rafmacalaba/data-use-mentions-extended huggingface dataset:
grand forms of corruption, as well as state capture<sup>9</sup> by elites and private interests (see Figure 9). The indicator appears to have stabilized at low levels – in contrast with other regions such as EAP or ECA, which have witnessed improvements. Recent survey data on citizens’ corruption perceptions also point to the resilience of corruption in SSA: more than half of respondents reported a perception that corruption was on the rise in the 2019 Transparency International Global Corruption Barometer for Africa (GCB), which covers 35 African countries, and 59% of respondents believed that their government was “doing a bad job at tackling corruption”.

**Figure 9. Trends in control of corruption**

<!-- Start of picture text --> Control of Corruption (percentile rank: 0 to 100)<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EAP ECA LAC MENA SA SSA<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br><!-- End of picture text --> _<mark>Source:</mark>_ <mark>Worldwide Governance Indicators (WB).</mark> _<mark>Definitions:</mark>_ <mark>Percentile rank ranges from 0 (lowest) to 100 (highest).</mark> _<mark>Regions:</mark>_ <mark>EAP (East Asia & Pacific); ECA (Europe & Central Asia); LAC (Latin America & Caribbean); MENA (Middle East & North Africa); SA (South Asia); SSA (Sub-Saharan Africa).</mark> _<mark>Note:</mark>_ <mark>All
br>FIN<br>SVK<br>CHE (2008) Unclassified BEL<br>CZE DNK<br>NLD CHE (2008)<br>HUN<br>CZE<br>GRC (2007)<br>ITA CHL (2008)<br>POL FRA (2008)<br>TUR<br>AUT<br>CHN<br>ZAF (2008) NLD<br>CHL (2008) LUX<br>MEX (2007) DEU<br>0 5 10 15 20 KOR (2008)<br>JPN<br>0% 10% 20% 30% 40% 50%<br>Source: OECD, Main Science and Technology Indicators Database, June 2011.<br><!-- End of picture text --> 33
perspective. We conduct a robustness check using the comprehensive financial development index of Svirydzenka (2016) as an alternative measure of financial deepening.

We include several control variables in our regressions that might affect carbon emissions per dollar of GDP, broadly following De Haas and Popov (2023). We control for the level of economic development, proxied by GDP per capita. And since the relationship between economic development and carbon emissions can be non-linear owing to the so-called environmental Kuznets curve (Hu et al. 2018), we also include the squared term of GDP per capita. To account for country and market size, which might affect the attractiveness for innovative investments, we use population (De Haas and Popov, 2023). Finally, the relationship between financial deepening and CO2 emissions per dollar of GDP might be affected by financial crises, because banks might be retreating from more risky, innovative, and potentially less carbon-intensive investments (Hardy and Sever 2021). Furthermore, financial crises, which make access to capital more difficult, might discourage investments—including investments in environmentally friendly technologies (Jalles 2024). To account for the potential effect of crises, we also include a dummy variable for banking crises from Nguyen et al. (2022) among the vector of control variables. Overall, we limit the number of control variables to retain the largest possible sample of countries. To account for time-invariant country-specific factors that might influence CO2 emissions (i.e., renewable energy potential, fossil fuel reserves, urbanization), we include country fixed effects. And, to account for time-specific factors that might influence CO2 emissions in all countries during a particular year, we add time fixed effects.

We explore responses of CO2 emissions per dollar of GDP to financial deepening conditional on four variables capturing different dimensions of a country’s institutional environment. First, we use the _number of climate change laws_ from the Climate Change Laws of the World database of the Grantham Research Institute to proxy environmental regulation. Second, we use the _rule of
irrigated land (0.86 against 0.27 for autarkic and 0.07 for landlord households). Unfortunately, the survey did not collect information on the prices paid for leasing land, thus preventing us from making inferences on the associated welfare effects. With tenants‟ profits per hectare more than double those by autarkic households or landlords, descriptive data suggest that rental transfers land to more productive production, something to be explored econometrically below.

At a descriptive level, comparing land rental to sales markets points to a number of differences. First, both output and profits per hectare by land purchasers are significantly lower than for those in autarky, suggesting that land sales may be motivated by factors other than a desire to increase productivity of land use. Also, while those renting in land receive the lion‟s share (52%) of their income from agriculture, those purchasing land obtain some 80% of their income from non-agricultural sources. Operation and impact of land sales markets may thus be structurally quite different from those for rental.

# **4.4 Econometric evidence on land market operation**

Results from the ordered probit regression for land rental and sales market participation are reported in col. 1 and 2 as well as 3 and 4 of table 4, with and without ability respectively. To interpret coefficients,

> 15 T-tests suggest that most of the differences in means for those on the supply- and the demand side and autarkic households are significant at conventional levels but are omitted here to reduce clutter. They are available from the authors upon request.

