rafmacalaba/datause-encoder-data huggingface dataset:
This paper studies the direction and magnitude of CPI bias for 16 Sub-Saharan African countries using the Engel curve method and estimates the implications of this bias on the measured change in the incidence of poverty. This Engel curve method has been applied to different countries in the world, such as Australia (Barrett and Brzozowski 2010), Brazil and Mexico (De Carvalho Filho and Chamon 2012), Canada (Beatty and Larsen 2005), China (Chamon and De Carvalho Filho 2014; Nakamura, Steinsson and Liu 2016), Indonesia (Olivia and Gibson 2013), the Republic of Korea (Chung, Gibson and Kim 2010), New Zealand (Gibson and Scobie 2010), Norway (Larsen 2007), the Russian Federation (Gibson, Stillman, and Le 2008), and the United States (Costa 2001, Hamilton 2001, Logan 2009). However, to the best of our knowledge, no such analysis exists for countries in Africa. The contribution of this paper is to provide comparable estimates of CPI bias for 16 countries in Sub-Saharan Africa to further our understanding of price changes and their implications on poverty trends in the region.

Using comparable consumption data from 16 Sub-Saharan African countries, namely, Burkina Faso, Cameroon, Côte d’Ivoire, Democratic Republic of Congo, Ethiopia, Ghana, Madagascar, Mauritius, Mozambique, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Togo, and Uganda, combined with monthly food, non-food, and overall CPI data from the respective national statistical offices (NSOs) our results suggest that the official CPIs mostly overestimate increases in the cost of living. In the 13 countries where our results indicate that CPIs overestimate inflation, the average annual CPI upward bias ranges from 0.7 percent in Cameroon to 43.9 percent in Nigeria. Three countries, Burkina Faso, Ghana, and Uganda, experience a negative bias between -8.9 percent a year in Burkina Faso and -5.8 percent in Ghana. These estimates of CPI bias are statistically significant, except for Cameroon and Ghana.

Armed with these estimates of bias in national CPIs, we study the implications for measured trends in international poverty. Correcting for CPI bias, poverty falls significantly faster than suggested by current international poverty numbers in 11 countries. Based on our estimates, the difference in poverty reduction resulting from CPI-bias adjustment could be as large as -5.7 percentage points per year in Tanzania between 2008 and 2012. Only two countries, Uganda and Ghana, experience significantly slower poverty reduction rates with the correction of CPI bias. The change in poverty trend due to CPI bias correction is statistically insignificant in Mauritius, Cameroon, and Burkina Faso. While we advise to interpret individual country estimates and the magnitude of point 3
# **2. Trends in PPI in Urban Infrastructure Developing Countries**

First, let us consider the trends in urban infrastructure investment in developing countries in the context of overall PPI over the last several years. These data are drawn from the World Bank PPI database. We constructed a database covering urban infrastructure as a subset of all infrastructure by identifying specific transactions in sectors in the database that can reasonably characterized as urban services (water and sanitation and a subset of transport projects). We calculated flows of finance by using the investment figures in the database, and excluded revenues from acquisition of assets. (See Annex 1 for details on the sources and construction of the dataset.) While it is the only source of statistical data on PPI, the database is not without its limitations, particularly for urban infrastructure. Unfortunately, the World Bank PPI database does not cover an important element of urban infrastructure, solid waste management and disposal, which is a potentially significant source of PPI transactions. Moreover, the database covers primarily transactions involving international investors, whereas local private investors could possibly have played a more important role in urban infrastructure sectors.<sup>6</sup> Table 1 shows both total PPI investment in infrastructure and urban infrastructure for the entire period 1983-2004 over which data have been collected. It shows that urban infrastructure investment has only had a very minor share in total PPI throughout the period, 10 percent in total. Urban infrastructure was slower to take off, with the first transactions captured in the database taking place in 1989. Investments peaked at roughly the same time as total, in the later half of the1990s, just as the optimism for PPI began to take hold.

**Table 1: Total PPI and Urban PPI**

> 6 The database is being upgraded to include local investors to some extent, however these data were not available at the time of writing of this paper.

