rafmacalaba/data-use-mentions-extended huggingface dataset:
Similar to the estimations shown in Tables 3 and 4, it is not the intensity of service input use, but the composition of services (complex services) and the origin of services (imports) that matter for productivity. The coefficient on services intensity is positive, and for most industries, it is statistically insignificant. An exception is the Chemicals and Pharmaceutical sector. An increase in service intensity in the pharmaceutical sector is associated with an increase in productivity. For other major manufacturing industries, a higher share of complex services is associated with higher productivity.

The use of imported services is positively correlated with productivity across manufacturing industries. Imports of services by firms in the electronics industry is associated with a 13 percent higher productivity relative to not importing services, while for firms in the textile and apparel industry the productivity premium associated with service imports is 7 percent. Firms in the Chemicals and pharmaceutical industry have a 9 percent productivity premium from importing services and firms in the automobile industry have a productivity premium of 15 percent. Exporting services is particularly important for firms in the electronics, chemicals and pharmaceuticals and IT service and ITES industries. Being internationally integrated in GVCs not only with goods, but also with importing and exporting services is particularly important for industries with highly sophisticated products and high technological content.

# 5. Concluding Remarks

The fragmentation of production across countries has allowed firms to increase specialization while reaching greater economies of scale through connecting to GVCs. This has enabled firms and countries to reap higher levels of productivity – an important factor for sustained per capita growth in the long run. Services play an important role as inputs as well as coordinators of the production process. Our findings indicate that Indian firms that are deeply integrated in GVCs are likely to be among the best performers and spend more on sophisticated services.

The findings from the analysis of the firm data show that: _First_ , firms integrated in GVCs have a productivity premium between 13 and 22 percent relative to domestic firms, with the productivity premium increasing with the extent of integration into
# 1. Data: High Frequency Phone Surveys (HFPS) in the EAP region

Starting from May 2020, the High Frequency Phone Surveys (HFPS) were conducted in 11 middle-income countries in the EAP in order to monitor the socioeconomic impacts of the pandemic. The surveys covered a wide range of topics, including employment, income, food insecurity, access to health and education services, and coping mechanisms, among others. World Bank teams working in each country led the design and collection of the data. The first rounds of surveys were administered in May-June 2020 and subsequent rounds continued into 2021. This paper utilizes data from seven of the 11 countries in which these data were collected (Indonesia, Lao PDR, Mongolia, Myanmar, Philippines, PNG, and Vietnam) and spans the period from May 2020 to May 2021. We include at least two survey rounds for each country to allow examination of the temporal aspects of the pandemic’s impacts. In the EAP, the HFPS relied on one of two sampling methods: Four countries drew from a sample frame of a recent representative household survey, while three used random digit dialing from a roster of phone lines (Table 1). Sampling weights were constructed for all surveys to ensure unbiased estimates from the sample. As the high-frequency surveys were phone-based, the samples are representative of households who have access to a phone. This may limit representativeness in areas and subpopulations for which phone penetration is low.

One major contribution of this paper relative to other studies that have used the HFPS is the use of household-level welfare data to estimate the distributional impacts of the pandemic. For countries that drew their sample from a previous pre-pandemic household survey (Mongolia and Vietnam), consumption data from these existing surveys could be used to identify each household’s welfare status before the pandemic. For the other five countries that employed random digit dialing methods or did not have prepandemic consumption information, welfare was imputed using a basic set of welfare predictors collected in the HFPS or from recent surveys.
|**Venezuela**<br>FB<br>1961|1971|1981|1990|2001|
|---|---|---|---|---|

- *The census year was derived from splitting an aggregated census.

a. FB is foreign born, NT is nationality, and ETH is ethnic group . FB(NT) means that the original data by nationality were amended and the resulting numbers are closer to foreign-born definition. Source: Authors‘ calculations based on data described in text.

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other African countries. Thus, the dependent variable, which is the quantity of crops sold, _Sij_ , is censored: Since _M_ is potentially endogenous, the instrumental variable tobit (IVTOBIT) model is used to estimate the equation. Unlike Equation (1), this specification allows to measure the impact of output market connectivity because the dependent variable is a precise measurement representing output market participation, explicitly ruling out subsistence production.

20. Similarly, an alternative specification is this: where _SHRij_ is the share of market sales of crop _j_ for household _i_ . This can be estimated consistently by the instrumental variable (IV) regression.

