rafmacalaba/datause-encoder-data huggingface dataset:
18 _Table 5 :_ **_Replication of the Davoodi and Zou model : Decentralization and economic growth_** _<u>Panel A : Davoodi and Zou, and Ebel and Yilmaz decentralization variables</u>_

|Subnational spending<br>ratio net of grants|<br>0,246<br>(0,254)|Multip<br>Per cap|le regre<br>ita GDP|ssions<br>growth||<br>-0,083<br>(0,466)|Simpl<br>Per capi|e regre<br>ta GDP|ssions<br>growt|h|
|---|---|---|---|---|---|---|---|---|---|---|
|Subnational tax<br>autonomy<br>Decentrali-<br>||-0,052<br>(0,828)|||||-0,121<br>(0,667)||||
|Subnational tax<br>sharing<br>zation<br>measure<br>|||0,05<br>(0,228)|||||-0,071<br>(0,023)|||
|Subnational fiscal<br>dependency<br>variables||||-0,093<br>(0,072)|||||0,019<br>(0,698)||
|Subnational non-tax<br>autonomy|||||0,198<br>(0,013)|||||0,217<br>(0,024)|
|GDP per Capita|-0,093<br>(0,032)|-0,06<br> (0,056)|-0,071 <br> (0,046)|-0,063 <br> (0,041)|-0,032<br> (0,144)||||||
|School enrollment<br>Control|-0,081<br>(0,538)|0,007<br> (0,953)|-0,188 <br> (0,280)|-0,156 <br> (0,398)|-0,162<br> (0,334)||||||
|Population Growth<br> <br>Variables|-0,905<br>(0,522)|-2,12<br> (0,091)|-4,03<br> (0,013)|-3,34<br> (0,024)|-1,15<br> (0,330)||||||
|Investment|0,406<br>(0,010)|0,294<br> (0,014)|0,42<br> (0,020)|0,466<br> (0,009)|0,437<br> (0,001)||||||
|AdjR-square|0,9221|0,9467|0,9927|0,9045|0,969|0,836|0,9016|0,8909|0,824|0,8686|
|Control variable joint<br>significance<br>P-Value|0,0325|0,0282|0,0081|0,029|0,0185||||||
|Nom. Obs.|30|30|30|30|30|30|30|30|30|30|

P-Values are in parentheses, showing levels of significance.

_Panel B : New decentralization variables_

||Multi<br>Per cap|ple regres<br>ita GDPg|sions<br>rowth|Sim<br>Per ca|ple regress<br>pita GDP|ions<br>growth|
|---|---|---|---|---|---|---|
|Subnational revenue<br>autonomy<br>Decentrali-|0,112<br>(0,084)|||0,223<br>(0,000)|||
|Subnational own<br>revenue ratio<br>zation<br>measure<br>||0,083<br>(0,679)|||0,386<br>(0,061)||
|variables<br>Subnational dependent<br>revenue ratio|||-0,449<br>(0,182)|||-0,767<br>(0,003)|
|GDP per Capita|-0,037<br>(0,217)|-0,058<br>(0,065)|-0,059<br>(0,052)||||
|School enrollment<br>Control|0,001<br>(0,996)|0,036<br>(0,755)|-0,22<br>(0,245)||||
|Population Growth<br>Variables|-1,783<br>(0,297)|-1,699<br>(0,164)|-3,193<br>(0,048)||||
|Investment|0,274<br>(0,030)|0,26<br>(0,031)|0,382<br>(0,003)||||
|AdjR-square|0,9729|0,93|0,9379|0,9808|0,8971|0,946|
|Control variable joint<br>significance P-Value|0,198|0,0885|0,0195||||
|Nom. Obs.|30|30|30|30|30|30|