4
17 would. More research is needed on<sup>this topic, particularly using Individual-level</sup> survey data now becoming available in many developed<sup>and developing countries.'</sup> 6 Other Factors: It should be emphasized that in some nations, older persons retire<sup>from</sup> the paid labor marke **t** o engage not In leisure<sup>activities, but in self employment</sup> and/or home production. In Hungary, for example, where large segments<sup>of the older population report working in agriculture,</sup> retirement has signified a change in<sup>industry and occupation -- a movement away from the state-run</sup> system to the non-socialist sector<sup>(Szalai 1991).</sup>

# B. Retirement Systems In Practice

Over the last two decades, many nations have<sup>developed innovative policies influencing</sup> retirement and work pattems, including (but<sup>not limited to) private and public pension systems. A</sup> 'traditional" motivation for this increased<sup>attention to retirement policy has been demographic aging --</sup> there are increasing numbers of persons<sup>requiring old-age income support. The population and labor</sup> force estimates discussed above imply<sup>growing concern along this dimension, given expected aging</sup> trends. A less traditional, but increasingly common, rationale for the new concem about<sup>retirement policy</sup> Is the fact that many nations have been adapting<sup>their retirement systems in times of slack labor demand,</sup> increasingly providing generous retirement<sup>payments in lieu of unemployment</sup> payments.

Benefits and financing arrangements of retirement systems differ greatly across<sup>the set</sup> of developed countries, as well as their reliance<sup>on private market provision, government regulation, and</sup> government provision. Despite these differences,<sup>it appears that many nations have increasingly relied</sup> on multiple-pillar systems to draw older workers out of the<sup>labor force. A review of experience from</sup> developed countries shows that many public<sup>retirement systems made early retirement financially</sup> attractive in the last decade. Such offerings<sup>included low early retirement eligibility ages, generous</sup> pension and other benefit levels, and strong<sup>disincentives</sup> to remain employed at older ages. High payroll tax rates also characterize many public retirement plans.
increases, as the expansion of agricultural sector slows down migration to the cities driving up wages of unskilled workers in urban areas. In the remaining two scenarios, agricultural sector is contracting releasing excess unskilled labor to be used in the fast expanding services sectors. Even though producers have at their disposal a relatively faster growing supply of skilled workers, the urban sector cannot fully substitute away from unskilled workers and its fast expansion is attracting unskilled workers to the cities. In rural areas, a declining agricultural output is produced with ever more shrinking labor force leading to a faster growth of rural wages. As a result, urban premium is falling in the scenarios with faster economic growth and stronger expansion of urban sectors.

**Figure 8: Wages of skilled workers under different growth assumptions**

<!-- Start of picture text --> Percentage growth of wages of skilled workers in urban<br>areas over 2005-2030<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>-50<br>Low Baseline High<br>EUR USA XHY CHN XEA SAS ECA MNA SSA ARG BRA CHL COL MEX PER CAC XLC<br><!-- End of picture text --> Source: World Bank staff calculations.

Note: ARG-Argentina, BRA-Brazil, CAC-Central America & Caribbean, CHL-Chile, CHN-China, COL-Colombia, ECA-Europe & Central Asia, EUR-EU27 & EFTA, MEX-Mexico, MNA-Middle East & North Africa, PER-Peru, SAS-South Asia, SSA-Sub-Saharan Africa, USA-United States, XEA-Rest of East Asia, XHY-Rest of high-income, XLC-Rest of Latin America 20
<!-- Start of picture text --> %<br>100<br>90<br>80<br>5 70<br>E<br>S 60<br>®@ 50<br>2<br>2 40<br>g<br>g 30<br>20<br>10<br>0<br>Q1 Q2 Q3 Q4 Q5<br><!-- End of picture text --> <!-- Start of picture text --> Quintile 5: TheQuintilePoorest1:<br>The Richest<br>7%<br>11%<br>oy,<br>44%<br>Q4<br>23%<br><!-- End of picture text --> <!-- Start of picture text --> 300<br>350<br>228<br>200<br>[-5<br>5<br>= 150<br>=3=2 100 81 120<br>a 58<br>50 34<br>0<br>Qi Q2 Q3 Qa Qs<br><!-- End of picture text -->
In order to answer these two questions might be useful to use georeferenced data, in particular looking at night-time light data as well as data on spatial and temporal incidence of droughts.

