rafmacalaba/data-use-mentions-extended huggingface dataset:
, electricity, transport, communications, postal services and the commercialization of basic or mass consumption products. The rest were financial and social institutions with programs in health, housing, education, culture, food and other products and services. SOEs still maintain control over important industries such as petroleum (Pemex) and electricity (CFE and LFC) that remain reserved areas for the State.

_Source_ : _Regulating Market Activities by Public Sector, 01-February-2005, Organization for Economic Co-operation and Development_

# **B. Sectoral highlights**

35. Government ownership in existing infrastructure and financial sector entities continues to exist despite increasing emphasis on private sector participation over the past decade.

# _Infrastructure_

36. Power utilities in nearly 85 developing countries--or 55 percent of all developing countries in the PPI database--are still owned and operated by the state. In a survey of 52 developing countries with generating capacity of between 29 megawatts (the Gambia) and 318 gigawatts (China), almost 70 percent had not started or completed the process of brining private sector participation into the sector, and a further 18 percent had just begun the process; by contrast, independent power providers had been established in 67 percent of the countries with
Table 9: Parental Investment by Proxies of Community-Level Collaboration

||Engage at School|Manage School|Engage With Child|Engagement Index|
|---|---|---|---|---|
|API Plus_×_ No Confict|0.161<br>[0.176]|0.159<br>[0.103]|0.370<br>[0.000]|0.364<br>[0.001]|
|API Plus_×_ Confict|0.926<br>[0.000]|0.281<br>[0.183]|0.592<br>[0.005]|0.914<br>[0.000]|
|Confict=No Confict|[0.000]|[0.596]|[0.329]|[0.020]|
|Mean Control (Confict)|-0.006|-0.059|0.152|0.011|
|Mean Control (No Confict)|-0.023|0.013|-0.013|0.003|
|Number of clusters|224|224|224|224|
|Observations|1045|1045|1045|1045|

_Notes_ : This table shows OLS estimates on parental outcomes measured after two academic years of exposure to the API program under the second experiment designed and implemented by the authors in collaboration with the government. The variable Conflict takes the value of one if least on hostile event related to land property, religion, elections, crime, or drug addiction is reported at the locality level in the population census (2010). For a detailed descriptions of the variables used in this table, see Appendices A.1–A.2. The dependent variables are standardized with respect to their means and the standard deviations in the control group. All _p_
0.19)<sup>_∗∗∗_</sup>|(0.24)<sup>_∗∗∗_</sup>|(0.40)<sup>_∗∗∗_</sup>|
|Adjusted _R_<sup>2</sup>|0.051|||0.179|||
|_F_|||2.602<sup>_∗∗∗_</sup>|||3.233<sup>_∗∗∗_</sup>|
|Hansen _J_|||74.043|||57.330|
|AR(2)|||-0.626|||-0.527|
|Estimator|OLS|RE|S-GMM|OLS|RE|S-GMM|
|Instruments|||74|||74|
|_N_|673|673|673|628|628|628|

> * Growth rates calculated from GDP data in constant 2005 international purchasing power parity-adjusted dollars. Sampling frame selected to maximize sample size, and is hence unbalanced. Data periodicity are five-year averages. All variables are in log form. Heteroskedasticity and autocorrelation-robust standard errors are reported in parentheses. A lagged dependent variable (GMM only), period fixed effects, and a constant term (all specifications) were included in the regressions, but not reported.<sup>_∗_</sup> indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.

36
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Vousdoukas, Michalis I., Panagiotis Athanasiou, Alessio Giardino, Lorenzo Mentaschi, Alessandro Stocchino , Robert E. Kopp, Pelayo Menéndez, Michael W. Beck, Roshanka Ranasinghe and Luc Feyen (2023), “Small Island Developing States under threat by rising seas even
resulting in omitted variable bias. Controlling for ideology and as many controls as possible reduces a part of this bias.

# **5.1 Explaining perceptions of (in)equality**

We start with regressing equation (3) in which equality perception is regressed against a set of individual variables that aim at proxying individuals’ perception of objective inequality and ideology as well as individual characteristics. The analysis is based on individual data and therefore exploits individual variation within a country, while clustering standard errors at the country level to account for clustered data. Using the perceived Gini index as the dependent variable, Table 2a shows the importance of the economic context, and not only of the objective Gini index, in explaining individuals’ perceptions about inequality. The same analysis using the categorical version of the variable representing perceptions is presented in Appendix Table A.3.

