rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9089

# **Abstract**

This paper uses data on directional trade flows and Regional Trade Agreements (RTA) to i) estimate the effects of RTA on trade flows and ii) assess the importance of Global Value Chains for these effects. Based on a Difference-inDifference identification strategy, we find that RTAs are associated with: (1) an increase in trade within the region, (2) a decrease in inflows to the region, and (3) an increase in outflows from the region. The first two findings can be understood as trade creation and trade diversion due to a shift in demand associated with the lower trade barriers within the region and Rules of Origin as an implicit trade barrier for imports from the rest of the world. Global Value Chains are most relevant to understand the third finding on the increase in outflows from the region. The key determinant of the increase in outflows is the importance of the _Regional Value Chains for imports of intermediates_ by members of the region.

This paper is a product of the World Bank’s _World Development Report 2020_ 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 authors may be contacted at francois.m.desoyres@frb.gov.

_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
_Table A2.2 Lee bounds for the intention‐to‐treat effects on main psychological and behavioral outcomes_

||(1)<br>(2)<br>Lower bound<br>Upper bound|
|---|---|
|_Main outcomes(survey‐ba_|_sed) – Psychological and behavioral_|
||‐0.005<br>‐0.002|
|Psychological wellbeing index|(0.961)<br>(0.989)|
|Risk index|‐0.052<br>‐0.049|
||(0.595)<br>(0.645)|
|Trust index|‐0.055<br>‐0.050|
||(0.590)<br>(0.641)|
|Crime and violence index|‐0.253***<br>‐0.105|
||(0.000)<br>(0.553)|
|Migration index|‐0.027<br>‐0.027|
||(0.641)<br>(0.826)|
|Observations|2292|

_Note: Outcome variables are listed on the left. Column (1) reports the lower bound. Column (2) reports the upper bound. P‐values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level._ 38
# Box 1: How consistent are the labor module, the AG module and the household enterprise module in LSMS‐ISA surveys?

This paper focuses on the labor module. But LSMS‐ISA surveys also collect employment data in the agricultural (AG) and household enterprise module.

- Relying on end‐of‐season recall, the AG module captures labor input per person and plot and, in some cases, by activity (land preparation, planting, harvesting) (see Gaddis and Palacios‐Lopez (2018) and Arthi et al. (2016) for a discussion of this approach and an assessment of recall error);

- The household enterprise module lists all household enterprises and records all household members involved as manager, owner or worker in the enterprise during the last month of operation in the past 12 months.

Using this information, we can verify the internal consistency of the labor, AG and household enterprise modules. Household members who worked on a plot – as captured in the AG module – should also have reported working in ‘HH farming in the last 12 months’ in the labor module. Similarly, household members listed in the enterprise module should also have reported being engaged in ‘running or helping in a non‐farm household enterprise’ in the labor module. We illustrate this approach for the 2016/17 Malawi LSMS‐ISA survey.

Table E in the Appendix compares responses in the labor and AG modules. Of 25,919 household members who worked on a plot in the rainy season, 2,034 (7.85%) were missing in the labor module. To explain this finding, we examined whether the share of ‘missing individuals’ differs by (1) the number of plots cultivated, (2) the type of activities carried out, (3) number of hours worked on all plots, (4) sex and (5) age of the individual. A clear pattern emerges. The share of individuals ‘missing’ in the labor module clearly decreases
the European Union, such as Ukraine, Armenia, Moldova and Georgia (and Russia soon)<sup>9</sup> show an average level of services sector liberalization for the region. Those who have not yet acceded to the WTO, such as Uzbekistan, Belarus, Tajikistan, Azerbaijan and Turkmenistan show more limited services sector liberalization. While Hoekman and Mattoo (2011) have shown that for incumbent members of the WTO, the offers on services within the Doha Development Agenda are much less liberal than the actual policies, for countries that accede to the WTO or to the European Union, the accession process has induced real liberalization.

# **II. Summary of the Six Empirical Studies of Services Liberalization**

# **Impact of Services Liberalization on Productivity of Manufacturing Firms: Evidence from Ukrainian Firm-level Data, by Oleksandr Shepotylo and Volodymyr Vakhitov**

The paper by Oleksandr Shepotylo and Volodymyr Vakhitov is a state of the art econometric test of the impact of services liberalization on productivity, and they expand the frontier of empirical research in this paper. Using a data set of over 40 thousand firms in Ukraine for the years 2001 to 2007, they establish important new results for the positive impact of services sector reform on total factor productivity in Ukrainian manufacturing.

