rafmacalaba/data-use-mentions-extended huggingface dataset:
turn, could help answer other types of questions, examining the effectiveness of a committee and the reasons for this. For example, are some committees more effective because of the role of private sector organizations which are inherently better organized and thus more effective in securing influence on reform committees? Are some committees unable to deliver because of the public sector fragmentation – political contention or conflicting incentives hindering the ability of the government to reach a clear decision and sustain implementation momentum? How does a reform committee assess its effectiveness if it not using KPIs? Even when using KPIs, are they in fact a good measure of success both to themselves and to external observers? These and many other related questions about the underlying components and complementary factors that enhance the effectiveness of successful reform committees could further be explored. The data provides also ample opportunities for operational advice, particularly to countries seeking guidance on how to establish or (improve) their own reform committee.

Furthermore, additional regular updating of reform committee data would allow tracking of the nature and extent of change. A follow-up the economies that did not participate in the first round may elicit additional information and allow for more regional and country-specific research, including additional case studies. With each round of data collection, additional key questions could be asked or refined on the basis of lessons learned from earlier data analysis. Finally, interviews from members of the different types of reform committees would provide a more detailed ‘inside view’ of the roles, responsibilities, and incentives of the reform committee members.

# **7. Conclusion**

Business regulatory reform is important for every government because it matters for private sector development, job creation, service delivery, and economic growth. After identifying what needs to be reformed, the next big challenge for governments is how to effectively implement the reform. Some do it through specifically designed reform committees, others through already existing structures in the government. Close to half of the reform committees globally are housed in the Prime Minister’s office reporting directly to him
Thus our study of entrepreneurship falls between two more common types of studies, thereby hitting on a key element of South Asia‘s future. On one hand, we purposely steer clear of self-employment measures. Even for the United States, self-employment is a second-best link for measuring entrepreneurship for job creation. These methodological challenges for low return or subsistence efforts are compounded in South Asia where the informal sector employs 90% of workers (e.g., Schoar 2009, Ardagna and Lusardi 2008). To realize long-term employment growth, it is necessary to distinguish transformative entrepreneurship from subsistence entrepreneurship, and Ghani et al. (2011) show the strong job growth linkage to entry into the formal sector. We thus focus our efforts on describing the spatial distribution of these entrepreneurs. This also accords with the emphasis of van Stel et al. (2007) to measure determinants separately across different types of entrepreneurs.

On the other hand, we also do not study the development of very high-growth entrepreneurship or venture capital markets in South Asia (except to the extent that they are part of our official statistics). This is not because these entrepreneurs are not important for South Asia; quite the opposite. Many recent studies focus on software‘s emergence, returnee entrepreneurs, diaspora, and similar exciting developments (e.g., Arora and Gambardella 2005, Kerr 2008, Nanda and Khanna 2010, Agrawal et al. 2011). Yet these specialized sectors are still an extremely small part of the Indian economy, and we focus more on identifying measures of entrepreneurship like young businesses that apply to the entire manufacturing and services sectors.<sup>4</sup>

# _Indian Entrepreneurship Data in Manufacturing and Services_

We employ cross-sectional establishment-level surveys of manufacturing and service enterprises carried out by the Government of India. Our primary manufacturing data are taken from surveys conducted in fiscal years 2005-06; the services sector data come from 2001-02. Even though these surveys were undertaken over two fiscal years, for simplicity we refer below to the initial
in each industry and year.<sup>39</sup> As in the tariff data, we begin with TTB data at the product-level, aggregate to the HS 6-digit level, extract HS 6-digit categories that correspond to final goods using the BEC classification, and then aggregate to WIOD industries. The TTB coverage ratio is the (unweighted) share of HS 6-digit final goods products within a WIOD sector for which a given importing country has a TTB in effect against a particular trading partner. We construct TTB coverage ratios for each year separately (1995, 2000, 2005, and 2009), which allows for both the imposition of new TTBs and removal of existing TTBs over time.

# **3.4 First Peek at the Data**

Before moving to formal analysis, we pause to introduce the bilateral tariff data, since their use is relatively new to the literature. We first review a few salient facts about bilateral tariff preferences, and then relate observed tariff variation to value-added content in an illustrative case to ideas.

**Tariff Preferences** Our identification strategy exploits differences between bilateral applied tariffs and applied MFN rates. Bilateral applied tariffs differ from MFN tariffs because countries offer preferential (lower-than-applied MFN) tariffs to selected partners under various preference schemes. We provide a summary description of these schemes and their relative importance here, with details provided in Appendix B.

