rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3**

# **Spouse Health and Work Status in Labor Supply Decisions of Adults Over 45**

Linear Probability Models, Dependant Variable: Worked at Least One Hour per Week

|||China (C<br>|HARLS)<br>|||Indonesi<br>|a (IFLS)<br>|||Korea (<br>|KLoSA)<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Ur|ban|Ru|ral|Ur|ban|Ru|ral|Ur|an|Ru|ral|
||Male|Female|Male|Female|Male|Female|Male|Female|Male|Female|work|work|
|Pension Eligible|-0.134*|-0.194***|-0.127*|-0.031|-0.235***|-0.240***|-0.129***|-0.136*|-0.104***|-0.020|-0.008|0.024|
||(0.08)|(0.06)|(0.07)|(0.12)|(0.03)|(0.05)|(0.03)|(0.07)|(0.02)|(0.03)|(0.03)|(0.05)|
|Spouse Pension Eligible|0.106|0.087|0.084|-0.136**|-0.104**|-0.015|0.110|-0.028|-0.050|-0.037*|0.047|-0.061*|
||(0.07)|(0.08)|(0.10)|(0.06)|(0.05)|(0.04)|(0.08)|(0.05)|(0.05)|(0.02)|(0.09)|(0.03)|
|Spouse Working|0.196***|0.202***|0.121***|0.193***|0.047**|0.091**|0.088***|0.231***|0.054***|0.025|0.354***|0.339***|
||(0.07)|(0.07)|(0.03)|(0.04)|(0.02)|(0.04)|(0.02)|(0.05)|(0.02)|(0.02)|(0.03)|(0.03)|
|Ln(Housing Wealth P.C.+1)|-0.062**|-0.001|-0.007|-0.026|0.002|-0.001|0.002|0.003|0.018**|-0.016***|0.015|-0.023|
||(0.03)|(0.02)|(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|
|(I)ADL Z-Score(W/Difficulty)|-0.061|-0.027|-0.083***|-0.083***|-0.101***|-0.069***|-0.063***|-0.071***|-0.034***|-0.012*|-0.062***|-0.007|
||(0.04)|(0.03)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|(I)ADL Z-Score(Unable)|0.005|-0.005|-0.079***|-0.048***|-0.107***|-0.070***|-0.133***|-0.093***|-0.032***|-0.007|-0.038***|-0.019|
||(0.04)|(0.03)|(0.01)|(0.01)|(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Spouse (I)ADL Z-Score(W/Difficulty)|0.068|0.031|0.031**|0.052***|0.016|0.005|-0.020**|0.031**|-0.016|0.003|0.005|0.008|
||(0.04)|(0.04)|(0.01)|(0.02)|(0.01)|(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.01)|
|Spouse (I)ADL Z-Score(Unable)|-0.016|-0.014|0.003|0.003|0.030|-0.052|0.008|0.016|-0.011|-0.008|-0.038|0.016|
||(0.05)|(0.04)|(0.01)|(0.02)|(0.02)|(0.03)|(0.01)|(0.02)|(0.01)|(0.01)|(0.03)|(0.01)|
|Observations|256|236|1,000|1,103|1,505|2,004|1,613|2,161|3,413|4,356|1,005|1,262|
|R-squared|0.489|0.482|0.320|0.343|0.332|0.138|0.330|0.164|0.386|0.169|0.329|0.265|

Notes: These regression models include age, age-squared, years of education, years of education-squared, average education of spouse and adult children, number of living grandparents, number of grandchildren, indicator variables for married, for spouse information not present and for no adult children, and city dummies (China: county; Indonesia: Kabupaten; Korea: metropolitan city and province). Standard errors in parentheses; ***p<0.01, **p<0.05, *p<0.1 36
(Figure 2). Productivity is significantly higher for the firms located in larger cities and in places with a higher share of college graduates, which reflects the benefits of agglomeration of skilled labor force.

Government efficiency and effectiveness are highest in the Southeastern cities (Fujian, Guangdong, Jiangsu, Shanghai, and Zhejiang), and are lowest in the lagging Southwest and Northwest. The firms in the coastal areas pay lower taxes and fees; have a much shorter customs clearance delay; spend less time dealing with bureaucracy; and spend less on entertainment and travel, which are good proxies of local corruption (Cai, Fang, and Xu forthcoming). The survey finds that foreign firms operating in the Southeast face considerably lower taxes and fees than elsewhere (Table 1) while combined average export/import clearance in the Southeast stood at 7.3 days in contrast to the Northwest at 16.8 (Table 2). In the coastal areas, firms reportedly have better access to finance, more reliable protection of property rights, and more effective contract enforcement.

