rafmacalaba/datause-encoder-data huggingface dataset:
Table SI.3. Determinants of civil war risk: 1946-2003 Natural resource endowments _mitigating_ the problem of weak states – indicator variables

|Context variable|Natural resources<br>Context variable|Fearon and Laitin<br>(2003) civil war|Prio \ Uppsala civil<br>war|
|---|---|---|---|
||Interaction|||
|Risk of coupd’état|Indicator variables ofproduction|||
||Lujalaprimarydiamondproduction(observations 5135)|-0.261(0.463)|-0.425(0.298)|
||Risk of coupd’état|0.199**(0.092)|0.214***(0.061)|
||**Interaction**|**-0.767*** (0.245)**|**-0.079 (0.169)**|
||Lujala secondarydiamondproduction(observations 5135)|0.324(0.325)|0.182(0.232)|
||Risk of coupd’état|0.262***(0.096)|0.230***(0.065)|
||**Interaction **|**-0.479***(0.160)**|**-0.072(0.108)**|
||Lujala oilproduction dummy- offshore(observations 5135)|0.062(0.358)|0.177(0.221)|
||Risk of coupd’état|0.194**(0.095)|0.226***(0.063)|
||**Interaction**|**0.011 (0.167)**|**-0.028 (0.102)**|
||Lujala oilproduction dummy- onshore(observations 5135)|-0.388(0.292)|0.048(0.208)|
||Risk of coupd’état|0.152(0.104)|0.203***(0.069)|
||**Interaction**|**0.116 (0.134)**|**0.025 (0.082)**|
|Coup d’état history in<br>past 5years|Quantitative measures of resources endowments|||
||Indicator variables ofproduction|||
||Lujalaprimarydiamondproduction(observations 6775)|-0.090(0.365)|-0.212(0.268)|
||Couphistory|0.331(0.261)|0.413**(0.183)|
||**Interaction **|**0.636 (0.831)**|**0.355 (0.666)**|
||Lujala secondarydiamondproduction(observations 6775)|0.400(0.271)|0.401*(0.206)|
||Couphistory|0.570**(0.273)|0.489**(0.205)|
||**Interaction**|**-0.952 (0.591)**|**-0.127 (0.357)**|
||Lujala oilproduction dummy- offshore(observations 6775)|0.166(0.337)|0.299 (0.217)|
||Couphistory|0.421(0.265)|0.527***(0.190)|
||**Interaction**|**-0.371 (0.693)**|**-0.477 (0.460)**|
||Lujala oilproduction dummy- onshore(observations 6775)|-0.219(0.250)|0.230(0.184)|
||Couphistory|0.027(0.345)|0.413*(0.242)|
||**Interaction**|**0.724 (0.456)**|**0.052 (0.315)**|
|Militaryregime|Indicator variables ofproduction<br>|||
||Lujalaprimarydiamondproduction<sup>20</sup> (observations 6832,6857)|0.084(0.339)|-0.068(0.254)|
||Militaryregime|0.537**(0.271)|0.744***(0.187)|
||**Interaction**|**-**|**-0.552 (1.049)**|
||Lujala secondarydiamondproduction(observations 6857)|0.468*(0.252)|0.515**(0.200)|
||Militaryregime|0.754***(0.289)|0.932***(0.212)|
||**Interaction**|**-1.103* (0.602)**|**-0.632* (0.351)**|
||Lujala oilproduction dummy- offshore(observations 6857)|0.250(0.327)|0.450**(0.213)|
||Militaryregime|0.627**(0.281)|0.959***(0.191)|
||**Interaction **|**-0.986 (0.831)**|**-1.480***(0.568)**|
||Lujala oilproduction dummy- onshore(observations 6857)|0.055(0.243)|0.383**(0.184)|
||Militaryregime|0.535(0.351)|1.008***(0.244)|
||**Interaction **|**-0.091 (0.489)**|**-0.520 (0.328)**|

> 20 There are no large civil war onsets (Fearon and Laitin) in countries that either have civilian governments, produce no primary diamonds or both.

