rafmacalaba/datause-encoder-data huggingface dataset:
#### **Figure 1: Revealed Capability Intensity Estimates in Selected Sectors**

<!-- Start of picture text --> Machinery/Electronics<br><!-- End of picture text --> <!-- Start of picture text --> Transportation<br><!-- End of picture text --> <!-- Start of picture text --> Requirement Requirement<br>Proximity to Proximity to<br>Markets Markets<br>Access to 1 Natural Access to 1 Natural<br>0.5 0.5<br>Inputs 0 Capital Inputs 0 Capital<br>‐0.5 ‐0.5<br>Market Human Market Human<br>‐1 ‐1<br>Access ‐1.5 Capital Access ‐1.5 Capital<br>Wage Physical Wage Physical<br>Competitiv... Capital Competitiv... Capital<br>Logistics/Co Institutional Logistics/Co Institutional<br>nnectivity Capital nnectivity Capital<br><!-- End of picture text -->

## **Clothing**

<!-- Start of picture text --> Requirement<br>Proximity to<br>Markets<br>1<br>Access to Natural<br>0.5<br>Inputs 0 Capital<br>‐0.5<br>Market Human<br>‐1<br>Access ‐1.5 Capital<br>Wage Physical<br>Competiti... Capital<br>Logistics/Co Institutional<br>nnectivity Capital<br><!-- End of picture text -->

## **Textiles**

<!-- Start of picture text --> Requirement<br>Proximity to<br>Markets<br>1<br>Access to Natural<br>0.5<br>Inputs Capital<br>0<br>‐0.5<br>Market Human<br>‐1<br>Access Capital<br>‐1.5<br>Wage Physical<br>Competitiv... Capital<br>Logistics/Co Institutional<br>nnectivity Capital<br><!-- End of picture text --> Source: Authors’ calculations

# V. Application to Southern Africa

### A. Context

Having established the capability requirements globally and within GVC sectors, we now turn to an application of this methodology to a region that has been largely on the periphery of global value chains: the Southern African Customs Union (SACU) countries, comprising South Africa, Botswana, Namibia, Lesotho and Swaziland. SACU countries rank far behind many other regions in total GVC exports. Table 7 13
Contrary to popular belief, public banks do not appear to surpass private banks in the extent to which they lend to small businesses. This would seem to indicate that the argument that privatization of public banks would hurt small businesses is, at best, weak.

Regarding the impact of foreign bank entry on lending to small businesses we find, consistent with conventional wisdom, that, on average, foreign banks in the four countries generally lent less to small businesses (as share of total lending) than private domestic banks (at least by end of period). However, the difference appears to be primarily due to the behavior of small foreign banks. In all four cases, small foreign banks lent considerably less to small businesses than small domestic banks. In contrast, the difference was considerably smaller for large and medium-sized banks. In fact, after controlling for other factors that might affect lending, large foreign banks actually appear to lend more to small businesses (as share of total lending) than large domestic banks in two of the four case study countries, Chile and Colombia.

Finally, in Argentina and Chile, the two cases where the ratio of banking sector assets to GDP consistently grew over the sample period, lending to small businesses by medium and large foreign banks was positive and was growing more quickly than for similar domestic banks. In contrast, lending by small foreign banks was shrinking and was growing far more slowly than for similar domestic banks.

# **References**

- Barajas, A., R. Steiner, and N. Salazar, "Foreign Investment in Colombia's Financial Sector," In _The Internationalization of Financial Services: Issues and Lessons for Developing Countries,_ edited by Stjin Claessens and Marion Jansen, Boston, Mass.: Kluwer Academic Press, 2000.

- Berger, A., R. De Young, H. Genay, and G. Udell, "Globalization of Financial Institutions: Evidence from Cross-Border Banking Performance," _Brookings-Wharton Papers on Financial Services,_ No. 3, 2000.

- Berger, A. N., L. Klapper, and G. F. Udell, "The Ability of Banks to Lend to Informationally Opaque Small Businesses", mimeo, World Bank, Washington DC, 2000.

