rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

- Baker, D, Andrew Glyn, David Howell, and John Schmidt, 2004. “Labor Market Institutions and Unemployment” in D. Howell (ed.) Questioning Liberalization: Unemployment, Labor Markets, and the Welfare State, Oxford, University Press.

- Barakat, Bilal, Johannes Holler, Klaus Prettner, and Julia Schuste, 2010, “The Impact of the Crisis on Labour and Education in Europe”, Vienna Institute of Demography Working Paper.

- Bargain, Olivier, Herwig Immervoll, Andreas Peichl, Sebastian Siegloch, 2010. Who are the losers of the labour-market downturn? A scenario analysis for Germany. Mimeo.

- Bell, David and David Blanchflower, 2009. Youth Unemployment: Déjà Vu. Working paper. Dartmouth University.

- Buvinic, Mayra, 2009. The Global Financial Crisis: Assessing Vulnerability for Women and Children, Identifying Policy Responses. Commission of Status of Women, United Nations.

- Cai, Fang, Yang Du, John Giles, Albert Park, and Meiyan Wang, 2010, “Shock and Recovery in China’s Labor Market: Flexibilty in the Face of a Global Crisis,” mimeo

- Corbacho, Ana, Mercedes Garcia-Escribano, and Gabriela Inchauste, 2007, “Argentina: Macroeconomic Crises and Household Vulnerability,” _Review of Development Economics_ . vol 11 no. 1.

- Cunningham, Wendy and William F. Maloney (2000) “Measuring vulnerability: who suffered in the 1995 Mexican crisis?” Mimeo, The World Bank, Washington, D.C.

- Choudhry, Misbah, T., Enrico Marelli, and Marcello Signorelli, 2010. “The Impact of Financial Crises on Labor Markets- A Gender Perspective” The 11th bi-annual EACES conference, University of Tartu.

- Elsby, Michael, Bart Hobijn, and Aysegul Sahin, 2010, “The Labor Market in the Great Recession”, NBER Working Paper 15979.

- Fallon, Peter and Robert Lucas, 2002. The Impact of financial Crisis on Labor Markets, Household Incomes, and Poverty: Review of Evidence. _The World Bank Research Observer_ , 17(1). pp 21-45.

- Freeman, Richard. 2005 “Labour Market Institutions without Blinders: The Debate over Flexibility and Labour Market Performance”, NBER working paper 11286

- Freeman, Richard. 2009, “Labor Regulations, Unions, and Social Protection in Developing Countries: Market Distortions or Efficient Institutions”, NBER working paper 14789

Ghosh, Jayati, 2010. “Financial Crises and the Impact on Women” _Development,_ v. 53(3) p. 381-385.

- Glewwe, Paul and Gillette Hall, 1998. Are Some Groups More Vulnerable to Macro Shocks Than Others Hypothesis Tests Based on Panel Data from Peru. _Journal of Development Economics, 56._ pp. 181-206

23
This conditionality leads to a second stylized fact: the initial parameters of the income distribution matter both for growth and the efficiency with which growth is able to reduce poverty. According to a well-established theoretical argument, initial conditions dull economic growth and its impact when market failures translate into credit constraints that trigger diminished investments in physical and human capital or, worse, leave investment opportunities entirely unexploited. In particular, if credit rationing combines with investment indivisibilities, this is especially harmful for the poor (Aghion and Bolton 1997; Banerjee and Duflo 2003; B ́enabou 1996; Durlauf 1996; Galor and Zeira 1993; Hoff 1996; Ljungqvist 1993; Piketty 1997).

Built on similar arguments, an array of empirical studies on the constraints and determinants of growth has thus tested the role of the initial parameters of the distribution in growth models and confirmed that either higher initial poverty (Ravallion 2012) or higher initial inequality (Alesina and Rodrik 1994; Clarke 1995; Deininger and Squire 1998; Knowles 2005; Persson and Tabellini 1994; Ravallion 1998) are significant constraints to future growth rates. Moreover, some studies have also demonstrated that such unfavorable initial parameters tend to curb the impact that a given growth rate can exert on the proportionate rate of poverty reduction, as revealed by diminished elasticities of poverty to growth (Bourguignon 2003; Lopez and Serv ́en 2006; Ravallion 1997, 2004, 2007, 2012).

Most of the empirical literature on (income) convergence does not explicitly address the influence of the initial distribution of income on subsequent poverty reduction and growth. In Ravallion’s (2012) sample of almost 90 countries that have recorded noticeable rates of growth and poverty reduction and in which there are unambiguous signs of income convergence, there is no significant evidence that countries starting out poorer experienced higher relative rates of poverty reduction thereafter. This counterintuitive result is attributed to initial poverty, which, as revealed by a decomposition of the speed of poverty convergence, offsets the advantage of higher growth rates among poorer countries, that is, income convergence and the growth elasticity of poverty reduction.