There are four main sources of tariff preferences in our data. The first is the Generalized System of Preferences (GSP), which accounts for the majority of preferences. It is an explicitly unilateral (non-reciprocal) preference scheme, in which developing countries receive preferential treatment from high-income importers.<sup>40</sup> An important feature of the GSP program is that each GSP-granting country unilaterally chooses the set of GSP-receiving countries to which and sectors in which it extends preferences, and these choices differ across GSP-granting countries and time.

Free trade agreements and customs unions, authorized under WTO Article XXIV, are a second source of preferences. These
We present three main findings. First, we find that firm-level productivity has been stagnant, as suggested by macro-data indicators, and that the aggregate gains have been mostly driven by more productive firms gaining market shares. Indeed, firms’ average productivity has not grown since 2015. Second, we find that in Pakistan, as in many other contexts, internationally linked firms perform better than domestic-owned or domestic-oriented firms. For foreign-owned firms we find suggestive evidence pointing to the productivity premium being explained both by a self-selection element – foreign investors target more productive firms in the first place – but also by foreign-owned firms increasing productivity after they are acquired by foreign investors. For exporting firms, we find suggestive evidence that the productivity premium is mostly explained by increases in productivity after starting exporting. Third, we find that integration with the global marketplace of upstream sectors – both for goods and services – has led to increased productivity in downstream sectors. Tariff reductions upstream helped increase productivity downstream mainly for non-exporters, and relatively smaller exporters – for which the process of claiming import duty exemptions is too cumbersome. FDI in upstream services sectors, instead, tends to benefit the productivity of those firms downstream that are further away from the technological frontier. We also find that the type of FDI encourages investments in intangible assets, pointing to a potential mechanism through which upstream services FDI increases productivity downstream.

Finally, data constraints prevent us shedding more light on the causal links between exporting and foreign ownership and productivity in Pakistan. Exploring this is important both from an analytical and a policy perspective. That should be an important task for future research.

# **References**

Ackerberg, D. A., Caves, K., and Frazer, G. 2015. Identification Properties of Recent Production Function estimators. _Econometrica_ 83(6): 2411-2451.

Ali, S. 2015. Trade Costs and Trade Composition: Firm-level Evidence from Pakistan. IGC Working paper.

Amiti, M., and Konings, J. 2007. Trade Liberalization, Intermediate Inputs,
growth. It was only with the recent quantification of specific improvements in management practices, such as better ways to monitor production information, to set binding operations, inventory and quality control targets, and to incentivize workers with merit-based pay and promotion, that it has become possible for economists to rigorously compare management technologies across firms. Based on data across 17 countries, Bloom and Van Reenen (2010) rank average Indian, Chinese and Brazilian management practices of domestic firms (the only developing countries in the sample) as significantly below those of OECD countries, with a large lower tail of very badly-managed firms; foreign multinationals residing in these countries, on the other hand, are well managed across all countries. Robust positive associations are found between the average firm management score and labor productivity (sales per employee), profitability, Tobin‘s q, sales growth and survival, controlling for country and industry fixed effects and general firm-level controls. And in a follow-on randomized experiment on large multi-plant Indian textile firms, Bloom et al. (2011) show the causal impact of adopting better management technologies: five months of extensive consulting to upgrade management practices raised average TFP by 11% in the first year, increased the use 12
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Several recent papers suggest refinements to the Shift-share model (e.g. Borusyak et al. 2022) and provide a new framework for shift-share instrumental variable regressions following a quasi-random assignment of shocks allowing exposure shares to be endogenous. Their analysis claims that identification and consistency may arise from the exogeneity of shocks while providing a new guidance for shift share instrumental variable (SSIV) estimations. Further versions of this study might include these new methodologies.

# **_Data_**

The shift-share Bartik analysis draws on labor market indicators from the _Enquête Nationale sur l’Emploi_ (Labor Force Survey, LFS) and on trade flows from the United Nations (UN) COMTRADE database. The LFS, a nationally representative survey conducted by the _Haut Commisariat au Plan_ , includes detailed information on the active population’s main demographic and professional characteristics, enabling the study of Moroccan labor market trends. It provides employment, formality, and participation data from 2000 to 2018.

