rafmacalaba/data-use-mentions-extended huggingface dataset:
the dataset includes information from 21 different dimensions and each dimension is associated with five different summary measures. For example, we summarize the atmospherically resistant vegetation index for each municipality using the minimum, maximum, mean, sum, and standard deviation of the index. The same procedure is applied to all dimensions.<sup>9</sup> We then have two alternative sets of covariates that we use in our simulations: the geo-referenced data and the standard set of sociodemographic characteristics from the census.

> 8The equation used to calculate inclusion probabilities assumes sampling with replacement, but is used here as an approximation of inclusion probabilities under proportional selection without replacement. This should provide a reasonable approximation in this case since there are a relatively large number of clusters present in the frame. The design weight for each household is simply the inverse of the inclusion probability. In a typical survey, the design weights would be further adjusted for nonresponse and calibrated to known population characteristics. However, since the sampling is only a simulation exercise, there is no nonresponse and thus no nonresponse adjustment is required. Calibration or post-stratification could be performed, but was not implemented to simplify the process.

> 9The 21 dimensions are as follows: enhanced vegetation index, normalized difference vegetation index, normalized difference built-up index, built-up index, simple ratio, atmospherically resistant vegetation index, urban index, index of biological integrity, normalized difference water index, modified normalized difference water index, new builtup index, band ratio for built-up area, normalized built-up area index, built-up area extraction index, normalized difference snow index, visible atmospherically resistant index, soil-adjusted vegetation index, optimized soil-adjusted vegetation index, normalized difference moisture index, digital altitude model, and digital slope model.

6
Extensive margin only|61.3|15.2|7.7|15.8|
||Intensive margin only|50.3|37.7|6.5|5.5|
||Extensive and intensive margins|37.9|32.5|18.3|11.4|

# **4.3 Stylized Fact 3: The recovery among firms is heterogeneous and more sensitive to firm-level attributes rather than to country-level differences in the severity of the initial shock.**

The analysis of firm outcomes, focusing on average trends, masks significant heterogeneity in performance. Such heterogeneity is well-noted in many studies, including the one using the first wave of BPS (Apedo-Amah et al., 2020). The second wave of BPS data allows us to explore the heterogeneity in recovery process within and across countries.

_First_ , firms in countries with more severe initial shock (as measured by the drop in mobility drop) are catching up in performance relative to their peers in countries facing a lower severity of the shock. For instance, while firms in countries with more severe mobility drop during the start of the pandemic witness a decrease in sales by 49% in wave 1 and 29% in wave 2, those in countries with milder disruptions in mobility observed a decline in sales by 39% during wave 1 and 28% in wave 2 (Figure 7, panel a). A similar pattern is also found with the probability of being open (Figure 7, panel b).

14
# **Figure 22. SSA: Investment growth slowdown**

_Investment growth has slowed sharply from about 8 percent in 2010 to near-zero in 2015, despite significant public investment until 2014. The slowdown has reflected a severe terms-oftrade deterioration in commodity exporters as well as long-standing structural bottlenecks and political tensions._

# **A. Investment growth**

# **B. Output growth**

<!-- Start of picture text --> Percent Percent<br>14 1990-2008 avg 2003-08 avg 14 1991-2008 avg 2003-08 avg<br>12<br>10 10<br>8<br>6 6<br>4<br>2 2<br>0<br>-2<br>-2<br>-4<br>Overall Com. exp. Com. imp.<br>Overall Com. exp. Com. imp. EMDE SSA EMDE<br>C. Share of SSA EMDEs with weak D. Contributions to investment growth<br>investment growth<br>Percent Percentage points<br>75 Below long-term average 20 Public Private<br>Contracting 15<br>10<br>50<br>5<br>0<br>-5<br>25<br>Commodity exporters South Africa and<br>0 excl. South Africa and Nigeria<br>2010 2011 2012 2013 2014 2015 Nigeria<br>E. Terms of trade change F. Political stability<br>Percent ICRG index, 0-100 (100=best)<br>15 Energy exporters 65<br>Non-energy commodity exporters<br>10<br>60<br>5<br>0 55 Energy exporters<br>Non-energy commodity exporters<br>-5<br>50<br>-10<br>45<br>-15<br>-20 40<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br>Sources:
Policy Research Working Paper 9814

