rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> 100<br>as a de Y US ARR ia<br>80 KGZ M@EOGB CYP MUR Sueur on<br>cons OS LT SGP 80 CH C az<br>esRFRA D p phi ISASL LUX ARM BGR RUSRE‘au<br>RIGA INBSE yee ZAF Do aa B sop<br>VNM VERDENEURYaSIRY MKBIDN Efeaylys<br>@ IR, TUNDARG WT o IND MG Rees<br>g MANO | SAUWT gat g RAGA VNMIRV<br>& 60 TGCRA N q NE WRIRYH ARE 8 {TUN ARE<br>FSaeG aiBevICyERIENEBTNNBMDes OY BHR 3o pReser OUR pgayon CHN<br>D eee syc BHS = ght 028 cAQAHR<br>aedY GMGIN LBworSwzGUYVCT TTO D 1 LBNOREMBLAONIC© caneSDNAR steYercute “Ttpyc<br>COBNB COG IRQ 1kG<br>TCD<br>GAB GAB<br>KIR SSD Shay w<br>20 y = 0.692 + 7.400 In(x), R2 = 0.432 20 MH| y = 15.015 + 87.977 x, R2 = 0.618<br>(4.737) (0.544) (3.222) (5.072)<br>1,000 10,000 100,000 0.3 04 05 0.6 0.7 0.8 0.9<br>Log GDP Per Capita Human Capital Index<br>Source: All indicators come from the World Bank.<br><!-- End of picture text -->
2): 139–191.

Matsuyama, K. 1992. “Agricultural Productivity, Comparative Advantage, and Economic Growth.” _Journal of Economic Theory_ 58 (2): 317–334.

McMillan, M. 2013. “The Changing Structure of African Economies.” Unpublished paper.

McMillan, M., and K. Harttgen. 2014. _What Is Driving the Africa Growth Miracle?_ Working Paper 20077. Cambridge, MA: National Bureau of Economic Research.

McMillan, M., and D. Rodrik. 2011. “Globalization, Structural Change, and Productivity Growth.” In _Making Globalization Socially Sustainable_ . Geneva: International Labour Organization and World Trade Organization.

Mokyr, J. 1976. _Industrialization in the Low Countries, 1795–1850._ New Haven: Yale University Press.

Mundlak, Y., R. Butzer, and D. F. Larson. 2012. “Heterogeneous Technology and Panel Data: The Case of the Agricultural Production Function.” _Journal of Development Economics_ 99 (1): 139–149.

Murphy, K., A. Shleifer, and R. Vishny. 1989. “Industrialization and the Big Push.’’ _Journal of Political Economy_ 97 (5): 1003–1026.

38
# **1 Introduction**

Research on the measurement, drivers, and consequences of economic inequalities has blossomed since the early 2000s. Despite an active ongoing debate on precise levels and trends, there is an increasing consensus that income inequality has substantially increased in most high-income countries since the 1980s, often accompanied by a decline of labor share in total income (Auten & Splinter, 2023, Piketty, 2001, 2003, Piketty & Saez, 2003, Piketty & Zucman, 2014, Smith _et al._ , 2022).

For a long time, Latin America was seen as an exception to that global trend of rising income inequality. After the ”lost decade” of the 1980s and the structural adjustment in the 1990s (Birdsall _et al._ , 2011), economies in the region were buoyed by the commodity boom of the 2000s and, starting from very high levels, registered large decreases in measured income inequality in the following decade (Alvaredo _et al._ , 2023, Azevedo _et al._ , 2013, Gasparini & Lustig, 2011). The fundamental reasons behind this trend are still up for debate, but most countries in the region faced strong wage increases for workers at the bottom of the distribution coupled with the introduction of robust cash transfer systems (Lustig _et al._ , 2013).