The rising concentration of economic activity in prospering coastal areas has resulted in the rising provincial disparities of GDP per capita. Income inequality has been on the rise steadily since 1980 and then grew exponentially during the 1990s (Chan, Henderson, and Tsui 2008). The ratio of the rural per capita annual net income to urban per capita annual disposable income was 61% in 1990 and 41% in 2004. Per capita GDP gap between the wealthiest and poorest provinces more than doubled between 1988 and 1998. This pattern coincided with a substantial net migration into the eastern provinces and a net outflow from the central and western provinces (Fan 2005).

Countries at their early stages of development tend to experience widening regional disparities in living standards before slowly converging (World Bank 2008). The PRC’s rapid growth was due to its ability to exploit scale economies and take advantage of the world market of unprecedented size. The PRC’s GDP per capita today is roughly equivalent to that of Britain in 1911. London at that time had a GDP per capita around 1.7 times the national average, whereas East Anglia had a GDP per capita two thirds of that average. In the PRC today, the comparable figures are 3.3 for Shanghai and one third for the lagging area of Guizhou. Shanghai has a GDP per capita ($16,044), roughly equivalent to the British average in 1988, while Guizhou has a level ($1,653) close to the British average in 1830 (World Bank 2008).

Meanwhile, the structural transformation also led to stronger regional specialization. Urban manufacturing employment declined steadily throughout the 1990s as the trade sector grew more slowly than services (Table 3). Regions became increasingly specialized over time (Chan, 5
69

# TABLE 10: Number of Cattle by Tenure Category 1970 & 1981

||Private|EJldo|Mixed|
|---|---|---|---|
|'000 Head||||
|ALL. FARMS||||
|Total Herd||||
|1970|13,003|4,734|na|
|1981|14,067|7,613|823|
|Purebred Cattle||||
|1970|1,995|160|na|
|1981|3,163|620|97|
|FARMS<br>UP TO 5 HECTARE|S|||
|Total Herd||||
|1970|2,633|na|na|
|1981|855|2,378|187|
|Purebred Cattle||||
|1970|124|na|na|
|1981|88|189|16|

SOURCE: Agricultural Census, 1970 & 1981.
Levine 1997, Bluedorn 2001). If such cultural differences are also correlated with gender inequality then our main empirical results could suffer from spurious correlations. For example, violence and conflict resulting from cultural differences may adversely affect health services in the country and this may be particularly so for women than men since women typically are the underprivileged in society. To guard against this source of spurious correlation, we use three separate variables due to Alesina et al. (2003) that capture the degree of ethnic, linguistic and religious fractionalization in the country.

In addition to differences within a country, social, cultural and religious differences across countries can also spuriously affect our results (see for example, Dollar and Gatti 1999). For example, culturally motivated or sanctioned attitudes towards women are likely to influence gender inequality in the labor market, health, etc. Also, the same social, cultural and religious factors contribute to social capital through for example, the social values they inculcate among individuals, emphasis on honesty, hard work, and creativity, etc. (see for example, Knack and Keefer 1997, Rose 2000). We follow the literature and guard against the implied omitted variable bias here by controlling for the percentage of population that is Catholic, Muslim and Protestant. The data source for the variable is La Porta et al. (1999).

Geography and trade openness have also been linked to economic growth. For example, a number of studies have shown that landlocked countries and countries that are closed to international trade tend to have lower growth than others (see for example, Frankel and Romer 1999, Mackellar et al. 2002). If gender inequality also happens to vary systematically with trade openness and geography then our main estimation results could suffer from spurious correlation. To this end, we control for a dummy variable equal to 1 if the country is landlocked and 0 otherwise ( _Landlocked_ ), country size as measured by (log of) population of the country in 2005 10
Green’ scenario where Romania will meet EU’s long-term road map of reducing 80% of GHG emissions in 2050 from 1990’s level.<sup>6</sup> The study finds that Romania can meet the mitigation obligations likely under the EU 2030 framework in energy and electricity at moderate costs. However, meeting the prospective requirements of the EU 2050 Roadmap (i.e., super-green scenario) is both expensive and challenging to implement.

