rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 8: Average Annuity Rate and Risk Free Rate**

(percentage points) <!-- Start of picture text --> 8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1993 1995 1997 1999 2001 2003 2005 2007 2009<br>Average Annuity Rate (RV‐04) BCU‐10<br><!-- End of picture text --> Sources: SVS, Central Bank.

The average MWRs estimated for March 1999, 2002, 2003, 2004 and 2005 are consistent with the movements in the two rates. The MWR is slightly lower than 1 in 1999, consistent with a slightly higher risk-free rate and higher than 1 in the following years, consistent with a higher annuity rate. In general MWRs are higher in the period after 2002, when the annuity rate has exceeded the risk-free.. The MWRs for 2004 and 2005 are similar, consistent with a stable relationship between the two rates. We have not estimated MWRs for previous years, but the relationship between the two rates suggests that MWRs were lower in the 1990s and increased significantly after 2000. All in all, these results suggest that annuitants in Chile in the mid 2000s were receiving better value for their premiums and are also receiving a better deal, on average, than annuitants in other countries.

# **3.5 Replacement Rates**

Despite the collection of massive amounts of data, Chilean statistics do not contain information on the replacement rates achieved by retiring workers. Projections of replacement rates are based on assumptions regarding the length of active work and retirement lives, the average rate of wage growth and the average rate of net investment returns. Assuming an active life of 40 years, a retirement life of 20 years, an average growth rate of 2 percent and a net investment return of 5 percent would produce a replacement rate of 60 percent of the final wage for a 10 percent contribution rate. If the net investment return is 3.5 percent, the replacement rate would amount to 38 percent.

Chile closed down its old social security system and until recently did not operate a public pillar. The introduction of a solidarity pillar in 2009 is an implicit zero/first pillar that tops up pensions if they fall below the PMAS. The amounts of PBS and PMAS are set in the law and are expected to reach about USD 150 in 2009 and USD 500 in 2011 respectively. Both pensions will be indexed according to inflation.<sup>34</sup> The solidarity pillar was designed to provide income support

> 34 Laws 20.255 and 20.366

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- 4 -

1.18 As a result, the contribution of the property tax to municipal revenues is small. In 1984--the most recent year for which aggregate data are available--property taxes contributed 8 percent of total municipal tax and transfer revenue. The service tax contributed 12 percent. Transfers--chiefly fixed shares of state and federal taxes--contributed the remaining 80 percent.

1.19 The size of this contribution has declined since the present structure of local finance was established in 1967 (see Figure 1). During the 1970s, property tax revenues increased by 250 percent in real terms. Revenues from transfers increased at an equivalent rate, and business taxes, somewhat faster. As a result, the contribution of property taxes to total municipal revenues fluctuated between 10 percent and 15 percent during this period.

1.20 Beginning in 1979, however, property tax revenues began to decline in absolute terms. This downturn corresponded to a period of accelerating inflation and recession in Brazil. As shown in Figure 2, annual inflation began to exceed 50 percent in 1979, and growth in GDP virtually ceased during the first four years of the decade. Property tax revenues-1 which had closely paralleled GDP growth during the 1970s--declined by 19 percent between 1979 and 1980, and continued to decline steeply during the following four years.

<!-- Start of picture text --> Figure 2<br><!-- End of picture text --> <!-- Start of picture text --> Property Tax Buoyancy<br>Trends in GDP, Inflation, and Revenue<br>an<br>S nu<br>2.5 _<br>2<br>1.5<br>.. ... xx<br>I........-<br>' i to 7i 72 n 7 i 'A i C'i 2 A I<br>Yew<br>- - T.fr1"WtVtO . .l<br><!-- End of picture text -->

1.21 The importance of the property tax varies widely among municipalities, in both relative and absolute terms. The per capita

revenues of selected municipalities are illustrated in Figure 3. The figure divides municipalities between Brazil's two major economic regions: the wealthier south/southeast, and the less industrialized north/northeast. Individual municipality data is provided for Brazil's nine largest cities (all state capitals). Aggregate data is provided for other state capitals and all other municipalities in each region.

