rafmacalaba/datause-encoder-data huggingface dataset:
system in general, and with investor rights, accounting standards and transparency rules (on the stock market) in particular. The inflation rate is an indicator of both the government’s management of the economy and whether long-term contracting is likely to be widespread. It characterizes also the opportunity cost of holding money. Debt contracts may be specified in nominal terms. So we expect a negative correlation between the rate of inflation and firms’ indebtedness. Two other control variables for asset markets conditions are: the real interest rate and the cost of equity.<sup>55</sup> Finally, the volatility of inflation is a proxy for macroeconomic instability.

# _(3) Financial System Characteristics_

The financing patterns of firms, especially their access to external finance,<sup>56</sup> depend on the characteristics of the financial system. This in turn affects the ability of firms to have a higher rate of growth than the one permitted by their internal resources.<sup>57</sup> The stock market and banking sector variables provide a control group guarantying that our contractual savings variables are not simply a proxy for the level of development of the financial system.

# **(a) The Stock Market**

First, we measure the size of stock markets by the stock market capitalization (in percentage of GDP). This variable has been widely used in the recent literature. The ability of the stock market to provide risk diversification opportunities and information also depends on its level of activity and liquidity (Levine and Zervos (1998)). Greater liquidity will encourage investors to acquire stakes in risky firms<sup>58</sup> and will enhance information acquisition by large investors (Holmstrom and Tirole (1993)). Greater informational content in prices will increase the efficiency of capital allocation. And better public information may have a spillover effect on the long-term debt market by reducing initial informational asymmetries, as illustrated in the model. Activity on the stock market is measured by the turnover ratio, that is the total value traded, in proportion of the stock market capitalization.

# **(b) The Banking System**

Banks have a comparative advantage in acquiring private information on borrowers and in monitoring their actions. A sound and efficient banking sector is obviously essential for firms especially those that do not have access to capital markets. The use of short-term debt reduces the scope for opportunistic behavior, thus reducing the

> 55 The cost of equity Req is: Re _q_ = 1 + _<u>g</u> P_ / _E_

> where g is the average rate of growth of future earnings and P/E the current price-earnings ratio. For g we use the average rate of growth of earnings over the period, and we use the P/E ratio index in a given year provided by Datastream.

> 56 Demirguc-Kunt and Maksimovic (1996, 1999), Rajan and Zingales (1998) and Carlin and Mayer (1999).

> 57 See Beck, Demirguc-Kunt, Levine and Maksimovic (2000) for a synthetic approach.

> 58 And make efficient restructuring decisions, see Maug (1998) for a theoretical argument.

21
Table 3. Turkey AZverage Daily Trading Volume on the Istanbul Stock Exchange 1986-89 (in millions of TL)

||1986|1987|1988|1989|1990 1/|
|---|---|---|---|---|---|
|Treasury bills and government bonds|5.998|15,304|38,572|115,946|<br>261215|
|Revenue sharing certificates|242|1,618|1,701|8,717|11183|
|Private sector bonds|340|2,256|4,849|10,593|14962|
|Stocks|35|425|589|6,809|44176|
|Total avg. daily tradinA volume|6 615|19 603|45,711|142.065|331 536|
|T.tal avg. daily tradina<br>volume at 1986 prices 2/|6.615|8 446|14 187|29.789|50.694|
|Memorandum items||||||
|(percent of total)||||||
|Treasury bills and government bonds|90.7|78.1|84.4|81.6|<br>78.8|
|Revenue sharing certificates|3.7|8.3|3.7|6.1|<br>3.4|
|Private sector bonds|5.1|11.5|10.6|7.5|<br>4.5|
|Stocks|0.5|2.2|1.3|4.8|<br>13.3|
|Total|100.0|100.0|100.0|100.0|<br>100.0|

1/ Most recent data available January-April 2/ Deflated by WPI (1986-100) Source: Istanbul Stock Exchange without undue difficulty, provided the sales are spread through time to prevent the stock exchange from becoming saturated with SEE shares. This would be a revenue-raising effort and would presumably have little, if any, impact on enterprise performance.