P-Values are in parentheses, showing levels of significance.
**Table 7.** Risk Exposure Limits Imposed on Licensed MFIs, 2002

|_Item_|_Benin:_<br>_credit unions and authorized_<br>_MFIs_|_Ghana:_<br>_licensed banks, NBFIs,_<br>_and credit unions_|_Tanzania:_<br>_licensed banks_<sup>_a_</sup>|
|---|---|---|---|
|Ceiling on total loans|200 percent of total deposits|No ceiling specified|No ceiling specified|
|Unsecured loans ceiling|No ceiling specified|No ceiling specified|10 percent of core capital|
|Single borrower limit|10 percent of total deposits|� Unsecured loans: 10 percent of<br>net worth<br>� Secured loans: 25 percent of net<br>worth for banks; 15 percent of net<br>worth for NBFIs and S&Ls|25 percent of core capital|
|Connected lending limit|20 percent of total deposits|� Unsecured loans (individual): 0.67<br>percent of net worth<br>� Secured loans (individual): 2<br>percent of net worth<br>� Unsecured loans (aggregate): 10<br>percent of net worth|Less than or equal to the value of the<br>officer’s shares and deposits; in total<br>less than or equal to 25percent of the<br>bank’s net worth|
|Ceiling on fixed assets|No ceiling specified|No ceiling specified|10 percent of net worth|

a. Data for Tanzania are as of June 2003. _Source:_ Gallardo and Randhawa (2003); Ouattara (2003); Steel and Andah (2003).

# **_Liquidity Risk_**

64. Credit unions and licensed MFIs in Benin and licensed banks, and deposit-taking NBFIs in Ghana and Tanzania are required to maintain primary liquidity reserves against deposit liabilities. The requirement is 15 percent of total deposits in Benin and 20 percent of demand deposits in Tanzania (table 8). A related requirement in Tanzania is that total loans may not exceed 80 percent of total deposits. For S&Ls in Ghana the primary reserve is 10 percent, plus a 15 percent secondary reserve on total deposits. Ghana’s Credit Union Association requires member credit unions to maintain a 10 percent reserve on deposits. In an effort to improve the solvency of rural banks in Ghana, the secondary reserve requirement on total deposits was raised to 52 percent in 1996, in addition to the 10 percent primary reserve requirement. By restricting risky loans and forcing investment in treasury bills with high interest rates (more than 30 percent per year), this restraint helped raise the number of rural banks rated as satisfactory. Nevertheless, it severely hampered their ability to serve their intended rural clientele, and the authorities subsequently lowered the secondary reserve requirement to 20 to 30 percent in 2002, depending on the loan recovery rate of each individual rural bank. Credit unions and licensed MFIs in Benin must comply with a specified liquidity ratio, namely, current assets must not exceed 80 percent of current liabilities, and a maturity matching standard, where medium- to long-term assets must be fully covered by matching medium- and long-term liabilities. This is a fairly difficult standard to meet because of the dearth of medium- to longterm deposits or borrowed funds in Benin as in many other developing countries.

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In terms of our variable of interest (age), older individuals are more likely to believe that combating climate change should be prioritized over economic growth, but they are also less willing to pay higher taxes or give up a part of their income to achieve that objective. The positive correlation between age and beliefs that climate action should be prioritized at the expense of economic growth is robust to the inclusion of other determinants of climate beliefs. In fact, when all the additional confounding variables are included in the model, the marginal effect of age increases relative to the baseline specification. In the case of willingness to pay for climate action, the negative association with age is robust to the inclusion of most confounding variables but becomes insignificant once we account for differences in risk aversion in the specification that includes all the confounding variables.

# **6. Sensitivity analysis**

This section examines the sensitivity of our results to the potential bias introduced by the endogeneity of climate beliefs in our climate action regressions, as well as the sensitivity of the age gradients to alternative framing of the climate action questions.

# _Addressing the endogeneity of climate beliefs_

One concern with the results presented in <u>Table 5 has to do with the possibility that perceived risks</u> of climate change for one’s self or children could be correlated with unobserved characteristics of respondents that affect their willingness to pay for climate action or their beliefs about the tradeoff between climate action and economic growth. For example, political conservatism, which we do not capture with LiTS data, could be correlated with less pessimistic views about the risk of future climate impacts and, at the same time, a lower willingness to support climate action, either politically or financially.

To account for such unobservable factors, we instrument the perceived risks to one’s self or children with past exposure to climate shocks based on the question “ _Have you personally experienced disruption or damage due to flooding, drought, or other natural disasters?_ ” Regressing the perceived risk for one’s self (children) on the instrumental variable, conditional on other variables in the model, results in _t_ statistics associated with the coefficient of the instrumental variable of 16.4 and 17.4, respectively, confirming that our instrument is correlated with the endogenous variable, conditional on other model covariates. The exclusion restriction for this 19
# _3.4 Country-specific differences in ECA countries_

To test whether the results described above hold within the diverse ECA sample, we conclude by conducting country-specific analysis using the LiTS data, which covers most relevant countries in two waves. The results, shown in Table 5, produce a heterogeneous picture, with 17 countries exhibiting an S-shaped relation between age and well-being. For all other countries, the cubic term is insignificant, but the squared-term specification yields significant coefficients, suggesting a U-shape relation; a heterogeneity also evident from the variation in minima (32.9 to 51.3) and maxima (67.4 to 101.8).

