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

- Adenikinju, A., 2009. Energy pricing and subsidy Reforms in Nigeria. OECD Conference Centre, Paris 9- 10 June 2009. Accessed on January 25, 2012.http://www.oecd.org/dataoecd/58/61/42987402.pdf

- Arze del Granado, F. J., Coady, D., Gillingham, R., 2012. The Unequal Benefits of Fuel Subsidies: A Review of Evidence for Developing Countries. World Development, 40(11): 2234-2248.

- Bazilian, M., Onyeji, I., 2003. Fossil fuel subsidy removal and inadequate public power supply: Implications for businesses. Energy Policy, 45: 1-5.

- BBC, 2012a. Nigeria Reveals ‘$6bn Fuel Fraud’. BBC, April 24, sec. Africa. http://www.bbc.co.uk/news/world-africa-17823959.

- ______ 2012b. Nigeria’s President Jonathan ‘Must Act over Fuel Scam’. BBC, May 28, sec. Africa. http://www.bbc.co.uk/news/world-africa-18238973.

- Birol, F., Aleagha, A.V., Ferroukhi, R., 1995. The economic impact of subsidy phase out in oil exporting developing countries: a case study of Algeria, Iran and Nigeria. Energy Policy, 23(3): 209-215.

- BP, 2011. British Petroleum Statistical Review of World Energy. June 2011. bp.com/statisticalreview. Accessed on March 2, 2012.

- BusinessDay, 2013. Increasing Nigeria’s oil refining capacity. Thursday, 14 February 2013. http://www.businessdayonline.com/NG/index.php/oil/51502-increasing-nigerias-oil-refiningcapacity. Accessed on April 16, 2013.

- Coady, D., Gillingham, R., Ossowski, R., Piotrowski, J., Tareq, S., Tyson, J., 2010. Petroleum product subsidies: Costly, inequitable and rising. IMF Staff Position Note, SPN/10/05. Washington D.C: International Monetary Fund.

- Dartanto, T., 2013. Reducing fuel subsidies and the implication on fiscal balance and poverty in Indonesia: A simulation analysis. Energy Policy, 58: 117-134.

Davis, L.W., 2013. The Economic Cost of Global Fuel Subsidies. NBER Working Paper 19736, December.

- EIA, 2012. US Energy Information Administration report of Nigeria. October 16, 2012. http://www.eia.gov/countries/analysisbriefs/Nigeria/nigeria.pdf. Accessed on April 16, 2013.

- El-Said, M., Leigh, D., 2006. Fuel price subsidies in Gabon: Fiscal cost and distributional impact. IMF Working Paper, 06(243). Washington D.C: International Monetary Fund.

25
28

- Kuznesof, Elizabeth Anne. 1985. "The role of the female-headed household in Brazilian modernization: Sao Paulo 1765 to 1836," Journal of Social History.

- Lam, David, Guilherme Sedlacek, and Suzanne Duryea. 1992. "Increases in women's education and fertility decline in Brazil." University of Michigan Research Report No. 92-255, September, 1992.

- McLanahan, Sara and Larry Bumpass. 1988. "Intergenerational consequences of family disruption," American Journal of Sociology, Vol. 49, No. 1 (July 1988): 130-52.

- Merrick, Thomas W. and Marianne Schmink. 1983. "Households headed by women and urban poverty in Brazil." In Women and Povert, in the Third World, ed. Mayra Buvinic, Margaret A. Lycette and William P. McGreevey. Baltimore: Johns Hopkins University Press.

- Moynihan, Daniel P. 1965. The Negro Family: The Case for National Action. Washington D.C.: U.S. Department of Labor, Office of Family Planning and Research.

- National Commission on Children (U.S.). 1991. Beyond Rhetoric: a New American Agenda for Children and Families. Washington D.C.: U.S. Government Printing Office.

- Pastore, Jos6, Helio Zylberstajn and Carmen Silvia Pagotto. 1983. Mudanca Social e Pobreza no Brasil: 1970-1980 (O gue Ocorreu com a Familia Brasileira?). Sao Paulo, Brazil: Estudos Economicos, FIPE/PIONEIRA.