63. During the first quarter of 1990, the MHPPA sold public shares on the TSE for about TL 400 billion (US $160 million). The MHPPA has little inhouse stock market expertise, and some brokers contend that in some cases the sales were at prices 30-40 percent below what could have been obtained had they been handled more professionally. The MHPPA has become the major participant on the ISE. It has evolved into a market maker. There have been charges of r%rket-rigging. The MHPPA has intervened to prevent the price of 35
|Trade Variables|||||||
|---|---|---|---|---|---|---|
|Effective<br>Rate of Protection|40.012<br>(0.007)|-0.010<br>(0.007)|.0.009<br>(0.007)|_-0.352*_<br>(0.022)|-0351<br>(0.021)|.0320<br>(0.021)|
|% Exports|-0.lS9<br>(0.194)|-0.003<br>(0.178)|-0.045<br>(0.182)|-0.137<br>(0.208)|_-0.135_<br>(0.187)|-0.198<br>(0.190)|
|InDuts|||||||
|In (Senegalese<br>Unskilled)|0361<br>(0.056)|0362<br>(0.055)||0394<br>(0.060)|0392<br>(0.058)||
|In (Other Africans<br>Unskilled)|6.9E-6<br>(7.62-5)|||-8.7E-5<br>(83E-5)|||
|In (Non-African<br>Unskilled)|4.8E24<br>(1.8E.4)|_42E4_<br>(1.72-4)||7.7-4<br>(2.0E4)|6.8E-4<br>(1.92-4)||
|In (Senegalese<br>Skilled)|2.52-4<br>(1.5S4)|32E4<br>(1.4A-4)||4.12-4'<br>(1.E-4)|4.02B4'<br>(1.5-4)||
|In (Other Africans<br>Skilled)|2.7E2-4<br>(9.5E-5)|2.724'<br>(8.4B-5)||2.9E-4'<br>(9.9E-5)|**2t.4'**<br>(8.62-5)||
|In (Non-Africans<br>Skilled)|12E4<br>(9.6E-5)|_3.525_<br>(1.0E-4)|||||

(Continued) '0
Despite the critical role that the insurance sector may play for financial and economic development and reasonable evidence that the sector has promoted economic growth, there have been few studies examining the factors that drive the development of the insurance sector. Moreover, the bulk of the existing empirical research focuses on the growth of the life sector, as measured by life insurance premiums. This paper contributes to the body of research in several ways. First, it builds a large and recent database comprising 90 developed and developing countries over the 2000-08 period. Second, it examines the factors that drive the development of both the life and non-life sectors. Third, it explores the impact of a broad set of potential determinants, including new variables that have not yet been tested. Finally, it measures the development of the life and non-life sectors using both premiums and assets.

The paper is structured as follows. Section 2 provides a review of the empirical research on the determinants of the insurance sector’s development, with focus on the more recent studies. Section 3 discusses the data and the methodology used in the paper. Section 4 presents and discusses the main results, including univariate, two-group comparison tests and regression results. Finally, section 5 summarizes the main findings and identifies the major policy implications for the development of the insurance sector.

# **2. A Review of the Empirical Literature on the Insurance Sector**

The empirical research on the determinants of the insurance sector has essentially focused on the life sector. Early contributions to the empirical literature were mostly focused on the US, and explored the role of education, income, religion, and cultural factors, as well as prices. By way of illustration, Hammond, Houston and Melander (1967) stress the importance of the occupation of the main wage earner. Headen and Lee (1974) show that the demand for life insurance depends on savings and interest rates. Burnett and Palmer (1984) show that education, income and religion are key determinants of the demand for life insurance. Babbel (1985) shows that prices have a negative effect on the demand for life insurance. Beenstock, Dickinson, and Khajuria (1986) provided one of the first empirical studies exploring cross-country data. Using a dataset of 10 developed countries, the authors conclude that income, life expectancy, and the 3
Table 4: Plant level productivity, markup, value added and revenue.

||(1)<br>TFPQ|(2)<br>Marginal cost|(3)<br>Markup|(4)<br>Value added|(5)<br>Revenue|
|---|---|---|---|---|---|
|ETR _×_ secondary|0.029***|-0.086***|0.051***|0.028***|0.041***|
||(0.005)|(0.020)|(0.010)|(0.006)|(0.004)|
|Observations|53,517|53,517|53,517|53,517|53,517|
|R-squared|0.999|0.747|0.744|0.885|0.914|
|Plant FE|yes|yes|yes|yes|yes|
|Industry-year FE|yes|yes|yes|yes|yes|
|Other technologies|yes|yes|yes|yes|yes|

The table presents OLS estimates of the relationship between plants’ exposure to robots, productivity, markup, value added and revenue. The dependent variables are TFPQ (1) the log of plant level real marginal cost (2), markup (3) real value added (4) and real revenue (5). Secondary is the 2006 share of plants’ employment with secondary education. Other technologies are capture by an index of innovation activities in 2006, interacted with year fixed effects. Standard errors are clustered at the 2-digit industry- and year-level. The coefficients with<sup>_⋆⋆⋆_</sup> are significant at the 1% level, with<sup>_⋆⋆_</sup> are significant at the 5% level, and with<sup>_⋆_</sup> are significant at the 10% level.

