttime lights / Population raster|
|Electricity grid|All<br>All|around 2006<br>most recent|Foster and Briceno-Garmendia (2010)<br>gridfnder.organd Arderne et al.(2020)|
|Internet|All|2009-2019|Africa Bandwidth Maps 2009-19|

We gathered geospatial maps of road expansion using government sources as well as previously harmonized collections of road networks (Foster and Briceno-Garmendia, 2010; Jedwab and Storeygard, 2020). The quality of the network and associated features and the frequency of updates vary across countries. We first used road networks from Foster and Briceno-Garmendia (2010) which relied on surveys and governments sources as a baseline for years between 2000 and 2007. Additional government surveys provided more recent road networks for Cameroon and Chad. We used the road network from Ali et al. (2015) for Nigeria which included road survey data from the Nigeria Federal Roads Maintenance Agency (FERMA) and the World Bank’s Fadama project.<sup>4</sup> Panels of roads from the same source are rare. Related works include a similar paper applied to the Horn of Africa (Herrera Dappe and Lebrand, 2021) and Moneke (2020) whose focus on ‘all-weather’ (i.e. gravel, asphalt or bitumen surface) roads is closer to ours.
***|0.0232***|-0.00608|0.000570|-0.00645|0.0121**|-0.0127**|0.00938|
||(0.00915)|(0.00869)|(0.00425)|(0.00386)|(0.00600)|(0.00492)|(0.00571)|(0.00674)|
|Trade Openness|0.437*|-0.461***|0.106|-0.0847|0.194|-0.112|0.138|-0.287**|
|_(% of GDP, logs)_|(0.248)|(0.152)|(0.118)|(0.0675)|(0.166)|(0.0860)|(0.155)|(0.118)|
|Financial Openness|-0.0123|0.306***|-0.0160**|0.00110|-0.00231|0.108***|-0.00340|0.171***|
|_(Chinn‐Ito Index)_|(0.0173)|(0.0339)|(0.00777)|(0.0148)|(0.0110)|(0.0189)|(0.0108)|(0.0263)|
|_External Factors_|||||||||
|Foreign Growth|-0.000306|-0.0127|0.00497|0.00275|0.00245|0.000869|-0.00317|-0.0148**|
||(0.0140)|(0.00817)|(0.00664)|(0.00361
to conflict, for the years in which both NA and survey data are available, the average ratio of average per capita consumption from household survey to average private consumption per capita from NA was 0.93, whereas the corresponding ratio using GDP per capita was 0.61 (Table 8).<sup>22</sup> _Table 8:Ratio of survey household consumption to Private Consumption and GDP_

||**Ratio of average pc consumption from HH**<br>**survey to average pc private consumption**<br>**from NA**|**Ratio of average pc consumption from HH survey**<br>**to pc GDP**|
|---|---|---|
|**2003**|1.18|0.71|
|**2007**|0.84|0.56|
|**2009**|0.79|0.57|
|**Average**|0.93|0.61|

_Source_ : World Bank staff calculations, based on WDI and CBS data

> 21 The international recommendation is to update the base year at least every five years.

> 22 The lower ratio when using GDP is expected because GDP includes more than private household consumption.