- Rosenhouse, Sandra. 1989. "Identifying the poor: Is "headship" a useful concept?," Living Standards Measurement Survey. Working Paper No. _58._ World Bank. Washington. D.C.

- Vickery, Claire. 1977. "The time-poor: A new look at poverty." Journal of Human Resources 12(1):27-48.

- Wilson, William Julius and Kathryn Neckerman. 1986. "Poverty and family structure: The widening gap between evidence and public policy issues." In Fighting Poverty, ed. Sheldon Danziger and Daniel H. Weinberg. Cambridge: Harvard University Press.

- Wood, Charles H. 1989. "Wowen headed households and child mortality in Brazil, 19601980." Deparanent of Sociology, University of Florida, Gainesville.
of lawyers and judges, measures how efficiently the courts of a country enforce contracts. DLLS have constructed measures based on how courts handle two types of cases: Collection of a bounced check, and eviction of a (non-paying) tenant. A higher score in either category implies that the court system is slower (with more bureaucracy) and less efficient. We took the average of these (highly positively correlated) indexes to construct a single formalism index. From Table 2-D, India’s has a higher formalism index than the average of English origin countries, and is only lower than that of the French origin countries.

Second, measures on government corruption used in Tables 2-D and 2-E come from Transparency International’s survey-based annual reports, which provide the most comprehensive examination of corruption within the government and legal institutions around the world. Based on its 2005 Corruption Perception Index, India has a score of 2.9 out of 10 (a higher score means less corruption), which ranked 88th out of 140 countries (with the range of scores from 1.5 to 9.7). In the 2003 survey, India had a score of 2.8/10 and ranked 95th out of 145 countries. From Table 2-E, India’s corruption score is below that of China, Brazil and Mexico, all of which have much weaker legal protection of investors, and is only ahead of Pakistan (Argentina has a score of 2.8).<sup>11</sup> Further, in its survey of investment climates around the world, the World Bank ( _World Development Report 2005_ ) found that corruption was the number one constraint for firms in South Asia. Another survey by the same organization, “Corruption in South Asia in 2002” found that the two most corrupt public institutions identified by the respondents in India (as well as in most countries in South Asia) were the police and the judiciary.

Third, we have two measures for the quality of accounting systems. The disclosure requirements index (from 0 to 1, higher score means more disclosure; LLS 2006) measures the extent to which listed firms have to disclose their ownership structure, business operations and

> 11 For more information on the cross-country and time series of corruption scores, go to the website of Transparency International <u>http://www.transparency.org/publications/annual_report.</u>

10
column (2) of Table 8 is positive, but not significant.<sup>30</sup> As sales may react with a lag to lower prices, column 3 regresses next period’ sales on PSI. The coefficient becomes positive and significant. Having expressed inventories in logs allows us to compare the elasticities. Consistent with the idea that lower markups have the objective to boost sales and empty out inventories, we find that the increase in sales is of similar magnitude as the increase in inventories.

To further corroborate the mechanism just described in Appendix Table A10, we provide two further tests. First, the coefficient of input PSI showed in column (1) shows that the application of this type of NTM on inputs does not have any impact on inventories of finished goods, which should be only partially impacted by restrictions on intermediate materials. Second, in column (2) of Appendix Table A10, we show that input PSI does not have any impact on employment, consistent with the idea that the increase in sales may not be related to a successful growth strategy of the plant.

# **6. Conclusions**

This paper builds a novel NTM dataset on all NTMs applied to the universe of goods produced in Indonesia from 2008 to 2015 and uses the data to explore the impact of NTMs in a large panel of manufacturing plants. The results suggest that on average NTMs do not harm plants’ performance, whether through the input or the output channel. To the extent that some of the benefits of NTMs in addressing market failures may not be fully captured by the plant-level outcomes, NTMs might even have a net positive impact on welfare. This is different to the case of import tariffs, which have a negative impact on productivity and - unlike NTMs - they also do not appear to have any positive impact on consumers.