21. The current paper relies on a household expenditure and consumption survey that was carried by the Comorian National Institute of Statistics and Economic and Demographic Studies (INSEED) in 2014. The survey has an agricultural module, in which households were asked how many crops were produced in monetary terms. In total, 36 crops were identified. For each crop, households were also asked whether they used fertilizer and improved seeds. The availability of intermediary services to sell produce was also interviewed. Three types of services were itemized: (i) Aggregation at the cooperative level (denoted by _COOP_ ), (ii) Direct access to large markets ( _MKT_ ), and (iii) Collection of crops by intermediate traders, i.e., collectors ( _TRAD_ ). Note that these data are available at the crop level. In addition, households were asked whether they sold some of their produce, and how much.
# **4.2 Intra-household labor allocation**

This section unpacks the intra-household composition of the observed reduction in agricultural labor provision as a result of the intervention, as well as its determinants, testing the predictions in Section 2. We restrict attention to the 1,834 married producers with complete time-use data for spouses.<sup>18</sup> Table 7 shows that the oxen had different impacts for different household members, with time on household plots decreasing for women and girls. Columns 1 and 2 show that assignment to treatment had no significant impacts on the producer’s time use. Columns 3-8 show that treatment households decreased the time spent on household plots by almost 6% and 9% for wives and girls, respectively. The large decrease in household plot activities for girls represents around two-thirds of a larger decrease in their total reported active time.<sup>19</sup> In this table, we report results for both the first spouse and other spouses for polygamous households. The impact of the treatment on time use, including household plot activities, is not statistically different between first spouse (column 4) and other spouses (column 6). Results on other spouses being noisier but equivalent to results on first spouse, the rest of the paper studies the average impact of treatment on spouses, by taking the average of outcomes across the producer’s wives, when applicable. Finally, boys in treatment households increased time spent on household chores by almost 0.6 hours, representing an increase of over 30%.<sup>20</sup> These findings speak directly to our model: the significant decrease in female plot labor supports the presence of sizable social prescriptions dictating that women should not handle traction oxen ( _a f >_ 0). Relatedly, the lack of significant impacts on domestic work suggests a near-zero response of the marginal product of labor to a capital shock ( _HTK ≈_ 0). We examine the ambiguous observed impact on non-farm labor in
entrepreneurs (mean age of 38 in our sample vs. 41 in FinScope), and led by better educated individuals (67% have at least matriculated in our sample vs. 42% in Finscope). In addition, these businesses are more likely to be formally registered (42% in our sample vs. 21% in Finscope) and hire more employees (2.4 employees in our sample vs. 1.24 in Finscope). Access to formal business credit is also different but notably low in both samples (6% in our sample vs. 4% in Finscope).

In Panel B of Appendix Table 7, we repeat the comparison but restrict the Finscope sample to those with above median number of employees (i.e., at least one employee) to roughly align the Finscope firms to our sample.<sup>10</sup> While significant differences are still present on some margins, the two samples are more similar on some key characteristics such as the gender of business owner, formal registration status, and access to credit. Hence, while we cannot claim our sample is nationally representative of a typical South African small business, the comparisons with FinScope data suggest our study sample is more similar to the average business that employs at least one worker.

An important aspect to note is that such businesses are highly prevalent in the economy. To illustrate this point, we use the recorded GPS coordinates of all businesses from the baseline survey to plot their precise locations on a map. Appendix Figure 1 shows that our sample has wide coverage and is spread all across the greater Cape Town area rather than being geographically confined to one particular neighborhood or sub-district. Hence, the businesses in our study are not an unusual sample concentrated in one particular geographic area, but rather firms like these are present throughout the region.

The GPS mapping also helps allay a second concern of knowledge spillovers to control group businesses from businesses in the treated groups. Note that such spillovers will only undermine treatment effects
# 1. Introduction

In early 2020, the COVID-19 pandemic forced the suspension of in-person data collection around the world. As data is a critical means to better understand the impacts of the pandemic, many governments, national statistical offices (NSOs), international organizations, and researchers came to rely on phone surveys in lieu of in-person interviewing. Phone surveys offered advantages for rapid data collection in an emergency both in terms of cost and flexibility (United Nations and World Bank, 2020). While already commonly used tools in high-income countries (National Research Council, 2008; Slavec and Toninelli, 2015), prior to the COVID-19 emergency phone surveys were far less prevalent in low- and middle-income countries where limited phone penetration had in the past been an obstacle to their implementation (GSMA, 2018, 2020). Phone surveys had for instance been used for specific populations and purposes, such as in response to the 2014 Ebola outbreak (Etang and Himelein, 2020) and the 2017 drought and conflict crisis in the Republic of Yemen, Somalia, South Sudan, and Nigeria (Hoogeveen and Pape, 2020), but only in a handful of regional or national projects (Ballivian et al., 2015; Dabalen et al., 2016). The COVID-19 emergency, however, prompted a rapid and wide-scale uptake of phone surveys in these countries, supported not only by the urgent need for real-time data but also by recent expansions in mobile network coverage (Tomlinson et al., 2009; Dillon, 2012; Demombynes et al., 2013; Ballivian et al., 2015; Larmarange et al., 2016; Lau et al., 2019).