The four columns of Table 2a present different specifications all of which include the four main macroeconomic variables at the country level: the unemployment rate, the poverty headcount rate, the Gini index of income inequality, and the government expenditures in education. The sign of the correlations is as expected, and the coefficients are precisely estimated. The higher the unemployment rate, the Gini index, and the poverty rate, the more unequal society is perceived. This is not surprising given the cross-country correlations found before. This might mean that the Gini index is more difficult to ‘observe’, while poverty and unemployment, also correlated with the actual Gini index, are easier to understand and directly observe or experience. The fact that a fairly abstract measure such as the Gini index is correlated with equality perceptions may reflect that such index does indeed capture income inequality that can be perceived by individuals. We also introduce government expenditures in education, as in contrast with social expenditures these are not mechanically correlated with current inequality, but rather with future income inequalities to the extent that education expenditures might increase equal opportunities. The coefficient for government expenditures in education has the expected sign: more government
external dependence elsewhere in the world. Thus, the degree of US firms’ dependence on external finance is good proxy for the demand of external funds on other countries (see Rajan and Zingales, 1998).<sup>1</sup> One potential criticism to this as well as to similar studies is that our results are driven by the way we measure dependence on external finance. We address this potential criticism in two ways. As a robustness check, we also consider alternative measures of external finance dependence as calculated by Klapper et al. (2006) for the US during 1980‐99, and by Fernandez de Guevara and Maudos (2011) for the UK during 1993‐03. Also in line with similar studies (Cetorelli and Gambera, 2001; Claessens and Laeven, 2005; Fernandez de Guevara and Maudos, 2011; Liu et al., 2014), we include a proxy of financial depth, i.e. domestic credit to GDP, taken from the World Development Indicators.

As for our measures of market power, we employ both structural and more direct indicators of bank market power. The former include the widely used five‐ and three‐bank concentration ratio, estimated as the share of total assets held by the five and three largest banks, respectively. These measures help us test the validity of the structure‐conduct‐performance paradigm (SCP), which assumes a causal relationship among the structure of the banking industry, firm conduct and performance. This paradigm suggests that fewer and larger firms are more likely to engage in anti‐competitive behavior, and therefore higher values of concentration signal greater market power.

> 1 For a critique of this approach see Von Fustenberg and Von Kalckreuth, 2006.

5
# **5 Conclusions**

The most used measures of welfare, mean income and the poverty headcount, are not distribution sensitive. Despite widespread agreement that inequality is important, the large menu of existing distribution sensitive welfare measures is rarely used in academic, policy, or public discourse. We argue that a main reason for this is their complexity and lack of intuitive units. To remedy this shortcoming, we have introduced a new family of distribution sensitive welfare, poverty and inequality measures that has a simple mathematical formulation and simple units. The key ingredient of these measures is the ratio of reference income z to individual individual incomes, z/yi, which captures the factor by which individual incomes must be multiplied to attain the reference level of income. This ratio formulation ensures that our measures satisfy all three definitions of distribution sensitivity in the literature. Our measures are based on averages of this ratio, giving a simple “average factor by which incomes must increase” interpretation to our measures. This simple average property also ensures that our measures are sub-group decomposable with population weights, further enhancing its ease of communication.

We demonstrate the empirical properties of our measures in the context of the world interpersonal income distribution over the period 1990-2019, as well as in selected country comparisons. Consistent with their strong distribution sensitivity, we find that our welfare and poverty measures decline more strongly than their non-distribution sensitive counterparts in a setting that has featured a substantial decline in global inequality. Finally, we exploit the simple structure and units of our new measure to propose a new “prosperity gap” measure that the World Bank could use to track progress towards its goal of promoting shared prosperity.

One area that requires further work to facilitate the implementation of these new measures is a more careful consideration of the treatment of low incomes in household surveys. As discussed in Section 3, by virtue of being based on z/yi, which makes them distribution sensitive, our measures also are sensitive to mismeasurement of very low or, at the extreme, zero
having work centrality, tenacity, and optimism traits increase their likelihood of employment. Interactions of gender role attitudes and personality traits, however, make some of these traits lose their significant effects, especially for women deciding on being employed.

The study also found the significant roles played by individual traits in the nature/type of employment (i.e., wage vs. self-employment) that women and men engage in. For men, being organized increases their likelihood of wage employment outcomes, although traditionalism tends to reverse this effect. Among women, impulsiveness appears to reduce their likelihood of of being employed in wage jobs. Like the results for males, traditionalism tends to reverse the direction of impact such that traditional and impulsive women have higher probability of being employed in wage jobs. These results, in general, suggest that while personality traits play an important role in observed employment outcomes, these effects are often dampened or reversed with increasing traditionalism.

Employment policies may need to focus on the development of soft skills in order to improve labor force participation and employment in the Ghanaian settings. Additionally, labor market policies that address certain constraits facing women and other general work should be encouraged. Policies that improve access to education and child care are also recommended.

