rafmacalaba/datause-encoder-data huggingface dataset:
# **II. Overview of Financing to the Private Sector**

## **_Equity Financing_**

Equity issuance has not played an important role in financing the private sector in Mexico during 2000-2005. As can be seen in figures 1 and 2, both the domestic stock market capitalization and the number of listed non-financial private sector companies are relatively small compared to international peers. In fact, the number of non-financial private sector issuers actually declined from 173 in 2000 to 155 at the end of 2005. The stock market is highly concentrated, with the top 10 companies representing close to 70 percent of market capitalization; ownership of listed firms also tends to be highly concentrated. While there was a significant growth in market capitalization during this period, it was due mainly to asset revaluation as opposed to new equity issues. Cumulative public equity issuance volume (both domestically and abroad) for 2000-2005 totaled only around MXP 90 billion, which represents just over one percent of Mexico’s 2005 GDP (see figure 3). Private equity is also not well-developed; according to industry estimates, Mexico attracted only 0.1 percent of global and 10 percent of regional private equity investments over the period 1993-2004<sup>5</sup> . Given the marginal impact of equity financing to the private sector<sup>6</sup> , subsequent analysis in this paper focuses solely on the evolution of debt financing.

Figure 1: Number of Listed Companies and Figure 2: Comparative Size of Stock Capitalization of Stock Market (2000-2005) Market Capitalization (2004) <!-- Start of picture text --> U.S.A. 140<br>35.0% 160<br>30.0% 140 Chile 124<br>25.0% 120 Spain 95<br>100<br>20.0% Korea 63<br>80<br>15.0% India 56<br>60<br>10.0% Brazil 55<br>40<br>5.0% 20 Argentina 31<br>0.0% 0 Poland 29<br>2000 2001 2002 2003 2004 2005<br>Mexico 25<br>Market capitalization/GDP Number of listed companies<br>- 50 100 150<br>Market capitalization / GDP<br>% of GDP<br>Number of companies listed<br><!-- End of picture text --> Source: Bolsa Mexicana de Valores and World Bank. Note: Only non-financial private sector equity issuers are included in the above Figure.

5 See NAFIN and US Trade and Development Agency (2004) for a recent description of the situation and main obstacles that impede the industry’s growth.

6 The recent passage of the new Securities Markets Law is expected to support greater equity financing by enhancing corporate governance and minority shareholder rights of publicly listed firms and by facilitating access to capital markets through the creation of a new corporate vehicle ( _Sociedad Anónima Promotora de Inversión_ or SAPI).

6
<!-- Start of picture text -->
- 7 =<br>140,000 140,000<br>120,000 120,000<br>100,000 100,000<br>80,000 80,000<br>60,000 60,000<br>40,000 40,000<br>° a | S a g & i E 0<br>-20,000 -20,000<br>-40,000 -40,000<br>~60,000 ~60,000<br>-80,000 -80,000<br>~_ RSSSSSASSRSRSESSSRSSRSSSASRSRSERSSSBSBSEBBERAERRRRRRRRRRRRARARRRARRRRRARAERRRRRARRRRR _—<br><s 59 10-14 15-19 >=20<br>m Extensive Margin Intensive Margin @ Net Job Creation<br><!-- End of picture text -->

<!-- Start of picture text --> Net Job Creation by TFP Quintiles Net Job Destruction by TFP Quintiles<br>40,000 °<br>30,000<br>-10,000<br>2Q2 22<br>=2 20,000 2a<br>-20,000<br>10,000<br>3 -30,000<br>2013 2014 2015 2016 2017 2018 2019 2020 2013 2014 2015 2016 2017 2018 2019 2020<br>GEGE quintiequintie14 MEEE quintie2quintile5 MEE quintile 3 GEEGEE quintie1quintie+ MEMEME quintie2quintie 5 ME quintile3<br><!-- End of picture text -->
- 10 -

P-er Characteristics. Peer group characteristics were indicated by four class averages: pretest score; average years of maternal education: proportion of students having fathers with white-collar occupations; and percentage of students who were female.

Comnarison of School Wyaes Table 1 presents the means and standard deviations of the variables related to student characteristics by type of school. Students in both types of private schools in the Dominican Republic came from distinctly more advantaged backgrounds than did their public school counterparts. This finding is not surprising given that private schools charge fees and public schools do not. On average, the private school students had more educated mothers, were more likely to have a father in a white-collar occupation, and were more likely to live in a "blockn house than were the students attending urban public schools.