- Berger, A., A. Saunders, J. Scalise, and G. Udell, "The Effects of Bank Mergers and Acquisitions on Small Business Lending", _Journal of Financial Economics,_ 50(2), pp. 187-229, 1998.

- Berger, Allen N. and Gregory F. Udell, Relationship Lending and Lines of Credit in Small Firm finance, _Journal of Business,_ 68, pp. 351-382, _1995._

21
<!-- Start of picture text --> 8<br>8 78 tT 357577 Th 74 73 TT 78 17 TT 5<br>—- =a a er =Frrcsee “Fess oo<br>5 59 6<br>Cc<br>= me 1970<br>S 4 mm 1980<br>o mm 1990<br>ao<br>2<br>a<br>ee Rc peea ao°a oe oeye on?<br>agare” oe pta at ear<br>ow” roe Pas ow<br>2 aos whao?<br><!-- End of picture text -->
The solution to this set of coupled difference-differential equations yields a geometric distribution for establishment size at time  conditional on survival where  (|) is the parameter of the size distribution. The solution of this system can be used to derive the moments of the growth rate of the number of products an establishment owns. The expected growth rate and the variance of growth rate of the number of products conditional on initial size 0 are given as

The expected growth rate of total size relative to aggregate expenditure can also be determined. Size of a −type establishment is given as  () grows due to the growth of the number of products.

# 3 Model’s Implications

The model has four novel implications, each of which explains a regularity observed in the Chilean dataset and several other empirical studies on industry evolution. Below, I describe three of these regularities, present evidence from te Chilean dataset on each one, and show how the model explains them. The fourth regularity, which is the long right tail of the size distribution, is explained in the simulation results section.

12
**Figure 4. Distribution of pre-earthquake asset index**

<!-- Start of picture text --> Asset Index Density Before Earthquake<br>0 20 40 60 80 100<br>Asset Index<br>EFSA I<br>EFSA II<br>kernel = epanechnikov, bandwidth = 3.7832<br>.04<br>.03<br>.02<br>.01<br>0<br><!-- End of picture text --> Source: Own computations using February 2010 (EFSA I) and June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. Asset index weights are calculated using EFSA I pre-earthquake assets.

To compare the retrospective answers in both surveys, we have built an asset index, obtained from a multiple component analysis using pre-earthquake housing characteristics and household durable goods. This asset index is thus a proxy for household wealth. Weights are estimated with the February survey and are used to build asset index in both surveys. Figure 4 shows that pre-earthquake asset index distributions are very similar. So, recall bias might be small in the June survey.

# _(c) Assets_

Based on the June survey, an asset index is calculated using a wider set of pre-earthquake dichotomous variables, namely some durable goods not declared in the February survey and access to basic utilities.<sup>14</sup> Table 2 (in Appendix) reports both weights and contributions to inertia. Weights have signs consistent with interpretation of the first component as an assetpoverty index. In directly affected areas, contribution to inertia of lighting appears to be particularly high (26.7%). Water source also contributes in a large extent to inertia (18.9%).

> 14 Note that all our results have been replicated using a more restrictive definition of the asset index for which water source, water filtration, lighting, type of toilet and tools/material were not included in the multiple correspondence analysis. Regression results (presented later) in particular were not very different when using this alternative asset index. What is more, results do not vary very much when using factor analysis instead of multiple correspondence analysis to build the index.

11
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda However, no specific data was available to confirm this supposition. Under the circumstances, two variants of the baseline scenario were modeled to look at the sensitivity of the projections to mortality rates assumptions: 1) “baseline variant 1”, using the general population mortality rates; and 2) “baseline variant 2”, assuming lower mortality rates for civil servants. Mortality data for various populations groups in India were used as a benchmark for variant 2.<sup>33</sup> A 70% mortality multiplier was applied to mortality rates for old age pensioners, resulting, e.g., in an increase of life expectancy for a 60 year old men in the base year by 3 years. See Table 18 for the assumed life expectancy changes in variant 2.