# **References**

- Affum-Osei, E., Asante, E.A., Forkouh, S.K., Aboagye, M.O., and Antwi, C.O. (2019). Unemployment trends and labor market entry in Ghana: job search methods perspective, _Labor History_ , 60(6), 716-733. DOI: 10.1080/0023656X.2019.1640356

- AHIES (2022) Ghana Annual Household Income and Expenditure Survey – Quarterly Labor Force report. Ghana Statistical Service (2022) <u>https://www.statsghana.gov.gh/gssmain/fileUpload/pressrelease/AHIES%202022%20Q1 %20and%20Q2%20Labor%20Force%20Report.pdf</u>

- Alesina
# **5 Discussion and Conclusions**

A recent paper by Fowlie et al (2014) notes that “Widely publicized engineering estimates [ _. . ._ ] suggest that consumers are systematically bypassing opportunities to invest in costeffective energy efficiency improvements that lower their energy expenditures and reduce externalities associated with energy production. As a result, many policymakers are championing programs designed to encourage energy efficiency as a cost-effective strategy to confront climate change.” Like these authors, we find that bottom-up estimates tend to overstate the benefits of energy efficiency mandates in terms of energy and emissions savings. But our analysis comes at this issue from the opposite direction, examining the aggregatelevel environmental and economic effects of technology deployment strategies for low-carbon economic economic growth. Building on the fundamentals of engineering cost assessments, we develop a stylized bottom-up/top-down general equilibrium model which accounts for the several influences: the opportunity costs of increasing the installed base of energy efficient technology-specific capital, the direct productivity benefits to economic sectors of the resulting increases in energy efficiency, and the broader impact of rebound effects. We then implement this model as a CGE simulation which integrates energy technology data into the system of open-economy input-output accounts for Armenia and Georgia.

Our main finding is that mandated increases in the penetration of bottom-up energy efficiency technology options can mitigate CO2 up to around 4 % of baseline emissions without adversely impacting real GDP or welfare. Even so, as capacity in these more efficient but relatively expensive technologies expands, the productivity benefits to technology-rich sectors are progressively diminished by increasing opportunity costs. The good news for transition economies such as Armenia and Georgia is that even modest increases in energy efficiency (e.g., our 1% or 2% per annum deployment rates) appear to generate both welfare improvements and reductions in the rate of growth of emissions—albeit small in magnitude. The bad news is that, depending on the structure of the economy, the direct abatement realized by higher energy efficiency tends to be undermined by substantial emission leakage—due to increased fossil
India’s Net Domestic Product (NDP) per capita in the period 1958-1991 was under 2% per annum, but it was more than double this rate in the period since 1992.<sup>13</sup> The picture that emerges from the accumulating evidence from India’s National Sample Surveys (that started in the 1950s) indicates that economic growth in India had in fact been poverty reducing. Ravallion and Datt (1996) showed that the elasticity of the incidence of poverty with respect to mean household consumption was -1.3 over 1958-1991.<sup>14</sup> Given the modest rate of growth over this period, success at avoiding rising inequality prior to the 1990s was key to this finding.<sup>15</sup> Many observers came to the view that too little growth was the reason for India’s slow pace of poverty reduction. However, a deeper exploration of the data suggests that the sectoral pattern of growth also played a role. Using data up to the early 1990s, Ravallion and Datt (1996) found that rural economic growth was more poverty reducing, as was growth in the tertiary (mainly services) and primary (mainly agriculture) sectors relative to the secondary (mainly manufacturing and construction) sector. They also found that spillover effects across sectors reinforced the importance of rural economic growth to national poverty reduction. Urban growth and secondary sector growth had adverse distributional effects that mitigated the gains to the urban poor, while urban growth brought little or no benefit to the rural poor. The slow progress against poverty reflected both a lack of overall growth and a sectoral pattern of growth that did not favor poor people.

There was much hope in India that the higher growth rates attained in the wake of the economic reforms that started in earnest in the early 1990s would bring a faster pace of poverty reduction.<sup>16</sup> However, there have also been signs of rising inequality in the post-reform period, raising doubts about
# **1. Introduction**

In this paper, we address the following two questions: Does the introduction of mandatory and voluntary standards affect Chinese exports significantly? What is the effect of international harmonization on agricultural products? The aim of this paper is to attempt to answer these questions by assessing econometrically China‟s development of mandatory and voluntary standards on its exports of agricultural and food products. We use new data that link Chinese food standards for seven groups of food products (meat, fish, vegetables, cereals, milk, tea, and sugar) to trade data.