# **Abstract**

This paper explores the relationship between the use of service inputs, participation in global value chains, and firm productivity. Services play the role of both an intermediate input in production and a coordinator. Using a detailed Indian firm-level data set from 1990–2017, the paper estimates the productivity premium associated with varying - depths of global value chain integration and different inten sities and types of services used in the production. The study finds that firms in global value chains have a productivity premium between 13 and 22 percent relative to domestic firms, with some variation based on the depth of global value chain integration and the sector to which the firm belongs. Both the type of service inputs used (composition of services) and the origin of services (whether sourced domestically or from abroad) matter for firm performance. While higher aggregate service input use (as captured by the share of expenditure on service inputs) is not necessarily associated with an increase in productivity, increased use of complex services and information technology services is associated with higher productivity. The use of imported services is associated with higher productivity. Moreover, firms that are more deeply integrated in global value chains benefit more from importing services.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rmanghnani@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly.
0 1S (6) Equation (6) adds to Equation (5) a vector of control variables cc,cc cc,cc cc,cc cc cc cc,cc , which includes the added YY = γγ + γγ SS + γγ<sup>′</sup> XX + θθ + σσ + εε values in manufacturing, agriculture, forestry, and fishing, and trade values (all as shares of cc,cc XX country GDP), which are measures of the sectoral composition of the economy. All these three variables are measured as a percentage of the country GDP. also includes the gross primary school enrollment rate. further includes either an index of political institutions as measured by cc,cc XX WGI (when the outcome variable is log of GDP per capita) or country income levels as measured cc,cc XX by log of GDP per capita (when the outcome variable is WGI).

Since we do not have all the country-year observations for these control variables (except for the WGI), we impute for the missing observations with the nearest available values. The percentage of missing values ranges from 1 percent (agriculture, forestry, and fishing added values as a share of GDP) to more than 25 percent (gross primary school enrollment). We focus on the resulting balanced panel data for 159 countries with data for the SPI, WGI, and other control variables between 2016 and 2022. The main reason that most countries do not have an overall SPI score in 2016 is due to data unavailability from Open Data Watch’s Open Data Inventory (ODIN), which was used for the SPI measures of data openness and geospatial information. The other reasons are missing human capital index scores or trade data. As such, Equation (5) is our preferred model for analysis but Equation (6) can offer useful robustness checks.

It is important to emphasize that these econometric models are unlikely to allow us to identify the causal impacts of the SPI on GDP growth or governance (
**|34.8|**28.7**|**6.1**|
|**Poland**|**1998**|**36.9**|**32.0**|4.9|

_Source:_ Country data (Bulgaria and Poland) and authors' calculations

**3.10** Women live significantly longer than men do in the **FSU.** Life expectancy and the expected length of the retirement period are central in valuing the pension. When unisex annuities are used (as they are in a PAYGO system, and in most defined contribution systems), women get a higher rate of return on contributions than men. Table 4 shows the gender gap difference in life expectancy in the four countries under consideration. The largest gap is observed in Kazakhstan.

**Table 4. Life Expectancy**

|**Country**|**Kazakhstan**|**Kyrgyz Republic**|**Latvia**|**Moldova**|
|---|---|---|---|---|
|**Year**|**1996**|**1997**|**1998**|**1997**|
|**AtBirth**|||||
|Female|70.4|71.4|74.9|**70.3**|
|Male|**59.7**|**62.6**|**63.8**|**62.9**|
|**Age60**|||||
|Female|20.4|**19.1**|**19.9**|**18.8**|
|Male|**13.7**|14
led to a differential increase in German acquisitions and performance improvements in sectors in which Germany had a comparative advantage at the moment of accession.

# **6 Concluding remarks**

The belief that integration with high-income markets can help firms in less developed countries to reduce efficiency gaps is a key argument for pursuing deep agreements. In this paper, we studied the interplay between industrial structure and foreign acquisitions in shaping the effects of deeper integration on firm performance. We examined the extent to which this interaction played a role in driving the patterns of catch-up growth observed in Poland following accession to the European Union in 2004. Drawing on detailed data on Polish manufacturing firms 17
population living in cities with one million or more taken from the United Nations’ _World Urbanization Prospects (UNWUP)_ . To check for robustness, the 750,000 cut-off will also be used.<sup>_21_</sup> In the UNWUP, the population data are available every five years. The data for other years are interpolated, assuming a constant growth rate during each 5-year period. Two sources of data are used to calculate the share (in %) of people in agriculture, _sA_ : FAO’s database and the World Bank’s _World Development Indicators_ (WDI). The coverage of FAO’s database is larger than that of WDI, and the FAO data are used whenever they are available. The share of the population engaged in non-farm activities located in the intermediate space or the “middle”, _sN_ , is defined as the residual, i.e. _sN_ ,=100- _sU-sA_ . Given the (deliberately) narrow definition of urban areas (i.e. only the mega cities), _sN_ includes people living and employed in secondary towns as well as those engaged in off-farm employment in rural villages.<sup>22</sup> The number of floods during the spell are taken from the International Emergency Database (EMDAT)<sup>23</sup> and included to control for possible differences in migration patterns to metropoles and the middle in response to natural hazards. The composite polity indicator compiled by the Polity IV project of the Center for Systemic Peace (Marshall, Jaggers, and Gurr) is used to reflect the autocratic/democratic nature of the political regime. The indicator takes on values between -10 (autocratic) and +10 (democratic).<sup>24</sup> The sample is limited to low and middle-income countries according to the World Bank’s classification in 2008 and spans about a quarter of a century, from 1980-2004. The complete list of available poverty spell observations in _Povcal_ at that time consists
mode effects thus do not appear to be driving the observed difference between survey and administrative data.