This surge in phone surveys provided an unprecedented opportunity for NSOs and international organizations operating in low- and middle-income countries to build experience and the necessary infrastructure to implement such surveys at scale. Gourlay et al. (2021) provide an overview of how this acceleration materialized, drawing on the experience of the Living Standards Measurement Study (LSMS) survey program as well as
# **Appendix: Supplementary Tables and Figures**

# **_Tables_**

**<u>Table A1: Descriptive Statistics Based on Administrative Data, 1981–2019</u>**

||<br>**Total **|<br>**1981–99**|**2000–09**|**2010–14**|**2015–19**|
|---|---|---|---|---|---|
|**Initial registration**||||||
|<br>No. of leases issued|58,582|6,641|4,845|39,189|7,907|
|per year|1,502|350|485|7,838|1,581|
|Plot area (m<sup>2</sup>)|1,060|1,494|1,025|972|1,173|
|Sporadic|24,160|6,641|4,845|6,111|6,563|
|Systematic|34,422|||33,078|1,344|
|<br>Sporadic/year|619|350|485|1,222|1,313|
|Systematic/year|883|||6,616|269|
|_Approach and land use type_||||||
|Sporadic registration|0.412|1.000|1.000|0.156|0.830|
|Systematic registration|0.588|0.000|0.000|0.844|0.170|
|<br>Residential land|0.892|0.853|0.924|0.889|0.921|
|Commercial land|0.046|0.115|
# **4.1 Data**

## **– 4.1.1 CPS US**

Our data for the United States come from the March supplement to the Current Population Surveys (CPS) for 1995 and 2007.<sup>9</sup> The dataset provides the distribution of labor earnings in the US in 1995 and 2007 for full-time workers of both genders. We use the following variables for our analysis. _Y_ denotes real hourly labor earnings (sum of annual pretax wages, salaries, tips, and bonuses, divided by the number of hours worked annually). The vector **_X_** consists of three covariates of _Y_ , namely: (i) the worker’s age in years; (ii) a gender dummy; and (iii) a categorical variable for the highest educational level attained (“high school”, “some college” or “college”).

We restrict the sample to individuals aged 16 to 65 that reported a positive value for real hourly earnings in the previous year. Individuals with missing values for any of the four variables in _Y_ and **_X_** were excluded from the sample. After applying these filters, we trimmed the sample by dropping the top and bottom 0.5% of the distribution of hourly wages in each year, to eliminate outliers. Hourly wages are in US dollars of March 2007. The Consumer Price Index was used to inflate 1995 incomes: nominal values in 1995 were multiplied by 1.36 to be expressed in 2007 dollars. The final sample contains a total of 165,245 observations. Summary statistics are presented in Table 1.

– Table 1: Summary Statistics US

||Mean|S.D.|Min.|Max.|Observations|
|---|---|---|---|---|---|
|CPS 1995||||||
|Hourly Work Earnings|16.730
Policy Research Working Paper 9839

# **Abstract**

International commodity price shocks may have large impacts on producers in developing countries. In this paper, a unique household panel data from Ethiopia is utilize to show that a decrease in international coffee price has strong pass-through to the consumption of households that rely on coffee production as a main source of livelihood. It also results in decreases in on-farm labor supply (particularly male labor supply) and induces reallocation of labor towards non-coffee fields, but has negligible effect on off-farm labor supply. The decline in consumption has significant consequences on child malnutrition: children born in coffee-producing households during low coffee price periods have lower weight-to-age and weight-to-height z-scores than their peers born in non-coffee households.

This paper is a product of the Development Policy Team, Development Economics. 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 author may be contacted at hundanol.kebede@siu.edu.

_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. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The paper contributes to the literature on the economic analysis of conflict. Most of this literature focuses on the causes of conflicts or analyzes their economic consequences in a macro or cross-country framework (Collier and others 2003, Collier and Hoeffler 2001, Collier 1998, 1999). With the notable exception of the work by Verwimp on the Genocide in Rwanda (Verwimp, 2004a, 2004b, 2003a, 2003b), few studies have used micro-level household data to look at the causes or consequences of conflict.<sup>1</sup> At the same time, by looking at the long-run consequences of mortality, morbidity and negative educational shocks, this study can contribute to a better understanding of the fertility decisions, the marriage market and the long-term benefits associated with education and good health.

This paper is structured as follows: Section II documents the excess mortality that took place during the period and decomposes it by birth cohort, gender, and family background. Section III analyzes fertility behaviors and the marriage market. Section IV investigates the long-term impact of the period on health, while section V describes the consequences on the educational attainment of the population. Section VI concludes.