**Results.** Their key finding is that a one standard deviation increase in services sector liberalization leads to a 9 percent increase in total factor productivity of Ukrainian manufacturing. The estimated productivity gain from services liberalization is higher than the estimates of previous studies, but Ukraine started with more protected, less liberal services sectors than in other countries where comparable studies were performed. The larger estimates

> 9 Russia was invited to join the WTO in December 2011. But as of late February 2012, its Duma had not yet ratified the agreement, where ratification is required in order to become a member of the WTO.

8
that are implemented in one visit, and therefore the source of the discrepancies should originate somewhere else.

Thus, we focus next on potential implementation differences between the individual-level recall arm (T3) and the one-line recall arm (T1) that could explain the results. In particular, we look into changes in protocols that were introduced to T3 to improve FAFH collection but that could have unexpectedly impacted the at-home module’s reported consumption. Taking advantage of the paradata that were collected through the implementation of CAPI, we explore two hypotheses: interruptions to the interview and “group interviewing”. Both hypotheses stem from the fact that enumerators were expected to collect data from all adult members in the household when implementing T3 but not when implementing T1.

Interruptions to the interview in general - or the at-home module in particular - may have occurred more often in the individual-level recall arm because enumerators may have tried to get adult members’ information as they were leaving or coming into the house as a way to maximize response rates. This practice, in turn, would have extended the interview time. Results do not show, however, support for this hypothesis. The recorded time between the start of the survey and the start of the AH module, as well as the time it took to complete the AH module, are statistically the same in T1 and T3 interviews.

The hypothesis of “group interviewing” also builds on the idea that interviewers would try to get any adult member as they stop by. In this case, however, instead of interrupting the ongoing interview, the interviewer would ask them to ‘stand by’ for a bit. The potential implication of this practice is that extra people may have participated in answering the AH section. We explore this hypothesis by looking into two indicators. First, we test whether the share of at-home items with positive incidence is higher among T3 households, based on the rationale that the presence of more individuals should have reduced omission. Second, we analyze the correlation between the value of AH
.223**|2.804*|
||(0.424)|(7.466)|(0.030)|(1.261)|(1.364)|(1.432)|
|=1 if Unconditional Schoolgirl|-0.549*|10.688|0.001|-0.586|-0.880|-0.817|
||(0.285)|(12.721)|(0.055)|(1.441)|(1.524)|(1.876)|
|p-value UCT vs. CCT|0.278|0.829|0.838|0.032|0.034|0.127|
|p-value Treatment|0.121|0.420|0.939|0.030|0.044|0.137|
|Mean in Control Group|0.902|33.302|0.250|18.638|23.342|20.774|
|Sample Size|465|2,049|2,049|2,006|2,021|2,040|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Effective daily wage is calculated using earnings and activities in the past seven days. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
nascent entrepreneurs should plan a human capital investment strategy which is wellbalanced across different competences and fields of expertise. Using cross section analyses, both Lazear (2005) and Wagner (2003) found that students who ended up as entrepreneurs had studied a much more varied curriculum than those who ended up working for others. Overall, these researchers conclude that accumulation of a balanced skill-mix ( _i.e._ general human capital) causally involves entrepreneurship and aboveaverage post-entry performance (in contrast with the positive role of specific human capital discussed above)<sup>23</sup> .

Turning our attention to DCs and taking into account that in this context entrepreneurship and self-employment are often carried out within the informal sector of the economy, the impact of education turns out to be controversial. In fact, higher education, either general or specific, augments the managerial capabilities which are necessary to run a business enterprise, but also increase the outside option for salaried employment in the formal sector of the economy. This is probably the reason why Van der Sluis, Van Praag and Vijverberg (2005), in their comprehensive survey of the previous literature focusing on DCs, found that in the majority of cases education lowers the likelihood of entering self-employment as contrasted with wage-earning employment. In contrast, Goedhuys and Sleuwaegen (2000), running logit estimations on data concerning the owners of 141 manufacturing firms in Côte d’Ivoire, found that the probability of being an entrepreneur is strongly stimulated by both apprenticeship and formal education, with the positive effect of education steadily increasing going from lower to higher levels of education. Similarly, Ghani, Kerr, and O’Connell (2011b), using cross-sectional establishment-level surveys of manufacturing and services companies in Indian districts, conclude that higher education in a local area significantly increases the supply of entrepreneurs<sup>24</sup> . However, this relationship becomes non-significant when the informal manufacturing sector is taken into account. This is an interesting outcome and confirms the fact that education may render the

