rafmacalaba/data-use-mentions-extended huggingface dataset:
GVCs and classify exports into 19 subcategories as presented in a concordance table in the Appendix.

# **_3.1.2 Skill data_**

Human capital is potentially an important determinant of country participation in global markets. However, data on human capital are often very difficult to obtain especially for SSA countries. This study derived skills data using information from the Global Trade Analysis Project (GTAP) input-output tables. We also used the recently published Human Capital Index developed by the WBG (2018) as an indicator of human capital endowment. This index measures the knowledge, 10
to be young, fairly well-educated and working in large firms in industrial regions of the country. The data indicates that few are aware of their entitlement to severance pay. This lack of awareness may be due to the complexity of the legislation regarding severance pay but, more than likely, it is exacerbated by the lack of formal contracts that explicitly define the employment relationship.

> 6 See figure 4.4 in World Bank (2011) for a detailed description of severance pay regulations

> 7 Question R11a of the Sakernas (2008) survey asks individuals to identify the type of business concern where they worked before the job separation occurred: government agency; state-owned firm; domestic private firm; foreign firm; and domestic worker.

> 8 The questions in the firing module of Sakernas (2008) pertain only to the industrial districts of Indonesia.
Policy Research Working Paper 8717

# **Abstract**

This study examines recall bias in farm labor by conducting a randomized survey experiment in Ghana. Hours of farm labor obtained from a recall survey conducted at the end of the season are compared with data collected weekly throughout the season. The study finds that the recall method overestimates farm labor per person per plot by about 10 percent, controlling for observable differences at baseline. Recall bias in farm labor per person per plot is accounted for by the fact that households in the recall group report fewer marginal plots and farm workers, denoted here as listing bias. This listing bias also creates a countervailing effect on hours of farm labor at higher levels of aggregation, so that the recall method underestimates farm labor per plot and per household and overestimates the labor productivity of household-operated farms. Consistent with the notion that recall bias is linked to the cognitive burden of reporting on past events, the study finds that recall bias in farm labor has a strong educational gradient.

This paper is a product of the Development Data Group, 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/research. The authors may be contacted at at igaddis@worldbank.org and apalacioslopez@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. 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
Tobit specification to deal with the zero-expenditure problem, they find that households’ socioeconomic characteristics and geographic region of residence drive their decisions to own or not a vehicle.

Turning now to studies that examine household transport expenditure along with other household expenditures, Sanchez et al. (2006) investigate the choice working households in the United States make between housing and transport expenditures. They use cluster analysis to show that these two types of spending must be analyzed conjointly. In a later study, Ferdous et al. (2010) indicate that households in the United States allocate their transport expenditures according to their socio-demographic characteristics. For instance, urban households spend a higher proportion of their income on housing and public transportation. We complement this first strand of related literature by providing stylized facts on transport expenditures at the household level based on various data sources from a heterogeneous set of countries. Transport-related questions, except for fuel consumption and asset ownership, have been difficult to harmonize across countries (Lebrand and Yin, 2022). We therefore harmonized expenditures from LSMS surveys for as many countries as possible and draw additional insights from larger harmonized survey efforts available at the country level.

Second, this paper is linked to the literature analyzing the relationship between household transport choices, energy demand, and pollution. One important margin to understand the impact of rising incomes on carbon footprint in low- and middle-income countries is the first-time acquisition of carbon-intensive vehicle by households. The growth in energy demand among first-time purchasers is likely to have important implications for macro-level trends in energy use and carbon emissions. Gertler et al. (2016) analyze household decisions to acquire energyusing assets, focusing on the role of rising incomes in the developing world, and show that non-linearities dues to credit constraints will have large impacts on energy use in developing countries. Among these studies, Dai et al. (2012) use household expenditure data in China from 1985 to 2009 to investigate how household consumption expenditure change with rises in income and infer the impacts on carbon emissions. As income rises,
. Impressively, their model achieved an average prediction accuracy of 87 percent. Furthermore, their research highlighted variations in activity patterns on an hourly, daily, and seasonal basis among adults aged 45 to 79. In a more recent study, Sundararajan et al. (2021) employed a Random Forest model to identify sleep patterns using accelerometer data. Their model differentiated between sleep and wake states with a 74 percent score on unseen data. Moreover, by integrating their sleep detection algorithms with daytime data, they adeptly utilized machine learning to ascertain whether the accelerometer was being worn, achieving a score exceeding 93 percent. Both studies underscore the precision with which machine learning models can predict physical activities.