Table 2 presents the means and standard deviations of the school variables by type of school. There were differences among the types of schools, but they were not consistently correlated with the status of the school. Private F-type schools appeared to be the most advantaged, with the most educated and experienced teachers, the highest proportion of students with textbooks, and the longest average periods of ir.struction. However, teachers in F-type schools also spent more time establishing order in their classrooms, on indication that they spent less time on instruction; they were also more likely to teach additional classes in other schools, a pattern that suggests they needed to supplement their salaries.
addresses the competitiveness loss not just in the domestic market but also in foreign markets.<sup>19</sup> <u>Figure 3a: Impact of Unilateral Emissions Reductions by Industrial Countries on their Output of Energy-Intensive Manufacturing ( % change: relative to 2005 for emissions reductions; and relative to BAU in 2020 for output)</u> <!-- Start of picture text --> 6%<br>BTADE<br>4%<br>2%<br>Reduction in emissions<br>0%<br>BTAFU<br>5% 10% 15% 20% 25% 30% 35% 40%<br>‐2%<br>BTADU<br>‐4%<br>NBTA17<br>‐6%<br>manufacturing<br>Change in output for energy‐intensive‐<br><!-- End of picture text --> <u>Figure 3b: Impact of Unilateral Emissions Reductions by Industrial Countries on China’s Manufacturing Output (% change: relative to 2005 for emissions reductions; and relative to BAU in 2020 for output)</u> <!-- Start of picture text --> 6%<br>4%<br>2%<br>Reduction in emissions<br>0%<br>NBTA17<br>5% 10% 15% 20% 25% 30% 35% 40%<br>BTADU<br>‐2% BTADE<br>BTAFU<br>‐4%<br>‐6%<br>manufacturing<br>Change in output for energy‐intensive‐<br><!-- End of picture text --> 19 The BTADE results are sensitive to the manner in which subsidies are calculated. In the analysis presented in the text, subsides are linked to ex ante (base-year) levels of carbon content as is likely to be done in practice. But insofar as exporters reduce the carbon content of their production in response to higher energy prices, they are in effect over-compensated and the higher output is the consequence. We also re-did the scenarios with border taxes based on the ex post carbon content (results are available from the authors upon request).

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focusing on within-cell changes with the implementation of political reservations. Industry-year fixed effects control for common patterns across states for each industry.

Outcome variables are indicated by column headers. Panel A models an indicator variable for a state-year observation where political reservations have been implemented in the state. Panel B includes an additional control variable that is the male-owned establishment analogue specific to the outcome variable studied. Thus, the dependent variable in the first column is the log count of new women-owned establishments in the unorganized sector measured by state-industry-year, and the additional regressor is the log count of new male-owned establishments in the unorganized sector in the same state-industry-year. This additional control provides a very powerful test by quantifying the relationship of reservations and women-owned establishments over-and-above contemporaneous male-owned business activity.

The results in Table 2 have a mixed message. On one hand, Columns 1 and 2 show a solid connection between the implementation of political reservations and women’s entrepreneurship. The elasticities suggest a growth in new women-owned establishments and associated employment of approximately 40% after political reservations were implemented. New women-owned establishments account for on average 15% of all women-owned establishments. In Column 3, this growth in entry is associated with a positive response in total counts of women-owned establishments, but this response is imprecisely estimated. This smaller aggregate response may be due to the relatively short time frame for these changes to accrue; it may also indicate that some of the induced entry is short-lived.

Column 4 considers the log employment of women overall in manufacturing for the stateindustry, and Columns 5-7 separately consider employment in male-owned unorganized establishments, female-owned unorganized establishments, and the organized sector. While point estimates tend to be positive, there is no measurable evidence that political reservations increased women’s employment in manufacturing. Thus, the implementation of political reservations in India connects to heightened entry of women-owned establishments but not to increased employment generally of women in manufacturing.<sup>15</sup> From here on, we report exclusively the

> 15 We confirm this limited employment response for women in manufacturing using the NSSO Employment-Unemployment survey stretching from 1983 to 2009. The estimates in the formats of Panels A and B of Table 2 would be 0.088 (0.353) and 0.089 (0.352), respectively, quite similar to Column 6.

