rafmacalaba/datause-encoder-data huggingface dataset:
- Liu, Ernest. 2019. “Industrial Policies in Production Networks.” _Quarterly Journal of Economics_ 134: 1883–948.

- McMillan, Margaret, and Albert Zeufack. 2022. “Labor Productivity Growth and

- Industrialization in Africa.” _Journal of Economic Perspectives_ 36 (1): 3–32.

- McNerney, James, Charles Savoie, Francesco Caravelli, Vasco M. Carvalho, and J. Doyne Farmer. 2022. “How Production Networks Amplify Economic Growth.” _Proceedings of the National Academy of Sciences_ 119 (1).

- Miller, Ronald E., and Peter D. Blair. 2009. _Input-Output Analysis: Foundations and Extensions_ , Second Edition. Cambridge, UK: Cambridge University Press.

- Miller, Ronald E., and Umed Temurshoev. 2017. “Output Upstreamness and Input Downstreamness of Industries / Countries in World Production.” _International Regional Science Review_ 40: 443–75.

- Ngai, L. Rachel, and Christopher A. Pissarides. 2007. “Structural Change in a Multisector Model of Growth.” _American Economic Review_ 97 (1): 429–43.

- Oberfield, Ezra. 2018. “A Theory of Input-Output Architecture.” _Econometrica_ 86: 559–89. OEF (Oxford Energy Forum). 2019. “Africa’s Oil and Gas Scene After the Boom.” _The Oxford Institute for Energy Studies_ January 117: 1–31.

- Osei, Robert Darko, Richmond Atta-Ankomah, and Monica Lambon-Quayefio. 2020. “Structural Transformation and Inclusive Growth in Ghana.” WIDER Working Paper 2020/36, United Nations University World Institute for Development Economic Research, Helsinki, Finland.

- Paul, Saumik, and Dhushyanth Raju. 2021. “Barriers to Growth-Enhancing Structural Transformation: The Role of Subnational Differences in Intersectoral Productivity Gaps.” Policy Research Working Paper 9683, World Bank, Washington, DC.

- PIAC (Public Interest and Accountability Committee). 2021. “Semi-Annual Report on the Management and Use of Petroleum Revenues for the Period January–June, 2021.” -

- <u>https://www.piacghana.org/portal/files/downloads/piac_reports/piac_2021_semi annual_report_p.pdf.</u>

- Skaten, Monica. 2018. “Ghana’s Oil Industry: Steady Growth in a Challenging Environment.” WPM 77, The Oxford Institute for Energy Studies, University of Oxford, Oxford, UK.

- Toews, Gerhard, and Pierre-Louis Vezina. 2022. “Resource Discoveries, FDI Bonanzas, and Local Multipliers: Evidence from Mozambique.” _Review of Economics and Statistics_ 104 (5): 1046–58.

- World Bank. 2022. _Africa in the New Trade Environment: Market Access in Troubled Times._ Washington DC: World Bank.

- WTO (World Trade Organization). 2018. _World Trade Statistical Review_ . Geneva: WTO.

21
The results of city fixed effects regressions are provided in Table 11. The excluded city in this case is the capital. Some clear patterns emerge. In terms of price dispersion, prices in Mexico City exhibit more dispersion in general and lower dispersions tend to be concentrated in cities in central states. This finding is not surprising since it is likely that our definition of markets, in a large city like Mexico City includes many markets. As stated before, our empirical definition of markets was likely to be more aggregated than one using cross-price elasticities would have determined.

Also, cities with more volatile prices tend to be located in the north. The magnitude of passthrough in Mexico City is generally higher than in most cities, but the speed of adjustment is lower.

The characteristics of goods matter in understanding other aspects in the functioning of markets. Goods that are less perishable and easier to transport, such as beverages, cement, salt, sugar and eggs have the lowest dispersions after controlling for other factors. In contrast, fruits and vegetables exhibit similar or relatively higher dispersion than rice, the excluded term. As suggested by the heat maps, the most volatile prices are for fruits and vegetables, while prices for meat are the most sensitive to changes to international prices.

Table 13 displays the results of the outlet FE regressions. Dispersion and volatility in supermarkets are in general higher than in other types of stores, but also in turn absorb shocks of international prices to a greater extent and at a more rapid pace. Table 14 shows semester FE results. This was done in order to identify how dispersion and volatility have evolved through time. The results indicate that dispersion is modestly increasing. Coefficients are relatively small but significant. Dispersion has increased by less than 1%. Volatility has been comparatively more stochastic across time; the coefficients suggest that volatility has decreased slightly by the end of the period.