1.22 As shown, the property tax makes its largest contribution--in both absolute and relative terms--in Brazil's largest cities. Property tax revenues in Sao Paulo--Brazil's largest city--were equivalent to US$11 in 1984 and provided 20 percent of recurrent revenues in 1984. Yields in other major cities in the south ranged from US$4 to US$9 per capita, and contributed 10 percent to 20 percent of municipal revenues. Yields in major northern cities were lower in absolute terms, but made an equivalent contribution to recurrent municipal revenues, comprising 10-15 percent of the total.
Policy Research Working Paper 10064

# **Abstract**

There is limited research on the determinants of firms’ green investment strategies in developing regions despite their importance to meet global climate change targets. Understanding how changes in firm climate investment affect environmental performance is essential for policy makers and firms alike. Based on unique data from the joint European Bank for Reconstruction and Development–European Investment Bank–World Bank Group Enterprise Surveys, this paper empirically examines the role of access to finance and green management practices in firms’ green investment strategies. Based on logistic regressions, the econometric analysis finds a positive influence of green management practices on the number of mitigation measures implemented. By contrast, firms that are financially constrained are less likely to pursue many mitigation measures. Finally, the results do not show significant differences in the impact of financial constraints on the type of green investment, but indicate that better green management practices lead to a higher likelihood of investing in both capital- and non-capital-intensive green measures.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 f.kalantzis@eib.org and schweigh@ebrd.com.

_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
least as large as those we calculate for our individual and aggregate indicators.<sup>13</sup> This underscores the need for caution in making cross-country comparisons with any type of governance indicator.

# **3.2  Changes over Time in Governance Using the WGI**

We now turn to the changes over time in our estimates of governance in individual countries. Figure 2 illustrates these changes for two selected governance indicators over the decade 1998-2008, Voice and Accountability and Rule of Law. In both panels, we plot the 1998 score on the horizontal axis, and the 2008 score on the vertical axis. We also plot the 45-degree line, so that countries above this line correspond to improvements in governance, while countries below the line correspond to deteriorations in governance. The first feature of this graph is that most countries are clustered quite close to the 45-degree line, indicating that changes in our estimates of governance in most countries are relatively small over the 11-year period covered by the graph. A similar pattern emerges for the other four dimensions of governance (not shown in Figure 2), and, not surprisingly, the correlation between current and lagged estimates of governance is even higher when we consider shorter time periods than the decade shown here.

Nevertheless, a substantial number of countries do show significant changes in governance. Over this period, we find that for each of our six indicators, on average 9 percent of countries experience changes that are significant at the 90 percent confidence level. Looking across all six indicators, 35 percent of countries experience a significant change in at least one of the six dimensions of governance over this period, roughly equally divided between improvements and deteriorations. We also note that the 90 percent confidence level is quite high, and for some purposes a lower confidence level, say 75 percent, would be appropriate for identifying changes in governance that are likely to be practically important. Not surprisingly this lower confidence level identifies substantially more cases of significant changes: 21 percent of countries experience a significant change on each indicator on average, and 59 percent of countries experience a significant change on at least one dimension of governance.

> 13 See Kaufmann, Kraay, and Mastruzzi (2004).

19
# **When the Lights Go Out: The Economic Impacts of Covid-19 on Cities Globally**

_Amjad Muhammad Khan, Hogeun Park, Mark Roberts and Putu Sanjiwacika Wibisana_<sup>_1_</sup>

**JEL codes:** O18, R11, R12 **Keywords:** Covid-19, nighttime lights, non-pharmaceutical interventions, cities, urban resilience

1 All authors are affiliated with the World Bank. Amjad Khan is affiliated with the World Bank’s Water Global Practice; and Hogeun Park, Mark Roberts, and Putu Sanjiwacika Wibisana with the World Bank’s Urban, Disaster Risk Management, Resilience, and Land Global Practice. **Corresponding author:** Amjad Khan (akhan31@worldbank.org).

This paper was produced as a background paper to the World Bank report _Vibrant Cities: Priorities for Green, Resilient, and Inclusive Urban Development_ . The authors thank Somik Lall and Forhad Shilpi for helpful comments and suggestions. They also thank Pui Shen Yoong and Ran Goldblatt for early discussions related to this work, and Andres Elizondo for his help with the processing of the nighttime lights data. 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.
_enterprise module_ on who owns/manages each business (in the enterprise module, the respondent for each enterprise may not be the same as the business owner/manager; in the data, there was also nearly total overlap between ownership versus management). Where men were the respondents in the non-farm enterprise module, they agreed with women who reported in the labor module that they ran a business in 68 percent of those cases. Women who were respondents in the enterprise module, however, agreed with men who reported operating a business in the labor module in 86 percent of cases. Gendered views of business ownership are likely a major factor for this discrepancy. In this case, the value-added of decision making questions over ownership/management of the enterprise is questionable without ensuring consistency of responses across men and women in the same household. For example, a similar analysis of the 2013-14 Uganda LSMS-ISA revealed a much lower degree of inconsistency, as there was closer matching of respondents across the labor and non-farm enterprise modules.

