rafmacalaba/datause-encoder-data huggingface dataset:
where _Yi_ , _c_ corresponds to the financial characteristics of bank _i_ in country _c_ , which include measures of size, profitability, riskiness, liquidity, and leverage; _CONNECTEDi_ , _c_ is a measure of the connectedness of the bank. We consider two such measures, the first is a dummy variable that takes the value one if at least one of the bank’s directors has been a politician or bank supervisor, and zero otherwise; the second is the fraction of a bank’s board members that has previously been a politician. _X i_ , _c_ are other bank characteristics included as control variables. Since we are not interested in modeling the determinants of bank’s financial characteristics, we typically include just a measure of bank’s size as captured by its total assets (except when the left hand side variable itself is a measure of size). Finally,<sup></sup> _c_ is a country fixed effect included to make sure that the identification comes from the within country differences between connected and unconnected banks, and  _i_ , _c_ is a residual term.

For our benchmark results we estimate the parameters of equation (1) by weighted-OLS, where the weights given to each observation are inversely proportional to the number of banks in a country for which we have information. This ensures that the results are not disproportionably driven by countries with a larger number of banks (see Demirguc-Kunt and Huizinga, 2000). Since bank level data are notoriously noisy, we measure all variables in logarithms to reduce the influence of outliers (variables corresponding to ratios that can plausible take negative values are expressed as the logarithm of one plus the variable). We estimate the parameters of the benchmark model separately for all the banks and banks that have no public ownership.

Table 3 reports the OLS estimates of  for the dummy measure of connectedness. The parameters obtained for the other controls are omitted. Different columns of the table report the coefficients associated with of size, profitability, leverage, and riskiness. Panel A reports the results obtained in the sample of all banks reporting board composition data, while panel B restricts the sample to include only banks with no public ownership.

The coefficients show that connected banks tend to be the largest in the country (Column (1)). Assets of connected banks are about 34 percent larger. Similar results are obtained for other measures of size, such as loans and country ranking (not reported). Connected banks also tend to be more profitable and typically have a return on average assets that is between 0.6 and 0.8 percent higher than the average bank (Column (2)). Regarding balance-sheet structure, leverage is significantly lower among connected banks (Column (3)). The ratio of equity to total assets is 2 percent higher in connected banks than in the average bank, and in the sample of fully privately owned bank this difference increases to 3 percent. Connected banks also seem to take on less risk 10
**<mark>Table 1 Share of employed population working NFEs (non-wage only)</mark>**

|_Country_|Burkina Faso|Ghana|Mozambique|Rwanda|Tanzania|Uganda|Weighted<br>Average|
|---|---|---|---|---|---|---|---|
|Primary employment|11.8|25.9|9.2|9.3|15.7|13.5|16.8|
|Primaryor secondaryemployment|28.8|37.5|20.1|21.4|28.9|21.0|27.6|

_Source: Annex table 1._ In sum, the HE sector in low income countries in SSA is likely to provide employment, either as a primary or secondary activity, to a substantial share of the labor force for the foreseeable future. There is no way to change this picture in the medium term. Instead of expecting this segment to disappear (or even actively discouraging it), policymakers and development professions will need to seek job creation solutions through improvements in the opportunities offered in this sector. In the following sections, we analyze the characteristics of this sector in more depth, focusing on owners of HEs and their households. Individuals for whom HE is either a primary or a secondary activity are included as owners, and when we analyze household livelihoods and incomes in section 5 we also include all economic activities of all members to obtain a full indication of the importance of HEs in household income generation.

# **4. Household enterprises and their owners**

A full understanding of how to promote employment and income growth through the development of HEs requires several perspectives – an understanding of HEs as business, and an understanding of HE ownership as an employment choice. This section presents our analysis on HEs from both perspectives using the survey data.

## **HE enterprises**

In the employment data above, the distinction between HEs and NFEs was usually not justified. But mostly, this does not matter. Over 90 percent of NFEs found through household surveys are HEs, with 7 out of 10 of these reporting no family help at all - just the owner operating the HE (Figure 6). Despite so few people engaged in each enterprise, HEs still provide employment to more than 80 percent of people employed in NFEs including the employees of MEs. HEs are the dominant group in the sector - a point which is often missed in policy discussions and rarely touched upon in the SME literature. As documented below, HEs and MEs are different enterprises.

12
## (USAID, 2005).