As shown in Figure 16, night-time light emission – a metric often used to proxy for economic activity - collapsed after the start of the conflict in 2011 and kept declining till 2016. In the following years, the rebound that followed the phase of conflict de-escalation was short lived, with emission declining again starting in 2019, the year of the financial crisis in Lebanon.<sup>35</sup> Interestingly, a drop in night-time light emissions is also visible in 2008-2009, corresponding to the severe drought that affected the country prior to the conflict start. Other drought periods affected agriculture production in 2014, 2016 and 2018 possibly driving additional decline in economic activity, as captured by night-time light emissions. More recently, drought conditions affected northeastern regions of Syria in 2021 and 2022.

Overall, evidence emerging from satellite data seems to suggest that – should a distribution neutral approach be used to nowcast poverty in Syria – using projections based on GDP in current prices deflated by the CPI is likely to provide a more accurate picture of poverty dynamics compared to GDP in constant prices which would provide monotonic poverty trends over the entire conflict period.

<!-- Start of picture text --> Figure 16: Trends in night-time light emissions<br>80000 conflict<br>70000<br>60000<br>50000<br>40000<br>30000<br>20000<br>10000<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>NTL NLT - Adj<br>Note: Night-lights data series shows in orange adjusted for intensity variations emanating from gas and oil flares.<br>Source: World Bank estimates based on Chen et al. (2021)<br>Night time light intensity<br><!-- End of picture text --> 35 In order to consistently analyze variation in night time light emissions over the period 2000-2020, we use the combined VIIRS, DMSP series. See Chen et al. 2021.
# II. INEQUALITY DURING PAST INDUSTRIAL REVOLUTIONS

Before we can talk about poverty, we need to understand what history tells us about inequality. It's important to understand first the industrial revolutionary forces driving inequality because they illustarte best the determinants of poverty eradication, namely the demand for and supply of unskilled labor. Having done so, we shall be better equipped to return to the poverty theme in Section III.

# The Kuzuets Curve: Contemporary Cross-Sections Once Nore

More than tbree decades ago, Simon Kuzuets (1955) noted that income inequality seemed to have declined in the industrialized nations across the mid-20th centur.?, and ventured the guess that it bad risen earlier just as the critics o, capitalism had asserted. While Kuznets was drawing on limited historical evidence, others rose to his challenge by pursuing the more abundant cross-sectional evidence.' Felix Paukert (1973), Hollis Chenery and his World Bank team (1974), Nontek Ahluvalia (1976; 1980), and Edmar Bacha (1979), all thought they saw contemporary cross-section evidence supporting the Kuznets Curve, namely that income inequality first rose and then declined with development.

The Kuznets Curve is illustrated in Figure 1, based on Ahluwalia's World Bank sample. A quadratic fits this 60-country sample fairly well, where the inequality statistic is simply the income share of the top 20%. The underlying data can and have been criticized. Indeed, some have argued that the data is much too fragile to resolve the debate. But whatever your position on the issue of data quality, two morals leap out from Figure 1. First, the more robust portion of the Kuznets Curve lies to the right: income inequality falls

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Finally, we explore whether the estimated effects could be the result of different migration or mortality rates affecting those that provided military service. Using the national ballot registry (voting is mandatory in Argentina), column (2) of Table 6 shows that conscription does not affect the probability of being alive and living in Argentina.

# **VI. Conclusions**

The objective of this study is to estimate the causal relationship between mandatory participation in military service and crime. A priori, different hypotheses could predict a positive or negative effect of military service on involvement in criminal behavior. We exploit the random assignment through a draft lottery of young men to conscription in Argentina to identify this causal effect. Our results suggest that participation in military service increased the likelihood of developing a criminal record in adulthood.

Additional evidence suggests two particular channels through which this effect could have operated. The significant effect of military service on arms-related crimes suggests that the firearm training received during military service may have reduced the entry costs into crime or the natural barriers to committing violent acts. Moreover, the significant effect of military service on crimes against property and the estimation of the largest effect for individuals that provided two years, rather than one, of military service may imply that military service delayed the insertion of the young into the labor market, affecting their future opportunities.