Figure 7. Malawi LSMS-ISA, 2016-17: share of respondents in NFE module, that agree with men and women who report in labor module that they run a business Total sample (proxy+own-reporting in labor module) <u>Notes: Sample sizes of men/women respondents in NFE module presented on each bar.</u> Within self-employment, an additional issue is that contributing family workers, often women, may also have significant roles in managing the business. Asking contributing family workers whether they were involved in the day-to-day administration of the business and earnings, as well as the frequency of involvement (on a regular basis; only during the current period/season; from time to time; or rarely), can help clarify economic roles for men and women beyond legal ownership of the business, and in doing so inform policy making (ILO, 2018c). Such questions were also recommended by the 20<sup>th</sup> ICLS in the revision of ICSE-18.

# Summary and ways forward

Since a large share of rural women’s employment is concentrated in contributing work in family farms/businesses, as well as informal wage employment that can be paid irregularly, strictly relying on individual earnings data to make comparisons on the value of women’s and men’s labor may not be very informative. Box 5 presents some recommendations from this section.

16
**Figure 7 Migration to high-income countries is more responsive to demographic divergence from middleincome countries, but not from low-income countries**

_Sources_ : Original estimation based on demographic data from UN DESA 2022 and migration data from Özden et al. 2011 and UN DESA 2020. _Note_ : The figure shows the relationship between demographic divergence and migration from low-income countries (in blue) and middleincome countries (in green) to high-income destinations. The lines represent the estimation of β from the equation: ln (Mo,d,t) = βGAP_ELDo,d,t + δod + γt + εo,d,t , where Mo,d,t is the number of migrants from origin country o in destination country d in year t per origin country population; GAP_ELD is the gap in the population share of the elderly (ages 65+) between destination and origin countries; δ represents corridor (origin-destination pair) fixed effects; and γ represents year fixed effects. The estimation is done separately for lowincome countries and middle-income countries. The scatterplot shows the residuals of ln (M) and GAP_ELD grouped into a hundred bins after the corridor and year fixed effects are taken out. The residuals are scaled by their respective mean values.

However, migration fueled by demographic transition also depends crucially on the income levels of origin and destination countries. For instance, as seen in panel a of figure 3, migration from low-income countries to other low-income countries with similar demographic profiles falls with increased development. Furthermore, as seen in Figure 7, migration to high-income destinations is more responsive to demographic divergence, measured by the gap in elderly shares of the population between destination and origin countries, from middle-income countries but not from low-income countries. This suggests that while demographic divergence can be a strong impetus for migration, the level of development matters for actual migration. This could be driven by the high costs of migration to high-income countries or other pertinent barriers in these corridors.

# 3.3 Heterogeneity by population size

A common cause for concern is whether the migration response described in the previous sections is similar for large countries, as well as small ones. The cross-sectional relationship, for instance, is a stark inverted-U for smaller countries with populations that are below the median. However, for larger countries, where 96 percent of the global population resides, the relationship is much more muted (appendix Figure A.1). This raises the question of whether the migration response is indeed driven by the response from smaller countries.

16
3

# considered a portfolio equity investment.2

There are numerous sources of data on PDI, and no single source is by itself necessarily adequate for a given purpo.e. For the most part, this study uses data on FDI flows reported by the International Monetary Fund's (IMF) Balance of Payments Statistics Yearbook and International Financial Statisiics. The IMF's data on FDI have several virtues: they provide relatively comprehensive country coverege, extend over many years, are based on a broad notion of FDI, and facilitate cross-national comparisons. In preparinlg these two volumes, IMF data have been supplemented by data froin she Organization for Economic Co-operation and Development (OECD), t-he Unitad Nations Centre on Transnational Corp rations (UNCTC), national governments, and other organizations and individuals.

One important difference between this paper and earlier World Bank Staff Working Papers on FDI is the principal source of FDI data that has been used.<sup>3</sup> Whejreas the present paper relies primarily on IMF data, the previous papers relied extensively on OECD data. Thus, any comparisons between data in those papers and the present volume should be made with considerable caution; this is particularly true with regard to time series analyses that extend back in ti-" prior to 1975.4 Appendix 1 of this report provides an in-dcpth oomparison between alternative sources of FDI data.