The paper is organized as follows. Section 2 briefly introduces the TIMES model and data used for the analysis. This is followed by discussions of results generated by the model under the alternative scenarios in Section 3. Section 4 highlights key policy implications drawn from modeling exercise carried out for this study and finally key conclusions are drawn in Section 5.

# **2. Methodology, Data and Scenarios**

The study employs the TIMES model to develop optimal supply mix under the various scenarios considered. The overall modeling framework is illustrated in Figure 1. As shown in the figure, a macroeconomic model (actually a CGE model) first provided projections on key macroeconomic variables, such as GDP, sectoral value added and outputs, household income, and commodity price indices for the 2015-2050 period.<sup>7</sup> These variables together with many other input data serve as the main drivers for end-use energy demand projections in various sectors of the economy (i.e., residential, non-residential, industrial, transportation) over the same time horizon (i.e., 2015-2050).<sup>8</sup> Demand side energy efficiency measures were considered in the demand modeling thereby developing two cases for energy supply projections. The first case assumed that energy efficiency measures will not be materialized due to several implementation barriers. We call it the high energy demand case. In the second case, we assumed that energy efficiency measures will be gradually implemented over the planning horizon (2015-2050). We refer this case as the low demand case. Each supply scenario (i.e., Base, Green and Super Green) is run under both demand cases thereby making six runs of the TIMES model.

# **Figure 1. Overall methodological framework**

> 6 The European Union has laid out a vision for mitigation through 2050 in a “Roadmap for moving to a competitive low carbon economy in 2050” was published in March 2011 by the European Commission. 7 Please see Jorgensen and Shkaratan (World Bank, 2016) for detailed information.

> 8 For the projection of end-use energy demand for Romania, which is used exogenously in this study, please refer to Malla and Timilsina (2016).

4
Giambona, E., R. Matta, J. Peydro, and Y. Wang, 2020, Quantitative easing, investment, and safe assets: The corporate-bond lending channel, working paper, Universitat Pompeu Fabra.

Grosse-Rueschkamp, B., S. Steffen, and D. Streitz, 2019, A capital structure channel of monetary policy, _Journal of Financial Economics_ 133, 357-378.

Hale, T., S. Webster, A. Petherick, T. Phillips, and B. Kira, 2020, _Oxford COVID-19 Government Response Tracker_ , Blavatnik School of Government.

Hartley, J., and A. Rebucci, 2020, An event study of COVID-19 central bank quantitative easing in advanced and emerging markets, NBER WP 27339.

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Kalemli-Ozcan, S., L. Laeven, and D. Moreno, 2020, Debt overhang, rollover risk, and corporate investment: Evidence from the European crisis, NBER WP 24555.

Kim, H. and H. Kung, 2017, The asset redeployability channel: How uncertainty affects corporate investment, _Review of Financial Studies_ 30, 245-280.

Krishnamurthy, A., and A. Vissing-Jorgensen, 2011, The effects of quantitative easing on interest rates, _Brookings Papers on Economic Activity_ , 215-265.

Kuttner, K., 2018, Outside the box: Unconventional monetary policy in the Great Recession and beyond, _Journal of Economic Perspectives_ 32, 126-146.

Luck, S., and T. Zimmermann, 2020, Employment effects of unconventional monetary policy: Evidence from QE, _Journal of Financial Economics_ 135, 678-703.

Maio, P., 2014, Another look at the stock return response to monetary policy announcements, _Review of Finance_ 18, 321-371.

Myers, S., 1977, Determinants of corporate borrowing, _Journal of Financial Economics_ 5, 147175.

OECD, 2020, Evaluating the initial impact of COVID-19 containment measures on economic activity, Staff Report.

Rigobon, R., and B. Sack, 2003, Measuring the reaction of monetary policy to the stock market, _Quarterly Journal of Economics_ 118, 639-669.

Rischen, T., and E. Theissen, 2019, Underpricing in the Euro Area bond market: New evidence from post-crisis regulation and quantitative easing, University of Munich working paper.

30
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The main dependent variable in our study, from the World Bank’s Country Policy and Institutional Assessment (CPIA), measures what World Bank country teams are attempting to achieve when they design public sector reform conditions to include in DPLs, i.e., an increase in the quality of public sector management. In contrast with the existing literature, we consider the number of cumulative conditions related to public sector governance in DPLs as the main variable of interest. We look at the accumulation of conditions since we believe supporting policy reform in the public sector is a multistage and long term process (see, e.g., Pritchett and de Weijer, 2010).

