rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10918

# **Abstract**

A widely held view in the media and among some researchers is that younger people are more worried about climate change and more willing to support the climate agenda than older generations. Such a “climate change age gap” is often explained by the longer time younger people expect to live under worsening climatic conditions. This paper develops a theoretical model that proposes an alternative explanation for the relationship between age and attitudes toward climate change. The empirical analysis is based on data from 38 countries in Europe, Central Asia, and the Middle East from the 2023 round of the Life in Transition Survey. The findings demonstrate a positive relationship between the respondents’ age and their concerns about climate change. Older people are more likely to object to higher taxes to finance public policies in general, including climate change policies, but even this result is sensitive to the framing of climate action questions.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region and the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at mlokshin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**<u>Table 2: Error-Correction Model Results: 1970-2005</u>**

|Variable|(1)|(2)|(3)<sup>1/</sup>|(4)|(5)<sup>1/</sup>|
|---|---|---|---|---|---|
|Error Correction Term|-0.37**|-0.30***|-0.78***|-0.29**|-0.69***|
||(0.17)|(0.147)|(0.21)|(0.14)|(0.17)|
|Δ Log (Open)|-0.42**|-0.36**|-0.53***|-0.36**|-0.51***|
||(0.19)|(0.16)|(0.16)|(0.16)|(0.12)|
|Δ Log (TOT)|0.12|0.08|0.14|0.23|-0.03|
||(0.21)|(0.17)|(0.18)|(0.19)|(0.14)|
|Δ Log (Aid)|-0.20|-0.22*|-0.26*|0.15|-0.13|
||(0.14)|(0.13)|(0.17)|(0.13)|(0.12)|
|Δ Log (Invest.)|0.03<br>(0.12)|||||
|Δ Log (Prod)|-0.76<br>(0.63)|||||
|Δ Log (Reserves)||||-0.08*|-0.14***|
|||||(0.04)|(0.03)|
|_Cons|-0.03|-0.01|-0.02|-0.01|-0.01*|
||(0.03)|(0.02)|(0.02)|(0.02)|(0.02)|
|No. of obs|35|35|35|35|35|
|Adjusted R<sup>2</sup>|0.35|0.37|0.52|0.42|0.70|

1/Independent variable incorporates parallel exchange rate.

# **V. The Policy Response to Aid Inflows**

This section builds the ECM results in the previous section to focus specifically on the policy response to aid inflows over the period 1995-2005. We undertake an accounting exercise in the spirit of the IMF (2004) to look for additional evidence that the policy response to aid inflows helps explain the negative and significant relationship between the REER and net aid inflows over this period. When aid flows into Tanzania the authorities must decide whether it is <u>to be used to finance expenditures or a tax</u> reduction. Following the **Figure 11. Fiscal Response to Aid (% of GDP)** IMF (2004) the spending of 4 aid is defined as the 3 widening in the 2 government’s fiscal deficit 1 0 before aid that accompanies -1 the increase in aid. The first -2 thing to note about these -3 series is that there has been a -4 94/95 95/96 96/97 97/98 98/99 99/00 00/01 01/02 02/03 03/04 04/05 steady increase in the amount of net aid financing Change in total expenditure Change in development expenditure that is recorded in the budget Change in net aid (Annex 3 Table A4). This has the effect of showing up <u>Source: IMF reports and authors’ calculations</u> as a steady increase in net aid inflows in the fiscal accounts over the entire period 199523
|sales|minorities to sales in 1994 for all nonfinancial firms in a given<br>country on the WorldScope database. Firm's stock market<br>capitalization held by minorities is computed as the product of the<br>stock market capitalization of the firm and the average percentage<br>of common shares not owned by the top three shareholders in the<br>ten largest nonfinancial, privately owned domestic firms in a given<br>country. A firm is considered privately owned if the State is not a<br>known shareholder in it.||
|---|---|---|
|Market cap/<br>cash-flow|The median ratio of the stock market capitalization held by<br>minorities to cash flow in 1994 for all nonfinancial firms in a given<br>country on the WorldScope database. The firm's stock market<br>capitalization held by minorities is computed as the product of the<br>stock market capitalization of the firm and the average percentage<br>of common shares not owned by the top three shareholders in the<br>ten largest nonfinancial, privately owned domestic firms in a given<br>country. A firm is considered privately owned if the State is not a<br>known shareholder in it.|WorldScope.|
|Debt/sales|Median of the total-debt-to-sales ratio in 1994 for all firms in a<br>given country on the WorldScope database.|WorldScope.|
|Debt/cash<br>flow|Median of the total-debt-to-cash-flow ratio for all firms in a given<br>countryon the WorldScope database.|WorldScope.|