# **3.2 Employment and Wages**

Having provided evidence of a significant productivity effect of robots, a key question in evaluating the economic impact of automation is whether the improved performance translates into increased labor demand at the plant level. In the terminology of taskbased models, the question is whether the productivity effect is large enough to offset the displacement effect of automation (Acemoglu and Restrepo (2018b); Acemoglu and Restrepo (2019)). Table 5 presents the results of estimating equation (3) using the log of total employment and the log of real average wages as dependent variables. The impact of robots on total plant level employment is negative and small in absolute magnitude (column 1). An additional robot per thousand workers in a plant with a mean share of secondary educated workers reduces the plant’s employment by 0.4 percent. While significant this effect is particularly small considering that the sample mean of robots is 0.18 per thousand. On the other hand automation does not have any significant effect on wages (column 2).

As robots are mainly used in production, we also test separately for the impact on employment of workers engaged in production versus other workers. Columns 1 and 2 in Table 6 present the results for _production_ workers, and columns 3 and 4 for _non-production_ workers. Exposure to robots is associated with lower production employment, but higher non-production employment, which includes a higher share of white-collar occupations 19
non-random assignment, prudential supervision is associated with less outreach. **_C. Staffing_** As noted in the introduction, the administrative work associated with preparing regular reports for supervisors is likely to be done in headquarters resulting in less staff working directly with clients in the field. To test that proposition, we use the percentage of staff located in headquarters as a dependent variable in the regressions in Table 7. We find that regular onsite supervision is positively associated with the share of staff located in headquarters. In the treatment effects and IV regressions, that relationship is even stronger.

Though the results point in the predicted direction, we must acknowledge that we cannot reject the hypothesis that the errors from the selection equation and the % headquarters staff equation are independent based on the likelihood ratio for the treatment effects regressions, and _big banks’ supervision x non-NGO/NBFI_ is significant in the OLS regression, indicating that it is an unsuitable instrument. However, the regressions provide some evidence consistent with the idea that supervised firms re-deploy personnel away from the field to comply with regulation. This re-deployment could also contribute to reduced outreach.

As a final exercise, we used the share of the loan portfolio that was delinquent at least thirty days as a dependent variable. Though our results to this point are consistent with the idea that prudential supervision imposes costs on MFIs, it might also be true that it helps ensure the safety of deposits, which could be an important goal given that MFIs tend to have small depositors as customers. In OLS regressions, the relationship between regular onsite supervision and delinquent portfolio share is positive and significant, indicating perhaps that supervised MFIs tend to take on more credit risk than others. In the treatment effects and IV regressions, the delinquent portfolio share variable is no longer significant, suggesting that prudential supervision can contribute to improved portfolio quality. However, data from only 37 MFIs enters those regressions, which pass neither the simple excludability test nor the likelihood ratio test indicating that the errors from the selection equation and the equation of interest are independent. We do not, 22
# Annex 4. Number Of Customers Identified in Literature as Served by Electricity SPSPs

