rafmacalaba/datause-encoder-data huggingface dataset:
efficiency term—see equation (4.8). In this case we sum up the different contributions of the INF, IC, and C factors of equation (4.8), but in absolute value so that the negative effects do not compensate for the positive ones and vice versa _,_ and we compute the relative contribution of each group within the IC group as a whole. Therefore, the relative contribution of the infrastructure group is the sum in absolute value of all individual infrastructure variables divided by the total absolute contribution of all INF and IC variables—multiplied by 100. The largest relative effects of infrastructure on aggregate log productivity are found in Malawi (60 percent), Uganda (50 percent), Benin (50 percent), Zambia (47 percent), and Ethiopia (46 percent). The lowest contributions are in Kenya, Swaziland, and Botswana. A similar ranking is provided by panel B, where the effects on the average log productivity are isolated from those from the allocative efficiency, as seen in panel C. Once again, Malawi, Benin, Senegal, Uganda, and Ethiopia lead a ranking closed by Mauritius, Egypt, Swaziland, Botswana, and Namibia. Panel C offers the results for allocative efficiency. In Malawi, Senegal, Namibia, and Algeria, the effects of infrastructure on the efficient reallocation of results among firms appear to be very significant, reaching the relative contributions of 54 percent, 48 percent, and 46 percent, respectively.<sup>21</sup>

# _Cross-country comparisons_

Table C.1 summarizes the empirical results discussed in previous sections. The first column shows ranking of African countries based on per capita income, the second based on the DBR (2007), the third column based on quality of overall infrastructure (1 minimum, 7 maximum) given in the ACR (2007), the fourth column the demeaned aggregate productivity, and the fifth column shows the ranking of firms’ perceptions of the quality of infrastructure (from 23rd being the poorest quality to 1st being the best quality in our sample). Columns 6 and 7 show the percentage of absolute contributions of infrastructure to average log productivity and to allocative efficiency, with TFP in logs, while columns 8 and 9 show the percentage absolute contributions of infrastructure to average productivity in levels and to allocative efficiency via simulations.

> 21 It is useful to clarify that the differences between the rankings of the contributions to the aggregate productivity via simulations of panel A of figure 5.5 and the rankings of the contributions to the aggregate log productivity of panel A of figure 5.6 come mainly from the role of the allocative efficiency. Notice that in figure 5.6 the allocative efficiency term based on log TFP does not have the same scale as the efficiency term when TFP is in levels. As a result, since aggregate productivity is simply the sum of the average productivity and the efficiency term in levels, the role of the efficiency term with TFP in levels will increase with respect to its counterpart in logs and therefore, could alter the rankings of countries based on the two alternative measures of aggregate productivity (weighted productivity).

37
per unit of fuel) to arrive at a figure for total emissions, and then use the assumed carbon tax rate to calculate total carbon tax revenue.

The revenue generated in 2030 ranges from $786 million under the direct tax scenario to $798 million under the sales tax reduction scenario (Fig 6.7). The differences in revenue among scenarios are due to the differences in economic growth which, in turn, affect the demand for fuel.

_Figure 6.7: Government revenue from carbon tax_ <!-- Start of picture text --> Government revenue from carbon tax<br>900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>2018 2022 2026 2030<br>DTAX STAX SAVINGS CORPOR TRANS<br>Millions of USD<br><!-- End of picture text -->

# **6.4 Distributional Impacts**

Although the impact of the carbon tax on overall economic growth is one indication of the economic consequences of the policy, a much clearer understanding of the full implications for economic well-being comes from looking at the impacts across heterogeneous households based on factors such as their location (urban, rural) and their abilities (skilled, unskilled). Households receive income from wages (employment), returns on land for which they have the right of use, returns on capital they own, and income transfers. The implication of a carbon tax on a household’s welfare, therefore, depends on how it affects each of these sources of household income.