Secondary source: LLSV(1998).

# **A.4. Definitions of different types of firms and banks in India**

1. **<u>Public Sector Undertakings (PSUs):</u>** Companies with federal and/or state government as the majority or sole shareholder.  Includes partially privatized companies.  Different from departmental undertakings like railways that are non-corporate in organizational structure.  The government is the de facto owner, and they choose managers to run the firm.

2. **<u>Small and Medium Enterprises (SME)</u>** <u>:</u> Firms with less than Indian Rupees (Rs.) 50 million (about US$ 1.11 million) in plant and machinery at original cost. Includes the small industry (less than Rs. 10 million or (about US$ 222,000) and the medium sector (Rs. 10-50 million).

3. **<u>Small Scale Industry (SSIs):</u>** Firms with less than Rs. 10 million (about US$ 222,000) in plant and machinery at original cost and (mostly) engaged in manufacturing activity.

4. **<u>Small Scale Sector Business Enterprise (SSSBE):</u>** Firms with less than Rs. 1 million (about US$22,000) in plant and machinery at original cost and engaged in non-manufacturing activity.

5. **<u>Non-SSI (NSSI):</u>** A firm that is not an SSI.

6. **<u>Non-SSSBE (NSSSBE):</u>** <u>A firm that is not an SSSBE</u> 7. **<u>Small Industry Development Bank of India (SIDBI):</u>** Specialized financial institution (not a commercial bank) created by the government of India for financing and promoting growth in the small scale sector.

8. **<u>State Finance Corporations (SFCs):</u>** State-level government financial institutions (not commercial banks) for financing and promoting growth, often in the small scale sector.

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bonds. Data for the United States in the 1 850s indicate that outstanding securities totaled between $1 billion and _$1.5_ billion, and were about evenly divided between bonds and equities. Other data for the same period show that about one-fifth of U.S. securities were held by foreign investors (table I 11.1). Foreign holdings of U.S. securities in 1853 were mostly bonds (about 93 percent) with only about 7 percent in stocks (equities). In other words, foreign investors had a much stronger preference for bonds compared with U.S. investors, who, after they exported bonds, held more stock than bonds at home.

It is understandable that foreign investors in the United States preferred to hold bonds. But why were U.S. investors willing to hold almost all of the outstanding U.S. equities? The answer to this question reveals why secondary trading markets for securities were important. Because good stock markets permit the conversion of equity securities into cash, more people become more willing to invest in equities. This is the benefit of a good stock market for a developing country: the country will find it easier to sell bonds to foreign investors. That, at least, was the U.S. experience more than a century ago. A final point I would make about historical securities markets concerns the size of corporate stock as a percentage of national assets (table 11.2). U.S residents, relative to their economy's total assets, had the biggest stock market in the world in the early nineteenth century, even though they were mainly a nation of farmers at that time. The United States maintained a stock market even larger than that of Britain, a country whose economy was more developed. Britain's earlier legislation-the Bubble Act and other laws dating from the early eighteenth century-made it difficult to establish corporations. Thus it is not surprising that Britain lagged behind the United States in equity market development, as the United States had no such constraints. By the middle of the nineteenth century the United States had outpaced other countries, such as Germany, by even more in terms of the ratio of stock to national assets. But the British did manage to catch up. And by the beginning of the twentieth century Britain surpassed the United States in corporate stock relative to national assets.