**Table 18: Projected life expectancy for civil servants**

|**Year**|**2003**|**2020**|**2050**|**2075**|
|---|---|---|---|---|
|_Male_|||||
|At Age 60|16.3|16.7|18.0|20.5|
|At Age 65|13.3|13.6|14.6|16.8|
|_Female_|||||
|At Age 60|17.0|17.1|18.6|22.9|
|At Age 65|13.8|13.8|15.2|18.8|

**Source** : WB calculations based on PROST

# **_Assumptions related to system participants_**

As mentioned before, this modeling exercise focused on the assessment of the system’s future liabilities leaving out the existing arrears. All benefits are assumed to be paid in full starting from the base year.

The following benefits are modeled as separate categories:

- Regular pensions paid to old age pensioners;

- CPG payments to newly retired old age pensioners (all new retirees are assumed to choose the CPG and a reduced pension<sup>34</sup> );

- Regular pensions paid to survivors.

All other benefits – gratuities paid to survivors, non-CPG gratuities and other types of benefits – are modeled in an aggregate way as pension system “other expenses”. No administrative costs are assumed to be borne by the system. Since the current system is non-contributory (the liabilities uniquely financed by the central or local budgets), no revenues are modeled in the baseline scenario.

Active employees and old age pensioners are modeled by tracking each age and gender cohort over time, using the initial distributions of system participants by age and gender, the assumed hiring policy for active members, retirement probabilities and mortality

> 33 A mortality study of annuitants conducted by the Life Insurance Corporation in India showed that, for example, life expectancy at age 55 for the covered group was 5 years higher than for the general population (see World Bank (2001))

> 34 Ugandan experts confirmed that almost everybody chooses this option.

- 83 -
# **Figure 3: Distribution of Final Round Real Profits by Treatment Status**

<!-- Start of picture text --> Distribution of Sept-Nov 2011 Real Profits<br>0 50000 100000 150000 200000 250000<br>Real Monthly Profits (LKR)<br>Control Assigned to Treat in 10, 20 or 40K<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> 25
Overall, the implication of this finding is that Cote d'Ivoire's resource mobilization problem is not merely one of extending taxes to an untaxed sector - for all businesses are taxed. Instead, the problem is one of reforming taxes to encourage private sector growth, and improving enforcement on firms already paying taxes to increase and reduce leakage. For all the attention that has been given to regulation and red tape, it is clear that the complexity and inconsistency of tax regulation and enforcement constrains business more than any other regulations (recall table 6).

Taxation of Formal Enterprises As indicated above, the government of CMte d'Ivoire derives its revenue from a complex network of taxes. During the 1980s, the burden of taxes has come to fall more heavily on a diminishing numiber of private enterprises, with the heaviest rate among formal enterprises falling on SMEs -- for whom taxation appears to have crossed the Lafferian turning point, past which progressively higher effective tax rates tend to yield lower revenues. Table 7 also suggests that, as profits of many firms shrank, the government has increased indirect (i.e. not based on profit) taxation. This could result in an untenable burden for struggling, marginal firms.

Table 7: Firm **Size** and Tax Burden (in CFAF) <!-- Start of picture text --> I Category//Year Taxes Paid (mn) 128.111133.21 1982 1983 142.75 145.37 159.84 153.37 175.75 1984 1985 1986 1987 1988i<br>NuTber of Firms 799 1 806 802 793 790 7 7<br>LargeIEnterprises Tax/Finm (000's) 16 0 . 3 4 165 . 2 7 177.99 183.31 202.33 196.88 224.74<br>Large Enterprises. -_ _<br>(priv. & pubtic) Value Addedmn) 691.43 724.23 747.19 806.72 825.94 M.49 815.33<br>Tax/VA (X) 18.67 18.39 19.10 18.02 19.35 19.85 21.56<br>(Tax-Prof. Tax)/VA 16.05 16.24 16.92 15.20 16.05 16.51 18.89<br>Taxes Paid (mn) 17.33 17.98 21.18 24.70 23.54 20.77 20.63<br>Number of Firms 1,684 1,680 1,624 1,631 1,571 1,484 1,510<br>Tax/Firm 10.29 10.70 13.04 15.14 14.98 14.00 13.66<br>Small & Nediua -...<br>Enterprises Vatue added (mn) 105.37 118.39 118.33 121.16 101.50 84.55 76.63<br>Tax/VA tX) 16.45 15.20 17.48U 24.341 27.85 27.10 26.92<br>(Tax-Prof. Tax)/VA 12.50 10.76 13.281 19.841 21.60 22.61 18.35<br><!-- End of picture text --> Source: BDF/CECPS Licensing: The licensing **tax** _(patente),_ is collected from virtually all enterprises with any kdnd of a fixed location, and is roughly assessed on the basis of the business premises' rental value. It 34
# **Table A22:  Latinobarometro (LBO)**