38
The risk measure performs better in predicting bank restructuring than the more straightforward measure of loan growth. Estimation of the previous binomial logit model with the average annual loan growth during the period 1992-96 as explanatory variable instead of our risk measure gives an insignificant coefficient for the loan growth variable (see Appendix, Regression 4 for further details). Although the risk measure is based upon loan growth during 1992-96, it follows that the risk measure contains additional useful information, namely the change in the efficiency of the bank (see equation (7)). In fact, the correlation between average annual loan growth and the risk measure is only 32%. The above suggests that the risk measure developed in this paper can be a useful tool for assessing the risk of a bank.

# 8. **Conclusions**

We have used a linear programming technique called Data Envelopment Analysis (DEA) to estimate the efficiencies of the commercial banks in Indonesia, Korea, Malaysia, the Philippines and Thailand for the years 1992-96. In this particular case, we found that in assessing the overall performance of a bank it is not enough to look at efficiency measures only, but that risk factors should be taken into account as well. The best bank is not simply the most efficient producer of loans, but is a bank that combines high efficiency with low risk-taking. We therefore introduced a measure of risk-taking based upon the well-established efficiency measure that enabled us to assess the overall performance of banks.

Our findings are that foreign-owned banks took little risk relative to other banks in the East Asian region, and that family-owned banks, were among the most risky banks, together with company-owned banks. Our risk measure does better than credit growth alone in predicting bank restructuring after the crisis of 1997. Not only do we find that banks that were restructured had excessive credit growth, but we also find that the restructured banks were mostly family-owned or company-owned, and almost never foreign-owned.

The results of our risk-taking model indicate that family and company ownership of banks should be discouraged, and that foreigners should be encouraged to become core group of investors of banks. These conclusions should, however, be interpreted with caution given the sensitivity of our technical methods. In particular, our analysis might have overlooked that some banks operated under more restrictions than others. It is, however, likely that foreign banks were not favoured by any of these rules and restrictions. We therefore argue that banking regulation 23
adoption of cutting-edge technologies in the Food Processing sector. FTM are 5 percent less likely of adoption computer testing for input testing. In addition, we observe large differences in adopting power equipment controlled by computers or robotics for packaging.

Given a large number of technologies across several business functions, we summarize the overall technological sophistication using four different technology indexes. Table 5 provide the descriptive statistics of technology indexes and the differences of these indexes between male- and female-managed businesses. The unconditional differences in technology indexes are positive, but when countries are controlled the coefficients become negative, but the differences are insignificant. This may be because in some countries a large share of male-managed businesses rely on less sophisticated of technologies. When region, size, and sector are controlled, we find female-managed businesses tend to adopt similar level of GBF technologies, but less sophisticated SBF technologies.

Table 5: Difference in technology indexes between male- and female-managed firms managers

||Ma|le|Fem|ale|D|iference||
|---|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|Uncond.|Cond.|Cond.|
|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|GBF - EXT|2.34|0.83|2.39|0.85|0.06*|-0.04|-0.03|
|GBF - INT|1.77|0.62|1.86|0.68|0.09***|-0.03|-0.02|
|SBF - EXT|2.09|0.98|2.18|1.04|0.08|-0.04|-0.09**|
|SBF - INT|1.64|0.72|1.70|0.76|0.06*|-0.03|-0.07**|

Note: Table shows averages for baseline using sampling weights. The fifth column presents the unconditional difference, the sixth column presents the coefficients of linear regressions of each variable on female top management controlling for country fixed effects, and the seventh column adds sector and size group dummies. GBF EXT = General Business Function Extensive Margin, GBF INT = General Business Function Intensive Margin, SBF EXT = General Business Function Extensive Margin, SBF INT = General Business Function Intensive Margin. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

# **5 Results**

## **5.1 Baseline**

The descriptive statistics show that a significant gender gap in productivity exists after controlling for region, sector, and size. However, the conditional gender gap may still reflect other factors. To understand the gender gap in productivity, Table 6 presents the results from estimating Equation (1). Column (1) presents a simple specification without including any capital or technology-related covariates. Column (2) adds the management human capital index, and column (3) includes the logarithms of capital per worker and total employment.