# **II.  Mortality**

Estimates of mortality under the Khmer Rouge regime vary widely and are the subject of an intense debate. Sometimes this debate is more ideological than scholarly. This is not surprising, given that the government overthrown by the Khmer Rouge, led by Lon Nol from 1970 until 1975, was pro-American, that the Khmer Rouge under Pol Pot advocated a radical form of Marxism and that the regime that took control of the country in 1979 was backed by the Vietnamese. According to former Khmer Rouge, the death toll did not exceed 20,000, while according to the Vietnamese government, there were over three million victims (Heuveline 1998).

An independent Finnish Inquiry Commission estimated the death toll at one million using demographic accounting (Kiljunen 1984). Probably the most careful exercise of demographic reconstruction, comparing the population structure before and
<!-- Start of picture text --> Figure 9. Historic power costs in select African countries (cents/kWh)<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Operating Costs Capital Costs<br>Source: Africa Infrastructure Country Diagnostic Power Tariff Database<br>Note: For details see annex 8.<br>South Africa Zambia Congo, Dem. Rep. of Ethiopia Mozambique* Malawi Nigeria Uganda Lesotho Cote d'Ivoire Namibia Senegal Ghana Chad Botswana Tanzania Kenya Madagascar Burkina Faso Rwanda Cameroon Cape Verde * Benin * Congo Niger Mali<br><!-- End of picture text --> But what drives costs? In general, hydro-based systems tend to be more skewed towards capital cost and thermal-based systems more skewed toward operating cost In terms of geography. Africa’s landlocked and island nations seem to bear a significant power cost disadvantage vis-à-vis the coastal countries. High power costs are also driven by the size of markets and their associated scales of production (figure 10).

<!-- Start of picture text --> Figure 10. Differences in average costs (US cents per kWh)<br>a. According to type of power system b. According to geography c. According to system size<br>20 20 20<br>15 15<br>10 10 10<br>5 5<br>0 0 0<br>Total Capital Operational Total Capital Operational Total Capital Operational<br>costs costs costs costs costs costs costs costs costs<br>Hydro Thermal Coastal Landlocked Island < 700 MW 700-1,500 MW > 1,500 MW<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ For details see annex 7.

# **d. Do average effective tariffs cover
many high-income countries. Building on Walmsley (1999) and working with a multi-region CGE model, Walmsley and Winters (2005) estimate that if OECD countries were to expand temporary access to foreign service-providers by the equivalent of 3 percent of their labor force, the global gains would be greater than those associated with full liberalization of merchandise trade. These gains are driven by higher labor productivity and thus wages of the workers that are permitted to move to locations where labor is scarcer. The gains to those who move outweigh losses incurred by those who stay behind, although these losses are partially offset by income that is expected to be remitted back to source countries (based on observed average rates of remittance transfers). Both developed and developing countries (including the citizens that are permitted to move) would share in these gains, and they would be largest if both high-skilled mobility and low-skilled mobility were permitted. There are of course large political obstacles that must be overcome for such mode 4 trade expansion to be feasible, but movement towards liberalization may be possible if designed appropriately. While this is an area where the GATS could play a role, it is more likely that countries will continue to rely on bilateral arrangements to manage such trade, which in turn may be sector-specific.<sup>29</sup> Rutten and Reed (2009) focus on the impact of health worker movement on the cost structure of the UK health system. They find that while a rise in the National Health Service (NHS) budget yields overall welfare gains, a nominally equivalent policy of enhancing access to the UK for foreign health care providers (yielding the same increase in services) yields even higher overall welfare gains. Cross-border health worker movement, whether temporary of permanent, is likely to become increasingly important given demographic trends in the OECD and the willingness of middle income countries to train such workers (as in the Philippines) for export.

Trade in health services can also occur through mode 2 type transactions insofar as patients move to the location of
- _QLi_ quality of employment aggregates (see below for detailed explanation)

- Χ vector of variables associated to the firm (size, region, manufacturing subsector, proxies for openness of the firm, technology adoption, quality control, and R&D)

For each firm, the measure of productivity used was value added productivity, which was calculated as the value of production minus the cost of materials. The wage variable is calculated as the sum of all wages, salaries, and compensations paid by the firm to its workforce.

The quality of labor aggregate takes into account the productivity of different types of labor. Take the simple economy set up above. Quality of employment is defined for the production function by: where:

- _L_ total workers in the firm

φ is the marginal productivity of type A workers relative to type B By substituting (3) into (1), we obtain a non-linear production function from which we can estimate φ , using firm-level data on value added, capital, and the number of workers and type decomposition of the workforce. Similarly, the quality of labor is introduced into the wage equation: However, we want to test these relationships for more than two types of labor. The types of labor are characterized by different categories in the following characteristics:

> 7 _LB_ , the number of type B workers in the firm is the reference group.

9