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country-industry-year tariff rates), and governance (% of firms that pay bribes).<sup>5</sup> These controls are employed to reduce the danger of the omitted variable bias: if the access to telecom services is correlated with the above determinants of firm performance (i.e., the level of development, geography, transportation costs, labor regulation, access to finance, competition, and corruption), the estimates of the telecom effects could be biased.

# _Empirical Results_

Firms grow faster and are more productive in countries where Internet access is better (see Table 1). A one standard-deviation increase in the percent of firms with websites (35%) increases firm growth by 5.8 percentage points (or 20 percent of one SD of firm growth) and log labor productivity by 28 percent (or 16 percent of one SD of log labor productivity). Similarly, a one standard deviation increase in fixed line Internet subscriptions (5.2%) increases firm growth by 2.7 percentage points (or 9 percent of one SD of firm growth) and log labor productivity by 35 percent (or 20 percent of one SD of log labor productivity). These effects are thus economically significant.

Firms with better access to fixed line and cellular services do not perform better: the coefficients on fixed line and cellular subscriptions are mostly statistically insignificant. The one significant coefficient—on fixed line subscriptions in the productivity regression—is negative. This result, however, is not robust: it becomes statistically insignificant in several regressions for smaller subgroups of firms.

We perform several checks to see if the main results for the Internet variables are robust. We summarize these results in Table 2 and an online appendix. The robustness checks include:

1. _Including country dummies_ .  Now country-level variables, including the country-level telecom variables, are dropped due to collinearity.

2. _Including additional controls_ (industry-country tariffs and local averages for markup and corruption). Such controls are potentially important if openness, competition and

> 5 Tariffs are from the UN TRAINS database.  All other variables are from the WBES.

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between cities that reflect differences in productivity rather than on differences in real wages, which would measure differences in the ‘standard of living’. We return to this distinction below, but for now, we examine productivity studies that typically estimate a regression such as log _wic_ ( _i_ ) = _α_ log _Popc_ ( _i_ ) + _ηc_ ( _i_ ) + _ui_ + _εic_ ( _i_ ) (1) where _c_ denotes cities and _i_ denotes individuals or groups of individuals. The dependent variable is log _w_ , the log of the wage, and the explanatory variables are log _Pop_ , the log of population as a measure of urban scale; _η_ , a city effect (usually proxied through a number of control variables at the city level); and _u,_ an individual effect (often proxied through observable individual characteristics). Finally, _ε_ is an error term. The estimated value of the coefficient of interest, _α_ , is usually positive and significant.

A few comments regarding the choice of variables are in order. The dependent variable is the log of the wage. Regressions similar to (1) can be proposed using measures of firm-level productivity as the dependent variable. Although the two types of measures are not exactly equivalent, they are close substitutes.<sup>3</sup> Using a population- or employment-based measure of agglomeration as the dependent variable of interest makes little difference to the results, given the high correlation between the two. After Ciccone and Hall (1996), density has often been favored relative to population because it appears to yield more reliable results. The reason is most likely that density-based measures of agglomeration are more robust to zoning idiosyncrasies. For instance, treating Washington and Baltimore as one large consolidated metropolitan area or two separate cities makes a substantial difference to their employment count but little difference to density.<sup>4</sup> _Is the relationship between city scale and productivity causal?_ 6
# **Essential Heterogeneity**

To identify the effect of income diversification on Nigerian farmers’ welfare given these identification constraints, a non-parametric selection model is estimated by local instrumental variables (Heckman and Vitlacyl, 1999; Heckman and Vitlacyl, 2001; Carneiro et al., 2001). The methodology employed here accounts for heterogeneity and selection. It has been used extensively in the analysis of the returns to schooling, nonetheless, in this instance, it is applied to the analysis of diversification among farming households. The methodology which is described below is based on the methodology described in Heckman and Li (2004), Carneiro et al. (2011), and Radchenko (2014).

