rafmacalaba/datause-encoder-data huggingface dataset:
**To assess the impact of PEMEX on the EOCK, we disaggregate investment flows for PEMEX and other sectors.** Assuming the same price elasticity of demand for investment funds as the rest of the private sector (Table 14), we recalculate the weights in this scenario for PEMEX, other investments, domestic savings, and foreign savings. The averages are included in Table 15. With the assumptions above, we generate the new weights and apply these to the tax distortions reported above for the four sectors (PEMEX, other investors, domestic savers, and foreign savers). The results, reported in Table 15, show a higher EOCK externality of 2.4 percent. The size of the PEMEX distortion has fallen in recent years as a result of its declining surpluses—a trend that could potentially continue over the long term—which suggests a more reasonable estimate of the economic externality of close to 2 percent.

**Table 14 Investment and savings shares and assumed price elasticities for the** **<u>disaggregated scenario, 2003–2011</u>**

||PEMEX investment<br>over GDP|Other private +<br>inventory investment<br>over GDP|DS/<br>GDP|FS/<br>GDP|η<br>PEMEX|η<br>(rest)|Eds|Efs|
|---|---|---|---|---|---|---|---|---|
|2003|1.5%|17.6%|22.9%|1.1%|−1|−1|0.3|3|
|2004|1.4%|19.1%|24.9%|0.9%|−1|−1|0.3|3|
|2005|1.4%|18.4%|24.4%|1.0%|−1|−1|0.3|3|
|2006|1.4%|20.4%|26.2%|0.8%|−1|−1|0.3|3|
|2007|1.5%|20.4%|26.5%|1.4%|−1|−1|0.3|3|
|2008|1.7%|19.6%|26.9%|1.8%|−1|−1|0.3|3|
|2009|2.1%|15.5%|23.8%|0.8%|−1|−1|0.3|3|
|2010|2.1%|16.0%|23.9%|0.2%|−1|−1|0.3|3|
|2011|1.9%|18.0%|25.1%|0.9%|−1|−1|0.3|3|
|Average|1.7%|18.3%|24.9%|1.0%|−1|−1|0.3|3|

_Source:_ Authors’ calculations using PEMEX and SHCP data.

**Table 15 Disaggregating PEMEX and the estimation of the EOCK tax externalities** **<u>per unit of capital</u>**

||PEMEX investment|Other investment|Domestic savings|Foreign savings|
|---|---|---|---|---|
||f0|f1|f2|f3|
|Average weights|5.5%|60.2%|24.7%|9.6%|
|Distortions/unit|32.3%|1.1%|−0.8%|1.4%|
|**EOCK externalities/unit**|**of capital**|||**2.4%**|

_Source:_ Authors’ calculations on the basis of previous tables.

**The reason for the increase in the tax externality when PEMEX is separated out is that PEMEX not only has a high tax distortion, but the investment share of PEMEX is higher than its asset share—over the 2003–2011 period it averaged 4.1 times higher** . This higher investment share puts a relatively higher weight on the PEMEX distortion when it is

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# **5. Conclusion**

There is a consensus in the literature that the explanation for the abnormally high level of violence is primarily due to the extent of inequality rather than the overall levels of development in Latin America. The fact that violence fundamentally hinges on inequality has important implications for the type of growth that needs to occur in order to achieve a reduction in crime and violence levels. Promoting pro-poor growth and equitable development to reduce the stark levels of inequality is key to curbing the violence pandemic. The main risk factors for violent and criminal behavior next to income inequality are the lack of education, low social capital, unemployment, unruly urbanization and inefficient criminal justice systems. Interventions should be geared towards the bottom quintile of the income distribution, attempting to reduce income inequality, ensure better access to education, jobs and justice, and build social capital. These preventive measures and innovative social policies are efficient and under-utilized strategies to address the problem. Violence prevention is inseparable from equitable development and social action.

To enhance our understanding of how violence affects the poor, victimization needs to be analyzed in more detail, especially among marginalized ethnic and social groups, which are largely excluded from existing studies. Another pertinent question is how new local governance structures can contribute to a reduction in crime and violence.<sup>99</sup> There is an urgent need for both analytical work and operational experimentation.

As a final note further research is suggested in three areas. Crime and violence research could benefit enormously from national data being updated annually and in a comparable fashion across countries. Moreover, evaluations of existing violence prevention programs are called for, and finally the literature could benefit greatly from a consistent model to calculate costs of crime and violence.

> 99 Ayers (1998).

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SEE group, the downsizing of the state sector would have been expected to have been stronger.