This paper also builds upon a large literature on poverty traps in general, and fragility traps in particular. The theoretical foundations of such traps were laid out by Azariadis (1997), and Lopez and Serven (2009) provide some recent empirical tests. Stewart and Brown (2009) portray fragile countries as countries characterized by dysfunctional institutions and conflict situations. Sachs (2007) introduced the idea that, because some developing countries are caught in a poverty trap, they will need significant amounts of aid to emerge. Collier (2007) broadened the concept to include the “conflict trap”, a notion that Zoellick (2008) linked back to the original poverty trap in a way that comes closer to what we develop here.

Finally, the idea that the widely-used performance-based allocation (PBA) method for aid may not be appropriate for fragile states has been raised by Guillaumont et al. (2010), who argue that “vulnerability” should be a criterion. Barungi and Davies (2010) suggest that the PBA formula does not take into account the special features of fragile states. For example, a 2005 study by the World Bank concluded that fragile states “received 40 percent less aid than their policy and institutional performance and poverty would predict.”

# 2 The basic structure of the model

This paper presents a formal equilibrium model in which political instability, insecure property rights, and corruption interact with minimum level of consumption to explain why Africa’s fragile states are caught in a low-level equilibrium trap. We start with a general equilibrium model in which three types of agents interact, namely: households, firms and the government. Population is exogenous, and development, which is endogenously determined, depends on institutional and political factors, and government policies. We consider an economy with a large number of individuals who live three periods in overlapping generations. At each point in time, children, young adults and older adults are alive. For simplicity we posit individuals within a generation (t) to be identical. In each period (t) the economy produces a single good that can be used for consumption or investment in physical capital, Kt.

Agents maximize lifetime utility during the two periods of adulthood, subject to a budget constraint in each period. During young adulthood, individuals work for firms, pay a proportion of their labor income τ t ∈ (0, 1) to the government as taxes, and use the remaining income to fund their own and their children’s consumption, and savings for retirement. Households do not work during their retirement and consume the principal and interest on their savings. In addition 3
Finally, in testing for the consequences of employing the estimation method of instrumental variables (test of the endogeneity or exogeneity of regressors), one acknowledges the trade-off between a possibly biased and inconsistent OLS estimator and the higher asymptotic variance of the IV estimator. The test employed here is the Durbin (1954)-Wu (1973)-Hausman (1978) version of the Hausman statistic, using the OLS estimate of the error variance, as opposed to the IV estimate.<sup>2</sup>

# **4. Data and Results**

<u>Data. We use cross-sectional data from the 2000</u> _Encuesta de Hogares por Muestro_ conducted by the National Statistical Office of Venezuela (OCEI). The working sub-sample is of male children of the head of household, working for wages and between the ages of 15 and 65. The use of only male wage and salary earners, as in some other studies, was decided in order to avoid complications due to gender issues. Using only children of the head of household allows the inclusion of father’s education and/or occupation in the earning function specifications.

The dichotomous instrument, _Zi_ , arising from the 1980 compulsory education reform was constructed by identifying the age in year 2000 (year of the survey) of those individuals who would have been affected by the reform. Given the year of initiation of the reform, affected individuals ( _Zi_ =1) are taken to be those who in year 2000 were in the 15-32 age bracket. Experimentation showed that a dichotomous instrument thus defined

> 2 Under the null hypothesis both estimates are consistent, but the OLS estimate is more efficient. Furthermore, the chosen flavor of the test has the additional advantage of performing better when the instruments are weak (Staiger and Stock 1997; Baum and others 2003).

10
product to the same country the year before. As indicated in Table A-1 in the Appendix, the proportion is 69.8% when considering foreign exports of the same product to other countries and 88.6% when considering foreign exports of other products to the same country.

The geographic and sectoral distributions of the new trade linkages established by Chinese domestic firms over the period are described in Table 3. The export starts are quite diversified in terms of destinations. The US is the main destination for new trade linkages over the period, but it only represents 1.8% of overall export starts, followed by Hong Kong, South Korea, and Japan, with between 1.6% and 1.7% of all export starts. However, new transactions are more concentrated in terms of the province of origin, the most dynamic exporters being, not surprisingly, Guangdong (8.5%) and Zhejiang (7.5%). The export starts are also more concentrated from a sectoral viewpoint: “Nuclear reactors, machinery etc.” account for 10.5% of new transactions over the period in comparison with 6.6% for “Electrical machinery etc.” and 4.4% for “Articles of iron and steel”.