Table 13. Outlet fixed effect results

||(1)<br>Dispersion|(2)<br>Volatility|(3)<br>PT magnitude|(4)<br>PT speed|(5)<br>PT symmetry|
|---|---|---|---|---|---|
|**Outlet** **FE** **(Supermarke**|**t** **excluded)**|||||
|Specialized store|‐1.911***|‐4.834***|‐0.096***|‐0.071***|‐0.074***|
||(0.452)|(0.305)|(0.026)|(0.015)|(0.018)|
|Public market|‐3.041***|‐5.240***|‐0.102***|‐0.062***|‐0.071***|
||(0.437)|(0.309)|(0.015)|(0.011)|(0.015)|
|Convenience store|‐1.671***|‐4.080***|‐0.064**|‐0.049***|‐0.051**|
||(0.458)|(0.346)|(0.024)|(0.017)|(0.023)|
|Informal market|‐2.228|‐4.452***|‐0.134***|‐0.012|‐0.044*|
||(1.403)|(0.626)|(0.024)|(0.028)|(0.022)|
|Warehouse club|‐3.212***|‐4.373***|‐‐|‐‐|‐‐|
||(0.704)|(0.612)|‐‐|‐‐|‐‐|
|Constant|15.264***|4.343***|0.178**|0.284***|0.022|
||(0.793)|(0.278)|(0.069)|(0.039)|(0.062)|
|Observations|412,017|663,508|4,475|4,475|4,475|
|R‐squared|0.149|0.646|0.340|0.238|0.043|

Clustered standard errors by city in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Regressions include additional controls: City FE, Good FE for all regressions; Time FE for columns (1) and (2).

30
on energy sources. It is on these distributions of spending and consumption constructed for 2014 that the subsidy reform is simulated and its distributive and fiscal effects estimated. The first step before beginning the simulation analysis is to look at a detailed outline of the current system of energy subsidies.

# **3.1. Structure of Residential Energy Prices**

The current price structure for residential electricity consumption follows a two-tier system.<sup>3</sup> A different structure—of which analysis is beyond the scope of this chapter—is applied to nonresidential users (which also differentiates between low and high tension use). Table 2 shows that for households consuming less than 200 kWh per month, a Volume Differentiated Tariffs (VDT) is applied to three consumption blocks and three distinctive prices apply: TND 0.075 per kWh, if consumption is 1–50 kWh; TND 0.108 per kWh (applied for all kWh consumed) if consumption is 51–100 kWh; and TND 0.140 per kWh if consumption is 101–200 kWh (also from the first kWh consumed). Households consuming more than 200 kWh per month are subject to an Increasing Blocks Tariffs (IBT) that includes multiple prices across different blocks of consumption. In this high-volume tier, TND 0.151 per kWh is charged for each of the initial 200 kWh consumed; TND 0.158 for each of the subsequent kWh in the 201 and 300 kWh block; TND 0.301 for the next 200 kWh block; and, finally, TND 0.501 per kWh for each of those kWh in excess of 500 kWh per month.

Table 2: Electricity Tariff Structure for Low Tension Residential Consumers (valid since May 1, 2014)

|Fee|Voltage<br>(millimes/kVa/month)|Price of ener<br>1–50<br>51–100|gy by month<br>(millimes<br>101–200|ly consum<br>/kWh)<br>201–<br>300|ption brack<br>301–<br>500|et<br>501<br>+|
|---|---|---|---|---|---|---|
|Economic (1 and 2 kVa and||75|||||
|<br>consumption under 200<br>|500|108|||||
|kWh)||140|||||
|Economic (1 and 2 kVa and<br>consumption over 200<br>kWh); normal (> 2 kVa)|500|151||184|280<br>250|350<br>295|

_Source:_ Societé Tunisienne d’Eletricité et du Gaz (2014). _Notes:_ Prices are in TND _millimes_ and before taxes.

> 3 The price structure described here became effective on May 1, 2014. The previous tariff structure, valid between January and April 2014, had slightly lower fees for the highest consumption block of the first tier, as well as for the second tier of residential consumption. Appendix 1 details the previous structure.

8
Our preferred method is to use fixed weights for the aggregation purposes. Equal weights are the most commonly used approach for index construction.<sup>14</sup> While there is no analytical reason for this choice, equal weights are a common approach that is often justified on the basis of transparency.<sup>15</sup> We also suggest using the min-max transformation function  ( _xk_ )   _xk_  min( _xk_ )  / max( _xk_ )  min( _xk_ )  which transforms the variable into a uniformly distributed variable between 0 and 1.