**Table 5. Country-specific differences of life satisfaction in age minima and maxima: LiTS data (2006-2010)**

|Country|Minimum|Maximum|Observations|
|---|---|---|---|
|_Countries with age minimum a_<br>|_nd maximum (S shape)_<br>|||
|Albania|37.4|81.3|1973|
|Bosnia and Herzegovina|42.2|70.7|1988|
|Czech Republic|39.5|79.0|1977|
|Hungary|38.5|84.6|2028|
|Moldova|40.5|92.0|1943|
|Montenegro<br>|37.2<br>|77.6<br>|1790<br>|
|Poland<br>|39.6<br>|84.1|2557|
|Romania|39.6|80.8|2030|
|Serbia|41.8|93.2|2420|
|Slovak Republic|39.7|79.4|1975|
|Turkey|36.2|73.2|1970|
|Ukraine|44.3|92.9|2369|
|Azerbaijan|40.1|78.2|1978|
|Estonia|40.1|95.2|1977|
|Kyrgyz Republic|32.9|67.4|1964|
|Latvia|41.8|91.7|1981|
|Lithuania|44.0|101.8|1968|
|_Countries with age minimum (_<br>|_U shape)_<br>|||
|Belarus|43.8||1587|
|Bulgaria|48.1||1981|
|Croatia|44.8||1878|
|FYR Macedonia<br>|38.3||1967|
|Slovenia|49.2||1917|
|Armenia|37.8||1899|
|Georgia|51.3||1978|
|Kazakhstan|45.7||1938|
|Russian Federation|49.8||2477|
|Tajikistan|40.4||1968|
|Uzbekistan|45.0||2443|

_Note:_ The dependent variable is self-rated individual life satisfaction (5-point scale). Age is specified using linear, squared, and cubic terms. The controls are gender, employment status (employed versus not employed as the reference), marital status (married, not married as the reference), education (medium level education and high level education, with low level education as the reference), income quintiles, self-reported health (5-point scale), and year dummies.

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|Burkina Faso|231.1|270.7|234|0.54|
|---|---|---|---|---|
|Chad|87.3|123.2|99|0.79|
|Guinea|246.4|289.0|251|0.57|
|Mali|280.6|281.4|244|0.054|
|Niger|216.5|264.2|228|0.70|

Turning now to comparisons at more aggregate levels, Figures 7, 8 and 9 compare the EBP geospatial estimates with direct estimates at the regional level. Figure 8 shows the same results as Figure 7 but excludes out-of-sample areas when aggregating the EBP estimates at the regional level. We decided to explore this latter approach considering the lower correlation between unit context model estimates and the unit-level model estimates for out-of-sample areas in Burkina Faso. Both Figures 7 and 8 aggregate the model-based estimates using WorldPop weights, while the direct estimates are aggregated using survey weights. Figure 9 remedies this inconsistency by using survey weights when aggregating the model-based estimates from target areas to regions. Overall, the results show that the model-based estimates are aligned well with direct estimates at the regional level. Some discrepancies are to be expected, however, because modelbased estimates are affected by shrinkage.

Figure 7: Small area estimates vs direct estimates at regional level (using WorldPop weights for aggregation) <!-- Start of picture text --> 1<br>.8<br>Burkina Faso<br>.6<br>Chad<br>Guinea<br>Mali<br>.4<br>Niger<br>.2<br>0<br>0 .2 .4 .6 .8 1<br>Direct estimates<br>EBP model estimates<br><!-- End of picture text --> Figure 8: Small area estimates vs direct estimates at regional level (including only in sample target areas and using WorldPop weights for aggregation).