Taking advantage of a unique panel dataset (1992–2014) on income, poverty, and inequality across municipalities in Mexico, this paper tests most of the above conclusions to provide a more disaggregated, longer-term perspective on the convergence paths and changes in well-being. Previous studies of convergence in Mexico have mainly focused on income growth paths among states, and, while some have found evidence of convergence in the years before the end of the import substitution model, most have consistently reported evidence of divergence after that. For 5
<u>Table 8: Passthrough between Household Final Consumption Expenditure (HFCE) growth from national</u> accounts and household welfare growth from surveys

|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|hfce_mean|0.711***|0.847***|0.747***|0.867***|1.031*|0.607**|
||(0.117)|(0.176)|(0.123)|(0.147)|(0.549)|(0.269)|
|initial headcount gini|||-0.00794|0.0192|0.0136|0.0133|
||||(0.0407)|(0.0493)|(0.0581)|(0.0405)|
|intial survey mean|||-0.005***|-0.004***|-0.00453***|-0.004***|
||||(0.00135)|(0.00133)|(0.0013)|(0.00132)|
|interval|||-0.000424|0.000170|-0.0002|-0.000270|
||||(0.00175)|(0.00168)|(0.002)|(0.00155)|
|Baseline: other regions|||||||
|SSA||||0.0162*|||
|||||(0.00864)|||
|hfce_mean x|||||||
|SSA||||-0.685***|||
|||||(0.255)|||
|initial headcount gini|||||-0.695||
||||||(1.314)||
|Baseline: <2000|||||||
|2001-2009||||||0.00777<br>(0.0148)|
|2010 and above||||||0.0189<br>(0.0134)|
|hfce_change x|||||||
|2001-2009||||||0.281<br>(0.309)|
|2010 and above||||||-0.0769|
|||||||(0.284)|
|Baseline:|||||||
|Consumption|||||||
|Income|||0.0354**|0.0356**|0.0351**|0.0371***|
||||(0.0151)|(0.0150)|(0.0149)|(0.0141)|
|Constant|0.00466|0.0203***|0.0312**|0.00924|0.0214|0.00862|
||(0.00490)|(0.00594)|(0.0142)|(0.0181)|(0.0232)|(0.0204)|
|country fixed effect|No|Yes|No|No|No|No|
|Observations|505|505|505|505|505|505|
|R-squared|0.198|0.444|0.249|0.267|0.250|0.268|

Notes: Table shows results of a regression of the annualized log change in survey mean consumption or income per capita on the annualized log change in private household expenditure per capita as measured from national accounts. Sample consists of all consecutive spells between 1981 and 2021, dropping spells with baseline and endline poverty rates lower than 2 percent. Sample drops from 575 to 505 because for some countries data on private household expenditures from national accounts are missing. Robust standard errors in brackets. ***: Statistically significant at 1%; **: Statistically significant at 5%; *: Statistically significant at 10%.

25
<!-- Start of picture text --> 4.0% |<br>3.5% |<br>3.0% |<br><!-- End of picture text -->

<!-- Start of picture text --> 2.5% -.~ dA : - —Brazil (2.3)<br>2.0% i/ > —mTM-China (1.2)<br>|-* . > A te india (2.1)<br>1.5%: |e svs~ f_. ————RussiaSouth (2.4)Africa (1.8)<br><!-- End of picture text -->

<!-- Start of picture text --> 0.5% |<br>1.0% | \/<br>0.0%<br>2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text -->
innovations, but not apply nearly as strongly for innovations which lower costs or improve operations firmwide, such as process and organizational innovations.

_Hypothesis 3: Diversification should be more strongly associated with product and marketing innovations than with process and organizational innovations._ However, to focus exclusively on the characteristics of the firm and the risktaking propensity of the owner is to abstract from the central role of the entrepreneur in the innovation process. The association of entrepreneurship with innovation dates back to Schumpeter (1934), who defines an entrepreneur as one who implements change in markets through the carrying out of new combinations –that is, who innovates. While some innovations spring from a sudden flash of inspiration, most result from a conscious purposeful search for innovative opportunities (Drucker, 1985). Some business owners will have greater ability to conduct such searches than others, and additionally, the personality traits of the owner may influence their propensity to focus on innovative activities rather than on the day-to-day running of the business. In a prior survey of Sri Lankan firms, we found that owner characteristics do distinguish own account workers (with no employees) from owners of businesses with 5 or more employees (de Mel et al., 2008). We will investigate here whether these owner characteristics also are associated with the likelihood of innovating.