Despite these data issues, basic patterns and trends can be discerned for the developing countries as a whole (see chapter 2) and for the eleven selectee countries (see chapter 3). Following these descriptions of the centra' tendencies and variations in the basic data, subsequent chapters consider the variables that influence these patterns and trends. Chapter 4 considers the host country economic and political environment, chapter 5 considers host country FDI policies, and chapter 6 considers the policies of home governments and international institutions. Chapter 7 concludes the study with an analysis of the prospects for the future of FDI in each of the eleven selected countries, as well as for all developing countvies collectively.
# **Table 6: Regressions Including the Interaction of Competition and Concentration with Government Bank Share**

The regressions below are estimated using country fixed effects and robust standard errors clustered at the country-year level. The dependent variable _Access to finance_ is a dummy variable that indicates whether the firm has access to a loan, overdraft, or a line of credit. The _Lerner index_ is a measure of competition (higher values imply lower levels of competition). The _Herfindahl index (HI)_ and _Concentration 3_ are measures of concentration _. Log firm size_ is the logarithm of the number of permanent employees. _Log firm age_ is the logarithm of the firm’s age in years. _Government-owned_ and _Foreign-owned_ are dummy variables that equal one if the firm has government or foreign ownership, respectively. _Exporter_ is a dummy variable that indicates if the firm is an exporting firm. _Manufacturing_ is a dummy variable that takes value 1 if the firm is in the manufacturing industry. _Financial development is_ measured as domestic credit to the private sector as a fraction of GDP. The _inflation rate_ is measured as the growth rate of the GDP deflator (annual). _Share of government banks_ is the fraction of banking system's assets in banks that are 50% or more government-owned. Results in column (b) are weighted by the inverse of the standard deviation of the Lerner Index. *** p<0.01, ** p<0.05, * p<0.1

|Variables||Acc|ess to finance||
|---|---|---|---|---|
||(a)|(b)|(c)|(d)|
|Lerner index|-0.018<br>[0.294]|0.045<br>[0.261]|||
|Concentration 3|||0.180<br>[0.198]||
|Herfindahl index||||0.338*<br>[0.183]|
|LernerShare government banks|-3.068**<br>[1.281]|-4.131***<br>[1.123]|||
|Concentration 3Share government banks|||-1.491*<br>[0.860]||
|H IndexShare of government banks||||-2.452**<br>[0.940]|
|Log firm size|0.086***|0.088***|0.088***|0.088***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Manufacturing|0.015|0.011|0.019**|0.019**|
||[0.009]|[0.009]|[0.010]|[0.009]|
|Exporter|0.036***|0.034***|0.036***|0.036***|
||[0.008]|[0.010]|[0.008]|[0.008]|
|Foreign-owned|-0.076***|-0.088***|-0.080***|-0.078***|
||[0.011]|[0.012]|[0.011]|[0.011]|
|Government-owned|-0.204***|-0.220***|-0.209***|-0.208***|
||[0.019]|[0.026]|[0.019]|[0.018]|
|Log firm age|0.010***|0.014***|0.008**|0.007*|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Financial development|0.354***|0.355***|0.266***|0.276***|
||[0.085]|[0.059]|[0.078]|[0.076]|
|Inflation rate|-0.357**|-0.428**|-0.309**|-0.316**|
||[0.150]|[0.184]|[0.150]|[0.156]|
|Constant|0.797***|0.937***|0.966***|0.643***|
||[0.214]|[0.206]|[0.336]|[0.193]|
|Observations|62,143|62,143|61,060|62,143|
|R-squared|0.222|0.192|0.222|0.219|

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15

# Table 2: CHARACTERISTICS OF COUNTRIES WITH NEGATIVE PER CAPITA GROWTH RATES 1970-87

||Total<br>number of<br>|Coun<br> <br>|tries wi<br>|th negati<br>rates<br> <br>|ve grow<br>|th<br>|
|---|---|---|---|---|---|---|
|Regions|countries|LSGR|GMGR|<br>RSLGR|IWGR|AMGR|
|Africa|36|19|20|16|23|19|
|Middle East and|||||||
|Eastern Europe|13|0|0|0|1|0|
|Asia|12|0|0|1|0|0|
|Latin America|22|9|7|7|11|6|
|All Developing Countries|<br>83|28|27|24|35|25|

# 5. WELFARE INTERPRETATION OF AGGREGATE GROWTH RATR

Let r2, r3 , ... , rn be the (n-1) growth rates calculated over the period from 1 to n years, we define the aggregate growth rate R as a unique function of each year's growth rates: satisfying certain desirable properties. Note that index R is unaltered if each value of x is altered by the same proportion. Thus, R is scaleindependent implying that x can be measured in any currency; (in US dollars or in constant local currency).

The question posed is what welfare interpretation can be given to index R. The social welfare function as introduced by Bergson in 1938 and subsequently developed by Samuelson in 1947 is generally defined over a set of individuals. Following the same idea, one can define a welfare function of an individual or a group of individuals over a period of time. For instance, x