21
events, for instance induced by changes in the El Niño Southern Oscillation weather pattern, some of the gains in poverty reduction are at risk of reversal. The economic recession induced by the 2019 drought coupled with subsequent inflation, and not least the onset of the COVID-19 pandemic in 2020, led to a poverty rate of 26.9% in 2021, more than three percentage points higher than pre-pandemic levels (23.5% in 2019) (World Bank, n.d.-b, 2023). While by 2022, poverty had fallen to 24.7%, the increases in poverty seen after this series of shocks highlight Paraguay’s vulnerability in the face of more severe and frequent weather shocks.

Paraguay’s climate is characterized by high humidity and warm temperatures throughout the year, with hot and rainy summers, as well as high temperature variability enabling both heat waves and frost across the country (World Bank, 2021). Between 1991 and 2020, annual average temperatures in Paraguay have increased by 0.29°C per decade, with particularly large increases in the summer months (World Bank, n.d.-a). Extreme weather events, such as flooding during the summer months (November to December), and heat waves or droughts, especially during December to February, are increasingly common. Warm spells and the number of heat days (days with temperatures above 30°C) have also become more frequent over time and are projected to further increase based on climate change projections at different emission scenarios (Magrin et al., 2014; World Bank, 2021). At the same time, inter-annual precipitation variability has been increasing, with greater rainfall in the summer months leading to flooding, and an increase in the number of days with heavy rainfall.

A combination of the increasing frequency and intensity of short-term weather shocks and the country’s economic structure makes Paraguay particularly vulnerable to the impacts of climate change (Coronel et al., 2015; Lovino et al., 2021; World Bank, n.d.-a). For instance, agriculture, forestry and fishing, a sector that is extremely dependent on changes in weather patterns and climate hazards (World Bank, 2021), contributed 11.6% to the country’s GDP in 2021, and employed 18.9% of the population in the fourth quarter (INE, n.d.-a; World Bank, n.d.-c). Farming in Paraguay is predominantly rain-fed, and thus, subject to extreme precipitation events and heat waves that pose large risks for crop production and agricultural yields, or can lead to livestock loss (World Bank, 2021). Furthermore, around 90% of producers are small rural farmers, whose income and consumption heavily dependent on agriculture and feel immediate economic and food security consequences of reductions in agricultural output while possessing limited capacity to adapt to negative consequences (Benitez Rodriguez, Wolf, Trotter, & Gurgel, 2023; Ervin & Gayoso de Ervin, 2019). Simultaneously, excessive rain or flooding can destroy crops and affect domestic food markets, leading to an increase in food prices that may eventually translate into a decrease in food consumption. Furthermore, heat shocks can exacerbate existing 6
**Figure 4:** Perception of households' social level in comparison with own social environment

<!-- Start of picture text --> (a) By classes (b) By quintiles<br>Poor Vulnerable Lower M-class M-class U-class Q1 Q2 Q3 Q4 Q5<br>Very Rich Rel Rich Middle Rel Poor Very Rich Rel Rich Middle Rel Poor<br>Very Poor N/A Very Poor N/A<br>100 100<br>80 80<br>percent60 percent60<br>40 40<br>20 20<br>0 0<br>2006 2013 2006 2013 2006 2013 2006 2013 2006 2013 2006 2013 2006 2013 2006 2013 2006 2013 2006 2013<br><!-- End of picture text --> _Source:_ Authors’ calculations based on Morocco’s household surveys.

In both rounds of the survey, the majority of households did not consider themselves as richer than the average households belonging to the social contexts in which they lived. It is worth noting that the share of households that considered themselves as poorer than other households was particularly high in the first quintile, and then declined quasi-monotonically through to the top one. Nonetheless, there are important nuances to this general trend. For example, while the (largely pessimistic) subjective perception recorded in 2013 among the poorest classes had slowly declined, as compared to 2006, something had clearly changed among the newly graduated vulnerable and the lower-middle class households. In both cases the (correct) perception of ranking in the middle declined considerably, proportionally raising their awareness of being relatively poorer than other households. Also, the middle classes considered that their perceived status had decreased; however, 70 percent of respondents from this social class did not feel poorer than other households. Finally, the “class-consciousness” of upper-class households declined slightly in 2013 but still stood above 80 percent.

The sense of frustration and perception of non-improvement shared by many Moroccans is further corroborated by an analysis of specific issues of concern to Moroccans in the course of their daily lives. For the sake of brevity, we decided to report only four results: two relating to the type of expenses that posed a problem for households over the past 10 years, for example, schooling and transports; and two relating to future issues of concern, for example, lower purchasing power and criminality (Figure 5).