Nonetheless, the consensus on falling Latin American income inequality in the last two decades has been challenged recently. Most of the measurement of inequality in the period relied on household surveys, which are known to underestimate top incomes and fail to capture sources of income that are relevant for high-income earners such as dividends. A new set of studies has complemented household survey data with tax registries to provide a more detailed view of top earners in the region. In doing so, they have documented that the share of income flowing to top earners has often been stable – if not increasing – in many countries in the region since the 2000s (Flores _et al._ , 2020, Medeiros _et al.
Policy Research Working Paper 6407

# **Abstract**

This paper exploits the emotional connections and viewer attentiveness of mainstream media to evaluate the economic impact of financial education messages on debt management delivered through a popular television soap opera in South Africa. The study uses a symmetric encouragement design to compare outcomes of individuals who were randomly assigned to watch a soap opera with financial messages, “Scandal!” to those of individuals who were invited to watch a similar soap opera without financial messages, “Muvhango.” Both shows overlapped in evening primetime and had similar past viewership profiles. The financial storyline spanned two months and featured one of the leading characters of the show borrowing excessively and irresponsibly through hire-purchase, gambling, and ending up in financial distress; and eventually seeking help to find her way out. Two intermediate and one final follow-up surveys were conducted as part of the study. The analysis finds individuals assigned to watch Scandal had significantly higher financial knowledge of the issues highlighted in the soap opera storyline, in particular messages delivered by the leading character. On behavior, Scandal viewers were almost twice more likely to borrow from formal sources, less likely to engage in gambling, and less prone to enter hire purchase agreements. Messages promoting a national debt mediation helpline delivered by an external character did not sustain traction beyond immediate interest. Three qualitative focus groups highlight the importance of emotional connections with the leading character in motivating behavior change.

This paper is a joint product of the Finance and Private Sector Development Teams of the Development Research Group and 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:// econ.worldbank.org. The authors may be contacted at gberg@worldbank.org and bzia@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues.
reports the number of destination markets by Colombian exporters over time.

# **3.2 Tariffs in LAC**

Colombian exporters saw declining tariffs in their regional destination markets between 2007 and 2017. The applied tariff decreased steadily from 6.7% in 2007 to only 4.1% in 2017, as seen in Table 5, as Colombia ratified numerous preferential trade agreements with regional partners over the period under investigation.<sup>5</sup> This is most evident for 2017 when tariff reductions of the Pacific Alliance between Colombia and Mexico, Peru and Chile, came into effect. At the same time, Most-Favored-Nation (MFN) tariffs negotiated at the World Trade Organization (WTO) declined only marginally from 9.1% in 2007 to 8.7% in 2017, reflecting the persistent deadlock in multilateral trade negotiations.

# **3.3 Non-Tariff Measures in Latin America**

The NTM dataset contains 6,211 regulations for Colombia’s 17 destination markets in LAC between 2007 and 2017. To study the variation of regu-

> 5The Pacific Alliance, signed in 2013 between Colombia and Mexico, Peru and Chile, reduced 92% of tariffs between members in 2016. Moreover, the free trade agreement (FTA) between Colombia and Costa Rica entered into force on August 1, 2016. As a result, 74% of industrial products became duty-free.

11
is an index of the disclosure standards for listed firms in a given country, compiled by the Center for International Financial Analysis and Research (CIFAR). Specifically, the Accounting variable informs on the presence, or absence, of 85 items in the annual reports of large publicly traded companies in 1993. The mean number of reported items is 72.73, as reported in Table 1.

Next, Getting credit is a measure of how easy it is to get credit as of 2006, with higher values of this variable meaning easier access to credit. The Getting credit variable reflects the existence of collateral and bankruptcy laws that facilitate lending as well as the coverage, scope and accessibility of credit information, for instance, through credit registries and credit bureaus. Greater ease of getting credit is expected to reduce a firm’s refinancing risk. The Getting credit index is available from the Doing Business database of the World Bank.<sup>9</sup> In addition, Contract enforcement measures the time and cost of resolving a commercial dispute through a local first-instance court in the year 2006, with higher values reflecting better enforcement. Faster and cheaper resolution of disputes is expected to facilitate lending, which should be especially important for firms that are more reliant on short-term debt financing as these firms have to refinance their debts more frequently.