The lack of homogeneous regional variables over time limits our LFS analysis. Only 10 regions can be homogenized across the entire 2000-2018 period, a low number of observations for econometric analysis. LFS’s “province” variable for the period between 2000 and 2009 contains 60 consistent observations, and so our analysis relies on these (excluding 2009 because of post-crisis shocks). The LFS also lacks industry codes for 20152017 and has a different classification system in 2018.

Annual bilateral trade flow data from 2000 to 2018 come from the UN Comtrade database. This analysis focuses on Moroccan exports, or its analog (world imports of the rest of the world from Morocco). We merge these trade data with labor market indicators using the concordance between ISIC rev 3.1 (from the LFS) and HS0 – 1988/92 trade classification (used by UN COMTRADE).

# **_Results_**

# <u>Informality</u>

An increase in exports per worker in Morocco is associated with falling informality **.** To the extent that our instrument controls for
The low engagement of women in occupations outside agriculture is problematic. Several factors could be holding women back from non-agriculture forms of employment, including low education and skills, social norms, legal restrictions and employer discriminatory behavior. Social norms are hard to decipher and are typically alluded to using polling data. A 2017 ILO Gallup poll found that in the DRC 81percent of respondents agreed that it is perfectly acceptable for any woman in the family to have a paid job outside the home if she wants one (gender disaggregation, male: 72 percent, female 89 percent). About 26 percent of male respondents would rather the women in the family stay at home and not work, while 13 percent of female respondents would rather stay at home. In comparison, in more egalitarian countries like Denmark, only 2 percent of male respondents would rather the women in the family stay at home and not work, while 6 percent of female respondents would rather stay at home. Thus, while social norms do point to a large portion of respondents in the DRC favoring women’s pursuit of work outside the home, it is still far lower than in societies where gender differences are less prevalent.

The legal environment in the DCR is also worth close consideration. At the time of the survey, women in the DRC faced several legal restrictions that have since been reformed. In 2012, the World Bank’s Women, Business and the Law (WBL) index, which measures the extent of legal discrimination that women around the world face as they navigate through their working lives, scored the DRC 42.5 out of 100, meaning that in the DRC at that time, women had less than half the amount of rights as men in the areas covered by the index. The global average score for the same year was 71.38.<sup>11</sup> The legal restrictions to women's economic opportunities in the DRC come in three main forms. One entails the requirement of spousal permission as noted in the
external training practices for current employees. Employers also provided information on the main sources of skills supply from education and training institutions. Together, these variables provide detailed insights into skills demand and supply from the perspective of employers and rich data to investigate the relationships between skills, skill deficits, strategies to mitigate them, and their productivity outcomes.

In common with other Enterprise Surveys, TESS elicited the following variables to characterize firms:

- Indicator variables for eight sectors, five regions and three firm sizes

- Firm attributes that research has suggested are related to (often unobserved) productivity attributes, including foreign ownership, female-owned firms, years in operation, and the educational attainment of the firm’s top manager

- Domestic sales and sales to international markets, whether as a direct or indirect exporter

- Engagement in innovative activities such as introduction of new products or services, quality control, R&D expenditure, and technology licensing

- Key variables needed to estimate firm performance using a simple production function model. Besides total sales and employment in the last fiscal year, data was elicited on the value of intermediate inputs and fixed assets, separately for machinery and equipment and buildings.

Skills encompass the broad set of cognitive, non-cognitive (social and behavioral) and technical competencies needed to perform effectively. Skills are acquired from education and training institutions, both before entering the labor market and while employed, in company-sponsored inservice programs. The TESS elicited information on the following skill dimensions:

- Education attainment of the workforce by four levels of schooling: primary, lower secondary, upper secondary and tertiary education, and their distribution in the workforce.

- The share of the workforce with formal pre-employment TVET qualifications as a separate category.

- Skill composition by seven occupational categories: managers, professionals, technicians and associate professionals, clerical jobs, sales, production operators, and other unskilled production or service workers. Each occupational category distinguished between Tanzanian and expatriate employees.

- Qualitative indicators of the adequacy of different workforce competencies (as judged by employers), including a
Research Record_ , 2163, 133–143.

- Moneke, N. (2020): “Can big push infrastructure unlock development? Evidence from Ethiopia,”Technical report, Mimeo.

- Mouelhi, R. B. A. (2009): “Impact Of The Adoption Of Information And Communication Technologies On Firm Efficiency In The Tunisian Manufacturing Sector,” Working Papers 506, Economic Research Forum.