16
Within the 38 non-empty stratum we then randomly allocated one-third to training only, onethird to training with a grant conditional on completion, and one-third to control, again subject to a quota of 40 firms per D.S. getting chosen to be offered training.

Randomization was done by the authors by computer, so any differences are due to pure chance. Table 1 shows that randomization succeeded in generating groups with similar observable characteristics.

# 3. **The training and take-up**

# **3.1 Training content**

The training program we use is the International Labour Organization (ILO’s) Start and Improve Your Business (SIYB) program. This program is designed to meet the needs of smallscale entrepreneurs in developing countries, and started in Eastern Africa in 1977 (ILO, 2003). More than 4.5 million people in 95 countries have participated in SIYB training.<sup>7</sup> In each country the materials are customized to local languages and context. We use the three main packages in the SIYB program:

- Generate Your Business (GYB) is a three day training course designed to help potential entrepreneurs decide if they should start a business, to generate feasible business ideas, and to choose the best idea from among this set of choices.

- Start Your Business (SYB) training works with potential entrepreneurs who want to start their own business and already have a feasible business idea. The training is a five day course covering the main aspects of starting a business, including selection of products and pricing, organization of staff, purchasing of equipment and other inputs needed to get started, and financial planning. Participants produce a business plan that they can use as a step-by-step guide to starting their business.

- Improve Your Business (IYB) is a five day course which trains existing business owners who want to develop their business. The modules covered are marketing, buying, costing, stock control, record keeping, and financial planning.

In addition to these core modules, we added one day of training of a more technical nature. The purpose of this was to provide exposure to some relatively high-return sectors in

> 7 See: http://www.ilo.org/empent/areas/start-and-improve-your-business/WCMS_159435/lang--fr/index.htm.

8
small amounts of electricity in their respective areas of jurisdiction. A few large industries have private generation facilities producing power for their own use.

**Figure 2  Energy sources used in electricity                        Figure 3  Energy sources used in generation in South Africa                                                      electricity generation in the world**

<!-- Start of picture text --> Oil 2% Oil 4%<br>Coal 6%<br>Coal<br>Gas <0.01% 7%<br>Gas<br>Renewables 0.1%<br>Renewables<br>Hydro/Storage Hydro/Storage 29% 37%<br>Nuclear<br>Nuclear<br>92%<br>1% 22%<br><!-- End of picture text --> South Africa is blessed by abundant coal. The country has the world’s seventh largest recoverable coal reserves--approximately 55 billion short tons, about 5 percent of the world total.<sup>10</sup> It is the world’s sixth largest coal producer—245.3 million short tons (mmst) in 2002. Out of the 171.6 mmst of coal consumed in 2002, 90 percent was used for electricity generation and the production of synthetic fuels.<sup>11</sup> South Africa, on the other hand, does not have natural gas fields. As a result, the profile of energy sources used in South Africa for the generation of electricity looks quite different from that of the rest of the world (figures 2 and 3). The bulk of South Africa’s electricity is generated from coal fired stations which in 2003 accounted for 92 percent of the gross energy sent out (202.5 TWh out of a total of 219.2 TWh—figure 2). During the same year, nuclear power accounted for 6 percent (12.7 TWh), with bagasse, hydro and pumped storage making up the remaining 2 percent (4 TWh).<sup>12</sup> Gas turbines made a negligible contribution.<sup>13</sup> In the rest of the world, during 2002, coal accounted for 37 percent and natural gas for 22 percent of electricity generation.

Eskom is by far the largest domestic consumer of coal. In 1994, the power utility used 110 mmst out of the total of 242.8 mmst produced by the South African mines. By 2009, it is expected that South Africa will burn between 120 mmst and 130 mmst a year

> 10 In recent years, the reliability of this figure has been questioned.  In 2004, the DME initiated a study to assess the country’s true reserves.  Energy Research Centre (University of Cape Town). 2004. _Energy for Sustainable Development: South African Profile_ .  Phase 1 final report.

> 11 Energy Information Administration (2005).  Country Analysis Briefs: South Africa

> (www.eia.doe.gov/emeu/cabs/safrica.html).

> 12 National Electricity Regulator (2003).  Electricity Supply Statistics for South Africa. Pretoria.

> 13 This might change in the near future as imported gas from Mozambique comes on stream.

11