The notable differences between aggregate employment growth and mean firm employment growth that we have observed in the above analysis suggest a closer look at the size distribution of firms is warranted. An indication of convergence in this exercise would be an increase in employment in small firms relative to employment in large firms in the transition economies. Indeed, planned economies had very few small firms, and the small firm sector would be expected to growth rapidly during the transition period to fill this gap (see, e.g., World Bank 2005 for detailed discussion of the Russian case). We would therefore expect to find that the size distribution is evolving towards the pattern of the developed market economies, and that the new EU members have caught up more than the poorer TEs.

This is indeed what we see in the BEEPS data. Figure III.9 shows the distribution of firm size in West Germany and the cohesion countries, the EU8 countries and the other TEs, by four broad size categories: micro (1-9 employees), small (10-49), medium (50-199) and large (200+ employees). Small and micro firms are most prevalent in the developed market economies of the EU, least common in the non-EU TEs, and the EU8 members occupy an intermediate position. Figure III.10 shows the size distribution of firms in the new EU8 moving steadily towards the developed market economy pattern of large numbers of micro and small firms, and by 2005 the distribution is close to that observed in West Germany and the cohesion group. Figure III.11 shows the same pattern in the poorer TEs, but these countries start in 1999 from a position of even fewer small firms, and although the small firm sector grows between 1999 and 2005, in 2005 it is still some distance from the market economy benchmark.

As noted already, another test of the convergence hypothesis is to use data on reallocation across industrial sectors in the course of transition, where we would expect to see a Kuznets-Chenery-type pattern. Raiser et al. (2004), in a study of 20-odd transition countries, divide total employment in to broad sectors (agriculture, industry, markets 21
Figure 4.5: Distribution of New Loans to Small Firms by Interest Rate (2006Q4) <!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Interest Rate 'Bucket'<br>By Number of Loans By Volume of Loans<br>0 to 0.99%1 to 1.99%2 to 2.99%3 to 3.99%4 to 4.99%5 to 5.99%6 to 6.99%7 to 7.99%8 to 8.99%9 to 9.99%10 to 10.99%11 to 11.99%12 to 12.99%13 to 13.99%14 to 14.99%15 to 15.99%16 to 16.99%17 to 17.99%18 to 18.99%19 to 19.99%20 to 20.99%21 to 21.99%22 to 22.99%<br>% of Total<br><!-- End of picture text --> Figure 4.6: Distribution of New Loans to Medium-Sized Firms by Interest Rate (2006Q4) <!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Interest Rate 'Bucket'<br>By Number of Loans By Volume of Loans<br>0 to 0.99%1 to 1.99%2 to 2.99%3 to 3.99%4 to 4.99%5 to 5.99%6 to 6.99%7 to 7.99%8 to 8.99%9 to 9.99%10 to 10.99%11 to 11.99%12 to 12.99%13 to 13.99%14 to 14.99%15 to 15.99%16 to 16.99%17 to 17.99%18 to 18.99%19 to 19.99%20 to 20.99%21 to 21.99%22 to 22.99%<br>% of Total<br><!-- End of picture text --> _Source: Authors’ analysis based on figures provided by SF. Note: The above figures show the distribution of SME loans by interest rate ’bucket’ that were originated in the fourth quarter of 2006.The relevant interest rate ceiling during that period was 22.6 percent._ A few banks mentioned the loan loss provisioning regime as another example of regulations that reduce the supply of SME credit. According to the credit risk management system ( _Sistema de Administración de Riesgo Crediticio_ or SARC) developed by the SF in recent years, provisioning should be based on a loan’s probability of default (PD) and loss-given default (LGD) estimates. PD estimates by rating classification and market segment may be derived either using banks’ own internal credit risk models (after the SF validates them), or using segment-specific ‘standard’ reference 24
These results are robust to three other measures of firm performance, namely, value added per worker, labor productivity and real labor productivity.<sup>11</sup> The logarithm of total hours of labor is included in the regressions as an additional explanatory variable. We observe that the more are the hours worked in a firm, the better this firm performs (appendix A, table A.11).

Additionally, simple simulations indicate that a number of characteristics, including human capital and managerial skills, could improve the performance of informal firms. The simulation relies on ordinary least squares (OLS) estimates to show how the performance of constrained gazelles and survivalists would improve if these firms shared the same environment with top performers. The findings reveal that the performance of constrained gazelles (value added per hour worked) would be enhanced by approximately 5 percent if the entrepreneurs in these firms had the same or similar educational attainment, financial knowledge, business networks, and managerial abilities as top performers. Similarly, given backgrounds and preparation resembling those of the top performers, the performance of survivalists would improve by 26 percent.

Simulations also indicate that human capital—especially vocational training (post-primary education)—is an important factor in the performance of firms (appendix A, figure A.8). Policies aimed at enrolling all top performers and constrained gazelles in vocational training could enhance their performance by 33 percent. Among survivalists, these policies could improve performance by 34 percent. The impacts of secondary education and reading knowledge on the performance of firms are not as strong. For instance, if all survivalists had access to secondary education, their performance would increase by 10 percent.