16
**Table 17: Number of workers in presumed comparative advantage industries by size groups of enterprises**

**(weighted estimates of totals) , UBI 2002 and UBI 2009**

||**_A. All comp _**|**_arative advanta_**|**_ge industries_**<br>|<br>||**_E. Comme_**|**_rcialfarming_**||||
|---|---|---|---|---|---|---|---|---|---|---|
|**Size Group**<br>**(number of**|Number o|f workers|% share<br>total|in sector|Average<br>growth|Number|of workers|% shar<br>total|e in sector|Average<br>growth|
|<br>**workers)**|2002|2009|2002|2009|rate(%pa)|2002|2009|2002|2009|rate(%pa)|
|less than 5|9,942|48,897|29.8|29.8|56.0|185|7,393|0.4|20.6|556.6|
|5‐20 workers|13,496|34,902|11.7|21.3|22.7|3,469|15,040|7.2|41.9|47.7|
|21‐100 workers|7,550|18,047|6.5|11.0|19.9|1,971|3,228|4.1|9.0|9.1|
|More than 100|84,424|62,293|73.1|38.0|‐3.7|42,323|10,228|88.3|28.5|‐10.8|
|Total|115,412|164,140|100.0|100.0|6.0|47,948|35,888|100.0|100.0|‐3.6|
||**_B. Compara_**|**_tive advantage_**|**_primary ind_**|**_ustries_**||**_F. Food a_**|**_nd beverages_**|**_industry_**|||
|**Size Group**<br>**(number of**|Number o|f workers|% share<br>total|in sector|Average<br>growth|Number|of workers|% share|in sector total|Average<br>growth|
|<br>**workers)**|2002|2009|2002|2009|rate(%pa)|2002|2009|2002|2009|rate(%pa)|
|less than 5|500|12,460|1.0|26.1|341.5|4,294|10,112|9.7|14.0|19.4|
|5‐20 workers|4,459|20,005|8.9|41.9|49.8|6,671|8,733|15.1|12.1|4.4|
|21‐100 workers|2,220|4,976|4.4|10.4|17.7|3,399|8,261|7.7|11.5|20.4|
|More than 100|42,736|10,358|85.6|21.7|‐10.8|29,758|44,895|67.4|62.4|7.3|
|Total|49915|47799|100.0|100.0|‐0.6|44121|72001|100.0|100.0|9.0|
||, <br>**_C. Compara_**|, <br>**_tive advantage _**|<br>**_manufacturin_**|<br>**_g industries_**||, <br>**G. Thega **|, <br>**rments indust**|<br>**ry**|||
|**Size Group**<br>**(number of**|Number o|f workers|% shar<br>total|e in sector|Average<br>growth|Number|of workers|% share<br>total|in sector|Average<br>growth|
|<br>**workers)**|2002|2009|2002|2009|rate(%pa)|2002|2009|2002|2009|rate(%pa)|
|less than 5|8,946|35,532|16.0|32.1|42.5|4,581|24,773|78.2|86.5|63.0|
|5‐20 workers|8,130|13,096|14.6|11.8|8.7|1,071|3,347|18.3|11.7|30.4|
|21‐100 workers|4,387|10,795|7.9|9.8|20.9|205|282|3.5|1.0|5.4|
|More than 100|34,308|51,249|61.5|46.3|7.1||240|0.0|0.8||
|Total|55,770|110,673|100.0|100.0|14.1|5,857|28,642|100.0|100.0|55.6|
||**_D. Compara_**|**_tive advantage_**|**_services_**<br>% h|i t|A|**H. Land t**|**ransport servi**|**ces**||A|
|**Size Group**<br>|Number o|f workers|sare<br>total|n secor|verage<br>growth|Number|of workers|% share|in sector total|verage<br>growth|
|**(number of**|||||||||||
|<br>**workers)**|2001|2009|2001|2009|rate(%pa)|2001|2009|2001|2009|rate(%pa)|
|less than 5|496|906|16.0|16.0|11.8|420|333|8.0|8.0|‐2.9|
|5‐20 workers|907|1,801|9.3|31.8|14.1|869|1,287|12.7|30.8|6.9|
|21‐100 workers|944|2,276|9.7|40.1|20.2|944|1,875|13.8|44.8|14.1|
|More than 100|7,380|687|75.9|12.1|‐13.0|4,592|687|67.3|16.4|‐12.1|
|Total|9,726|5,669|100.0|100.0|‐6.0|6,824|4,181|100.0|100.0|‐5.5|