This average neutral effect of NTMs masks a wide heterogeneity across individual NTMs. To tease out some of this heterogeneity, the paper focuses on two specific measures (APT and PSI), which we argue differ markedly in terms of how necessary they are to achieve legitimate policy objectives. In line with our hypothesis, the application of APT on imported inputs has a positive impact on TFPR through increases in markup, which we interpret as a sign of quality improvement linked to the measure. On the other hand, the results for PSI suggest that this acts mainly as an import barrier,

> 30Sales are the log of total production minus the yearly change in inventories of finished products, deflated by the plant-level output price deflator.

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Van de Poel, E., O. O’Donnell, and E. van Doorslaer. 2007. “Are Urban Children really healthier? Evidence from 47 developing countries.” Tinbergen Institute Discussion Paper TI 2007-035/3; Amsterdam, The Netherlands.

Wagstaff, A., and N. Watanabe. 2000. “Socioeconomic inequalities in child malnutrition in the developing world.” World Bank Policy Research Working Paper 2434; Washington, DC. Walker S.P., S.M. Chang, C.A. Powell, and S.M. Grantham-McGregor. 2005. “Effects of early childhood psychosocial stimulation and nutritional supplementation on cognition and education in growth-stunted Jamaican children: prospective cohort study.” _Lancet_ 366: 1804–7.

World Health Organization. 2006. “WHO Child Growth Standards: methods and development.” WHO Press, Geneva: Switzerland.

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# **Figures and Tables**

Table 1: Typology of regulatory barriers in services affecting (Foreign) Supply

||Impact on entry|Impact on operations(conduct)|
|---|---|---|
|Discriminatory|E.g., nationality quotas for<br>managers of affiliates; minimum<br>equity stake required for<br>national investors; economic<br>needs tests|E.g., foreign insurance firms not<br>permitted to offer certain types<br>of coverage or product<br>innovations; price controls|
|Non‐discriminatory|E.g., A limit of three mobile<br>phone providers permitted to<br>operate in the country|E.g., all retail banks required to<br>maintain a minimum level of<br>capital, independent of type of<br>legal entity|

Source: Francois and Hoekman (2010) Figure 1: Services regulation in the EU (2006‐2013) <!-- Start of picture text --> LU<br>SI<br>GR<br>PL<br>BE FR<br>PTEE<br>HU IT<br>CZ ATSKES<br>FI DE<br>IE<br>NLSE DK<br>GB<br>.1 .2 .3 .4 .5 .6 .7<br>Level of services regulation (2006)<br>.7<br>.6<br>.5<br>.4<br>.3<br>Level of services regulation (2013)<br>.2<br>.1<br><!-- End of picture text --> Source: OECD, and author’s own calculations; 0 is low regulations, 1 is high regulations.

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Gelb, A.H. (1988). _Oil Windfalls: Blessing or Curse?_ Oxford: Oxford University Press.

Gennaioli, N., R. La Porta, F.L. de Silanes, and A. Shleifer (2014). ‘Growth in Regions’. _Journal of Economic Growth_ , 19: 259–309.

Gylfason, T., T.T. Herbertsson, and G. Zoega (1999). ‘A Mixed Blessing’. _Macroeconomic Dynamics_ , 3(2): 204–25.

Gylfason, T., and G. Zoega (2003). ‘Inequality and Economic Growth: Do Natural Resources Matter?’ In T. Eicher and S. Turnovsky (eds), _Inequality and Growth: Theory and Policy Implications_ . Cambridge, MA: MIT Press.

Harding, T., and A.J. Venables (2016). ‘The Implications of Natural Resource Exports for NonResource Trade’. _IMF Economic Review_ , 64(2): 268–302.

Haughton, J.H., and S.R. Khandker (2009). _Handbook on Poverty and Inequality_ . Washington, DC: World Bank.

Henderson, J.V., A. Storeygard, and D.N. Weil (2012). ‘Measuring Economic Growth from Outer Space’. _American Economic Review_ , 102(2): 994–1028.