For each category _c_ , we can then create sub-indices _X_ 1 to _X_ 3 , where, where _K_ c is number of dimensions in category _c_ , and _K = K1 + K2 + K3_ .

Finally, we can take an equally weighted average of all the dimensions across the sub-indices to get at a DSI ranking within a sector; i.e., to get at the final index for a given sector.

Ultimately, there could be an overall aggregation of the three sectors index leading to an overall score and ranking of countries based on the three service delivery sectors.

In the aggregation process proposed above, we have implicitly assumed that all units sampled should carry the same weight. We could refine this further by weighing units by size of the catchment area. Thus, a clinic or school serving a smaller community would count less in the index than a clinic or school serving a larger community.

> 14 See: UNDP Human Development Index, UNESCO Education for all index, World Bank Doing Business Index. 15 Of course, they imply the assumption that there is a one-for-one trade-off between different dimensions. While equal weights are the most commonly used approach, there exist various data driven weighting schemes. One approach is to use frequency based weights, which are chosen relative to the proportion of the population suffering deprivation in a particular dimension. Alternatively, weights depend positively on the accuracy with which the data is measured. Another approach would be to use weights chosen to maximize the individual/country’s performance in the index. This is sometimes viewed as appealing from a political point of view and can be interpreted as “giving everyone the benefit of the doubt”. An alternative approach would be to use “normative weights”, which are constructed on the basis of asking experts on how they would trade-off different dimensions using the budget allocation or analytic hierarchy process. Also, one could use various statistical techniques to estimate weights that summarize the data. Approaches based on principal component and factor analysis have been used to control for multicollinearity between different dimensions and produce weighting schemes that penalize highly correlated dimensions and give more weight to dimensions that are orthogonal. Overall, none of these approaches is perfect and any weighting scheme that arises from them should always be assessed according to the reasonableness of the implied trade-offs between dimensions (for further discussion, see for instance Decancq and Lugo, 2008).
# **About Urban Mega Regions: Knowns and Unknowns**

## **Shahid Yusuf***

Japan, China and the Republic of Korea accounted for 84 percent of East Asia’s GDP in 2005. Within these three countries, just six urban regions generate 45 percent of the aggregate domestic production (see Map 1). Moreover, 42 percent of Korea’s population reside in the Seoul–Incheon region and 40 percent of the population of Japan live and work in the metropolitan areas of Tokyo, Osaka, and Nagoya. Because of its size, China’s population and industrial production are more evenly distributed. Nevertheless, 80–85 million people, over 6 percent of the total population live in the metropolitan precincts of Shanghai, Beijing-Tianjin and the Pearl River Delta (PRD) excluding Hong Kong and 37 percent of China’s GDP originates from the three sprawling regions. The notable characteristics of these urban regions, apart from size, is that they are attracting industries with high value added and are centers of knowledge intensive activities. The mega urban regions are not a passing phenomenon. They are likely to persist and to enlarge their economic footprints because they benefit from the advantages of market scale, agglomeration economies, location and the increasing concentration of talented workers.<sup>1</sup> Being polycentric and relatively well managed, and having invested heavily in transport infrastructure, the metropolitan regions have been able to contain some of the problems attendant upon a concentration of people and industry. Last but not least, with energy and water resources becoming relatively scarce and countries such as China anxious to preserve arable land for farming, the economic advantages of densely populated urban areas are on the rise because they have a lower resource utilization quotient.

> * The author would like to thank Alan Winters, Ian Porter, Indermit Gill, Yukon Huang and Kaoru Nabeshima for their insightful comments and suggestions and Marinella Yadao for her cheerful and highly efficient assistance with the production of the paper.

> 1 Using PPP rates, PwC estimates that the largest 30 cities in the world were responsible for 16 percent of global GDP in 2005 and the top 100 cities for a quarter of the GDP. Four East Asian cities were among the top 30 – ranked by GDP: Tokyo (1), Osaka-Kobe (7), Hong Kong (14) and Seoul (20). ("PricewaterhouseCoopers" 2007). See also the papers on global city regions in Scott (2001).