6
where the globalization coefficients are interacted with a country _c_ specific dummy 1( _i_ = _c_ ) to capture for those country-specific globalization dynamics. This follows FrancoBedoya (2023) analysis of country-specific globalization dynamics and its impact on the estimation of RTA coefficients.

Second, as the global economy has become increasingly structured around global value chains (GVCs), intermediate inputs often cross borders several times along the production process. The implication is that gross trade flows from gross trade statistics no longer reflect the domestic value-added content trade. For this reason, we use other dependent variables that allow us to decompose bilateral trade flows into domestic valueadded and GVC-related trade terms, as follows: where _GVCij_ , _t_ are different GVC trade flows. Total trade, as used so far in our empirical strategy, is the sum of traditional and GVC trade. Traditional trade is the value of trade flows that only cross one border, being absorbed by the direct trade partner. GVC related-trade is the value of bilateral trade that crosses at least two borders, flowing along a supply chain. GVC-related trade can be further decomposed into three components, pure forward, pure backward, and two-sided, as explained by Borin et al. (2021). "Pure backward GVC related-trade" is the value of goods and services produced with imported inputs and exported to the final destination market. "Pure forward GVC related-trade" is the value-added in goods and services entirely generated within the domestic economy (by different sectors) exported to partners, which in turn, re-exports it to other markets. "Two-sided GVC-related trade" is the value of goods and services produced with imported inputs and exported to partners, which in turn re-export them to other markets. Finally, note that the domestic value-added content of trade is the summation of traditional trade and pure forward GVC-related trade. These distinctions are important to understand the impact of a trade agreement on economic activity and will allow for gaining deeper insights regarding the specific effects of CAFTA-DR.<sup>13</sup> 13We access this
rate for offshores was raised to 10%, while the rate for onshore firms was reduced to 25%. Importantly, all other institutional features that benefited export-oriented firms remained unaffected.

We employ a differences-in-differences approach to assess the impact of the CIT rate increase on firms by comparing outcomes of interest for offshores versus onshore firms before and after the shock. The comparison is based on the universe of registered Tunisian firms, for which we collect administrative data from registries, social security, customs and tax records, to estimate the causal impact of the CIT reform on economic activity.

The key identifying assumption is that trends in the offshore and onshore sectors would have been similar absent the reform. We provide evidence in favor of that assumption, showing that onshore and offshore sectors often had very similar pre-reform trends, and also by documenting that our results are robust to a wide battery of aggregate and firm-level tests. These include robustness checks based on alternative samples which maximize the comparability of treated and untreated firms, as well as on a propensityscore matching estimator re-weighting firms on their observable characteristics. The results are also robust to controlling for possible shocks which could have differential impacts on offshore and onshore firms, including EU import demand, exchange rate and domestic demand.

The results at the extensive margin show that the number of firms in both the offshore and onshore sectors were growing significantly in the four years before the reform.<sup>2</sup> However, these trends diverged starkly in 2014, the first year of the reform. We estimate that relative to the counterfactual growth proxied by onshore firms, the number of offshore firms dropped by 20% four years after the reform. This effect is almost completely driven by a reduction in entry of new offshore firms after the change in policy, not by an increase of exit among incumbents.

The stark relative decrease in the number of offshore firms did not translate into a relative decrease in economic activity in the offshore sector. The differences-in-differences

> 2This growth in entrants was not associated with an
of characteristics obtained from the interviewed sample closely match those from the full representative survey sample. Provided the characteristics included in the calibration are closely associated with nonresponse, the calibration will serve to counteract the nonresponse bias. There are many complex calibration models that could be used (and are beyond the scope of this paper), but most are subject to an algorithm that minimizes the distance between the uncalibrated and calibrated weights and therefore might result in a lower increase in variation of the adjusted weights compared to the propensity modeling approach.