To sum up, our results do not encourage the introduction of military service on anticrime grounds.

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# **I. Introduction**

On the face of it, services play a relatively small role in international trade. Conventional trade statistics show that services trade accounts for only one-fifth of cross-border trade (Loungani et al., 2017). However, a significant part of goods trade includes trade in embodied services. In the United States, for example, more than a quarter of intermediate inputs purchased by manufacturers were from the services sector (USITC, 2013). For certain manufacturing sectors, such as computers and electronic products, this percentage — a measure of “services intensity” — is as high as 47.6 percent. Drawing on the Trade in Value Added Database (TiVA, 1995-2011), Miroudot and Cadestin (2017) show that services inputs account for about 37 percent of the value of manufacturing exports in the sample of countries covered. The development of the domestic services sector, as well as access to imported services inputs, can, therefore, be expected to influence comparative advantage in manufacturing trade. This paper seeks to understand this indirect role of services development drawing upon new data and new techniques.

The impact of services development is interesting because it is not straightforward. Since services are used as inputs in the production of manufactured goods, services development can help to increase manufacturing production. But since services and manufacturing compete for resources, the development of the former can be at the expense of the latter. For example, it is evident that the development of the services sector has drawn resources away from manufacturing not just in industrial countries like the United States and the United Kingdom, but also in developing countries like India.<sup>1</sup> We focus on two key services sectors: financial services and business services. Both have emerged as skill-intensive, dynamic, internationally traded services. These two services sectors are often regarded as the pillars of modern economies, and their value added shares in GDP have a strong positive correlation with countries’ income levels (see Figure 1). These are also the two sectors which represent the tension we discussed above in the sharpest form. On the one hand, manufacturing performance is critically dependent on the domestic availability of these services. On the other, these are the sectors that often provoke “de-industrialization” concerns – financial services in industrial countries like the United States and United Kingdom, and business services in developing countries like India and the Philippines.

> 1 See, for example, Kochar et al. (2006).

2
Becker, Gary S. and Nigel Tomes. 1986. “Human Capital and the Rise and Fall of Families,” _Journal of Labor Economics_ , 4(3/2: The Family and the Distribution of Economic Rewards):S1-S39 Behrman, J.R. and M.R. Rosenzweig. 2002. “Does Increasing Women’s Schooling Raise the Schooling of the Next Generation?” _American Economic Review_ , 92(1):323–334.

Behrman, Jere. R., Andrew D. Foster, Mark R. Rosenzweig and Prem Vashishtha. 1999. “Women’s Schooling, Home Teaching, and Economic Growth,” _Journal of Political Economy_ , 107(4):682-714. Bianchi, S.M. and J. Robinson. 1998-1999. "Family Interaction, Social Capital, and Trends in Time Use," Time diary data, University of Maryland.

Bianchi, SM. 2000. “Maternal employment and time with children: Dramatic change or surprising continuity?” _Demography_ , 37(4/Nov):401-414.

Black, S., P.J. Devereux, and K.J Salvanes. 2005. “Why the Apple Doesn't Fall Far: Understanding Intergenerational Transmission of Human Capital,” _American Economic Review_ , 95(1):437–449. Card, David. 1999. "The causal effect of education on earnings," in Orley Ashenfelter and David Card eds.

<u>Handbook of labor economics, (Amsterdam: Elsevier):1801-1863.</u> Carneiro, Pedro, C. Meghir, and M. Parey. 2007. “Maternal Education, Home Environments and the Development of Children and Adolescents,” _IZA Discussion Papers_ , No. 3072, Institute for the Study of Labor, Bonn.

Cawley, John and Feng Liu. 2007. “Mechanisms for Associations between Maternal Employment and Child Cognitive Development,” _NBER Working Paper_ No. 13609, November.

Chiburish, Richard, Jishnu Das and Misha Lokshin. 2006. "Comparing Linear IV and Bivariate-Probit Estimates," **Processed** .

Currie, Janet and Enrico Moretti. 2003. “Mother's Education and the Intergenerational Transmission of Human Capital: Evidence from College Openings”, _Quarterly Journal of Economics_ , 118(4):1495-1532.