Further, the Resolving insolvency index measures the time, cost and outcome of insolvency proceedings as well as the strength of the legal framework applicable to liquidation and reorganization proceedings in 2006, with higher values indicating a more efficient bankruptcy resolution. A more efficient framework for resolving insolvency is expected to reduce refinancing risk, as the suppliers of short-term credit potentially have to rely on insolvency proceedings more frequently to ensure partial or full credit repayment.

> 9 This variable is available only from 2006.

11
9.2|
|Nepal|19.99|14.89|4.7|9.0|23.4|16.3|
|Pakistan*|8.28|7.0|2.8|3.1|10.7|9.0|
|Sri Lanka|1.92|1.7|0.3|0.4|2.3|2.0|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). Pakistan’s values are from 2010.

# _Intertemporal deflation_

> 14 As an illustrative example, consider a country with two regions. The poverty line (PL) in a region can be interpreted as the cost of achieving a reference utility level in that region. The ratio between the poverty lines in two regions can be interpreted as a true-cost-of-living index, that is, as a spatial price index. Thus, the ratio between the regional poverty lines gives the cost of achieving the reference utility level in region 1 relative to region 2. This method makes strong assumptions, namely that the unobserved reference utility level underlying the poverty lines is constant across regions.

12
**Figure 1 Agriculture and cotton price indices (Real, MUV-deflated, 2000=100)**

<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>Cotton<br>50<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Source: World Bank<br><!-- End of picture text -->

# **Figure 2**

**Production growth decomposition into yield and area, 2000-04 to 2005-09**

<!-- Start of picture text --> 60%<br>Yield Area<br>38%<br>30%<br>51%<br>15%<br>31%<br>14% 13% 16% 13%<br>0% -1%<br>-17%<br>-22%<br>-5%<br>-30%<br>WORLD China India Africa<br><!-- End of picture text -->

**Note:** Growth decomposition has been calculated as: log(Q2005-09/Q2000-04) = log(A2005-09/A2000-04) + log(Y2005-09/Y2000-04), where Q, A, and Y denote production, area, and yield.

**Source:** Author’s calculations based on USDA data

_— 16 —_
- 13 -

where w', the present discounted value of wage costs over the lifetime of a machine, has been broken into the wage level and the nominal interest rate deflated by (expected) wage inflation (w). It is possible also to allow for intermediate inputs. Suppose that these are a fixed proportion of total output, that is, a Leontief technology prevails. Then, by taking a simple first-order approximation, it can be shown that an extra term in ^/(ps + p), where pH is the domestic price of intermediates, needs to be added to equation (8) to allow for the impact of changes in pM.

The fitted value from equation 8 can be taken as an estimate of capacity. Once Fhn is determined, capacity utilization can also be simply derived as the residual from equation 8. Indeed ln(h/hn) - ln(Fh) - ln(Fhn). Taking logs in equation 6 and substituting the calculated value of Fhn and h/hn, the export supply equation can be simply estimated as well.

# The data

The model has been applied to annual data for manufacturing exports from Turkey and Morocco, with the exclusion in the latter case of phosphate-derivative products. The price of export goods was corrected to allow for various export subsidies, whose value was taken from Milanovic (1986) and Yagci (1984) for Turkey and directly computed by the author for Morocco (see Appendix). Finally, in the construction of the price of
13 employment is associated with comparatively high per capita income and lower relative wages. But, of course, statistical associations say nothing about causality or its direction. Furthermore, the Wagner tendency is just that-a tendency--which, as the data for the OECD suggest, can be counteracted by deliberate policy. Concerning wages and employment, it would be especially risky to infer from cross-sectional data anything concerning the likelihood that wages would rise (or fall) if emplovment were reduced (or increased) in any particular country. All one can say is the obvious, albeit powerful, statement that retrenchment offers the _possibility_ of improving employee compensation, while employment expansion carries a strong _risk_ of eroding wages.
- ----------. 2016. "Growth Still is Good for the Poor." _European Economic Review_ 81: 68-85.