- Muller-Crepon, C., P. Hunziker, and L.-E. Cederman (2020): “Roads to rule, roads to rebel: Relational state capacity and conflict in Africa,” _Journal of Conflict Resolution_ , 65.

- Murdock, G. P. (1959): “Africa its peoples and their culture history.”

- Nelson, A., and U. Deichmann (2004): “The African Population Database. Version 4,”Technical report.

43
expected, the results show that firms are more likely to exit when they are exposed to a higher number of conflict events between two survey waves, and when their pre-exit sales levels are lower. This pattern suggests that, if anything, our estimates of the negative effect of conflict on sales may be downward biased—potentially underestimating the broader impact of conflict on economic activity.

# **2.2 Conflict data**

Our main source of data on political violence events is the Integrated Crisis Early Warning System (ICEWS, Shilliday and Lautenschlager, 2012). For each event, the ICEWS dataset provides information on the date, source and target actors, and precise location (geospatial coordinates). It captures all types of interactions—whether cooperative or hostile—among sociopolitical entities, including individuals, groups, and nation-states. These events are categorized according to the Conflict and Mediation Event Observations (CAMEO) classification (Schrodt and Yilmaz, 2012). Each category is then assigned an

> 10A related concern is that firms may strategically choose not to report their performance to avoid being targeted by fighters. However, it is worth noting that participation in the WBES is voluntary, and firms are under no obligation to respond to the questionnaire. Moreover, the number of missing values is higher for _Total Costs_ than for _Sales_ (see the number of observations in Table 1), which suggests that non-reporting is unlikely to be a strategic choice of firms trying to hide their economic performance.

6
# **2. Data and empirical methodology**

# _2.1 Data sources_

# <u>World Bank Enterprise Surveys</u>

The primary data source used in our analysis is nationally representative firm-level data from the World Bank Enterprise Surveys. The Enterprise Surveys are establishment-level surveys based on face-to-face interviews with firms’ owners or top managers. The surveys are designed to provide a representative sample of firms in the nonagricultural, non-extractive formal private economy with five or more employees, and to ensure that the data are comparable across countries and over time. The surveys have high coverage in terms of topics including access to finance, corruption, infrastructure, crime, competition, labor, the business environment, and performance.

The surveys are based on a stratified random sampling; stratified by industry, firm size, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ (large) employees. For most economies, the sector breakdown is manufacturing, retail, and other services; however, for larger economies, certain manufacturing sub-sectors are selected as additional strata, based on their contribution to economic activity. The within-country regional stratification is based on which cities or regions collectively contain the majority of economic activity. Survey weights are calculated and can be applied in order to make inferences about the whole population of firms in a country.<sup>6</sup> As of February 2021, the surveys had been completed using a global, internationally comparable methodology in 146 countries—we are able to match the firm level data with data on control variables for 141 countries.<sup>7</sup> In some countries, several waves of data collection have taken place; we use the most recent round of data collected for each country. While the Enterprise Surveys focus primarily on firms in developing economies, data have also been collected for several high-income countries and, therefore, our sample spans all regions and income categories.

# <u>Women
# **Figure C.1. Probabilities of Female-Headed Households Escaping Poverty in Second Year Conditional on Being Poor in First Year (percentage)**

<!-- Start of picture text --> Egypt 2017-2020 Iraq 2007-2012<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>Jordan 2010-2013 Mauritania 2014-2019<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>Palestine 2011-2017 Tunisia 2015-2021<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>FHH wo/children FHH w/children Non-FHH<br>75<br>45<br>70<br>40<br>65<br>35<br>Percentage (%) Percentage (%)<br>60<br>30<br>60 55<br>55 50<br>50<br>45<br>Percentage (%) Percentage (%)<br>45<br>40<br>40 55<br>35<br>50<br>30<br>45<br>25<br>Percentage (%) Percentage (%)<br>20 40<br><!-- End of picture text --> Note: Estimates are obtained with synthetic panel data and weighted with population weights where the second survey round is used as the base year. The figure shows the percentage of the population that moves out of poverty in the second year. FHH characteristics are measured in second period. Dashed red lines represent the national average for each period. Household heads' ages are restricted to 25-55 for the first survey round and adjusted accordingly for the second survey round. Standard errors are estimated with 1,000 bootstraps. ’s are estimated using Equation