Households can be separated into those who diversify and those who do not diversify, _Di_ = 1 _,_ and _Di_ = 0 respectively. Given these two groups, the returns in each option are given by: _Yi_ 0 is the returns for households who have chosen to not diversify, _Yi_ 1is the returns for those households who have chosen to diversify. _Xi_ is a vector of observable characteristics, that might affect the household’s expenditure per-capita and includes household characteristics and,characteristics of the head of the household, among others. Finally, _Ui_ 0 and _Ui_ 1 are the error terms.

The household’s choice to diversify is given by a standard discrete model of the form: In equation (5) _Di_<sup>_∗_isalatentvariable,andmaybeconsideredareducedformmodelfor</sup> diversification. A household will choose to diversify if it expects positive returns given unobservables, where: 12
**Table 1** : Government domestic spending data sources

|**Sector**|**Source**|**Variables**|
|---|---|---|
|Health|WHO GHED|•Domestic government health expenditure as % of GDP_(current only)_<br>•Domestic government health expenditure as % of total government<br>expenditure_(current only)_|
|Education|UIS, IMF|•Domestic government education expenditure as % of GDP_(current +_<br>_capital)_<br>•Domestic government education expenditure as % of total government<br>expenditure_(current + capital)_|
|Social<br>protection|ASPIRE|•Domestic government spending on social protection (defined above) as<br>a % of GDP_(current + capital)_|

The main source of information on country-specific human capital outcomes is represented by the HCI, which measures the human capital that a child born today can expect to attain by age 18, given the risks of poor health and poor education that prevail in the country where she lives (Kraay 2018). The index has three components:

- 1) **Survival** : measured as the complement of the under-5 mortality rate.

- 2) **School** : measured both through the _quantity_ and _quality_ of education. The quantity of education is measured by the number of Expected Years of School. The Quality of education is measured by the Harmonized Learning Outcomes (HLOs), that harmonize test scores from major international student achievement testing programs. A recent paper provides an in-depth discussion on the HLOs (Altinok, Angrist, and Patrinos 2018). The two measures are aggregated into the Learning Adjusted Years of School that vary between 0 and 14.

- 3) **Health** : measured by Adult Survival Rate (ASR), the share of 15-year-olds who survive until age 60, and the complement of the share of children under 5 who are below normal height for age.

A recent paper discusses in detail the aggregation methodology of the three components (Kraay 2018). The index, defined between 0 and 1, is measured in terms of productivity of the next generation of workers relative to the benchmark of complete education and full health. The index is currently available for 157 countries. Kraay (2018) describes the data used for each of the five ingredients.

Comparing or aggregating spending estimates across countries and over time requires assumptions that are not free of implications. When constructing regional- or income-level average spending for the three sectors, we give each observation equal weight so as not over-represent countries with the largest populations. However, changes in some of these averages might reflect the fact that the number of countries changes over time as result of increased data availability. Comparisons over time are particularly problematic for education since in some of the years the data coverage accounts for less than 50% of the potential universe (the universe being all possible country-years). It is different for health, where we have 5
|Freedom from|0.432|0.489|
|---|---|---|
|business regulations|||
||(0.527)|(0.562)|
|Urbanization rate<br>|0.003|0.022|
|(%)|||
||(0.034)|(0.032)|
|Women in|0.098|0.090|
|Parliament (%)|||
||(0.064)|(0.063)|
|Paternity leave<br>|0.391|0.796|
|(logs)|(0.475)|(0.485)|
|Square of Maternity<br>||-0.462|
|leave (logs)|||
|||(0.711)|
|Maternity leave<br>||-6.117**|
|(logs)*GDP per<br>capita (logs)||(2.680)|
|Maternity leave||0.783**|
|(logs)*Number of|||
|workers (logs)|||
|||(0.365)|
|Maternity leave||0.179|
|(logs)*Female<br>|||
|owner present Y:1|||
|N:0|||
|||(0.578)|
|Maternity leave||-4.497*|
|(logs)*Fertility rate|||
|||(2.440)|
|Maternity leave||0.022|
|(logs)*Gender parity|||
|in workplace laws|||
|||(0.050)|
|Maternity leave||-32.639|
|(logs)*Gender parity<br>|||
|in primary education<br>|||
|enrollment|||
|||(36.518)|
|Govt. Funded Y:1||1.200|
|N:0*GDP per capita<br>|||
|(logs)|||
|||(1.554)|