Das, Jishnu and Tristan Zajonc. 2009. “India shining and Bharat drowning: Comparing two Indian states to the worldwide distribution in mathematics achievement,” _Journal of Development Economics_ , **In Press, Corrected Proof** , Available online 16 March 2009 at http://bit.ly/1BFsPS 27
Table F.9: Productivity and Technology Sophistication, GBF and SSBF MOST Technology, Disaggregated Agriculture Sector

|VARIABLES|(1)<br>ln(VAPW)|(2)<br>ln(VAPW)|
|---|---|---|
|GBF EXT<br>|1.72***<br>(0.32)||
|GBF EXT<sup>2</sup>|-0.20***<br>(0.05)||
|GBF EXT*AGRI||1.96***<br>(0.45)|
|GBF EXT*MANF||-0.29***<br>(0.09)|
|GBF EXT*SVC||0.38***<br>(0.10)|
|SSBF MOST*Agriculture<br>|0.55***<br>(0.20)|0.63***<br>(0.20)|
|SSBF MOST*Livestock|-0.05|0.04|
||(0.56)|(0.58)|
|SSBF MOST*Food Processing|0.36**<br>(0.19)|0.38**<br>(0.19)|
|SSBF MOST*Apparel|0.65***|0.60***|
||(0.15)|(0.15)|
|SSBF MOST*Retail and Wholesale|-0.05|-0.15|
||(0.15)|(0.16)|
|SSBF MOST*Other Manufacturing|0.15*<br>(0.09)|0.12<br>(0.08)|
|SSBF MOST*Other Services|0.49*<br>(0.29)|0.48<br>(0.30)|
|Var(ABF EXT)|0.30***<br>(0.10)|0.37***<br>(0.10)|
|Firm Characteristics|_~~√~~_|_~~√~~_|
|Observations|2,746|2,746|
|R-squared|0.50|0.50|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors in parentheses. The regressions controlled for observable firm characteristics including size (i.e., small, medium, and large), sector (i.e., agriculture crops, livestock, food processing, wearing apparel, pharmaceutical, moto-vehicle, wholesale and retail, finance, land transport, hospital, other manufacturing, and other services), age (i.e., 0-5, 6-10, 11-15, and 16 or more), export, and multinational status.

102
agricultural products, however, are much higher. Table 2 lists the post-Uruguay Round tariff rates by commodity. Tariff rates on agricultural goods (see "Agriculture, exc. Fish: Estimate 2" in the table) are 7.6-18.5 percent). In addition to tariff protection, agricultural goods are also heavily protected by various nontariff barriers. For example, until very recently, Japan imposed rable 2: Binding and Levels of MFN Tariff Rates Before and after the Uruguay Round (percent)

||Levels and c<br>t|hanges weight<br>he world excl|ed by imports from:<br>. FTA'|
|---|---|---|---|
|Summary product category|Post-UR<br>applied rate|Tariff<br>reduction<sup>2</sup>|Post-UR bound<br>rate|
|Agriculture, excl. fish: estimate 1|25.0|32.4|32.4|
|Agriculture, excl. fish: estimate 2|7.6|4.5|18.5|
|Fish and fish products|4.4|4.4|5.2|
|Petroleum oils|1.7|_1.5_|3.1|
|Wood, pulp, paper and furniture|1.2|_5.1_|2.2|
|Textiles and clothing|9.8|3.1|12.4|
|Leather, rubber, footwear|6.4|3.1|7.8|
|Metals|2.9|4.3|4.1|
|Chemical & photographic supplies|4.8|_5.1_|7.3|
|Transport equipment|6.0|3.3|6.9|
|Nonelectric machinery|3.7|3.6|5.1|
|Electric machinery|4.6|4.0|5.7|
|Mineral prod., prec. stones & metal|1.6|2.8|2.6|
|Manufactured articles nes.|2.8|4.1|4.1|
|Industrial goods (lines 5-14)|4.2|3.9|5.7|
|All merch. trade (lines 2-14)|4.3|3.9|6.5|

Notes: 1. Value of imports from partner countries that do not participate in free trade agreements with the world.

2. Weighted average tariff reduction measured by dT/ (1 + T) in percent.

3. Average based on the 40 GATT members.

Source: Finger, Ingco, Reincke (1996).

6