Results from panel estimations reveal a significant quadratic relationship. These findings are robust to sample restrictions, additional controls, the use of alternative measures of public sector governance, and correction for endogeneity with system GMM. Further evidence is provided by instrumenting our variable of interest in a cross-sectional design. Falsification tests are also consistent with a causal interpretation from conditions to quality of public sector governance. Although results are statistically significant, the impact of PSG conditionality is modest. For instance, the predicted turning point in our preferred GMM specification is at 80 conditions, where the CPIA score is increased by about 0.247 points (or 0.3 standard deviations) higher on average, compared to the case of 0 conditions.

Next, we replicate the analyses after disaggregating the conditions and CPIA indicators by sub-sector. There are four CPIA indicators on public sector governance: (1) quality of budgetary and financial management, (2) efficiency of revenue mobilization, (3) quality of public administration and (4) transparency, accountability and corruption in the public sector. The PSG-related conditions in DPLs can be classified along similar lines. These disaggregated tests find that conditions are effective in supporting public financial management (PFM) and revenue systems, but not for promoting accountability and combating corruption. For quality of public administration, there is a significant quadratic relationship in our preferred GMM specification, but it is not robust to changes in sample 2
# **_3.1 Public Infrastructure and Economic Growth_**

In parallel to the increase of political interest in public investments toward the end of the 20<sup>th</sup> century, scholars began exploring the importance of infrastructure investment, particularly in relation to economic growth (Table 2). Taking advantage of different methodologies and various geographical locations, this strand of literature resulted in a wide range of estimates.

Focusing on North America, the literature offers evidence supporting the hypothesis that public infrastructure positively impacts economic growth (Munnell 1990; Garcia-Mila & <mark>McGuire</mark> 1992; <mark>Rives & Heaney</mark> 1995; Wylie 1996). For example, Munnell (1990) looked at the relationship between public capital and measures of economic activity at the state level. Munnell, then, concluded that public capital has a significant, positive impact on output, although the output elasticity was roughly one-half the size of the national estimate (Aschauer 1989a). Similarly, Garcia-Mila and <mark>McGuire</mark> (1992) used data on 48 contiguous states from 1969 to 1983 to estimate the impact of public infrastructure (captured by education and highways) on gross state product. The estimates were consistent with Munnell’s (1990) results, both in terms of the sign and the magnitude. Focusing on the economic performance at the community level, Fox and Smith (1990) and Rives and Heaney (1995) show investments contribute to U.S. local economies positively and significantly. Examining the role infrastructure on economic growth in Canada, Wylie (1996) finds that output elasticities of infrastructure in Canada are comparable and even larger than that in the United States.

The findings should, however, be interpreted carefully as the methodologies used in these studies have several limitations. The estimation techniques used to suffer from the endogeneity of the public capital stock and economic performance, spurious correlation, reverse causality where the causation also runs from the economic measures to the input of public capital, and measurement error due to lack of data.

14
are limiting more than non-members. In the second place, aspects of life quality more associated with the environment than the individual should be relatively similar for members and non-members (if our controls are reasonable matches) as the program does not specifically target the physical or social environment. Third, and finally. there may be factors in operation that seek to cancel each other out – for example, when it comes to worry and stress, social activities might reduce isolation whereas financial activity, particularly borrowing money, might contribute to increasing stress. As a result, while we are generally looking for evidence of capability expansions, it is important to recognize that some aspects of life quality might not be impacted (positively). The potential negative impact of empowerment programs on aspects of life quality has been recognized elsewhere – see for example van Kempen (2009). As this empowerment intervention encourages women to be aware of their opportunities and the treatment of women in their communities, it is perfectly possible that they may as a result become more aware of certain risks. And it is worth noting that there was no evidence of comprehension difficulties at the pilot stage: there are no psychometric results for this setting at present though a recent study by Vergunst et al. (2017) provides evidence on response reliability within a high income population. Our indicators are close to those used in that study but incorporate some minor modifications following an Indian piloting process.<sup>14</sup>

# **4. Data and Descriptive Results**

The data used for this study come from a sample of approximately 6,000 observations collected from women across all the 32 districts where the program is present. Three-

> 14 There are some similarities also with the capability indicators used in Tesoriero (2006), though he does not include data on controls and focusses his discussion on what he describes as modest contributions to community strengthening.

14