**Data Provider** Latinobarometro **Description** Nongovernmental organization based in Santiago, Chile **Website** www.latinobarometro.org **Data Source** Latinobarometro surveys **Type** Survey **Respondents** Households **Frequency** Annual since 1995 **Coverage** Sample of Latin American countries **Public Access** Full dataset is commercially available.  Averages of sub-indicators are publicly available in this spreadsheet. **Description** Latinobarometro administers a common questionnaire to households in Latin America with questions on areas such as Economy and International Trade, Integration and Regional Trading Blocks, Democracy, Politics and Institutions, Social Policies, Civic Culture, Social Capital and Social Fraud, the Environment, and Current Issues.

|**Voice and Accountability**|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|
|Satisfaction with democracy|X|X|X|X|X|X|X|X|X|
|Trust in Parliament|X|X|X|X|X|X|X|X|X|
|**Political Stability and Absence of Violence**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||||
|Trust in Government|X|X|X|X|X|X|..|..|X|
|**Regulatory Quality**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Rule of Law**||||||||||
|Trust in Judiciary|X|X|X|X|X|X|X|X|X|
|Trust in Police|X|X|X|X|X|X|X|X|X|
|Have you been a victim of crime?|X|X|X|X|X|X|X|X|X|
|**Control of Corruption**||||||||||
|Frequency of corruption|X|X|X|X|X|X|X|..|..|
|**Country Coverage**|**18**|**18**|**18**|**18**|**17**|**17**|**17**|**17**|**17**|
|**Year of publication**|**2007 **|**2006 **|**2005 **|**2004 **|**2003 **|**2002 **|**2000 **|**1998 **|**1996**|

60
of a Randomized, Placebo-Controlled Multiple Sclerosis Clinical Trial.” _Neurology_ 44 (1): 16–16.

Olken, Ben. 2015. “Promises and Perils of Pre-Analysis Plans.” Journal of Economic Perspectives 29(3): 61-80.

Oster, E. 2013. “Unobservable Selection and Coefficient Stability: Theory and Validation.” Working Paper 19054. National Bureau of Economic Research.

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41
all assets into the same index, but the second specification covers all three disaggregated asset indexes (Table 6). Results in Table 7 also come from the second specification, estimated with our preferred HTIV model for various subsamples. In all regressions, standard errors are clustered at the household level. Tables are structured so that results in the first column are obtained from the random effects estimator, which is inconsistent under conditions (i) and (ii) shown section 3.1. Under the same conditions, results in the second and third columns are consistent because they are obtained from the fixed effects and the HTIV estimators; results in the third column are our preferred results because the HTIV estimator is consistent _and_ more efficient than the fixed-effects estimator.