- Feenstra, R. C., Robert Inklaar, and Marcel P. Timmer. 2015. "The Next Generation of the Penn World Table." _American Economic Review_ 105 (10): 3150-3182.

- Ferreira, F. H. G., C. Lakner, M. A. Lugo, and B. Özler. 2018. "Inequality of Opportunity and Economic

- Growth: How Much Can Cross-Country Regressions Really Tell Us?" _Review of Income and Wealth_ .

- Frankel, J. 2017. "How to Cope with Volatile Commodity Export Prices: Four Proposals."  335, H. University, Cambridge, MA.

- Frankel, J. A. 2010. " _The Natural Resource Curse: A Survey_ ." National Bureau of Economic Research.

- Galego Mendes, A., and S. M. Pennings. 2017. "Consumption Smoothing and Shock Persistence: Optimal Simple Fiscal Rules for Commodity Exporters." Policy Research Working Paper Series WPS/8035, W. Bank, Washington, DC.

- Havranek, T., R. Horvath, and A. Zeynalov. 2016. "Natural Resources and Economic Growth: A MetaAnalysis." _World Development_ 88: 134-151.

- Hellebrandt, T., and P. Mauro. 2015. "The Future of Worldwide Income Distribution." LIS Working Paper Series/635, P. I. f. I. Economics, Luxembourg.

- Hnatkovska, V., and N. Loayza. 2003. "Volatility and Growth." Policy Research Working Paper Series WPS/3184, W. Bank, Washington, DC.

- Kraay, A. 2015. "Weak Instruments in Growth Regressions: Implications for Recent Cross-Country Evidence on Inequality and Growth." Policy Research Working Paper WPS/7494, W. Bank, Washington, DC.

- Lakner, C
.|
|Böhringer et al. (2012)|<br>Econometric analysis is used<br>for a panel dataset of German<br>manufacturing sectors.|The environmental regulations stimulate environmental<br>investment to be compatible with the pursuit of<br>production growth.|
|Lanoie et al. (2008)|<br>An econometric approach is<br>used for 17 sectors in the<br>Quebec manufacturing<br>industry for 1985-1994.|<br>The impact of environmental regulation on<br>productivity is negative in the short run but it is<br>positive in the long run. This effect is stronger in a<br>subgroup of industries that are more exposed to<br>international competition.|
|Hamamoto (2006)|A reduced-form econometric<br>model is used for 5 Japanese<br>manufacturing industries in<br>the 1960s and 1970s.|<br>The pollution control expenditures have a positive<br>relationship with the R&D expenditures and a negative<br>relationship with the average age of capital stock. Also,<br>increases in R&D investment have a significant<br>positive effect on the growth rate of TFP.|

# **4. Green/Clean Investment at the Sectoral Level**

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# **Growing a Developing City:**

# **A Computable Spatial General Equilibrium Model Applied to Dhaka**

Julia Bird, Anthony J. Venables University of Oxford

**Keywords** : Urban, land, computable equilibrium, Bangladesh **JEL classification:** R1, R10, R2

**Acknowledgements:** Thanks to Yue Li and Martin Rama for helpful comments, the South Asia Spatial Database project for background data and Lino Ferreira and Dewi Spijkerman for research assistance.  This work is supported by the Office of the Chief Economist, South Asia Region at the World Bank, and we also gratefully acknowledge the support of an Africa Research Program on Spatial Development of Cities at LSE and Oxford funded by the Multi Donor Trust Fund on Sustainable Urbanization of the World Bank and supported by the UK Department for International Development.

**Authors’ addresses:**

Julia Bird Department of Economics Manor Road Oxford OX1 3UQ UK <u>julia.bird@economics.ox.ac.uk</u> Anthony J. Venables Department of Economics Manor Road Oxford OX1 3UQ UK <u>tony.venables@economics.ox.ac.uk</u>
whether any section of the community found it hard to cultivate their land in 2004 because of insecurity. The predicted value represents the probability that the community would report being insecure in 2004 and therefore represents perceived risk.