> 23
,071|6,072|6,049|
|R-squared|0.634|0.711|0.614|0.714|0.630|0.702|
|1996 Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|
|2011 Controls|-|Yes|-|Yes|-|Yes|
|Geographical match type|Centroid|Centroid|Area>75%|Area>75%|Area>75%|Area>75%|
|Other restrictions|-|-|-|-|Unique|Unique|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). Definitions for Geographical match type: (1) Centroid: each community from 2011 is matched to the nearest 1996 community centroid. (2) Area>75%: If over 75% of the area of a 2011 community is contained in a single 1996 community, these are matched (note that several communities from 2011 can match a single 1996 community). Unique: restrict sample to one-to-one matches only. The dependent variable is the difference in log per capita income for 2011-1996. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. 2011 controls include: same as 1996 except for per capita income (which is excluded). All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at
by looking at what different types of banks do while controlling for country-year fixed effects. However, this strand of literature can tell us what banks do (for instance if they lend more or less during recessions), but does not tell us anything about the impact of different types of bank lending on economic activity. In order to address this question, it is necessary to link bank-level information with firm-level information. While we are aware of studies that match firm-level data with bank level data (including some studies that are able to do so at the loan-by loan level, see Jiménez et al., 2014), these studies require confidential information and usually focus on just one country. To the best of our knowledge, we are the first to use firm-bank matched data for a large number of countries.

We use confidential information from the World Bank Enterprise Survey (WBES) and the bank-level dataset described in Panizza (2022) to match bank-firm information for a sample of nearly 13,000 firms in 36 countries observed over 2012-2020.<sup>3</sup> We then use this matched bank-firm data to test whether firms that

> 3 We also have data for 5 advanced economies but do not include these countries in the analysis.

4
data from different waves of the World Bank Enterprise Survey. Specifically we compute the median real value of the capital stock per worker (in international dollars) for all of the manufacturing industries the data allows. The data is available for Indonesia in 2009, for Mexico in 2010 and for China in 2012.

> 39This is similar to Acemoglu and Restrepo (2018a), where robots offset the scarcity of young workers in advanced economies. Maloney and Molina (2019) also cite anecdotal evidence that Chinese firms choose to improve the quality of their export by employing robots, rather than investing in employees’ training.

> 40The average reading literacy score of Indonesian students is 371, against 555 for China, 420 for Mexico and 487 for the OECD average. Similarly, science performance is 396 for Indonesia, against 590 for China, 419 for Mexico and 489 for the OECD region.

> 41Evidence in Calì et al. (2019) presents a similar puzzle also in the context of the switching from

39
Figure A1: Cumulative firm distribution for innovation variables by sector World Bank Enterprise Survey Database (ISIC Rev 3.1)

|0.00<br>10.00<br>20.00<br>30.00<br>40.00<br>50.00<br>60.00<br>70.00<br>80.00<br>90.00<br>100.00<br>15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 45 50 51 52 55 60 61 62 63 64 71 72 93 98<br>h5<br>h1<br>e6<br>h8|
|---|

Source: Authors’ using World Bank Enterprise Survey. Numbers on the horizontal axis reflect 3‐digit ISIC 3.1 sectors.

34
seasonal rainfall, (2) number of days without rain, (3) mean seasonal temperature, and (4) growing degree days (GDD). These four variables are indicative of a number of different ways to measure precipitation and temperature (volume/degree v. count). We spend what may be more space than is typical in an economics paper discussing the descriptive statistics around these measures, but this is essential for understanding the way in which each EO product differs in its measurement of rainfall and temperature. In summarizing the descriptive statistics, we highlight a trends or stylized facts about differences in measurement (a) between EO products within a country and (b) within an EO product between countries.

Figure 4 presents the distribution of total rainfall (measured in mm) during the growing season, by country and EO product.<sup>9</sup> Not surprisingly, there are substantial differences in the distribution of rainfall across countries. Ethiopia and Uganda average around 650mm of rainfall a season while Malawi, Nigeria, and Tanzania average around 900mm of rainfall a season. By contrast, Niger averages only around 350mm of rainfall in a season. These level differences in the quantity of precipitation in a country are clearly due to geographic characteristics. When we consider all countries together, some general trends emerge with respect to EO sources. A first trend (T1) is that ERA5 tends to have long tails, indicating this reanalysis data product reports dramatically more rainfall than other EO source.<sup>10</sup> For example, in Ethiopia and Tanzania, ERA5 reports some households receiving 6,000-7,000mm of rainfall in a season - six to seven meters - while no other EO product reports even half that amount. A second trend (T2) is that CPC tends to measure less rainfall relative to other EO products. For example, in Ethiopia and Malawi, CPC reports only half as much rainfall as the other products.