# 3. Data and Methods

## <mark>3.1 High Frequency Phone Surveys – Ethiopia, Malawi, Nigeria, & Uganda</mark>

In order to examine bias in phone surveys and effectiveness of bias adjustment methods, this study utilizes four recent high frequency phone surveys (HFPS) in Ethiopia, Malawi, Nigeria, and Uganda. These four surveys were implemented as part of a broader initiative by the World Bank to support implementation of high frequency phone surveys in several developing countries to monitor the socioeconomic impact of the COVID-19 pandemic. The baseline rounds of the Ethiopia and Nigeria HFPS were conducted in April shortly after the rapid spread of the pandemic and safety protocols and restrictions put in place. The baseline rounds of Malawi and Uganda were implemented shortly thereafter in May and June. The four HFPSs contacted households drawn from the most recent round of Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). The LSMS-ISA are longitudinal, face-to-face, representative surveys conducted in partnership with National Statistics Offices in the respective countries. In Ethiopia, the fourth round of the Ethiopia Socioeconomic Survey (ESS) conducted in 2018/19 served as the frame for the HFPS. The fifth round of the Integrated Household Panel Survey (IHPS) 5
Figure A.5.1. Debt in selected countries <!-- Start of picture text --> A. Debt during the 1970s-80s A. Debt during the 1980s-2000s<br>Percent of GDP Percent of GDP<br>100 Government Private 160 Government Private<br>140<br>80<br>120<br>100<br>60<br>80<br>40 60<br>40<br>20<br>20<br>0<br>0<br>1987 1997 2002 1987 1997 2002<br>1972 1982 1987 1972 1982 1987<br>Thailand Chile<br>Mexico Indonesia<br>Source: Mbaye, Moreno Badia and Chae (2018).<br>Note: Figures show the evolution of government and private debt in different episodes. Government and private debt<br>are proxied by central government debt and credit to the private sector, respectively. Private debt data not available<br>for 1972 for Mexico and Indonesia.<br><!-- End of picture text --> 47
over night, but to get started a wide range of reforms are needed. Key policy steps, in our view, are as follows.

First, governments must for an extended period of time pursue a macroeconomic policy that is both sound and credible to domestic and international investors. The whole issue of governance is central, because if governance is poor, uncertainty will persist and investors’ response to other initiatives will be weak. We know from the firm data that uncertainty is a major factor in explaining why investment among African manufacturing firms has been low over the last decade. Reducing the uncertainty related to movements in the exchange rate and pursuing credible export programs is of course important to promote exports. Overall, the government needs to string together several years of good governance and good policies. This can be hard in a situation where people do not see quick results, and resisting policy reversals and populist policies will therefore be a challenge.

Second, scarce resources – land, skills, physical capital, raw material - must be channeled towards firms that are productive and in good position to export. One important issue is what to do with the now very large informal manufacturing sector in Africa. Two types of policy measures are conceivable here. The first involves designing policies purporting to encourage informal firms to grow and become formal. From the firm data we have some indications of how policy makers can best facilitate a transition process. Based on Kenyan firm data, Bigsten, Kimuyu and Lundvall (2004) argue that the informal sector is large primarily because being formal is costly. Formal firms in Kenya have experienced big problems in dealing with authorities relating to regulatory red tape, corruption, and lack of security. Informal firms, by and large, sidestep these problems, and so incur lower costs. Thus, addressing the basic governance and investment climate issues would be a step in the right direction as far as trying to encourage entrepreneurs to operate within the realm of the formal sector instead of outside it. The second type of policy is
with household survey data. In many countries, more than one schedule existed, and the least‐cost option was taken for the purpose of examining affordability. If the survey was undertaken when a different tariff schedule was in effect, then monthly nominal per capita expenditures were increased at the same rate as nominal per capita GDP in local currency to the year when the tariffs prevailing in July 2014 first came into effect and total household expenditures were then computed from the adjusted per capita expenditure. If the tariff schedule in effect in July 2014 had been first introduced before the survey date, no adjustment of expenditure was necessary—Burkina Faso, Ethiopia, Mali, Mozambique, Senegal, Sierra Leone, and Togo fell under this category and did not require any adjustment to total household expenditures. Connection charges were available for all but Mali and São Tomé and Príncipe. They were computed as multiples of total monthly household expenditures. If information on when the connection charges came into effect was not available, the dates of effectiveness were assumed to be the same as those for tariff schedules (see Table A.8 in the annex for more detail).

This paper computes the poverty gap for grid electricity in the same way the poverty gap is defined in economics as follows: where the required monthly payment is the monthly bill inclusive of taxes and other charges that a household has to pay to consume the corresponding amount of electricity, P is the total population living in households for whom the monthly payment exceeds 5 percent of their total monthly household expenditures (inclusive of freely acquired food and other items), and N is the total population of the country. Where the monthly electricity bill exceeds the 5‐percent share, electricity is deemed unaffordable, and the degree of unaffordability for a household is the size of the gap between the bill and the 5‐percent share when this is positive, zero otherwise. Although monthly consumption of 30 kWh is the basis for defining affordability, the poverty gap is also computed for 50, 100 (multi
**Table 2.** Deprivations included in the severe material and social deprivation rate (SMSD), Europe 2030