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other leaders in the same country, and higher growth than other leaders in the same region at the same time. If we instead compared leaders to an absolute standard of ≈2% global trend growth, the fraction of significant leaders is very similar (7.7%), as is the fraction of significant leaders who are democratic (42%).<sup>43</sup> Moreover, the overall highest- and lowest-growth leaders are the same as in Table 3. However, there is an overall shift towards leaders with positive significant contributions, driven by a greater fraction of leaders of European countries during the postwar boom and leaders of Asian countries, as these are no longer held against a higher regional growth standard.<sup>44</sup>

# **5. Conclusions**

In this paper, we start with a growth model where leaders matter for growth in general. We combine growth data and an optimal signal extraction methodology to estimate the size and significance of the growth contribution of each individual national leader since 1950. Many of these significant leaders are little known or long forgotten. We show that our optimal methodology strongly dominates the naïve method of attributing individual leader growth averages to leaders.

We find that only a small fraction of leaders (around 7%) have significant positive or negative growth contributions: knowing that leaders matter for growth _in general_ is very different from knowing _which_ leaders matter for growth. Our findings suggest much more caution is needed in policy debates that attribute growth to leaders. We do find significant leader effects for some famous “benevolent autocrats” and “bad emperors.” However, leaders in non-autocratic countries

> 43 Specifically, here we do not adjust for region X year observables in the first stage and we set for all leaders. 44 Using WDI growth data produces a similar fraction of significant leaders (6.6%) and democrats (31%). PWT 7.1 γγ= 0 produces smaller estimates of A , which reduces the fraction of significant leaders to 3.7% and increases the TT fraction of democrats. The set of significant leaders varies across data sets: see Online Appendix 3 for a discussion. μμ σσ

32
# 1.   Introduction

Armed conflict can have severe consequences for household welfare in conflict-affected areas. These negative consequences for local populations are indisputable (Blattman and Miguel 2010; Verwimp, Justino, and Brück 2018). However, the dynamic effects of victimization on household welfare remain understudied because longitudinal data about victimization are still scarce. Furthermore, the differential impacts of different types of victimization, ranging from violent attacks to attacks on property, are not well-known. Not all attacks against civilians in conflict-affected areas are committed by the main actors of the conflict. In conflict situations, crimes become more prevalent when law enforcement weakens and the opportunity cost of criminal activity decreases. Yet little is understood about how attacks committed by different types of perpetrators affect household welfare.

In this paper, we present microeconomic evidence on the effect of different types of victimization and different types of perpetrators on household welfare. Using panel data collected before, during, and after a large increase in conflict, we exploit plausibly exogenous variation in the timing, location, and intensity of household exposure to conflict to analyze the effects of victimization on household welfare. We conduct the analysis using panel data from the three most-conflict-affected regions in Nigeria, which all have witnessed an increase in conflict and violence since 2010.

Our first contribution to the literature is to quantify the effects of victimization on household consumption and food insecurity. We find that one additional conflict event leads to a 3–4 percent decrease in consumption. Both food and nonfood consumption are affected by victimization. By defining poverty as a threshold level of US$1.90 per capita per day consumption, we also find that poor households are more likely to stay poor after becoming victimized, whereas nonpoor households manage to prevent themselves from falling into poverty. We also find evidence of increased health and decreased educational expenditures relating to the herder-farmer clashes. This is in line with literature that documents a relationship between conflict and decreased schooling (Ichino and Winter-Ebmer 2004; Chamarbagwala and Moran 2011; Shemyakina 2011; Swee 2015; Monteiro and Rocha 2017; Bertoni et al. 2018).<sup>1</sup>

> 1 An effect not observed in a lower violence setting in Mexico (Márquez-Padilla, Pérez-Arce, and RodríguezCastelán 2015).

2
institutions), government (central, state, and local bodies), commercial banks, co-operative banks and societies, and micro-finance institutions; (ii) other institutional agencies; (iii) money lenders; (iv) business partners; (v) suppliers and contractors; (vi) friends and relatives; and (vii) others. In these rounds, the information on interest payment is also very detailed.

However, not all NSSO survey rounds provide such detailed information. For instance, only later rounds of the NSSO survey (specifically, the 56<sup>th</sup> , 62<sup>nd</sup> and 67<sup>th</sup> ) include loan information from business partners and suppliers, while different sets of institutions are included in earlier NSSO rounds. Therefore, to maintain consistency of information across all years and with the organized sector, we combine all institutional and other agency sources into one group, while loans from business partners and suppliers is grouped into the “others” category. In sum, we have the following categories in sources of loans and interest payments for establishments in the unorganized sector:

- Institutions and other agencies

- Money lenders

- Family and friends

- Others (including partners, contractors and others)

Appendix Tables 1 and 2 describe the detailed financial access data fields in the ASI and NSSO across the various survey years considered in our study. The two-digit industries and the basic traits included in our work are described in Appendix Table 3.