1
# **Hai-Anh H. Dang**

# Email: hdang@worldbank.org <u>https://sites.google.com/site/haianhhdang/</u>

|https://sites.google.com/site/haianhhdang/ <br>_Septe_<br>**Employment**|_mber 2023_|
|---|---|
|_Senior Economist,_Data Production and Methods Unit and Living Standards Measurement<br>Study Unit (Development Data Group), World Bank|2020- present|
|_(Consultant) Economist,_Poverty and Inequality Unit (Development Research Group);<br>Survey Unit, Analytics and Tools Unit, Data Production and Methods Unit (Development<br>Data Group), World Bank|2004- 2020|
|**Other Affiliations**||
|Co-editor,_Review of Development Economics_|2018- present|
|Co-editor for special issue on “Measuring Income, Wealth and Well-being in Africa”,|2022- present|
|_Review of Income and Wealth_||
|Editorial Board,_International Journal of Education Development_|2018- present|
|Editorial Board,_Economic Change and Structuring_|2020- present|
|Editorial Board,_Journal of Economics and Development_|2023- present|
|IZA Research Fellow|2019- present|
|Visiting Senior Fellow, International Inequalities Institute, London School of Economics and|2023- present|
|Political Science||
|Adjunct Professor & Non-resident Research Scholar, Paul H. O’Neill School of Public and<br>Environmental Affairs, Indiana University|2018- present|
|Global Labor Organization (GLO) Fellow|2018- present|
|Research Affiliate with Household in Conflict Network (HiCN)|2022- present|
|Visiting Senior Research Fellow, International School, Vietnam National University, Hanoi|2021- 2023|
|Guest lecturer, Georgetown University|2019- 2020|
|Visiting Scholar, Monash University|Nov. 2019|
|Visiting Scholar, Singapore Management University|Nov. 2017|
|Visiting Scholar, Beijing Normal University|Dec. 2016|
|Non-resident Senior Research Fellow, Centre for Analysis and Forecast, Vietnam’s<br>Academy of Social Sciences|2016- 2022|

# **Education**

|Ph.D. in Agricultural & Applied Economics, University of Minnesota, Twin Cities|2007|
|---|---|
|MSc. in Agricultural & Applied Economics, University of Minnesota, Twin Cities|2003|
|B.A. in International Economics, Foreign Trade University, Hanoi, Vietnam|2000|

# **Selected publications in the past 3 years**

# **_Journal Articles_**

1. Dang, Hai-Anh, Trong-Anh Trinh, and Paolo Verme. (in press). "Do Refugees with Better Mental Health Better Integrate?  Evidence from the _Building a New Life in Australia_ Longitudinal Survey." _Health Economics_ .

2. Dang, Hai-Anh, Paul Glewwe, Jongwook Lee and Khoa Vu. (2023). " <mark>What Explains Vietnam’s Exceptional Performance in Education Relative to Other Countries? Analysis of the 2012, 2015 and 2018 PISA Data"</mark> . _Economics of Education Review_ , 96, 102434.

3. Dang, Hai-Anh, and Peter Lanjouw. (2023). “Measuring Poverty Dynamics with Synthetic Panels Based on Cross-Sections”, _Oxford Bulletin of Economics and Statistics_ , 85(3), 599-622.

4. <mark>Dang, Hai-Anh, John Pullinger, Umar Serajuddin, and Brian Stacy. (2023). “Statistical Performance Indicators and Index: A New Tool to Measure Country Statistical Capacity”.</mark> 83
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Figure 2: MPL used to measure WTP for prepaid electricity in South Africa

||Electricity vs.|cash|
|---|---|---|
|Binary choice|Electricity voucher value|Cash value|
|_t_|Option A|Option B|
|Choice 1|R100|R60|
|Choice 2|R100|R80|
|Choice 3|R100|R92|
|Choice 4|R100|R97|
|Choice 5|R100|R99|
|Choice 6|R99|R100|
|Choice 7|R97|R100|
|Choice 8|R92|R100|
|Choice 9|R80|R100|
|Choice 10|R60|R100|

Notes: choices 1-10 were randomly shown in order (decreasing value difference) or in reverse order (increasing value difference). In addition, option A (electricity) and B (cash) were randomly shown either on the left or the right of the screen. Each binary choice was shown separately, accompanied by images showing the amounts either on a cash bill or on a stylized voucher. The respondent made choices by tapping directly on the image on the screen and then confirming the selection. See also figure S1 in appendix S2.

of those exhibiting MSB) have three switches, consistent with one error and subsequent correction.

The rate of MSB is relatively high, although not completely out of line with other MPL data from low-income populations. As discussed, one reason could be that within-choice randomization reveals errors that may otherwise remain undetected (e.g., in the form of NSB). That said, it is possible that the within-choice randomization increased error due to comprehension issues or difficulty using the screens.