_Hypothesis 4: Conditional on firm size and firm characteristics, owner characteristics still have an important role to play in predicting innovative activity, especially for smaller firms._ Which owner characteristics might matter for innovation? Gender and marital status are standard variables to include, although we do not have strong priors on their effect on the likelihood of innovating. Owner’s age is another standard demographic variable, and may be negatively correlated with the likelihood of innovation. We expect owner’s education to be positively correlated with innovation, as more human capital should increase the efficiency of innovation, lowering _D/λ._ Risk preferences and

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Policy Research Working Paper 7728

# **Abstract**

Climate change damage (or, more correctly, impact) functions relate variations in temperature (or other climate variables) to economic impacts in various dimensions, and are at the basis of quantitative modeling exercises for the assessment of climate change policies. This document provides a summary of results from a series of meta-analyses aimed at estimating parameters for six specific damage functions, referring to: sea level rise, agricultural productivity, heat effects on labor productivity, human health, tourism flows, and households’ energy demand. All parameters of the damage functions are estimated for each of the 140 countries and regions in the Global Trade Analysis Project 9 data set. To illustrate the salient characteristics of the estimates, the change in real gross domestic product is approximated for the different effects, in all regions, corresponding to an increase in average temperature of +3°C. After considering the overall impact, the paper highlights which factor is the most significant one in each country, and elaborates on the distributional consequences of climate change.

This paper is a product of the Development Prospects Group, Development Economics. 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://econ.worldbank.org. The authors may be contacted at roson@unive.it and martina.sartori@unive.it .

_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
<!-- Start of picture text --> 100<br>86<br>80<br>62<br>60<br>40 32<br>20 13<br>6<br>1<br>0<br>Down Same Up<br>Experienced Expect<br><!-- End of picture text --> Source: World Bank staff estimates based on data from Perceptions of Poverty, Prosperity and Economic Mobility in Nepal, 2014 Households in urban areas, in the Western Development region, in the hills, with heads employed in semiskilled, non-agriculture sector, in the top 40 percent of the wealth distribution and with both domestic and overseas migrants have the highest odds of perceiving upward mobility. In contrast, households in rural areas, in the Far Western Region, in the mountains and the Terai, and the bottom two quintiles are likely to have higher odds of lower mobility. Expected upward mobility, on the other hand, is highest in MidWestern region, mountains and lowest in the Terai and among semi-skilled workers in non-agricultural sectors. The fact that this latter group has experienced the highest upward mobility among all other occupation categories and yet is also the group that has the lowest expectation of upward economic mobility underscores an important aspect of the nature of structural transformation that has underpinned Nepal’s living standards improvement. The growth in labor income that has driven the improvements in living standards at the bottom of the distribution have come from expansion of opportunities as well as the rise in low productivity service sectors in rural as well as urban areas. But given the high degree of informality as well as the low prospects for productivity growth in these sectors, there is a dimmer outlook for continued improvements in living standards into the future. This is the population that constitutes Nepal’s growing vulnerable class.

This point is supported by evidence from other sources of data. Gallup World Survey, for example, also asks respondents similar questions about their perceived position in an 11-step Cantril ladder denoting various parts of the economic distribution within each country. Respondents are asked about current position as well as positions five years before the survey and expected position five years hence. Based on responses to these three questions, Gallup defines households as thriving, struggling or suffering. Thriving households have strong current life situation and have positive views about the next 5 years. Struggling households have wellbeing that is moderate but inconsistent while suffering households have well-being that is at high risk, they report poor ratings of their current life and also poor outlook for the future. Based on cross-country analysis of these categories, Gallup has found thriving respondents to have fewer health problems, less worry and stress, sadness and anger and more happiness, enjoyment, interest and respect. Struggling households on the other hand report higher daily stress, and worry about money than thriving 16
In our baseline analysis, we employ daily data of emerging market sovereign spreads over Treasuries on U.S. dollar-denominated debt for 58 countries included in the J.P. Morgan Emerging Markets Bond Index Global (EMBI Global). Table A2 presents our sample of countries, and the sample period covered is January 2, 2019 through May 27, 2022. The global factor used in our analysis is the EMBI Global index. Corporate spreads are captured by the J.P Morgan CEMBI IG+ index. To construct the regional factor, we adopt an approach similar to that used by Daehler et al. (2021). First, countries are grouped into geographic buckets according to the seven regional classifications defined by the World Bank’s World Development Indicators database.<sup>14</sup> The regional factor for a given country is then calculated as the daily average logged spread for the country’s regional grouping, excluding the country itself.