Table 5 shows how the aggregated asset index affects highest grade completed. The aggregated index has the expected sign, suggesting wealth has positive effects on children’s education. The positive coefficient on consumption expenditure, a proxy for household income, also suggests positive income effects. Among other controls, both having educated parents and access to a school in the village help children reach higher grades; the effects on child education of a 5 percent increase in total expenditure and an increase in parental education by one more level (such as primary to secondary school) are identical. Educated parents may expect a larger return from sending children to school, so they may not consider the opportunity cost of schooling for their children to be high. Similarly, children who live near a school may both attend school and occasionally take part in farm-household activities. This would lead to the positive effect for ‘school in village’ even if the child has to work in agriculture. After controlling for endogeneity, the effect of parental education on children’s highest grade completed becomes more than quadruple the result found by the random effects model. This implies the potential endogeneity of parental education and shows the importance of preferring the HTIV method to the fixed effects model.

Interestingly, having a male head of household adversely affects children’s grade level, but girls are more likely to reach higher grades than boys. This is consistent with evidence from other 19
# **Figure 5: Management Practices Score by Senior and Non-Senior Bureaucrats**

<!-- Start of picture text --> A. CS-Autonomy Score<br>Senior<br>Non-senior<br>-2 -1 0 1 2<br>Management Score<br>B. CS-Incentives/Monitoring Score<br>g g<br>Senior<br>Non-senior<br>-4 -2 0 2<br>Management Score<br>1<br>.8<br>.6<br>.4<br>Cumulative probability<br>.2<br>0<br>1<br>.8<br>.6<br>.4<br>Cumulative probability<br>.2<br>0<br><!-- End of picture text -->

**Notes:** CS-Autonomy (CS-Incentives) is an aggregate index of management questions relating to roles and flexibility (monitoring and incentives). Responses by senior/non-senior bureaucrats are averaged across all sub-topics and converted into z-scores before averaged into a single index of management. Officials are designated senior based on whether they classify themselves as a 'Director (Head of Division) or Acting Director' or as a 'Deputy Director or Unit Head (Acting or Substantive)'.
not hold all other factors constant, and the correlation between consumption and GDP/capita does not identify causation, it does suggest a strong positive linear relation between consumption and income at the world level for corn, soybean, rapeseed, and oil palm, and correlation coefficient of about 0.75 for rice and wheat (Table 2). The positive correlation computed above suggests that income is an important factor affecting consumption, and thus prices. Note that although globally the correlation between consumption and income is positive, in some regions it may be negative. Most notably, corn, rice, and wheat consumption in China declined during 2001 to 2007. Below we use income elasticity of demand from existing literature to incorporate income growth into our analysis.

**Table 2. Correlation between income and consumption of major agricultural commodities in major**

**regions** **<u>during 2001 to 2007</u>**

|**Region **|**Corn**|**Soybean**|**Rapeseed**|**Rice**|**Wheat**|**Oilpalm**|
|---|---|---|---|---|---|---|
|**Argentina**|0.87|0.99||0.39|-0.04||
|**Brazil**|0.89|0.89||-0.13|0.80|0.97|
|**China**|-0.53|0.97|0.10|-0.94|-0.44|0.93|
|**EU27**|0.68|-0.63|0.96|0.80|0.16|0.97|
|**India**|0.78|0.90|0.59|0.51|0.66|0.28|
|**US**|0.82|0.53|0.81|0.71|-0.50|0.89|
|**ROW**|0.98|0.35|0.98|0.98|0.86|0.99|
|**World**|**.98 **|**.98 **|**.94 **|**.74**|**.76 **|**.99 **|

Figures 4a-4f depict world consumption of various coarse grains and oil crops.

1. It illustrates the upward trend in global consumption from 2001 to 2007 for the various crops. From 2001 to 2007, demand grew, and the demand curve for the different crops shifted up and to the right. For some crops, however, the growth rate was larger than for others. Whereas corn demand grew by about 30%, rapeseed demand grew by almost 100% from 2001 to 2007.

2. In addition, growth was not symmetric across regions. Whereas globally consumption of all crops increased with income (at the world level, income is positively correlated with consumption, and world income grew throughout the period investigated), in some regions consumption of certain crops decreased. For example, corn, rice, and wheat consumption in China went down by 12.7%, 23.7%, and 20.9%, respectively, although global consumption increased by 24.0%, 3.2%, and 4.3%, respectively.

18