Table 3: Summary Statistics on Domestic Starts: Share in Total Export Starts Over the Period

|Destinations||
|---|---|
|USA|1.8%|
|Hong-Kong|1.7%|
|South-Korea|1.7%|
|Japan|1.6%|
|Malaysia|1.5%|
|Provinces||
|Guangdong|8.5%|
|Zhejiang|7.5%|
|Shanghai|7.0%|
|Jiangsu|7.0%|
|Beijing|6.7%|
|Sectors (HS2)||
|Nuclear reactors, machinery et al.|10.5%|
|Elect machinery et al.|6.6%|
|Art. of iron and steel|4.4%|
|Organic chem.|4.1%|
|Optical, photo.|4.0%|

13
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<u>Table 7: Model Predictions</u>

|||||**Holdout **|**Sampl**|**es**|**Ana**|**ntapur**_→_|
|---|---|---|---|---|---|---|---|---|
||**Est. **|**sample**|**Anan**|**tapur**|**E. Go**|**davari**|**E. G**|**odavari**|
||Data<br>(1)|Model<br>(2)|Data<br>(3)|Model<br>(4)|Data<br>(5)|Model<br>(6)|∆_π_<br>(7)|∆_π,_∆_R_<br>(8)|
|log(_I_)|1.67|1.68|1.06|1.01|2.32|2.60|1.47|2.56|
|_Pr_(_U_)|0.26|0.46|0.51|0.59|0.08|0.29|0.47|0.21|
|_Pr_(_A_)|0.23|0.15|0.47|0.16|0.26|0.10|0.15|0.10|
|_Pr_(_L_)|0.06|0.02|0.01|0.00|0.03|0.12|0.01|0.12|
|_Pr_(_P_)|0.21|0.15|0.01|0.20|0.05|0.19|0.13|0.25|
|_Pr_(_C_)|0.24|0.22|0.00|0.05|0.59|0.30|0.24|0.32|

_Notes:_ All figures are sample means. Log irrigated area, log( _I_ ), and choice probabilities, _Pr_ ( _j_ ), are simulated by drawing multiple values of _ε_ for each borewell and then averaging optimal choices. Column (7) reports mean predictions when each borewell in the E. Godavari sample is assigned well-flow state probabilites ( _πki_ ) drawn at random from the Anantapur sample. Column (8) is the same as column (7) except that the E. Godavari sample is _also_ assigned pipe widths ( _Ri_ ) drawn at random from the Anantapur sample.

6 shows that the model also reproduces, more or less faithfully, the bivariate relationship between area irrigated (relative to borewell plot area) and the coefficient of variation of groundwater supply.

Comparing the remainder of columns (1) and (2) of Table 7 shows that the model is quite successful at predicting choices of transfer arrangements ( _L_ , _C_ , and _P_ ), but far less so when it comes to unconstrained self-cultivation ( _U_ ) versus autarky ( _A_ ). While the latter result may not seem encouraging, it must be emphasized that our model is _extremely_ parsimonious. Moreover, as alluded to earlier, we are not only estimating choice probabilities. Indeed, it is remarkable that our likelihood, which recruits parameters such as _c_ ̄, _λ_ , and _α_ to fit the mean (and variance) of irrigated area without the benefit of any free constant terms (or fixed effects), ends up fitting choices as well as it does. That said, our model is, of course, not an exact description of reality.

30
**Table 4.B. Model comparison: Root mean squared error of oil price forecasts**

(U.S. dollars per barrel)