The country-specific economic indicators used as controls include the total external debt stock, primary balance (% of GDP), GDP per capita growth, and annual changes in consumer prices, all lagged by one year. Data for the external debt stock and primary balance is sourced from the spring 2022 vintage of the cross-country database of fiscal space created by Kose et al. (2022). GDP per capita growth is sourced from the IMF WEO database, and annual inflation data comes from the IMF as well.

Our policy-related variables are included to control for the influence of monetary policy decisions and pandemicinduced lockdowns and restrictions. These controls include dummy variables indicating dates of Federal Reserve and ECB announcements through the pandemic timeframe, and daily log changes in the Oxford COVID-19 Government Response Tracker Stringency Index. The Stringency Index, ranging from 0 to 100 (with 100 indicating the strictest response), is a composite of nine metrics measuring the stringency of school and workplace closures, stay-at-home requirements, and other government-mandated restrictions. Use of the Stringency Index serves as a control for variation in governments’ responses to the pandemic.<sup>15</sup> The Federal Reserve and ECB actions that we flag include interest rate cuts and any other monetary policy-related measures taken to reassure markets through the early stages of the pandemic. A priori, it is ambiguous whether Federal Reserve or ECB action would increase or decrease spreads during a crisis period. On one hand, by easing global risk aversion, action taken could lower spreads by encouraging capital flows into emerging markets. On the other hand, if Fed or ECB action fails to soothe

> 15 Our methodology using the Oxford COVID-19 Government Response Tracker Stringency Index follows Daehler et al., (2021), who investigated factors influencing credit default swap (CDS) spreads through the COVID-19 pandemic.

10
Policy Research Working Paper 10085

# **Abstract**

Social sustainability is often poorly understood and vaguely defined, despite growing appreciation of its relevance as a concept. This paper advances the empirical understanding of social sustainability by constructing a global database of 71 indicators across 193 countries and 37 territories between 2016 and 2020. The indicators are flexibly clustered around four dimensions—social inclusion, resilience, social cohesion, and process legitimacy—for which measurement indices are constructed. A simple empirical analysis using the database confirms that social sustainability is positively and strongly associated with per capita income, negatively and strongly associated with poverty, and negatively but weakly associated with income inequality. Much remains to be analyzed to understand the interactions between dimensions, but the results underscore that social sustainability matters not only in itself, but also to reduce poverty. Furthermore, extending access to markets, basic public services, and social assistance needs to be complemented with strengthening process legitimacy and social cohesion if inequality is to be reduced.

This paper is a product of the Social Sustainability and Inclusion 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 jcuesta@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
- 30 -

conditions: when imposed as a condition of granting an occupancy permit (to flag new construction) or registering a deed of sale (to flag changes in ownership, including new subdivisions). It would presumably be as effective in other countries, provided the process of property transfer and development employed similar systems of approval and registration. Cross-referencing, in the form of voluntary information-sharing amoni, agencies of government, does not have a good track record in Brazil. Experience with voluntary compliance is similarly mixed.

5.05 Episodic general resurveys, as practiced in Brazil, are a response to the failure of other techniques to provide updated property information. As a maintenance technique, they imply the acceptance of a high level of evasion prior to the general resurvey. Rio's experience suggests that general resurveys can be undertaken at relatively low unit-cost, but at the expense of continued omissions and distortions in the tax base. CIUTA's experience suggests that comprehensive general resurveys are not cost-effective if they are relied upon as the sole method of obtaining updated property information.

# Scarcity of Skills

5.06 The task of converting visible property characteristics to an estimate of market value is one that potentially requires a sophisticated knowledge of property markets and leaves considerable discretion to individual property valuers. Local governments in many developing countries have difficulty attracting such staff and have found that discretion invites corruption.

5.07 Brazil's response is to use a highly simplified form of mass appraisal to derive the majority of property valuations. In this respect, it is successful. The technique is based upon a few readily observable, measurable characteristics of each property. No skills, other than the ability to measure and write, are required of the valuer in the field. His work is readily verifiable through remeasurement of randomly selected properties.

5.08 The technique does require a degree of professional judgement in the initial calculation of unit costs. Here, CIATA appears to be a successful response, particularly in smaller jurisdictions where the need is most critical. CIATA maintains a centralized pool of expertise, which is available to municipalities at the time unit costs are recalculated.

# Inaccessible Market Information

5.09 Accurate data on market prices are also essential to the production of defensible valuations. They are rarely available through official sources. In countries defining value in capital or sales terms, data on transactions are understated to avoid real estate transfer taxes. In countries defining value in rental terms, a similar problem exists. Many of these countries impose rent controls. As these controls are often ignored or subverted in the market, data on actual--and therefore illegal--rents are not available to official sources.

5.10 Brazil has a workable approach to the calculation of unit costs for buildings: the municipalities use construction cost data derived from industry sources. Brazil has not found an acceptable means of calculating the unit cost