> 16 If sellers or lessors have left rural communities, descriptive statistics may be biased. Even if they remained in rural areas, land owners who no longer engage in agricultural production may not e captured in the LSMS survey where questions on participation in agricultural land markets appear in the agricultural module that is administered only to those who currently cultivate agricultural land (Deininger and Jin 2008).

> 17 A number of countries have initiated promising efforts to foster structural
br>Transf. made, biggest gift bf. Oct09 [MK]|428.75|1402.69|0.00|0.00|1000.00|2835|
|<br>Transf. received, biggest gift bf. Oct09 [MK]|482.44|1617.00|0.00|0.00|1150.00|2835|
|Net transf. made, biggest gift bf. Oct09 [MK]|‐58.76|1943.54|‐600.00|0.00|600.00|2835|
|Has fixed deposit account|0.07|0.25|0.00|0.00|0.00|2835|
|Not interviewed in follow‐up|0.10|0.30|0.00|0.00|0.50|3150|

Data based on two surveys conducted in February to April 2009 (baseline) and July to August 2010 (endline), and on administrative records of our partner institution. See Appendix B for details of the variable definitions.
that need-based free college combined with institutional funding for these supports might be the most cost-effective policy. A cautionary tale, however, comes from Oreopoulos and Petronijevic (2019), who find that these supports help students realize the need for greater effort but have little effect on college continuity or graduation. The reason is that students respond not by raising effort but by expecting less of themselves.

Moreover, our counterfactuals have assumed some highly favorable—perhaps unrealistic— conditions, as discussed before. One of them is no capacity constraints in higher education. Free college, however, would likely run into capacity constraints and force institutions to ration access. Bucarey (2018) explores the potential effects of free college ( _gratuidad_ ) in Chile, where it was introduced in 2016 for the bottom 60 percent of the income distribution. Data prior to 2016 shows that when low-income students are given additional financial aid, institutions respond by becoming more selective, thereby leading to a _lower_ share of low-income students. Using his structural model, Bucarey (2018) predicts that capacity-constrained institutions would follow a similar strategy under free college. We have also implicitly assumed that college quality remains the same after college becomes free. This may not happen if the policymaker reimburses colleges for less than the per-student average cost. Indeed, Murphy et al (2019) provide evidence that

> 23See, for instance, Bettinger (2004), Dynarski (2003), Hoxby and Turner (2013) and the references therein, as well as the surveys by Avery et al (2019), Deming and Dynarski (2009), Dynarski and Scott-Clayton (2013), Long (2008), Page and Scott-Clayton (2016).

> 24See Bettinger et al (2019), Castleman and Long (2016), Cornwell et al (2006), Dynarski (2000, 2004, 2008), Scott-Clayton (2011), Scott-Clayton and Zafar (2016), and the references therein.

> 25These programs vary across states in terms of eligible institutions (community colleges v. four-year
respondents are more likely to overstate the number of outages, though during widespread outage events, survey-based estimates of outage duration were more accurate than during more typical periods.

The results contribute to a growing literature investigating the sensitivity of survey results to design features and other potential sources of bias. For instance, Beegle et al. (2010) find that reference periods and mode (i.e., recall vs. diary-based data collection) can materially impact the results of consumption and welfare surveys, Bardasi et al. (2010) find similar relationships for labor force surveys, and Heath et al (2021) show large reference period and interview frequency effects in labor force surveys. In all cases, these studies establish the extent of qualitatively large and economically significant differences due entirely to survey design.

# I.II – Electricity Provision in Tajikistan

The Soviet Central Asian Power System (CAPS) was built in the 1970s and included what is now the independent country of Tajikistan. The system provided nearly universal electricity in the region, but production and distribution investments were optimized without regard to country borders. With independence, Tajikistan and other countries in Central Asia entered complex bilateral agreements for fuels, electricity, and water. Disagreements between the member countries led to a partial disintegration of the CAPS, and ultimately, Tajikistan’s exit from most cross-border arrangements including for electricity and gas. Official policy aimed for energy self-sufficiency with heavy reliance on hydroelectric power. However, low administered prices and a lack of alternative sources led to widespread use of electricity for heating in Tajikistan, leading to serious recurring electricity shortages in the winter months.

**Figure 1: Satisfaction with Electricity Provision in Europe and Central Asia**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>
increase). The schemes are revenue neutral for the government, meaning that the incremental government revenue after the introduction of the policies will be recycled to the economy.