||**Wor**|**ldwide estim**|**ate of SPSPs a**|**nd number of cl**|**ients served**||
|---|---|---|---|---|---|---|
|**Country**|**Number of**|**Average**|**Number of**|**Total households**|**SPSPs as part of**|**Significance on electricity**|
||**SPSPs**<br>[-]|<br>**clients per**<br>**SPSP**<br>[-]|**clients**<br>million|**electrified**<br>million|<br>**total electrification**<br>%|<br>**access in country**<br>high/medium/small|
|**SOUTH ASIA**|||||||
|Bangladesh|67|44,776|3.00|5.3|56%|high|
|Nepal|250|250|0.06|0.7|9%|medium|
|Sri Lanka|200|400|0.08|2.4|3%|medium|
|India|1,800|350|0.63|87.4|1%|small|
|Pakistan|20|300|0.01|14.6|0%|small|
|_Sub total_|_2,337_|_9,215_|_3.78_|_110.4_|_3%_||
|**EAST ASIA/CHINA**|||||||
|Philippines|119|44,538|5.30|13.2|40%|high|
|Cambodia|600|200|0.12|0.4|32%|high|
|Laos|125|200|0.03|0.2|13%|medium|
|Indonesia|2,000|200|0.40|22.5|2%|small|
|China|1,000|300|0.30|249.0|0%|small|
|_Sub total_|_3,844_|_9,088_|_6.15_|_285.3_|_2%_||
|**AFRICA**|||||||
|Kenya|20|5,000|0.10|0.5|21%|high|
|Mali|100|300|0.03|0.2|15%|high|
|Somalia|100|200|0.02|0.2|12%|high|
|Mozambique|10|1,000|0.01|0.3|4%|medium|
|Ethiopia|100|200|0.02|0.6|3%|medium|
|Uganda|5|500|0.00|0.2|1%|medium|
|Zimbabwe|30|300|0.01|1.0|1%|small|
|Cote d'Ivoire|3|10|0.00|1.6|0%|small|
|Ghana|3|10|0.00|1.7|0%|small|
|Senegal|3|10|0.00|0.6|0%|small|
|South Africa|10|1,000|0.01|5.7|0%|small|
|Tanzania|5|300|0.00|0.7|0%|small|
|_Sub total_|_389_|_736_|_0.20_|_13.2_|_2%_||
|**MIDDLE EAST/NORT**|**H AFRICA**||||||
|Yemen|300|200|0.06|1.5|4%|medium|
|Morocco|100|200|0.02|4.1|0%|small|
|_Sub total_|_400_|_200_|_0.08_|_5.6_|_1%_||
|**LATIN AMERICA**|||||||
|Bolivia|80|300|0.02|1.0|2%|medium|
|Nicaragua|10|1,000|0.01|0.5|2%|medium|
|Dominican Rep|15|1,000|0.02|1.1|1%|small|
|<br>Honduras|10|1,000|0.01|0.7|1%|small|
|Peru|35|1,000|0.04|3.8|1%|small|
|Argentina|10|1,000|0.01|7.0|0%|small|
|Brazil|100|300|0.03|34.2|0%|small|
|Guatamala|10|500|0.01|1.5|0%|small|
|_Sub total_|_270_|_763_|_0.14_|_49.9_|_0%_||
|**ECA**|**_-_**|**_-_**|-|-|||
|**Total**|**7,240**||**10.35**|**464.3**|**2%**||

**Note:** countries listed have been selected after literature review - list might change after field surveys

33
Table 1 goes about here

# 4.2. Estimation Strategy

All the analyses described below rely on within-district variation in exposure to conflict and are thus robust to correlation between the intensity of conflict at the district level and any other time-invariant characteristics of the district that also influence the explained variable. Although specifications vary, the approach is essentially of the difference-in-difference type. The main threat to identification is therefore the possibility that time-varying omitted factors may be correlated both with the left-hand-side variable and conflict exposure.

Research on the causes of the Nepalese conflict summarised in Section 3 has shown that conflict was more intense in more mountainous and forested regions, and up until 2002, in poorer areas. These characteristics are also likely to have an effect on education and the marriage market and it is therefore essential to control for regional differences. This is done in several ways. First, the inclusion of district fixed effects ensures that any time-invariant differences in the explained variable between districts with different degrees of conflict are differenced-out. Second, all regressions include region-specific time dummies, i.e., for each year except the first one, which is the omitted category, there are five dummies corresponding to each of the five development regions of Nepal. Figures in World Bank (2005, p.6) show that poverty has decreased at a different pace across these five regions, and this may confound the effect of conflict if the intensity of violence is caused not only by levels but also by trends in poverty. The region-specific time dummies fully capture these different regional trends, and imply that the source of identification of the effect of conflict is the variation in conflict intensity within development region, net of district-specific constant characteristics and time-varying characteristics that are common to all districts in a given region. Third, I test for the presence of differential pre-conflict trends correlated with future conflict, which is equivalent to carrying out a series of placebo experiments. Fourth, I use several identification strategies, relying, in turn, on (i) a comparison of cohorts that are exposed (treated) and not exposed (control) to conflict, and (ii) a comparison of outcomes for individuals of a given age observed just before the conflict escalated (in 2001) and at the end of the conflict (2006), thus comparing individuals born only five years apart but experiencing very different conflict intensity.