Additional factors have important effects on the success of firms. Discipline problems have a relatively greater impact on the performance of survivalists, which thereby decreases by 41 percent, than of top performers and constrained gazelles (approximately 39 percent). Likewise, wider access to financial knowledge and business associations could improve the performance of firms, but the impact is slightly stronger among survivalists.

# **8. Conclusion and Policy Recommendations**

The informal sector accounts for an important share of the economic activities in Sub-Saharan Africa. The literature on the informal sector has focused mainly on explaining the causes of the emergence and productivity of informal firms and on ways to formalize the informal sector. Using 1-2-3 survey data on the Democratic Republic of Congo, this paper contributes to the empirical literature in four main areas. First, following Grimm, Knorringa, and Lay (2012), the

> 11 Labor productivity is the ratio of production to the number of workers. Real labor productivity is the ratio of turnover to the number of workers.

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Promoting Democracy in Fragile States access to information, voter coordination, and voter security. The experiment randomly assigned combinations of a monthly civic education and town hall program and a security committee program over the course of nine months prior to the October 2011 elections. Programs were assigned to 101 villages, with 41 villages randomly assigned to control. The civic education and town hall program used a curriculum developed by the National Elections Commission and facilitated town hall discussions in communities. The security committee program linked communities to the United Nations (UN) peacekeeping mission. Outcomes were measured using polling place data on votes, a voter survey, and a set of novel behavioral measures.

The results show pronounced effects of the civic education and town hall program but negligible effects from the security committee program. The civic education and town hall program substantially increased enthusiasm for civic participation and preferences for national versus parochial candidates. This is consistent with the type of “coordinated shift” that our theory proposes can occur when third parties introduce new information and coordination opportunities in otherwise remote communities. The civic education program also increased sensitivity to voter intimidation. The security monitoring program only had a weak effect on increasing preferences for national versus parochial candidates.

We begin with our theoretical framework. This is followed by a discussion of the interventions and the context. Following that is a discussion of our experimental design, how we operationalize outcomes, and our estimation methods. We then present our main results, followed by an exploration of reasons for some of the unexpected findings. A conclusion draws out implications.

3
<!-- Start of picture text --> GUY<br>MOZ<br>ETH<br>MWI<br>DJI<br>MDGRWA MLI BDI<br>COM BENBFAKHMSEN BGD NPL HND NIC<br>CAF NERBOLTGO PAKYEMGHAUGA COGUZBMRT<br>CIVSLE GIN TZAKEN LKAARMMDA<br>SDN<br>CMR<br>.05 .1 .15 .2 .25 .3<br>Private Investment/GDP<br>.15<br>.1<br>.05<br>0<br><!-- End of picture text --> Figure 2: This graph plots the over-time average of government investment as a fraction of GDP (on the vertical axis) against the over-time average of private investment as a fraction of GDP (on the vertical axis). Total investment and total government investment are drawn from the IMF’s World Economic Outlook database. Private investment is the difference between the two. Annual averages are calculated over all years between 1980 and 2012 for which data are available.

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Since some of our sectors are more aggregated than the original ISIC Rev. 3.1 classification, we aggregate tariffs up using import shares as weights. Tariff data are aligned with the survey year of a country’s enterprise survey. In a few cases where tariff data are unavailable for the specific survey year, we choose the observation of the nearest available year before the survey year.

# **3.3 Measures of Foreign Spillover Potential**

We now turn to the mediating factors MF. In a first step we present measures of FDI spillover potential at the sectoral level based on available data in the Enterprise Surveys Indicator Database. All of the following variables are averages across all foreign firms within a specific sector of a country.

- _own_ = a sector’s average percentage of foreign ownership in a country;

- _market_ = a sector’s average percentage of FDI sales to the domestic market in a country. This measure serves as a proxy for a sector’s average FDI motive in a country, whereby a higher share is associated with market-oriented FDI;

- _inp_ = a sector’s average percentage of domestic input purchases of FDI firms in a country. This measure captures a sector’s average sourcing strategy of foreign firms in a country, whereby a higher share is associated with more local sourcing;

- _tech_ = _iso_ + _tech_for_ + _website_ + _email_ with 0 <= _tech_ <= 4, where _iso_ = 1 if firm owns internationally-recognized quality certification and 0 otherwise, _tech_for_ = 1 if firm uses technology licensed from foreign firms and 0 otherwise, _website_ = 1 if firm uses own website to communicate with clients or suppliers, _email_ = 1 if firm uses email to communicate with clients or suppliers. The technology indicator serves as a proxy for a sector’s average FDI technology intensity in a country.

# **3.4 Measures of Absorptive Capacity**

In a second step, we include the following measures of absorptive capacity that were available in the Enterprise Surveys Indicator Database.