|..||||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|---|---|
|**Approach**|**Horizon**|**Consensus**|**Futures**|**correlations**|**BVAR**|**OEM**|**learning**|
|Consensus|3|6|...|...|...|...|...|
|Futures|3|*|3.5|...|...|...|...|
|Bivariate correlations|3|*||3.5|...|...|...|
|BVAR|3|*|*|*|13.1|...|...|
|OEM|3||*|*||8.5|...|
|Machine learning|3|*|||*|*|4.1|
|Consensus<br>|6<br>|6.6|...<br>|...|...|...|...|
|Futures|6||5|...|...|...|...|
|Bivariate correlations|6||*|7.5|...|...|...|
|BVAR|6|*|*|*|15.7|...|...|
|OEM|6||||*|7.7|...|
|Machine learning|6||||*||5.6|
|Consensus|9|7.9|...|...|...|...|...|
|Futures|9||6.2|...|...|...|...|
|Bivariate correlations|9|||6.5|...|...|...|
|BVAR|9|*|*|*|21.1|...|...|
|OEM|9||||*|7.4|...|
|Machine learning|9||*|*|*||10.1|
|Consensus|12|9.2|...|...|...|...|...|
|Futures|12||7.2|...|...|...|...|
|Bivariate correlations|12|||6.6|...|...|...|
|BVAR|12|*|*|*|26.6|...|...|
|OEM|12||||*|8.1|...|
|Machine learning|12|*|*|*|*|*|16.1|
|Consensus|15|10.3|...|...|...|...|...|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15|...|...|...|...|...|...|
|BVAR|15|*|...|...|31||...|
|OEM|15||...|...|*|8.4|...|
|Machine learning|15|*|...|...||*|21.4|
|Consensus|18|11.1|...|...|...|...|...|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18|...|...|...|...|...|...|
|BVAR|18|*|...|...|32.7|...|...|
|OEM|18||...|...|*|8.9|...|
|Machine learning|18|*|...|...||*|24.2|
|Consensus|21|11.9|...|...|...|...|...|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations|21|...|...|...|...|...|...|
|BVAR|21|*|...|...|34.4|...|...|
|OEM|21||...|...|*|9.5|...|
|Machine learning|21|*|...|...||*|24.6|
|Consensus|24|12.8|...|...|...|...|...|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24|...|...|...|...|...|...|
|BVAR|24|*|...|...|33.1|...|...|
|OEM|24||...|...|*|10.8|...|
|Machine learning|24|*|...|...||*|23.7|

_Source_ : World Bank.

_Note_ : The forecast period is 2015Q1-2022Q1. Forecasts for OEM are only available at the semi-annual frequency; forecasts for all other approaches are available at the quarterly frequency. Diagonal entries indicate average RMSE estimates for the respective approach. * indicates significant difference between the forecasts of any pair of approaches, at the 5 percent significance level according to an F-test (RMSE).

39
<!-- Start of picture text --> Figure 7c: The Evolution of Access to Adequate Environment<br>Prevalence of Adequacy in Environment<br>81<br>71<br>49<br>45<br>38<br>36<br>32 31<br>17 14 16<br>10<br>5<br>0 1 2<br>BGD, HK BOL IDN NPL ZWE<br>BGD, IFPRI ETH KHM PER<br>Year 1 Year 2<br>Source: Author estimates. Data for Bangladesh from 2010 (HK) and 2011 (HK, IFPRI); Bolivia 2003, 2008; Cambodia 2005, 2010;<br>Ethiopia 2000, 2011; Indonesia 2010; Nepal 2000, 2011; Peru 2005, 2012; Zimbabwe 2005, 2010.<br>Note: (1) Bolivia (2003 and 2008) do not include improved sanitation.<br>(2) Ethiopia, Nepal, Bolivia, and Indonesia do not include community sanitation.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text -->

**Figure 7d: The Evolution of Access to Adequate Health Services**

<!-- Start of picture text --> Prevalence of Adequacy in Health<br>78<br>42<br>35<br>27<br>21 22<br>18 18<br>16<br>13 13<br>8<br>4 5 4<br>0<br>BGD, HK BOL IDN NPL ZWE<br>BGD, IFPRI ETH KHM PER<br>Year 1 Year 2<br>Source: Author estimates. Data for Bangladesh from 2010 (HK) and 2011 (HK, IFPRI); Bolivia 2003, 2008; Cambodia 2005, 2010;<br>Ethiopia 2000, 2011; Indonesia 2010; Nepal 2000, 2011; Peru 2005, 2012; Zimbabwe 2005, 2010.<br>Note: (1) Bangladesh (IFPRI) has Vitamin A supplementation for 6 to 24 month olds only.<br>(2) Indonesia does not have information on prenatal checkups or Vitamin A supplementation.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text --> Children in resource‐poor households are less likely to have access to adequate environment and health services than children in resource‐rich households. Thus, in contrast to the situation with 31
**Figure 6: Cost of core components of education interventions across settings (in 2011 USD)**

<!-- Start of picture text --> Cost (2011 USD) Cost (2011 USD)<br>$30<br>$200<br>$180<br>$25<br>$160<br>$140 $20<br>$120<br>$15<br>$100<br>$80<br>$10<br>$60<br>$40<br>$5<br>$20<br>$0 $0<br>Primary teacher salary Transportation cost per Parental daily wage<br>school/facilitation visit<br>Nepal Kenya<br>Uganda<br>Liberia<br>Indonesia<br>Afghanistan<br>India Kenya Madagascar<br><!-- End of picture text --> Source: All data are from J-PAL (2014a) with calculations by authors, except for teacher salary data, which are averages for civil service teachers in primary education from a database extracted from World Bank Public Expenditure Reviews (World Bank 2014). The reported primary teacher salaries presented in 2011 USD are calculated by converting the teacher salaries in local currency in the various base years (the year the data were collected) to USD in the given base year, before inflating to 2011 USD using the inflation GDP deflator (annual %).