53
differential impact on the growth rates of firms across different states. In this section we try to provide additional insight into why we are finding seemingly different results from the vast finance and growth literature that has established a causal impact of finance on firm growth.

<mark>First, our results may be reflecting the low level of financial development of the country overall. On the one hand, states within India exhibit large differences in income and quality of institutions. On the other hand, India does not rank very high in terms of financial development in the global economy along a number of different indicators. According to the Global Financial Inclusion (2011) database, only 35% of adults in India have an account at a formal financial institution and only 11% of adults save at a financial institution giving India a rank above 75 along both dimensions in a sample of 147 countries. In terms of private credit provided by deposit money banks as a share of GDP, India ranked 52 out of 103 economies in 1983 at the beginning of our sample period and 69 out of 171 economies in 2005, the end of our sample period. Thus it may be the case that the Indian banking system seems to be uncompetitive and inefficient according to global rankings making within country differences marginal. If lending decisions are politicized, financial development as captured by bank credit to state domestic product is not likely to be very meaningful. Indeed, Rajan and Prasad (2008) also emphasize that the Indian financial sector if “unleashed from government strictures” can have an “enormous multiplier effect on economic growth.”</mark> <mark>Second, it could be that there are other exacerbating factors in the economy such as corruption or infrastructure weaknesses that are greater in magnitude than the differences in financial sector development across states which we are not focusing on in this paper.</mark> <mark>Finally it could be that firm idiosyncratic factors such as managerial skill (e.g. Bloom, Eifert, Mahajan, Mckenzie, and Roberts, 2013) or firm characteristics at birth (e.g. Ayyagari,</mark> 30
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2442 AFirms's-Eye View of Policy and<br>Fiscal Reforms in Cameroon|Bernard Gauthier<br>Isidro Soloaga<br>James Tybout|September 2000|L. Tabada<br>36896|
|WPS2443 The Politics of Economic Policy<br>Reform in Developing Countries|Richard H. Adams Jr.|September2000|M.Coleridge-Taylor<br>33704|
|WPS2444Seize the State, Seize the Day:<br>State Capture. Corruption, and<br>Influence in Transition|Joel S. Hellman<br>Geraint Jones<br>Daniel Kaufmann|September 2000|D. Billups<br>35818|
|WPS2445 Subsidies in Chilean Public Utilities|Pablo Serra|September 2000|G. Chenet-Smith<br>36370|
|WPS2446 Forecasting the Demand for<br>Privatized Transport: What Economic<br>Regulators Should Know, and Why|Lourdes Trujillo<br>Emile Quinet<br>Antonio Estache|September 2000|G. Chenet-Smith<br>36370|
|WPS2447 Attrition in Longitudinal Household<br>Survey Data: Some Tests for Three<br>Developing-Country<br>Samples|Harold Alderman<br>Jere R. Behrman<br>Hans-Peter Kohler<br>John A. Maluccio<br>Susan Cotts Watkins|September 2000|P. Sader<br>33902|
|WPS2448 On 'Good" Politicians and "Bad"<br>Policies: Social Cohesion,<br>Institutions, and Growth|Jo Ritzen<br>William Easterly<br>Michael Woolcock|September 2000|A. Kibutu<br>34047|
|WPS2449 Pricing Irrigation Water: A Literature<br>Survey|Robert C. Johansson|September 2000|M. Williams<br>87297|
|WPS2450 Which Firms Do Foreigners Buy?<br>Evidence from the Republic of<br>Korea|Caroline Freund<br>Simeon Djankov|September 2000|R. Vo<br>33722|
|WPS2451 Can There Be Growth with Equity?<br>An Initial Assessment of Land Reform<br>in South Africa|Klaus Deininger<br>Julian May|September 2000|33766|
|WPS2452 Trends in Private Sector Developme<br>in World Bank Education Projects|nt Shobhana Sosale|September 2000|S. Sosale<br>36490|
|WPS2453 Designing Financial Safety Nets<br>to Fit Country Circumstances|Edward J. Kane|September 2000|K. Labrie<br>31001|
|WPS2454 Political Cycles in a Developing<br>Economy: Effect of Elections in<br>Indian States|Stuti Khemani|September 2000|H. Sladovich<br>37698|
<!-- Start of picture text --> 8%<br><!-- End of picture text --> <!-- Start of picture text --> 100% Chinese owned<br>8%<br>21%<br>Joint venture with local<br>private firms<br>71%<br>Host government<br>contracts<br><!-- End of picture text --> _Source: Shen and Zhai._ The average project size of Chinese investment is about US $4 million, based on available data from six host countries. There is some notable diversity in project size among the participating host countries, which correlates largely with the different sectoral spreads in those countries. For instance, Nigeria, with a number of large capital-intensive projects in energy and infrastructural sectors, has the largest project size among the participating host countries, with an average $10 million per project. Following Nigeria, Rwanda shows an average project size of $5 million, Ethiopia $2.3 million, Liberia $1.2 million, and Zambia $0.9 million. Ghana, with nearly 60% of the projects concentrated in the service sector, has the smallest project size, averaged at $163,000.