Establishments in both the organized and unorganized sectors (which broadly capture the difference between formal and informal sectors) provide information on the value of the land and buildings that they own. Although both sectors provide this information, only the organized sector offers the distinction between land and buildings consistently for all the survey years under consideration. For the unorganized sector, this separation between land and buildings stopped after the 56<sup>th</sup> round of NSSO survey in the year 2000.

Not all plants own the land that they operate on. Thus, in both the organized and the unorganized surveys, the questionnaire asks plants to also provide information on the rent paid for land and 14
**Table 4.6: Unemployment rates by region and state**

||**1997**|**1998**|**1999**|
|---|---|---|---|
|**Northeast**|**6.7**|**7.1**|**8.0**|
|Maranhao|3.5|3.4|4-3|
|Piaui|3.8|4.9|3.4|
|Ceara|6.1|6.2|6.3|
|RMFortaleza|10.3|11.0|12.2|
|RioGrande doNorte|8.9|7.6|9.2|
|Paraiba|5.6|5.6|7.8|
|Pemambuco|8.5|8.1|10.1|
|RM Recife|13.2|14.7|14.1|
|Alagoas|7.5|11.4|13.7|
|Sergipe|6.0|10.2|8.9|
|Bahia|7.7|8.1|9.1|
|RMSalvador|16.2|17.2|19.2|
|**Southeast**|**9.0**|**10.8**|**11.2**|
|MinasGerais|6.4|8.2|8.7|
|RMBeloHorizonte|9.7|12.7|14.3|
|EspiritoSanto|6.5|6.7|8.2|
|RiodeJaneiro|9.3|10.8|11.4|
|RMRiodeJaneiro|9.6|11.1|11.5|
|SaoPaulo|10.3|12.4|12.6|
|RMSaoPaulo|12.6<br>|14.9<br>|15.8<br>|
|**Brazil**|**7.8**|**9.0**|**9.6**|

_Source: IiBGE_

# **5. Migration and Schooling of Children**

We have seen evidence that migration tends to make the migrants themselves better off. Recent migrants to both the NE and then SE are not as generally well off as longer-term migrants and migrants, particularly in the NE, seem to improve their employment prospects over time. A remaining question is the impacts of migration on use of public infrastructure, in particular schooling. While the decision to migrate is primarily taken by the household head, all family members incur potential costs. Non-monetary resettling costs might be particularly high for children, as they have to adjust to different schools and curricula. The difference in school attendance probabilities between children of migrants and non-migrants in both regions is not very pronounced and participation rates for all children are close to 90 percent (Table 5.1). However, school attendance for children from migrants to the SE is about 5-percentage-points lower than for the average school-aged child in the SE, suggesting that children of recent migrants may be educationally disadvantaged.

Differences in school performance, as measured by age-appropriate grade enrollment, for migrant versus non-migrant children are more evident. Children of 30
# Table 5: Digitalization heterogeneity