Our research contributes to the existing empirical literature in at least three innovative ways. First, we focus on standards implemented by the _exporting_ country for its produced goods, whereas previous empirical studies mainly focus on standards used by the _importing_ countries. Second, we analyze the impact of standards harmonization in a developing country. Due to data availability, research on the impact of standards harmonization has so far been limited to industrialized countries. To our knowledge, there is no empirical study estimating the impact of national product standards and harmonization in exports from developing countries. Third, our dataset allows differentiation between mandatory and voluntary standards, whereas this was not possible in previous studies due to data limitations. In addition, each type of standard can be identified as being harmonized to international norms or not.

We use an extended gravity model to examine the impact of food standards in China on its agricultural exports. Although estimates vary slightly across products, the results confirm the export-promoting effect of standards. This is particularly true with regard to mandatory standards. In particular, based on our analysis, we confirm the positive impact of standards harmonization. We find that the impact of an additional standard that is based on international measures has a larger impact than the marginal impact of purely Chinese domestic standards. Thus our results provide new evidence on the positive impact of standards on export performance, especially when standards are harmonized to international norms.

The paper is structured as follows. In
debt compared to 47 percent of households in the highest quintile. Finally, only 4.6 to 7.6 percent of all households are at risk before a shock (i.e., the last dark blue column in each panel of Figure 4). The low levels of vulnerable households, as well as the low levels of debt incidence, suggest that household debt is unlikely to become a drag on aggregate economic activity.

<!-- Start of picture text --> Figure 4. Distribution of Household Debt and Household Vulnerability by Income Quintile<br>Panel A. Arbitrary Threshold Panel B. Financial Margin Threshold<br>50<br>40<br>30<br>20<br>10<br>0<br>I II III IV V TOTAL I II III IV V TOTAL<br>Indebted Households Households at Risk<br><!-- End of picture text --> Source: Croatia HBS and authors’ calculations.

It should be noted that the financial margin threshold makes most households in low income quintiles vulnerable. Do banks know something about their borrowers that is not captured in HBS? One can speculate as to possible explanations. A non comprehensive list includes banks’ knowledge about informal sources of income and assets. Kraft (2007) highlights other factors that make household lending less risky, including informal incomes originating in family ties with Croatians living abroad the traditionally stringent banking lending conditions (e.g., codebtor or guarantor requirements). This also points to weaknesses in Croatia’s HBS that are thoroughly discussed in World Bank (2010). In particular, the original HBS sample was framed following the 2001 population census, but consistency checks carried out in recent years using macro data point to an underestimation over time in incomes and consumption expenditures. As a result, household vulnerability in the financial margin threshold calculations would be biased upwards as subsistence consumption levels are imposed on a sample of underestimated incomes. To a lesser degree, this
Impact Evaluation to Development Impact (i2i) fund, the Trade and Competitiveness Impact program (Compel), the Strategic Impact Evaluation Fund (SIEF), the Knowledge for Change Program (KCP), the Korea World Bank Group Partnership Facility (KWPF), the Development Economics Research Support Budget (RSB) and the Development Research Group (DECRG) at The World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its executive directors, or the countries they represent. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper. This study was pre-registered in the AEA RCT registry with RCT ID AEARCTR-0001001. Ethical clearance was granted by the Ethics and Scientific Review Board at the African Medical and Research Foundation (Approval no. AMREF-ESRC P94/2013), the Kenyan Ministry of Health and authorities at participating facilities. Computational reproducibility verified by DIME Analytics.

> †Development Impact Evaluation (DIME) department, World Bank

> ‡Georgetown University and NBER
|**SE**|**P-value**|
|---|---|---|---|---|---|
|**Availability & Reliability Index**||||||
|No Irregular Supply|0.62|0.41|0.20|0.23|0.379|
|No Low Pressure|0.64|0.15|0.49*|0.22|0.022|
|No Seasonal Shortage|0.49|0.04|0.45**|0.14|0.001|
|**Household Satisfaction Index**||||||
|Overall Satisfaction|3.5|2.9|0.64**|0.20|0.001|
|Regularity of Supply|3.4|2.5|0.97***|0.24|0.000|
|**Household Cost of Service Index**||||||
|HH Opinion on Contribution|2.1|2.4|-0.29|0.32|0.362|
|HH Opinion on Tariffs|2.1|2.0|0.05|0.19|0.813|
|**Operation & Maintenance Index**||||||
|Yearly Analysis of Water Quality|0.28|0.69|-0.41+|0.24|0.082|
|Number of Breakdowns Last Year|3.4|8.7|-5.3**|1.93|0.006|
|Scheme Facing No Water Shortage|0.60|0.39|0.20|0.23|0.367|
|� p�0.1, *p<0.05,