**Table 3: Schemes to recycle savings or increased government revenues**

|**Scenario Name**|**Scenario definition**|
|---|---|
|Excise tax|Use the savings or revenue to cut excise or indirect tax rates on all goods and<br>services consumed in Bangladesh|
|Income tax|Use the savings or revenue to cut income or direct tax rates imposed on<br>individuals and firms in Bangladesh|
|Lump-sum|Transfer the savings or revenue to households increasing their disposable income|
|Investment|Invest in the economy to increase the total capital stock in Bangladesh|

A large number of other options, such as using the savings for public consumption (or providing larger fiscal space), using the savings for reducing public debt, and using the savings to subsidize or finance clean energy technologies, are possible. However, we have not included them here because these additional schemes may not be necessarily better than the ones we considered here. Reviewing a large number of existing studies on the recycling of carbon tax revenues, Timilsina (2018) implies that using the savings or new revenues for public consumption would be the least preferred option because of its costs (GDP and welfare loss). The study also shows that recycling revenues for cutting public debt or financing clean energy technologies would not be efficient from the options considered here.

# **3.3 Data**

The main data required for this analysis are as follows:

- (i) Social Accounting Matrix (SAM) for Bangladesh

- (ii) Elasticity of substitution and other parameters

- (iii) Direct government transfers (subsidies) made to electric utilities in Bangladesh

- (iv) Prices of domestically supplied natural gas in Bangladesh and delivered price of LNG in India

The latest SAM available for Bangladesh is for
|Income|NUTS1|No|
|LFS|2009-2013|Imputed Income|NUTS2|Yes|

This paper applies well established econometric techniques of survey to survey imputation (Elbers, Lanjouw, and Lanjouw, 2003) to estimate poverty rates at the NUTS2 level (26 regions in Turkey) by combining information from the SILC with the Turkish Labor Force Survey (LFS). This approach is necessary since, as in most other countries in the world, income collected by the LFS includes only wage income from employment,<sup>8</sup> which is insufficient for measuring of total household income used for welfare measurement. By definition, total household income should include both labor and non-labor components. In the LFS, important sources of non-labor income such as social assistance, pensions, asset liquidation or private transfers are missing. Total household income and household characteristics from the SILC are used to impute total household income into the LFS. Moreover, the fact that Turkey has both the LFS and SILC conducted in parallel allowed the use of different vector of coefficients between 2006-2011.<sup>9</sup> In the case of the 2005, the coefficients from 2006 are used (given that there are concern regarding the quality of the first year of implementation of the SILC 2006, and in 2012 and 2013, the coefficients of the 2011 are used). As a result of the impuation, poverty is measured as total household income poverty in per capita terms. Another advantage of using the LFS is the availabliity of CPI at the NUTS2 level in Turkey which allows for spatial deflation.

> 7 Turkish Statistical Institute (TUIK) has started to collect NUTS2 level data in the 2014 SILC at the NUTS2 level. This data should be made available to the public in 2016.

> 8 Wage income is only available for regular and casual employees in the LFS which accounts for around 60% of total employment. There is no other monetary income value for the rest of the working population.

> 9 It is important
> where _exit_  1 if a firm’s export share >= _ζ_ and 0 otherwise.

# **3.2 Empirical Specification**

We focus on the following equation: _exi_   0  _firmi_  _regr_  _aggr_  _Dc_  _Ds_   _i_ (4)

> where  0 designates the constant, _Dc_ country fixed effects, _Ds_ sector fixed effects, and<sup></sup> _i_<sup>the</sup> idiosyncratic error term.

Following the literature on the firm-level determinants of exporting described above, we include firm size, firm age, foreign ownership, as well as measures of workers’ skills and productivity. This leads to the following version of equation (4):

> where _D_

> _r_ denotes region fixed effects.

The firm-level indicators from the Enterprise Surveys Indicator Database combined with data from the Enterprise Analysis Unit<sup>9</sup> are defined as follows:

- _emp_ = total number of permanent and temporary employees in logarithms,

- _age_ = years of operation in logarithms,

- _fdi_ = 1 if foreign private ownership >= 10% and 0 otherwise,

- _comp_ = average real compensation per worker (including wages, salaries and bonuses) in logarithms,

- _labprod_ = labor productivity defined as real value added per worker in logarithms, and

- _tech_ = _iso_ + _tech_for_ + _website_ + _email_ ∈ {0, 1, 2, 3, 4 }, where _iso_ = 1 if firm owns internationally-recognized quality certification and 0 otherwise, _tech_for_ = 1 if firm uses technology licensed from foreign firms and 0 otherwise, _website_ = 1 if firm uses

> 9 We obtained compensation and value added data from Frederica Saliola and Murat Seker from the Enterprise Analysis Unit of the World Bank. Value added has been calculated by subtracting materials and energy costs from total value of sales. All local currencies have been converted into US dollars and deflated using a GDP deflator in USD (base year 2000). Exchange rates and
# **3. Three Sub-Saharan African Economies**

This section describes the data used in the empirical analysis and provides a sketch of the economies of Cote d'IvoiTe, Ghana, and Zimbabwe. The data used in the analysis are the (log) real GDP in the industry, agriculture, and service sectors from the World Bank African Regional Database for 1965-97 (figure 1).6 The plots reveal an overall positive trend in all output series.<sup>7</sup> First-order differences of the series (not shown) appear stationary, indicating that the series are integrated of order one, I(1). This is further supported by results from augmented Dickey-Fuller tests (see the appendix). Therefore, the series may possibly co-integrate.