**<mark>-</mark>** **<u><mark>Table 5: Hypothesis 3 Survey Mode</mark></u>**

|**Panel A:**Interviewing phone survey main respondents in-person||
|---|---|
|Phone Survey Main Respondents, Apr - Jun 2022|46.0<br>(41.0 to 51.1)|
|Admin data (adults 15+), June 5|33.4|
|_N (Phone Survey Main Respondents)_|2,403|
|t-Test,Main respondents(survey)vs. Admin(adults 15+)|p< 0.0001 ***|
|**Panel B:**Collecting vaccination status of all phone survey household|members in-person|
|All Phone Survey Household Members, Apr - Jun 2022|30.5<br>(27.2 to 33.7)|
|Admin data (general population), June 5|20.1|
|_N (All Phone Survey Household Members)_|10,786|
|t-Test,All HH members(survey)vs. Admin(generalpopulation)<br>Note: Weighted estimates of vaccine uptake in face-to-face data from the Ethiopia Socio|0.0001 ***<br>economic Survey, Wave 5|

Note: Weighted estimates of vaccine uptake in face-to-face data from the Ethiopia Socioeconomic Survey, Wave 5 (ESS 5) (Apr-June 2022). Administrative vaccine coverage rate for adults conservatively assumes that no children below the age of 15 were vaccinated at the time of reporting. All values in percent. 95%-Confidence Intervals in parentheses.

# **Panel conditioning**

Respondents may change their vaccination behavior in response to being repeatedly surveyed on the topic of COVID-19 vaccination. We therefore predicted that respondents
electricity sector in the original I-O table is divided based on the GTAP electricity database with ad-hoc adjustments for the Turkish electricity balance tables. Nuclear power is introduced with a small share in electricity supply based on the average cost structure of nuclear power activities in the GTAP power database.

# 2 MFMOD additional feature

The World Bank’s macrostructural model, MFMod is used in quantifying the economy-wide impacts of the energy efficient infrastructure investment. The customized standalone model for Türkiye extends the investment channels along the several dimensions. (1) Capital stock is differentiated by public and private contributions and (2) capital stock is differentiated between infrastructure and non-infrastructure capital,<sup>17</sup> and (3) the time horizon is extended to 2040.

In the model, investment decisions are based on the difference between returns and the cost of capital. Specifically, the dynamic investment equation is a function of past investment decisions (to reflect the “sticky” nature of asset decisions) and the marginal Tobin’s Q ratio, which reflects the return vs. the cost of capital. In the long-run, the investment to capital ratio is a function of long-run economic growth plus the rate of capital depreciation. The private sector infrastructure equation is written as:

> 17 In (Hallegatte, Jooste, & McIsaac, 2022), they use a modified version of the model to explore the vulnerability to floods and earthquakes. <u><mark>http://hdl.handle.net/10986/37060</mark></u>

27
Subset of Eligible Adults Interviewed**|**802**|**32%**|
|3 out of 4|106|4%|
|2 out of 4|92|4%|
|1 out of 4|29|1%|
|2 out of 3|167|7%|
|1 out of 3|65|3%|
|1 out of 2|343|14%|
|**Average # of Adults Interviewed**|**1.8**|**9**|

We calculate weights to correct for non-response in the IHPS by running a logistic regression of individual response status among adults eligible for individual interviews. The results from the logistic regression are presented in the Appendix Table A1.<sup>20</sup> Subsequently, we (1) take the

> 19 Among the eligible men who did not respond, 44 percent were heads of household and 35 percent were children of the household head, and among the non-responding women, only 6 percent were household heads, 32 percent were spouses of the head, and 37 percent were children.

> 20 Van den Broeck and Kilic (2019) use a similar approach to correct for attrition bias in panel data samples, in a study of labor market dynamics in Sub-Saharan Africa. Our right-hand-side predictors of “response” include (i) fixed effects for districts, interview months and enumerators, (ii) individual covariates, including age; a dichotomous variable identifying females; a series of dichotomous variables on educational attainment; dichotomous variables identifying whether the individual is currently married, and separately, whether he/she is head/spouse of head; and individual’s

10
with poverty by examining the extent to which young people with disabilities live in households with lower economic status. Section 4 investigates the long run association with poverty by examining the association between disability and school participation among school-aged youth.