In this subsection, we highlight the heterogeneous impacts on labor demand (employment) and household consumption. Figures 6.8 and 6.9 provide the difference in employment trend of low-skilled individuals located in the urban and rural sector. The carbon tax on fuels increases the price of goods reliant on fuel-using transport. As a result, it shifts demand away from those goods towards others. Sectors that are transport and thus fuel dependent include the service and manufacturing sectors. Both sectors are overwhelmingly concentrated in urban areas. As a result, there will be a decrease in urban unemployment (Fig. 6.8) among low-education workers. Because we assume full employment for highskill individuals, they adjust to the carbon tax through a wage decrease rather than an employment decrease.

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The results strongly suggest a moderate negative effect on informality when _SP_ is deployed in a given municipality. The results are very similar across the two columns, and indicate that the introduction of _SP_ increases informality by about 0.7 percentage points, which amounts to an additional 1.5 percent of the baseline level.

We next assess the robustness of our results to the introduction of random and individual fixed effects (Columns 3 and 4, respectively), making full use of the longitudinal nature of the data. It should be emphasized that this identification strategy is very demanding, as it is based on comparing the change in the probability of being formal of a _given household_ around the introduction of _SP_ in a given municipality. Because we can follow households during at most five quarters, our windows of observation around given thresholds are small. The advantage of this methodology is that it ensures that we compare the same households over time and therefore that the results are not due to composition effects resulting from aggregating different households over time—or due to the migration of individuals with a higher probability of being informal toward locations with _SP_ . This aspect is particularly important because household surveys in Mexico are not designed to be representative at the municipality level. This implies that cross sectional estimates may suffer from the aggregation at the municipality level of non-representative averages over time, a problem avoided with fixed effects estimates, as they are based on comparing the behavior of given households over time. Given their superior possibilities to control for unobservables and make good use of existing data, most of our results are inferred from estimates that take full advantage of the longitudinal nature of the survey.

The results for the random and fixed effects model yield very similar coefficients and in both cases about half the ones obtained in the cross sectional model. Thus, the coefficient of _SP_ is negative and statistically significant and it indicates a magnitude of the impact of 0.4 percentage points. Nonetheless, the Hausmann test rejects the null hypothesis of equality between random and fixed effects. We therefore only estimate fixed effect models in the remainder of the paper.

We next assess the robustness of our basic results to changes in the specification. Column (1) in Table 5 reports the results of excluding state trends from the FE specification. This yields an 19
<!-- Start of picture text --> Share of sectors with FEL<br>Percent<br>0 20 40 60 80 100<br>PR No.7 of 2007 tt6/5 5 TN<br>PR No.36 of 2010 ttt:7 0(O<br>PR No.39 of 2014 50221<br>PR No.44 of 2016 CCS<br>PR No.10 of 2021 III)21 ttt ZS<br>PR No.49 of 2021 III21 tS<br>MNoFEL FEL applied<br><!-- End of picture text --> <!-- Start of picture text --> Share of sectors with restrictions<br>Percent<br>0 20 40 60 80 100<br>PR No.7 of 2007 Zl 217<br>PR No.36 of 2010 ltt12<br>PR No.39 of 2014 521751:<br>PR No.44 of 2016 SSSGAS<br>PR No.10 of 2021 III t=<br>PR No.49 of 2021 012INN 7<br>Not restricted m Restricted<br><!-- End of picture text -->
the income distribution exhibited by the two measures. As is well-known, Theil is much more sensitive to extreme values, while Gini is most sensitive to the values around the mode. Since our data for pre-industrial economies are calculated from the social tables with a limited number of social classes, the top of the distribution is truncated, or more exactly “squeezed”. The top is represented by the mean income of the richest class, not by the individual incomes of the richest individuals. Hence, Theil may tend to show much lower values than if we had more finely-grained (individual-level) data. The gap between the measured and maximum Theil may thus be overestimated.

Second, for high values of α the distance of the calculated (actual) Theils from the Inequality Possibility Frontier is much greater than the analogous distance of the Gini. The reason is that the IPF within the Theil framework increases linearly in logs while within the Gini framework IPF is concave. Thus, for example, for countries with α>4, the average IER using Gini is 66 percent; using Theil, it is less than half (30 percent).<sup>13</sup> But while the levels of IER calculated with Gini and with Theil differ, the correlation between the two IER measures is quite high: both the linear and rank correlations are 0.89.