Hodler, R., and P.A. Raschky (2014). ‘Regional Favoritism’. _Quarterly Journal of Economics_ , 129(2): 995–1033.

Humphreys, M. (2005). ‘Natural Resources, Conflict, and Conflict Resolution: Uncovering the Mechanisms’. _Journal of Conflict Resolution_ , 49(4): 508–37.

Isham, J., M. Woolcock, L. Pritchett, and G. Busby (2005). ‘The Varieties of Resource Experience: Natural Resource Export Structures and the Political Economy of Economic Growth’. _World Bank Economic Review_ , 19(2): 141–74.

Ismail, K. (2010). ‘The Structural Manifestation of the “Dutch Disease”: The Case of Oil Exporting Countries’. Working Paper 10/103. Washington, DC: International Monetary Fund.

Kim, S. (2008). ‘Spatial Inequality and Economic Development: Theories, Facts, and Policies’. In M. Spence, P.C. Annez, and R.M. Buckely (eds), _Urbanization and Growth_ . Washington, DC: World Bank.

Kotsadam, A., and A. Tolonen (2015). ‘African Mining, Gender, and Local Employment’. Policy Research Working Paper 7251. Washington, DC: World Bank.

Labus, M.P., G.A. Nielsen, R.L. Lawrence, R. Engel, and D.S. Long (2002). ‘Wheat Yield Estimates Using Multi-Temporal NDVI Satellite Imagery’. _International Journal of Remote Sensing_ , 23(20): 4169–80.

Lederman, D., and W. Maloney (2008). ‘In Search of the Missing Resource Curse’. _Economía_ , 9(1): 1–57.

Lujala, P. (2009). ‘Deadly Combat Over Natural Resources: Gems, Petroleum, Drugs, and the Severity of Armed Civil Conflict’. _Journal of Conflict Resolution_ , 53(1): 50–71.

20
23 statistics<sup>25</sup> . Furthermore, as seen in appendix 12, the increase in the coefficient of POSTLIB is robust to the introduction of lags of several independent variables<sup>26</sup> .