- _gap =_ domestic firm’s LP relative to median LP of multinational firms in sector in natural logarithms;

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misunderstanding of the units of measurement, they may reflect a language barrier, or they may be intentional incorrect answers.

Data on climate was gathered from two sources (Dinar et al. 2008). We relied on temperature data from satellites operated by the Department of Defense (Basist et al. 2001). The Defense Department uses a set of polar orbiting satellites that pass above each location on earth between 6am and 6pm every day. These satellites are equipped with sensors that measure surface temperature by detecting microwaves that pass through clouds. The precipitation data comes from the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003). This dataset, created by the National Oceanic and Atmospheric Association’s Climate Prediction Center, is based on ground station measurements of precipitation.

It is not self-evident how to represent monthly temperatures and precipitation data in a Ricardian regression model. The correlation between adjacent months is too high to include every month. Kurukulasuriya et al. (2007) explored several ways of defining three-month average seasons. Comparing the results, the authors found that defining winter in the northern hemisphere as the average of November, December and January provided the most robust results for Africa. This assumption in turn implies that the next three months, February, March and April would be spring, May, June and July would be summer, and August, September and October would be fall (in the north). The seasons in the southern hemisphere are six months apart, i.e. winter in the southern hemisphere is defined as the average of May, June and July. These seasonal definitions were chosen because they provided the best fit with the data and reflected the mid-point for key rainy seasons in the sample. The authors adjusted for the fact that seasons in the southern and northern hemispheres occur at exactly the opposite months of the year. The authors also explored defining seasons by the coldest month, the month with highest rainfall, and solar position, but found these definitions did a poorer job of explaining current agricultural performance.

Soil data were obtained from FAO (2004). The FAO data provides information about the major and minor soils in each location as well as slope and texture. Data concerning the hydrology was obtained from the results of an analysis of climate change impacts on African hydrology (Strzepek and McCluskey 2006). Using a hydrological model for Africa, the authors calculated flow and runoff for each district in the surveyed countries. Data on elevation at the centroid of each district was obtained from the United States Geological Survey (USGS 2004). The USGS 6
and the Caribbean, 7 percent each are on Europe and Central Asia and Middle East and North Africa, and 6 percent are on the United States (Table 1). The geographic distribution is broadly similar for estimates on the poor’s exposure to climate shocks and the more adverse impact of such shocks on the poor (Table 2).

The majority of studies (57 percent) in our sample focus on general climate change, followed by studies on temperature anomalies (about one third), flooding, droughts, and general natural disasters (about one fifth each), and less than 10 percent of studies focusing on landslides, typhoons/cyclones, or earthquakes (Table 1). These indicators are not mutually exclusive since some studies examine multiple shocks. About one fifth of studies focus on heat, a subset of studies on temperature anomalies. Almost a quarter of estimates in our sample focus on the poor’s exposure to floods, and about a third focus on the more adverse impact of climate shocks on the poor (Table 2). The share differences at the study and estimate levels reflect the estimates from multiple specifications documented in the studies.

We assess the representativeness of the studies in our sample against the reported incidence of regional climate shocks in the global EMDAT (Emergency Events Database) database by performing a two-proportion t-test. EMDAT is a comprehensive global database that collects and provides information on the occurrence and effects of more than 22,000 mass disasters worldwide since 1900. The database includes information on natural disasters (geophysical, meteorological, hydrological, climatological, biological, and extraterrestrial) and technological disasters (industrial, transport, and miscellaneous accidents). The database includes disasters in which 10 or more people died, 100 or more people were affected, a state of emergency was declared, or international assistance was provided. We used EMDAT data from 2000 to 2023 from each region in the world for comparison (Table 3).<sup>5</sup> We find that, in our sample, the share of studies on subSaharan Africa is similar to the reported incidence of climate shocks in EMDAT. The share of studies on South Asia is higher than the reported incidence of shocks. Other regions are underrepresented. The results show that the coverage for floods and heat are lower in our sample, while it is higher for droughts in our sample of studies relative to the reported incidence in EMDAT.

> 5 Last accessed November 28, 2023.

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<!-- Start of picture text --> Figure 4.3. Systematic Sensitivity Analysis—sample frequency distributions from 30,000<br>simulations for the richest and poorest deciles<br><!-- End of picture text --> <!-- Start of picture text --> 4.5<br>4.0<br>Richest decile Poorest decile<br>Percentage of 3.5<br>solutions<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>4 6 8 10 12<br>Hicksian Equivalent Variation as a percentage of consumption<br><!-- End of picture text --> 95 percent confidence intervals (assuming a normal distribution): Poor (6.1, 9.0); Rich (5.1, 8.3). 99 percent confidence intervals (assuming a normal distribution): Poor (5.6, 9.5); Rich (4.6, 8.8).

t 107