# **2. Data**

The data used for this analysis are from 11 nationally representative household surveys from 9 countries. Three of the surveys are associated with the Living Standards Measurement Study (LSMS) surveys: Jamaica 1998, Jamaica 2000, and Romania 1995. Three of the surveys are national socio-economic surveys (SES): Cambodia 1999, Indonesia 2000, and Mozambique 1996. One survey is a Demographic and Health Survey (DHS): Cambodia 2000. Four of the surveys are End of Millennium Multiple Indicator Cluster Surveys (MICS2) carried out under the guidance of UNICEF in 2000: Burundi, Myanmar, Mongolia, and Sierra Leone.<sup>3</sup> These surveys are typically used to calculate poverty statistics, or derive basic health indicators such as child mortality or the use of health services, and underlie much empirical poverty and social analysis in developing countries. Most of the surveys have a sample size of between about 4,000 and 25,000 households (with Jamaica and Myanmar being outliers with 1,800 and over 65,000 households surveyed respectively).

In order to select these datasets all LSMS, DHS, and MICS surveys were reviewed for any questions on disability and all those with a clear question on disability for a relevant age-range were included. In addition, the SES from Cambodia, Indonesia and Mozambique are accessible from national statistics offices and are some of the most recent in the world with information on disability. There is relatively little data of this kind in developing countries: the datasets, and therefore the countries, for this analysis were selected on the basis of data availability. The countries were not selected to be representative of developing countries in general.

This is clearly a heterogeneous group of countries. Population living on less than a dollar a
>Second, a number of variables must be “jointly observed” in both the donor and the recipient samples. Van Hook and colleagues (2015) demonstrate that the violation of the latter condition results in severe bias both in magnitude and direction.</mark> <mark>For our purpose, both conditions are satisfied: Both DHS and H</mark> CES <mark>are nationally representative household surveys that collect a large range of variables on housing conditions. Further, location and household size are jointly observed in DHS and H</mark> CES <mark>. While DHS does not collect information on expenditure or income</mark> _<mark>per se</mark>_ <mark>, the survey still calculates a wealth quintile based on other available data collected which are closely correlated with wealth.</mark> <mark>Countries where DHS data were imputed include Albania (structural quality and cooking), Bangladesh (structural quality and cooking), Colombia (cooking), Ethiopia (structural quality), Indonesia (overcrowding), Nepal (structural quality), Pakistan (structural quality and cooking), Philippines (structural quality and cooking), and Senegal (cooking). For India and Mali, due to data quality issues in the H</mark> CES combined with rather old survey data (2011 and 2009 respectively <mark>), we reverted to DHS for all seven adequacy dimensions. For five countries</mark> (Kazakhstan, Russian Federation, Ukraine, Uzbekistan, and Vietnam <mark>) imputation was impossible due to missing data in both the H</mark> CES <mark>and DHS surveys and one dimension remains missing. Overall, this implies that we are potentially underestimating the inadequacy for those countries where data on one adequacy dimension is missing as a stricter definition of adequacy has shown to increase housing deficit estimations (cf. World Bank 2021).</mark>
manufacturing sector survey have a graduate (B.A.) or higher degree, as opposed to 79 percent of the owners/managers of the businesses selling on Chaldal. More than 90 percent of the firms in the Bangladesh manufacturing sector survey are managed by the owner or their family, in comparison to 82 percent of Chaldal sellers. Thus, firms selling on Chaldal have higher levels of managerial education and a lower prevalence of family management than typical manufacturing firms in Bangladesh.

Given the small average size of Daraz sellers, a recent representative survey of family business in India may be an appropriate comparison sample for the Daraz survey.<sup>20</sup> In India, among family businesses with at least one hired worker, only 25 percent of the owners have tertiary (above senior secondary) education. In comparison, the majority of Daraz sellers have tertiary education. The average age of the owners of Indian family businesses is about 45 years, compared to 30 years in the case of Daraz sellers. Daraz sellers may be small, but they are a highly educated and youthful subset of small business owners in South Asia.

# **6 Conclusion**

The quantitative case studies presented in this paper have contributed to the scant evidence base on the ways in which selling on e-commerce platforms impacts small firms in developing countries. The main channel of impact highlighted by the surveys is the greater access to product markets enabled by the reduction in transactions costs. This finding is significant in light of the growing evidence that there are important demand-side constraints to firm growth, such as the high costs of gaining initial market access (Atkin and Osman (2017)) and slowly building a customer base (Foster and Syverson (2016)). The case studies also suggest that this easing of demand-side constraints to growth increases e-commerce sellers’ incentives to formalize and undertake complementary changes to business practices and technologies. Strikingly, micro and small-sized firms from the informal sector report these benefits to the same extent as do medium to large-sized formal