**Table 3: Trade Liberalization and GDP Per Capita Growth Controlling for Other Growth Factors**<sup>1</sup>

|||Inde|pendentV|ariable:G|DP PerCa|pita Grow|th||
|---|---|---|---|---|---|---|---|---|
|postlib|1.686|2.327|2.392|1.996|1.737|2.116|2.592|2.504|
||(6.10)**|(4.05)**|(3.98)**|(3.21)**|(2.55)*|(2.92)**|(3.54)**|(3.43)**|
|l.gdppcg||0.141|0.138|0.117|0.167|0.244|0.222|0.225|
|||(4.00)**|(3.90)**|(3.05)**|(3.90)**|(5.32)**|(4.84)**|(4.93)**|
|ll.lpop||-16.206|-17.326|-18.37|-16.658|-18.345|-16.518|-16.302|
|||(3.85)**|(4.05)**|(3.81)**|(3.24)**|(3.47)**|(3.13)**|(3.10)**|
|gdptous||-4.091|-4.188|-4.708|-4.025|-4.707|-4.895|-4.592|
|||(5.43)**|(5.31)**|(5.36)**|(4.21)**|(4.70)**|(4.74)**|(4.42)**|
|postpops|||-0.003|-0.004|-0.005|-0.005|-0.006|-0.006|
||||(1.74)|(2.34)*|(2.56)*|(2.75)**|(3.08)**|(3.10)**|
|postini|||0.049|0.037|0.074|-0.04|-0.022|-0.012|
||||(0.40)|(0.29)|(0.50)|(0.25)|(0.14)|(0.08)|
|rxrdev||||-0.016|-0.017|-0.02|-0.021|-0.023|
|||||(2.88)**|(2.94)**|(3.17)**|(3.33)**|(3.54)**|
|disast||||-0.939|-0.947|-0.482|-0.487|-0.522|
|||||(2.22)*|(2.12)*|(0.97)|(1.00)|(1.07)|
|ltot|||||1.017|0.959|2.257|2.099|
||||||(0.87)|(0.67)|(1.56)|(1.45)|
|infg|||||-0.452|-0.649|-0.632|-0.651|
||||||(2.39)*|(3.07)**|(3.02)**|(3.11)**|
|fbal||||||0.182|0.173|0.179|
|||||||(3.42)**|(3.29)**|(3.43)**|
|cur|||||||-0.211|-0.211|
||||||||(3.84)**|(3.83)**|
|ll.igdp||||||||-0.075|
|||||||||(1.62)|
|Constant|0.356|251.254|269.08|284.199|257.072|292.922|250.054|254.725|
||(1.92)|(3.77)**|(3.97)**|(3.71)**|(3.10)**|(3.28)**|(2.89)**|(2.87)**|
|Observations|856|764|764|665|549|454|450|449|
|Numberofcountries|39|39|39|37|34|30|30|30|
|R-squared|0.04|0.19|0.19|0.21|0.24|0.33|0.35|0.35|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% 1Year dummies are included (except in the first regression) though their coefficients are not shown in this table 25 Unfortunately we cannot make a direct comparison between our results and those in WW because the latter are calculated for subperiods that do not coincide with our periods of estimation. Appendix 11 however presents estimates using our methodology and WW identification of reform episodes. There is indeed some divergence between the estimates in Table 2 and Appendix 11; a difference ranging between 0.06 and 1 percent. 26 In appendix 12 we introduce lags of time-varying independent variables that were statistically significant in Table 3. Since the introduction of lags brings a reduction in the number of observations, the coefficients of several variables lose statistical significance.
with large exchange-rate spreads, the informal sector may be much larger than the above average. In particular, we estimate that informality is most extreme in Sub-Saharan Africa, Eastern Europe and Central Asia.

Over time, the informal sector has been shrinking, especially in Latin America and Asia. To some extent, this development has generated misleading impressions about the true speed at which remittances are growing. Reductions in transaction costs would encourage a further shift of remittances towards the formal sector. Such cost reduction could be usefully pursued through measures to enhance competition in the banking sector, promote financial development, and reduce exchange rate volatility.

Future work should focus on collecting data on the cost of sending remittances from a wider range of institutions, and for a wider range of originating countries. Such information would not only prove valuable for research purposes, but would also strengthen competition in the remittance marketplace and hence aid migrants wishing to send money home. Similarly, conducting more detailed surveys, in more countries and regions, on how remittances are sent would prove extremely useful in understanding the relative importance of the informal sector.

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**Figure 6: Coefficient estimates individual-level regressions for different life satisfaction scales**

<!-- Start of picture text --> Regression estimates across scales : Unemployed<br>MNETUR KG Z<br>CZE<br>HUN MNG<br>HRV UZB TJK<br>SVN GEO RUS<br>KSV<br>BGRKAZ MDASRBLTUBLR<br>POLFRA LVABIH ROU ARMSWE UKR ITASVK ALB<br>MKD<br>GBR<br>EST AZE<br>DEU<br>-.8 -.6 -.4 -.2 0 .2<br>Dep Var: 10-step scale<br>Source: LiTS II, 2010.<br>Regression estimates across scales : Post-secondary education<br>HUN<br>HRV<br>MKD<br>DEU ALB<br>SVK MDACZEKGZROUKAZPOLBIHLVAITA GEOBGRSRB MNE<br>UKR ARM<br>GBR EST SVN<br>UZB LTU TUR<br>MNG SWE<br>RUS AZE FRA TJK KSV<br>BLR<br>0 .5 1 1.5<br>Dep Var: 10-step scale<br>Source: LiTS II, 2010.<br>.1<br>0<br>-.1<br>-.2<br>Dep Var: 5-step scale<br>-.3<br>.6<br>.4<br>.2<br>Dep Var: 5-step scale 0<br>-.2<br><!-- End of picture text --> <!-- Start of picture text --> Regression estimates across scales : Married<br>HRV<br>HUN<br>ITA<br>SVK<br>GEO<br>GBR<br>RUS<br>ALB MDA AZEMNECZE LVA SRB POL<br>BGR UKR SWEDEU<br>KSV BIH KGZ FRA<br>KAZ BLR ROU TJK<br>ARM<br>LTUMNGUZB<br>EST SVN<br>TUR MKD<br>0 .2 .4 .6 .8 1<br>Dep Var: 10-step scale<br>Source: LiTS II, 2010.<br>.4<br>.3<br>.2<br>Dep Var: 5-step scale<br>.1<br>0<br><!-- End of picture text -->