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Size|Age|Female-led|Subsector|Size|Age|Female-led|Subsector|
|||||**Pane**|**l A**||||
||Star|ted using|or increased d|igital||Has o|nline sales||
|Informality|-0.335**|-0.254**|-0.306**|-0.358**|-0.251**|-0.228**|-0.259**|-0.258**|
||(0.0323)|(0.0259)|(0.0206)|(0.0306)|(0.0400)|(0.0339)|(0.0261)|(0.0380)|
|Micro (less than 9 employees)_×_Informality|0.0658*<br>(0.0360)||||0.0134<br>(0.0437)||||
|Young_×_Informality||0.0244||||0.0524|||
|||(0.0451)||||(0.0536)|||
|Established_×_Informality||-0.110**||||-0.0662*|||
|||(0.0324)||||(0.0400)|||
|Female-led_×_Informality|||0.0870**||||0.102**||
||||(0.0406)||||(0.0480)||
|Retail_×_Informality||||0.110**||||-0.0471|
|||||(0.0433)||||(0.0588)|
|Other Services_×_Informality||||0.101**||||0.0431|
|||||(0.0356)||||(0.0428)|
|Observations|14037|14037|14037|14037|14124|14124|14124|14124|
|||||**Pane**|**l B**||||
||Inv|ested in d|igital technolo|gies|Share of|employees|workingrem|otely (%)|
|Informality|-0.283**|-0.238**|-0.231**|-0.268**|-5.071**|-3.655**|-4.251**|-1.373|
||(0.0329)|(0.0270)|(0.0211)|(0.0317)|(1.978)|(1.507)|(1.244)|(1.739)|
|Micro (less than 9 employees)_×_Informality|0.0656*<br>(0.0361)||||1.797<br>(2.001)||||
|Young_×_Informality||0.0388||||-1.772|||
|||(0.0449)||||(2.729)|||
|Established_×_Informality||-0.0267||||0.390|||
|||(0.0328)||||(1.569)|||
|Female-led_×_Informality|||-0.0691*||||2.495||
||||(0.0413)||||(2.198)||
|Retail_×_Informality||||0.0434||||-2.716|
|||||(0.0447)||||(1.834)|
|Other Services_×_Informality||||0.0408<br>(0.0356)||||-4.529**<br>(1.819)|
|Observations|14214|14214|14214|14214|19033|19033|19033|19033|
|Panel controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Initial productivity|No|Yes|No|Yes|No|Yes|No|Yes|
|Change in sales|No|No|No|No|Yes|Yes|Yes|Yes|

**N** otes: All continuous variables are winsorized at the 1% and 99%. Robust standard errors in parentheses

* p<0.10, ** p<0.05, ** p<0.01

Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).
Table 1. Descriptive Statistics: Treatment and Outcome Variables

||N|Mean|SD|Min|Max|
|---|---|---|---|---|---|
|_Panel A. Treatment Variables_||||||
|Taliban exposure|5,592|0.25|0.08|0.20|0.59|
|U.S. allies exposure|5,592|0.60|0.27|0.00|0.80|
|Contested territories exposure|5,592|0.16|0.21|0.00|0.65|
|_Panel B. Outcomesfor Integration_||||||
|Integration index (SD)|5,592|0.00|0.93|-3.52|4.36|
|<br>Feel comfortable if your child socialize with host children (1-5)|5,285|3.97|0.73|1.00|5.00|
|<br>Feel welcomed in this city (1-5)|5,535|4.17|0.61|1.00|5.00|
|Feel part of this village (1-5)|5,469|4.06|0.71|1.00|5.00|
|Feel that Tajikistan is your home (1-5)|5,592|7.53|2.56|1.00|10.00|
|Number of refugee friends|5,592|4.20|6.83|0.00|100.00|
|Number of Tajikistan friends|5,589|2.26|3.89|0.00|50.00|
|_Panel C. Outcomesfor Educational Attainment_||||||
|Education index|5,592|0.60|0.23|0.00|0.75|
|Literate [=1]|5,592|0.91|0.29|0.00|1.00|
|Some education [=1]|5,592|0.85|0.36|0.00|1.00|
|Primary [=1]|5,592|0.14|0.35|0.00|1.00|
|<br>Secondary [=1]|5,592|0.49|0.50|0.00|1.00|
|_Panel D. Outcomesfor Mental Health_||||||
|Mental health issues index|5,590|0.16|0.19|0.00|1.00|
|Felt little interest in doing things [=1]|5,554|0.30|0.46|0.00|1.00|
|Had sleeping troubles [=1]|5,570|0.14|0.35|0.00|1.00|
|Had poor appetite or overeating [=1]|5,573|0.11|0.31|0.00|1.00|
|Suicidal thoughts [=1]|5,568|0.01|0.10|0.00|1.00|
|<br>Mental health assistance[=1]|259|0.77|0.42|0.00|1.00|
|_Panel E. Outcomesfor Labor Market Access_||||||
|Labor index (SD)|5,592|0.12|1.10|-0.47|2.72|
|Employed [=1]|5,592|0.22|0.41|0.00|1.00|
|<br>Monthly wage (somoni)|5,592|357|1,086|0.00|40,000|
|<br>Weeklyhours worked(somoni)|5,592|11.75|23.72|0.00|84.00|
|_Panel F. Outcomesfor Income and Consumption_||||||
|Income and consumption index (SD)|5,592|0.01|1.00|-7.03|8.29|
|<br>Monthly house income (somoni)|5,587|5,159|3,412|0.00|30,850|
|Monthlyconsumption(somoni)|5,592|11,151|234,477|0.00|12,000,000|