**Figure 1. Sectoral GDP for C6te d'Ivoire, Zimbabwe and Ghana**

<!-- Start of picture text --> ind_civ=_~_ ~ agr_Piv=._.s.p.ciu=._.<br>27.6 - 28.5 -<br>i n&..gl,a=__ ag!_gha=,.... ser_sha=___<br>26.4 27._ 27<br>25.8 25. H m- 26.4 , 26 26.7 75 ' _<br>264 -26. 25.<br>2in _____wr _-we= ___ . _<br>25.2 - {-,2 26.4 5<br>1985 1985 1985<br>22 21*.6**-222.5-<br>28.4 ~~~~26. 21<br>21.6 2 * 22<br>21.8 ~ ~ V-I2. 2<br>1985 1995 1995<br><!-- End of picture text --> _Note:_ The series are log(real GDP, factor
on four variables capturing different dimensions of a country’s institutional environment. First, we use the _number of climate change laws_ from the Climate Change Laws of the World database of the Grantham Research Institute to proxy environmental regulation. Second, we use the _rule of law index_ taken from the World Governance Indicators (WGI) of the World Bank to capture the conditioning role of institutional quality. Third, we study whether the response varies in countries with more market-based versus bank-based financial systems. De Haas and Popov (2023) express this ‘financial structure’ as the ratio of the size of the equity market to the size of the entire financial 7
The identification of the PAF impact is based on a design that involves a randomized phasein approach, in which certain localities were randomly assigned for earlier intervention than others. Not surprisingly, the actual PAF intervention does not perfectly comply with the random assignment. However, there is a strong correlation of planned and actual intervention status. Using two rounds of survey data (pre and post-intervention) and difference-in-difference combined with instrumental variable estimation method, we find significant PAF effect on key welfare indicators. The treatment-on-the-treated impact is 19 percent growth on real per capita consumption is, 19 percentage points decline on incidence of food insecurity (defined as food sufficiency for six months or less) and 15 percentage points increase in school enrollment rate among 6-15 year olds. Impacts (positive or negative) are yet to be detected on indicators associated with child malnutrition, social capital and female empowerment. The policy implications of these results should be of interest to the government and to development partners in determining what may be effective instruments to deliver services to marginalized communities in what remains a fragile and difficult political environment.

The study contributes to evidence on social funds evaluation in three ways. We use an experimental design to evaluate the impact of a large-scale intervention. Moreover, we estimate the causal impact even when there is no perfect compliance between the actual intervention and the randomized assignment. Imperfect compliance is a reasonable real world setting and allowance for such can be deemed to maintain external validity of the impact estimates. Lastly, in addition to using per capita household consumption, we also report impact on food security, an important indicator of vulnerability in conflict settings.

Like most evaluations, our study has its limitations. First, the treatment-on-the-treated instrumental variable estimate does not necessarily represent the average treatment effect for the entire population, but only of the sub-population that was actually treated because of random assignment. This is a well-documented qualification of the IV method. Second, our analysis has focused on the outcome indicators only but there are important process related questions that this impact evaluation does not yet
<!-- Start of picture text --> Figure 8: Median Survival Time (adjusted for exporters) based on Fixed<br>Costs by Country<br>Ukraine<br>Kazakhs ...<br>Kenya<br>Greece<br>Portugal<br>Turkey<br>Morocco<br>Jordan<br>Italy<br>Russia<br>Peru<br>Colombia<br>0 4 8 12 16<br><!-- End of picture text --> <!-- Start of picture text --> 20<br><!-- End of picture text --> <!-- Start of picture text --> Weeks<br>Note : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,395<br><!-- End of picture text --> The analysis here also offers a partial answer to the puzzling result in Tables 2-4 that Schumpeter’s theory does not find support in our data. Previous analyses have shown that exporters are among the most productive firms in any economy (for example, Wagner 2007). As exports are among the most affected sectors of the economy during economic distress periods that involve health concerns, productive firms are in effect subjected to financial strain more than the average firm. The Schumpeter theory of creative destruction no longer holds.

# **VII. Policy Response**

The evidence in Section VI suggests that significant government response is warranted to prevent mass insolvency. Such response can proceed in two steps.

First, governments need to temporarily suspend bankruptcy procedures, which often dictate that illiquid firms’ assets get transferred to their secured creditors, mostly banks. A number of countries have already taken this step. For example, in France bankruptcy law normally gives 45 days from the moment a debtor can no longer pay its debts to filing for bankruptcy. The new ordinance says that the firms will have three months