# **_4.2. Systematic biases: Consistency of subjective relative income assessments within households_**

For the economic ladder, we test the presence of systematic biases in reporting by examining within-household differences in ladder assessments. In 40 percent of the households in the sample, the economic ladder question was asked to two respondents – the head of the household who answered questions about household composition, assets and expenditures, and the randomly chosen household member who provided answers to the rest of the LiTS modules (including all attitudinal questions). There is clear variation in the economic welfare assessments provided for the same household by two different household members. However, the assessments are “roughly” consistent – for most of the ladder steps, more than 80 percent of the evaluations by the second household member are within -1 /+1 interval from the evaluation given by the head of the household.

The two variables that are available in the LiTS data for all respondents are their age and sex. It is thus possible to examine whether answers to the economic ladder question are influenced by these demographic characteristics. Table 4 presents household fixed effects regressions for the 17
<!-- Start of picture text --> Figure 9. Historic power costs in select African countries (cents/kWh)<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Operating Costs Capital Costs<br>Source: Africa Infrastructure Country Diagnostic Power Tariff Database<br>Note: For details see annex 8.<br>South Africa Zambia Congo, Dem. Rep. of Ethiopia Mozambique* Malawi Nigeria Uganda Lesotho Cote d'Ivoire Namibia Senegal Ghana Chad Botswana Tanzania Kenya Madagascar Burkina Faso Rwanda Cameroon Cape Verde * Benin * Congo Niger Mali<br><!-- End of picture text --> But what drives costs? In general, hydro-based systems tend to be more skewed towards capital cost and thermal-based systems more skewed toward operating cost In terms of geography. Africa’s landlocked and island nations seem to bear a significant power cost disadvantage vis-à-vis the coastal countries. High power costs are also driven by the size of markets and their associated scales of production (figure 10).

<!-- Start of picture text --> Figure 10. Differences in average costs (US cents per kWh)<br>a. According to type of power system b. According to geography c. According to system size<br>20 20 20<br>15 15<br>10 10 10<br>5 5<br>0 0 0<br>Total Capital Operational Total Capital Operational Total Capital Operational<br>costs costs costs costs costs costs costs costs costs<br>Hydro Thermal Coastal Landlocked Island < 700 MW 700-1,500 MW > 1,500 MW<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ For details see annex 7.

# **d. Do average effective tariffs cover historic costs?**

Existing effective tariffs allow for the recovery of operational costs for close to 80 percent of African countries (figure 11b). But when capital investments are considered, the picture looks less rosy. On average, sample countries recover only two-thirds of their capital costs. Indeed, barely one-third of the 21
Measuring Quarterly Economic Growth from Outer Space<sup>∗</sup> Robert C. M. Beyer<sup>†</sup> Yingyao Hu<sup>‡</sup> Jiaxiong Yao<sup>§</sup>

**Keywords:** Nighttime lights, economic measurement, quarterly GDP, national accounts, COVID-19

**JEL Classification:** C10, E01, R12

> ∗The views expressed here are those of the authors and do not necessarily represent the views of the IMF or the World Bank, their Executive Boards, or their respective managements.

> †World Bank (email: rcmbeyer@worldbank.org).

> ‡Department of Economics, Johns Hopkins University (email: yhu@jhu.edu).

> §International Monetary Fund (e-mail: jyao@imf.org).