The vector of independent variables, _distance_ , represents the distance of community _i_ from the closest LRA attacks in previous years. Separate distance measures are constructed from the NUS and ACLED data. The NUS data only contain information for attacks in 1992 and 1999. The ACLED data contain yearly data for 1997 until 2003. There are therefore nine independent variables (two from the NUS and seven from the ACLED data). The same independent variables are used for both the estimation of statistical and perceived risk. _E_ is an error term.

The results from the estimation of equation (2) to construct the measures of statistical and perceived risk are presented in table 1.<sup>25</sup> The errors in the logistic regression are clustered at the community level. In the first column, the dependent variable is a binary variable for whether or not a community was attacked by the LRA in 2004. Consequently, as previously discussed, the predicted values represent the probability of the community being attacked in 2004, that is the statistical risk. Similarly, the second column uses a binary variable for the perceived insecurity within the community. As previously noted, the fitted values from column (2) are estimates of the likelihood that the community feels insecure and therefore of the perceived risk.

Overall, while the variables are strongly jointly statistically significant, only several of the variables are individually significant, likely reflecting the multicollinearity among measures of distance to the closest attacks in different years. This is not a problem as we are only interested in how fell the model “predicts” attacks as opposed to the relevant contribution of any of the

> 25. This estimation only captures the part of insecurity which is correlated with the proximity of communities to historical violence. While this likely determines the statistical risk of violence, perceived insecurity
the DIVA database from the DINAS‐ COAST project (Vafeidis et al. 2008). WMP indicates the potential for wetlands, including mangroves, to migrate landward in response to a 1‐meter rise in sea level. The migratory potential is based on a few geophysical characteristics of the coastline: coastal type, topography, tidal range, and other information when available (e.g., whether mangroves are

> 12 Mangroves have adapted special aerial roots, support roots, and buttresses to live in muddy, shifting, and saline conditions. Mangroves produce peat from decaying litter fall and root growth and by trapping sediment in water. The process of building peat helps mangroves keep up with sea level rise. Mangroves can expand their range despite sea level rise if the rate of sediment accretion is sufficient to keep up with the sea level rise.

13
Growth, with an Update on Africa.” _World Development_ 63:11–32. 2.2, 5, 7, A.1

- Mensah, E.B., and A. Szirmai. 2018. “Africa Sector Database (ASD): Expansion and Update.” MERIT Working Papers 2018-020, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology, Maastricht, Netherland. 2.1

- Ngai, R., and C. Pissarides. 2007. “Structural Change in a Multisector Model of Growth.” _American Economic Review_ 97:429–443. 1

- Restuccia, D., D.T. Yang, and X. Zhu. 2008. “Agriculture and aggregate productivity: A quantitative crosscountry analysis.” _Journal of Monetary Economics_ 55:234–250. 2

14
on food among the analyzed social groups, in part because of the economies of scale in these households and the significant role of in‐kind income from subsistence farming. This is worrisome, as it may be indicative of worsening nutrition in households with children, although we cannot confirm this with the data at hand. A significant reduction of expenditures on food was also observed among the working age population with a low level of education, i.e., the low paid labor force, and in the rural population, who have a very high probability of being poor. In contrast, such groups as residents of Minsk, retirees, households without children, working age population with a high level of education, that have a very low risk of poverty, spend on food much more than the average household and did not cut these expenses substantially during the crisis.

_Figure 24: Real change in food consumption during crisis (2014‐2016) for major socio‐economic groups depending on initial level of food consumption, %_ <!-- Start of picture text --> 2<br>food expenditure per capita in 2014, in BYN in prices of 2014<br>0 partly_employed<br>40 50 60 70 80 90 100 110 120 130<br>minsk<br>-2<br>retiree_hh<br>small_cities retiree<br>-4 hh_single_parent high_edu hh_no_kids<br>average<br>child large_cities<br>nonemployed<br>-6 hh_1child working_ageemployed<br>sec_specialized_edu<br>-8 hh_2kids rural<br>other_edu<br>hh_3more_kids<br>-10<br>-12<br><!-- End of picture text --> Source: Authors’ estimates based on HBS data.

21