However, there are important exceptions to these trends.
measurement errors in consumption and income data from developing countries (see Deaton, 1997; Glewwe, 2005; Lee, 2009 for detailed discussion). Education is also considered a vital policy instrument in the creation of equal access to economic opportunity, leading to higher social mobility and economic progress (Bowles, 1972; Becker and Tomes, 1979; Piketty, 1995; Hertz et al., 2007; Corak, 2013; Reeves, 2014). Furthermore, the region serves as an excellent case study for intergenerational education mobility. It endorsed the United Nations goal of Education for All agreed in Dakar in 2000, a commitment to provide quality basic education for all children and adults by 2015. Accordingly, many countries in the region undertook major reforms in education systems, including the abolition of school fees (Thakur, 1991; Tomasevski, 2006). Thus, gauging how such policy changes induce intergenerational mobility has important policy implications.

Drawing on nationally representative survey data, the study analyzes the trends, levels, and patterns of intergenerational im(mobility) in educational attainment in the Comoros, Ghana, Guinea, Madagascar, Malawi, Nigeria, Rwanda, Tanzania, and Uganda over 50 years, with a special focus on gender differences. The paper contributes to the existing literature in several ways. First, the study extends the existing evidence by creating comparable recent estimates for nine Sub-Saharan African countries so that we may begin to draw conclusions about the inclusiveness of the recent investment in education in the sampled countries. Using two widely applied measures of intergenerational persistence, we provide persistence estimates for 10 successive five-year birth cohorts at the aggregate and gender levels in each

> 1South Africa is the exceptional country in the region where there exists a reasonable literature on intergenerational transmission of economic status.

2
<br> <br>|**/11‐2030)**<br>|
|Investment‐to‐output ratioI/Y<br>_(2)_|0.235|
|Public investment‐to‐output ratioI<sup>�</sup>/Y<br>_(2)_|0.094|
|Private investment‐to‐output ratioI<sup>�</sup>/Y<br>_(2)_|0.141|
|Human capitalgrowthg�<br>_(3)_|0.015|
|*Multiply by 100 to obtain parameter/variable values in percent sh<br>terms (%).<br>Note (see Section 2.1 for further discussion):<br>_(1)_<br>Calculated using female and male LFPRs from ILO‐modelled<br>projections, May 2018. We carry out linear interpolation fo<br>based on the ILO‐reported values of series end points (2010<br>turning points (2020‐21 for the male participation rate) so<br>smoothened. Female and male shares of working‐age po<br>United Nations (2017) are kept unchanged at 2010 levels.<br>_(2)_<br>I/Yis based on IMF estimations suggesting higher GCF (% of G<br>The assumption of public investment share of total invest<br>used in the central projection is maintained.<br>_(3)_<br>Long‐term average based on calculations using both Cohen,<br>and Barro and Lee average years of schooling data respectiv<br>4(a)‐(b).|are or growth<br>estimates and<br>r annual values<br>and 2030) and<br>that trends are<br>pulation, from<
competitiveness. Recent work (mostly based on US data) has found evidence that environmental policy does have a statistically significant negative effect on competitiveness. However, the economic magnitude of this effect tends to be small.

What are the policy implications of the finding that more stringent environmental policy does, at the margin, reduce the competitiveness of polluting industry? In one sense, this should neither be a surprise nor cause for concern. A reduction of output in a polluting industry does not imply that tightening up environmental policy is welfare-reducing. As noted above, weak environmental policy is essentially a subsidy for the use of environmental services. Removing such subsidies may shift production towards cleaner activities and this can be expected to be welfare-improving. Moreover it does not imply a loss in competitiveness for the economy as a whole; it simply leads to a reallocation of production towards cleaner activities.

Nevertheless, there may be legitimate concerns that the affected polluting industries are central to a country's long run growth strategy. In the presence of market failures relevant to the industrialization process (such as learning spillovers, agglomeration effects, and coordination problems) there can be an efficiency rationale for subsidies and other forms of government support. However, it is difficult to make the case that weak environmental policy would be either a desirable or effective way of delivering support to such industries. This is not simply because of the social costs of the environmental damage that results from such an approach. Weak environmental policy is unlikely to be effective in stimulating local production because the evidence shows that the effects of environmental policy on production and plant location are small for most industries. Moreover, weak environmental policy would not address the market failures that would justify some form of industrial policy. Other forms of support, in combination with a policy of internalizing environmental externalities, would likely be much more effective than simply leaving lax environmental policy in place.

15
the erosion of effective tax rates has no doubt been the weakness of tax administration, especially in a rapidly growing small-scale and self-employed private sector which is difficult to bring into the tax net. However, adjusting or modifying the income tax law so as to facilitate compliance and enforcement may well be the most expeditious way of improving tax administration.

10/A similar analysis of the revenue performance and distributional impact of the 1997 tax changes requires an examination of a more recent household survey data. Future research employing such data is highly encouraged.