The share of never-switchers in the data is also high. Since options were randomized within binary choice, it is unlikely that this is due to inattention that made the subject simply repeat the same choice many times. Only one individual always chose the same side of the screen, that is, exhibited a form of framing-based never-switching. The neverswitchers give us a first opportunity to look into the magnitude of order biases. Never switchers are an interesting case because their choices are consistent with a very strong preference for one of the two options. Figure 4 shows the proportion of never-switchers who prefer the electricity voucher.

The left panel shows NSB subjects as a whole. In our data, 54% of all never-switchers 24
# 1984-86. It takes a value of 4 for the least and 1 for the most human rights abuses31.

Table 8: Political liberties and project performance

||______<br>______Spec|ifiation|2:_|_<br>_<br>_<br>_<br>_<br>_|
|---|---|---|---|---|
|_________|A|B|C|D|
|Freedom House (Political)b|1.16|-.016|1.05|-. 115|
|(N=649)|(.0087)c|(.977)|(.026)|(.840)|
|Index of Human Rights Viol<br>Dummy variable for each lev|ations (IHRV<br>el of violatio|) (N =425)<br>ns (default is m|ost violations,**IH**|**R' 1)**|
|iIHRV=2|5.46|3.33|4.23|2.92|
||(.018)|(.193)|(.078)|(.292)|
|IHRV=3|0.08|1.66|1.30|3.04|
||(.974)|(.534)|(.586)|(.310)|
|IHRV=4 (least violations)|0.90|3.96|4.87|7.83|
|l_____________________|(.788)|(.297)|_(.154)_|_(.045)_|

Notes: a) for description of the specifications see table 2; b) Annual values from 19781987. IHRV is a single value extrapolated to cover 1980-1986; c) p-levels in parenthesis. Source: Authors' calculations The results reported in table 8 do not show any striking positive impact of purely political rights on ERR. The Freedom House index shows a significant positive effect in the least demanding specification (A), but whereas civil liberties is robust, the political result is not. It is driven out in significance by the policy controls and, in both magnitude and significance, by the inclusion of regional variables.

The index of human rights violations (IHRV) similarly does not show any clear pattern of impact on project performance. Although in specification D there is some significant effect, the result is highly non-robust and appears to be driven by a few observations with very `2 Actually, the highest possible value on the scale is 5, which only one country, Jamaica, achieves so for our purposes Jamaica is re-classified to 4, the level which includes Costa Rica, Venezuela, Mexico, Singapore and Nigeria from 1984-86.

- 27 -
29 and Warner note that trade reform is usually just one part of a government’s overall reform plan for integrating an economy with the world systems. They argue that trade liberalization is usually accompanied by a much broader range of reforms, such as price liberalization, budget restructuring, extensive privatization, deregulation and attempting to achieve macroeconomic stability. This has been especially clear in the postcommunist countries. In almost all cases, trade reform has been merely part of the overall institutional harmonization with the advanced market economies. As such, to some degree, the Sachs and Warner measure of openness serves as a proxy for an entire array of features that are characteristic of a healthy economic regime, and hence is an appropriate measure for use in our regressions in explaining economic growth.

# **Institutional Quality**

Knack and Keefer (1995) examines the effects of property rights on economic growth and find that property rights were able to explain about 8 to 10 percentage points of the observed variation in cross-country growth rates for the period 1974 to 1989. As proxies for the levels of property rights in countries, they used indicators provided by two private international investment risk services who evaluate country risk to potential foreign investors. More specifically, the data were obtained from the Business Environmental Risk Intelligence (BERI) and the International Country Risk Guide (ICRG). ICRG data were generally taken for the year 1982 and focused on expropriation risk, rule of law, repudiation of contracts by the government, corruption in government and the quality of the bureaucracy. On the other hand, BERI data were generally for the year 1972 and include indicators for bureaucratic delays, nationalization potential, contract enforceability and infrastructure quality. For this paper, we use the simple average of the ICRG indicators as a measure of institutional quality.

# <u>Expropriation Risk and the Rule of Law</u>

Both of these measures are proxies for the security of property and contract rights. The former measures the risk of expropriation while the latter measures whether there are established peaceful mechanisms for adjudicating disputes. Low scores on these dimensions are likely to imply that investor risk is high and investors may lose proceeds from the investment, or the entire investment itself. Such situations tend to induce potential investors reduce their investment and channel their resources to activities that are more secure from the threat of expropriation, leaving such countries with low investment levels and hence lower economic growth rates.

# <u>Repudiation of Contracts by the Government</u>

This is another indicator of contract enforcement and also of government credibility. Knack and Keefer argue that it is likely that if the government does not respect and recognize the contracts it has with private agents, then the government cannot be relied on to enforce contracts between two private parties. Hence, in the absence of