Most responding country investment authorities provided data that allow comparing Chinese investment with the overall FDI they attracted and with FDI from other major source countries. These data show that, in terms of size and sector, Chinese investment is similar to that from other emerging economies, notably India and South Africa, the two other top source countries mentioned by host countries IPAs. The data also show that investment originating from western countries is of relatively large size and more engaged in resource-extracting sectors. Figure 9 compares the project sizes of all major FDI source countries, with China being the smallest of all.

**Figure 9: Average Size of Chinese Investment Projects Compared with that of Projects from Other Origins** (in million USD)

15
Promoting Democracy in Fragile States

# **1 Introduction**

One way to characterize the “quality” of elections is in terms of the clarity with which they convey voters’ policy preferences (Powell, 2007). In “fragile states,” which are defined by a lack of consolidated political authority operating under the rule of law (North, Wallis and Weingast, 2009), a few factors interfere with election quality in these terms. These include problems in citizens’ access to information, opportunities to coordinate, and vulnerability to intimidation by patrons of political factions. We develop these ideas theoretically and then use a field experiment to study the effects of strategies to improve voter information, coordination, and security on electoral quality in post-war Liberia.

Our study is motivated by the fact that citizen-oriented democracy promotion is a major component of external aid to “fragile” states like Liberia, where institutional weakness is either a pre-cursor or product of violent conflict (OECD, 2011). The United States has since 2007 spent about $13 billion per year in official development assistance to fragile and confict-affected states, with about 10% of this going to citizen-oriented governance and democracy promotion programs.<sup>1</sup> Based on OECD accounting, about 12% of the $127 billion dollars in 2012 development aid went to governance programming.<sup>2</sup> Such programs are based on assumptions about individuals’ desire for democratic expression combined with the belief that “elections with integrity” contribute to “the ability of a society to resolve

> 1Of course the lion’s share has gone to programs in Iraq and Afghanistan. These figures come from examining United States official development assistance data for congressional years 2007-2010, as posted to http://www.usaid.gov/results-and-data/progress-data/data. Fragile states are identified as those that are shown in the UCDP-PRIO Armed Conflicts Database (v.4) as experiencing political violence since 1990.