Two issues of particular concern when estimating the impact of any conflict episode are mortality selection and migration. In the case of Nepal, the number of conflict-related deaths was 13
|Guinea_2013|338|169|50%|162.0|4%|
|---|---|---|---|---|---|
|Guinea-Bissau_2014|11|7|64%|—|—|
|Kenya_2015|2,299|2,228|97%|1,512.0|32%|
|Lesotho_2010|72|—|—|138.0|—|
|Liberia_2014|22|14|65%|10.9|24%|
|Madagascar_2014|569|450|79%|—|—|
|Malawi_2014|351|341|97%|323.9|5%|
|Mali_2014|352|—|—|245.0|—|
|Mauritania_2013|170|107|63%|110.0|-3%|
|Mauritius_2013|776|684|88%|441.1|36%|
|Mozambique_2014|2,520|—|—|831.0|—|
|Namibia_2013|487|392|80%|614.0|-57%|
|Niger_2014|126|—|—|146.1|—|
|Nigeria_2014|10,584|6,493|61%|—|—|
|Rwanda_2013|93|—|—|92.0|—|
|São Tomé and Príncipe_2014|33|—|—|—|—|
|Senegal_2013|757|684|90%|483.0|29%|
|Seychelles_2014|104|—|—|59.0|—|
|Sierra Leone_2012|94|—|—|—|—|
|Somalia_2011|16|—|—|—|—|
|South Africa_2014|47,027|44,895|95%|36,170.0|19%|
|South Sudan_2013|22|—|—|22.0|—|
|Sudan_2014|3,686|—|—|—|—|
|Swaziland_2014|95|—|—|226.3|—|
|Tanzania_2015|1,626|—|—|934.6|—|
|Togo_2013|264|—|—|162.6|—|
|Uganda_2014|892|—|—|580.0|—|
|Zambia_2014|2,366|2,211|93%|—|—|
|Zimbabwe_2012|1,970|1,257|64%|1,546.0|-23%|
|**Total for countries with available capacity data (20 countries)**|77,501|67,110|87%|||
|**Total for countries with available capacity data**<br>**(excluding South Africa)**|30,474|22,215|73%|||
|**Total for countries with available capacity data and peak demand**<br>**data(14 countries)**||56,357||45,940|18%|
|**Total for countries with available capacity data and peak demand**<br>**data(excluding South Africa)**||11,462||9,770|15%|

Source: utility annual reports and other documents _Note_ : The year following each country name is the reference year for countries included in the QFD analysis, or the latest year with available data for capacity, peak demand, or both for countries not included in the QFD analysis. For the purposes of the availability factor, reported available capacity is assumed to correspond to domestic capacity excluding regional projects. Reserve margins calculations are based on available capacity relative to peak demand. — = not available 87
Table A.3: Baseline Parameters (continued)

|Parameter|Description|Units|Value|
|---|---|---|---|
|_μ_<sup>_b,_1</sup>|GHG Emissions from Combustion of 1G Biofuels|tCO2e per toe|1.729|
|_μ_<sup>_b,_2</sup>|GHG Emissions from Combustion of 2G Biofuels|tCO2e per toe|0.609|
|_μ_<sup>_x_</sup><br>|GHG Emissions from Petroleum Combustion|tCO2e per toe|2.902|
|_μ_<sup>_P_</sup>|GHG Emissions from Manure on Pasture Land|tCO2e per Ha|0.208|
|_μ_<sup>_l_</sup>|GHG Emissions from Livestock|tCO2e per ton|4.641|
|_μ_<sup>_~~w~~_</sup>|Forest Carbon Stocking Density|MgC per _m_<sup>3</sup>|1.1|
|_φ_|Share of Stored Carbon in Harvested Forest<br>Products||0.3|
|Preferences|and Welfare Parameters|||
|_αg_|AIDADS Marginal Budget Share at Subsistence<br>Income for Processed Grain Products||0.21|
|_αl_|AIDADS Marginal Budget Share at Subsistence<br>Income for Processed Livestock Products||0.14|
|_αe_|AIDADS Marginal Budget Share at Subsistence<br>Income for Energy Services||0.11|
|_αw_|AIDADS Marginal Budget Share at Subsistence<br>Income for Processed Timber Products||0.03|
|_αr_|AIDADS Marginal Budget Share at Subsistence<br>Income for Ecosystem Services||0.02|
|_αo_|AIDADS Marginal Budget Share at Subsistence<br>Income for Other Goods and Services||0.49|
|_βg_|AIDADS Marginal Budget Share at High Income<br>for Processed Grain Products||0.03|
|_βl_|AIDADS Marginal Budget Share at High Income<br>for Processed Livestock Products||0.04|
|_βe_|AIDADS Marginal Budget Share at High Income<br>for Energy Services||0.05|
|_βw_|AIDADS Marginal Budget Share at High Income<br>for Processed Timber Products||0.02|
|_βr_|AIDADS Marginal Budget Share at High Income<br>for Ecosystem Services||0.05|
|_βo_|AIDADS Marginal Budget Share at High Income<br>for Other Goods and Services||0.81|

11