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Table A34: Indexes at medium-term follow-up

|**_Loading factor_**|
|---|
|**Knowledge and awareness** _(Cronbach’s alpha = 0.24)_|
|Knows that father determines sex of child<br>0.540|
|Knows that 1/3 of women in the world experience violence<br>0.433|
|Thinks that VAW is an issue in India<br>0.596|
|Mentions either virginity ritual or menstruation rituals as prevalent gender norms<br>0.406|
|**Attitudes toward gender norms/roles** _(Cronbach’s alpha = 0.60)_|
|Thinks that is it wrong to follow the above-mentioned gender norms<br>0.367|
|Agreement (1-5) on “women should be virgin until marriage”<br>0.359|
|Agreement (1-5) on “a woman should be banned from entering the kitchen or household<br>shrine during her period”<br>0.338|
|Agreement (1-5) “women should be able to marry whomever they want, regardless of their<br>parents’ views”<br>0.087|
|Agreement (1-5) “girls should be allowed to wear whatever they want without being harassed”<br>0.232<br>Agreement (1-5) “parents should maintain stricter control over their daughters than their<br>sons”<br>0.430|
|Agreement (1-5) “when women get rights they are taking rights away from men”<br>0.370<br>Agreement (1-5) “it is more important that a boy goes to school than a girl”<br>0.444|
|Agreement (1-5) “nowadays men should participate in child rearing and household chores<br>rather than leaving it all to the women”<br>0.091<br>Thinks that husband and wife should have the equal say in deciding how many children to<br>have<br>0.181|
|**Attitudes toward VAW** _(Cronbach’s alpha = 0.71)_|
|Agreement (1-5) on “a husband is justifed in hitting or beating his wife if she goes out<br>without telling him”<br>0.479|
|Agreement (1-5) on “a husband is justifed in hitting or beating his wife if he suspects her of<br>being unfaithful”<br>0.467<br>Agreement (1-5) on “a husband is justifed in hitting or beating his wife if she neglects the<br>house or the children”<br>0.522|
|Agreement (1-5) on “a husband is justifed in hitting or beating his wife if she shows disrespect<br>for in-laws”<br>0.504<br>Would tell anyone if fnds out that a friend beats or physically hurts his partner<br>0.159|
|**Beliefs on others** _(Cronbach’s alpha = 0.69)_|
|Imagine to pick 10 of your closest Facebook friends. According to you, how many of them<br>think that women should be virgins till marriage?<br>0.496<br>Imagine to pick 10 of your closest Facebook friends. According to you, how many of them<br>think that a husband is justifed in hitting or beating his wife if he suspects her of being<br>unfaithful?<br>0.630|
|Imagine to pick 10 of your closest Facebook friends. According to you, how many of them<br>think that a husband is justifed in hitting or beating his wife if she goes out without telling<br>him?<br>0.598|

Notes: The _Global Index_ (Cronbach’s alpha = 0.74) is created using all variables reported above with the exception of those contained in the _Beliefs on others_ index. All variables were re-oriented so that the impact of treatments on each component of the index should be positive. The column on the right reports the loading factors used for the construction of the indexes with principal component.

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