||**Deprivations at the Household Level**|**Deprivations at the Individual Level**|
|---|---|---|
|**Severe material**<br>**and social**<br>**deprivation rate**<br>**(SMSD)**|1. Capacity to face unexpected<br>expenses<br>2. Capacity to afford to pay for a one-<br>week annual holidayawayfrom home<br>3. Capacity to be confronted with<br>payment arrears (on mortgage or<br>rental payments, utility bills, hire<br>purchase installments, or other loan<br>payments)<br>4. Capacity to afford a meal with meat,<br>chicken, fish, or vegetarian equivalent<br>everysecond day<br>5. Ability to keep home adequately<br>warm|1. Having an internet connection<br>2. Replacing worn-out clothes with some<br>new ones<br>3. Having two pairs of properly fitting<br>shoes (including a pair of all-weather<br>shoes)<br>4. Spending a small amount of money<br>each week on him/herself<br>5. Having regular leisure activities|
||6. Have access to a car/van for<br>personal use<br>7. Replacingworn-out furniture|6. Getting together with friends/family<br>for a drink/meal at least once a month|

Source: Eurostat - <u>https://ec.europa.eu/eurostat/statistics explained/index.php?title=Glossary:At_risk_of_poverty_or_social_exclusion_(AROPE)</u>

**Other multidimensional poverty indicators are commonly used in other
investment incentive systems that have often constrained operations of larger enterprises in the formal sector of the economy.

24. Size of an enterprise is- commonly measured by the number of workers, because statistics on employment are more easily available and the fact that employment size is more easily envisaged. In industrial countries small-scale often means less than 200-300 workers. However, in developing countries the average plant size is typically smaller, and an enterprise with 1-49 workers is generally recognized as small-scale. Finer distinctions may be appropriate, depending on the specific issues under consideration. Other measures of size are the value of capital or assets employed, or volume of sales revenue in a given year. Discussions in various forums about the concept of the "informal" sector have led to a general understanding that enterprises in this informal sector are labor-intensive, provide goods and services for the poor by the poor, and offer opportunities to absorb much more employment than do larger firms for any given level of investment.<sup>8</sup> 25. Notwithstanding the diversity of MSB activities, the two most important characteristics common to all MSBs operating in the informal sector are (a) extremely limited access to institutionalized sources of finance, and (b) significant lack of access to business support services such as quality maintenance and control, record-keeping, marketing linkages and efficient business management. The limited access to external finance from institutional sources and the reliance on own sources of capital result in much lower tolerance for operating losses in comparison with the much larger enterprises. The MSB entrepreneur can protect operations against this risk through operative relationships with suppliers and buyers, but usually at the expense of earnings. The related results of "captive relationships" are restrictions on MSBs for sources of business support services as well as for information, particularly about better production and processing technology and emerging market opportunities.

26. The vast majority of private sector firms start very small in terms of both capital and the number of employees. The bulk of initial capital is most often provided by
economic growth, though the specific component of inequality of opportunity –that inequality that correlates with circumstances at birth—does not show a robust effect by itself. One of the main contributions of that paper is the attempt to distinguish between income inequality and indicators of equity –in this case, inequality of opportunity. Along the same lines, Marrero and Rodriguez (2013), using the PSID database for the U.S. in 1970, 1980 and 1990, find a robust support for a negative relationship between inequality of opportunity and growth, and a positive relationship between inequality of effort and growth. Thus, the indicator of inequality used in the empirical work –based on different notions of what is the inequality that “matters”—may lead to different results. On the other hand, equity indicators –such as access to basic services and access to markets—may have a more delayed effect on growth, as they affect the long-term productivity of the poor or previously excluded groups.

_Revisiting equity-efficiency trade-offs_ As discussed in Kanbur (2013), there is no shortage of theoretical models showing why equity is associated with growth.<sup>4</sup> If the economy is “distorted enough” –producing within its full productive capacity, lifting constraints to productivity might actually improve both efficiency and equity. Political economy considerations might also lead to a situation where redistribution enhances efficiency (Levy and Walton, 2005).

> 4 There are diverging views on the link between economic growth and equity.  Aghion et al. (1999) present three arguments that support the view that wealth inequality should be growth-enhancing. The first argument is Kaldor's hypothesis that the marginal propensity to save of the rich is higher than that of the poor. Stiglitz (1969) and later Bourguignon (1981) formalized this argument and demonstrated that more unequal economies will grow faster. The second argument has to do with investment indivisibilities. In the absence of a well-functioning market for shares, wealth needs to be sufficiently concentrated in order for