29
Smith, A. (1776). _An inquiry into the nature and causes of the wealth of nations_ (republished, eds. R.H. Campbell and A.S. Skinner. Oxford: Clarendon Press, 1976).

STATSSA (2022). Available at: https://www.statssa.gov.za/?page_id=739&id=1 United Nations (2016). “Leaving no one behind: The imperative of inclusive development.” _Report on the world social situation 2016_ . New York: United Nations.

World Bank (2005). “Empowering people by transforming institutions: Social development in World Bank Operations.” _Social development strategy paper_ . Washington, DC: World Bank.

World Bank (2013). “Inclusion matters: The foundation for shared prosperity.” Washington, D.C.: The World Bank.

<mark>World Bank (2018).</mark> _<mark>South Africa - Systematic country diagnostic: An incomplete transition - Overcoming the legacy of exclusion in South Africa</mark>_ <mark>(English). Washington, D.C.: The World Bank.</mark> <mark>World Bank (2022). World Bank Poverty and shared prosperity Report 2022: Correcting course. Washington DC: The World Bank</mark> **<mark>.</mark>** <mark>World Bank (2022a).</mark> _<mark>Peru Systematic Country Diagnostic Update (English)</mark>_ <mark>. Washington, D.C: The World Bank.</mark> Yonzan, N., A. Cojocaru, C. Lakner, D. Gerszon Mahler, and A. Narayan. A. (2022). “The Impact of COVID-19 on poverty and inequality: Evidence from phone surveys.” _World Bank Data_ (blog), January 18, 2022. https://blogs.worldbank.org/opendata/impact -covid-19-poverty-and-inequalityevidence-phone-surveys 31
Interestingly, recurrent expenditure has a small but positive impact on output. As defined above, recurrent expenditures include salaries and treatment, public spending in goods and services and transfers and subsidies. The positive impact on output reflects the first two items. An increase in salaries and treatment leads to either immediate rise in private consumption or an increase in private savings, ultimately transformed into demand for investment goods. Either way, a rise in salaries and treatment affects output. Public spending in goods and services can also support in a straightforward fashion. Indeed, some components of current spending such as maintaining of roads and schools are important to maintain the quality of the services financed by public capital and contribute to ensuring the profitability of infrastructure investments. Spending on operation and maintenance that keeps existing infrastructure in good condition or spending that contributes to health outcomes and the accumulation of human capital–can promote growth more effectively than capital expenditure per se (Moreno-Dodson 2008). Finally, a reallocation of subsidies to public investment or recurrent spending is growth-enhancing, all things being equal.

There are not many studies of the impact of dynamic fiscal shocks to output on countries similar to Tunisia. The study by Restrepo and Rincon (2006) on Chile and Colombia is an exception. These authors use an SVAR and SVEC model respectively to calculate spending multipliers for Chile and Colombia using quarterly data over 1989 through 2005. They find that a one Chilean peso increase in government spending has a transitory positive effect of 1.9 pesos on real GDP growth, which stabilizes at 1.37 cents. For Colombia, the authors find that a one peso increase in public spending translates, at impact, into a 0.12 increase in the level of GDP and stabilizes at 0.15 peso.

The size of fiscal multipliers depends, among other things, on leakages into saving and imports. Multipliers tend to be smaller for economies more open to trade (i.e., higher leakages into imports) and more susceptible to financial market constraints (i.e., upward pressure on real interest rates), or when it is subject to monetary policy that offsets the fiscal stimulus (e.g., IMF, 2008). Many cross-country studies have found small fiscal multipliers and in some cases multipliers with a negative sign (Christiansen 2008). The most notable studies with “negative multipliers” are found in the literature on expansionary fiscal contraction initiated by Giavazzi and Pagano (1990) and surveyed in Hemming, Kell, and Mahfouz (2002).

Finally, a result that requires further investigation is the small impact of private investment on growth. Indeed, real private investment has a positive but smaller impact on GDP that public 9