2From http://www.oecd.org/statistics/.
grants, and loans with a grant element of more than 25 percent. This definition excludes most IMF lending, and a substantial share of World Bank lending, which goes to middleincome borrowers charged near-market rates of interest.

Following standard practice in the empirical literature on natural resource rents, we proxy for these using fuel exports as a share of gross national income, and a similar measure for exports of ores and metals. As with aid/GNI, we average these values for the 1999-2006 period for which ERM data are available.

Control variables used in one or more tests include the initial (1999) ERM value, log of initial (1999) income per capita, average annual per capita income growth over the 1999-2005 period (based on purchasing power parity income data from the _World Development Indicators_ ), and political rights. Table 1 shows summary statistics for the instruments and other variables, for the 100-country main sample used in the analysis.

# **4. Analysis**

The first column of Table 2 presents results of OLS estimation using a simple specification. The impact of aid, fuels and metals on ERM is tested controlling only for initial (1999) ERM values. Aid and fuels both have negative and statistically significant coefficients, consistent with the hypothesis that the availability of sovereign rents weakens incentives to improve tax policy and administration. These impacts are fairly modest in size: an increase in aid’s share of GNI by about 26 percentage points, or an increase in fuel exports’ share of GNI of about 60 percentage points, are each associated with a half-point reduction in the 1-6 ERM scale. The impact of aid is also larger than for fuels when measured in standard deviation units. A standard-deviation increase in aid 8
# **5.2. Predictive Power of Estimated GDP-Employment Elasticities**

To evaluate the predictive power of the four estimation approaches discussed in section 3, this section uses the elasticities estimated using data until year 2011 (described in the previous section), to perform an out-of-sample evaluation of predicted employment growth based on the estimated elasticities and observed GDP growth. These predicted employment growth values are benchmarked against observed employment growth for years 2012-2016. In addition, the predictive power of average annual elasticities estimated from more recent data (years 2000-2011) is also tested. To assess and compare predictive power, Mean Absolute Prediction Percentage Error (MAE) and Root Mean Squared Prediction Percentage Error (RMSE) are computed for each of the country-sector employment predictions generated with the elasticity estimation approaches. Table 1 shows the results of this analysis.

Table 1. GDP-Employment Elasticities Predictive Power ( **bolded numbers** identify the estimation methodology with highest predictive power in each case)

||Average Ann<br>Full<br>Sample|ual Elasticities<br>Recent<br>Sample|Decomposition<br>based<br>Approach|Regression<br>based<br>Approach|SAM Multiplier<br>Approach<br>(EYL,i,j,H= 1.0)|
|---|---|---|---|---|---|
|Manufacturing||||||
|MAE|**2.34**|**2.50**|3.47|2.73|<br>8.64|
|RMSE|15.39|15.56|21.56|**11.28**|47.96|
|Services||||||
|MAE|**0.38**|**0.43**|0.83|1.06|1.39|
|RMSE|**0.54**|**0.58**|1.22|1.54|2.94|
|Agriculture||||||
|MAE|1.51|1.73|**1.33**|2.50|7.10|
|RMSE|4.31|4.78|**2.11**|8.23|17.59|

The results imply that the average annual elasticity estimates have the highest predictive power, by at least one of the performance measures utilized in the evaluation, for the manufacturing and services sectors. For agriculture, the decomposition-based approach has the highest predictive power, but with MAE and RMSE values closely followed by the average annual elasticity estimates. Therefore, across sectors the average annual elasticity estimates have either the highest or second highest predictive power among the methods tested. It is worth noting that using the full or more recent (short) sample to produce 17