In conclusion, Gini and Theil seem to rank countries according to their inequality extraction ratios very much alike: more “exploitative” countries will be deemed such by both approaches. But the implied levels of IER are significantly lower with Theil than with Gini. This, in turn, has implications on our view regarding how close to the inequality frontier were different societies. It also seems that, given the nature of social tables that are used to estimate pre-industrial inequalities, Gini would be more likely to capture inequality well, and in this case at least, it would be reasonable to prefer it to Theil.

> 13 There are 7 countries with α>4.

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# **3. Results**

# **Price Changes**

Consistent with the CPI, the HPI shows that inflation exploded between 2015 and 2017. The mean price index increased substantially from 1.31 in 2015 to 8.07 in 2016 and exploded to 30.75 in 2017 (Figure 1). The results show that non-food price inflation is drastically higher than food price inflation during this period. We use an adjusted Wald test to test the significance of the differences in the means across the three years. The test confirms that the differences are statistically significant (p<0.01), meaning that inflation has increased significantly over time.

Figure 1: Recent trends in price index <!-- Start of picture text --> 60.00<br>40.00<br>20.00<br>0.00<br>Food price index Non Food price index Total price index<br>2015 2016 2017<br><!-- End of picture text --> Source: Authors’ calculations based on HFS market prices data The increase in prices varies across states and have been much higher in the Eastern Equatorial, Central Equatorial, and Northern Bahr El Ghazal states than in other states (Figure 2). This is mainly driven by the fact that food price inflation increased much more in these states. Since the price developments have been so different across the country, it is important to consider this significant geographic variation when analyzing the impact of inflation on livelihoods.

Figure 2: Inflation by state - 2017 (Base year 2015=100) <!-- Start of picture text --> 500%<br>400%<br>300%<br>200%<br>100%<br>0%<br>Northern Bahr El Western Bahr El Lakes Western Equatoria Central Equatoria Eastern Equatoria<br>Ghazal Ghazal<br>Food inflation Non Food inflation Total inflation<br><!-- End of picture text --> Source: Authors’ calculations based on HFS market prices data 8
Reinhart, C. M., and K. S. Rogoff. 2009. This Time is Different: Eight Centuries of Financial Folly. Princeton: Princeton University Press.

Rogoff, K. 2013. “What’s the Problem with Advanced Economies?” Project Syndicate, December 4.

Rogoff, K., D. Robinson, and T. Bayoumi. 2002. “Was It a Global Recession?” Box in World Economic Outlook: Recessions and Recoveries, by the International Monetary Fund, April, International Monetary Fund, Washington, DC.

Romer, C. D., and D. H. Romer. 2012. “A Rehabilitation of Monetary Policy in the 1950’s.” American Economic Review 92(2): 121-127.

Rossiter, J. 2010. “Nowcasting the Global Economy.” Bank of Canada Discussion Paper 2010-12, Bank of Canada, Ottawa.

Schmelzing, P. 2020. “Eight Centuries of Global Real Interest Rates, R-G, and the ‘Suprasecular’ Decline, 1311–2018.” Staff Working Paper 845, Bank of England, London.

Sichel, D. E. 1994. “Inventories and the Three Phases of the Business Cycle.” Journal of Business & Economic Statistics 12(3): 269-277.

Stock, J. H., and M. W. Watson. 2012. “Disentangling the Channels of the 2007-09 Recession.” Brookings Papers on Economic Activity 2012(Spring): 81-135.

Summers, L. H. 2015. “Have We Entered an Age of Secular Stagnation? IMF Fourteenth Annual Research Conference in Honor of Stanley Fischer, Washington, DC.” IMF Economic Review 63(1): 277-280.

Taylor, J. B. 2014. “The Economic Hokum of ‘Secular Stagnation.’” Wall Street Journal, January 1.

Terrones, M. E. 2019. “Do Fixers Perform Worse than Non-fixers during Global Recessions and Recoveries?” MPRA Paper 97524, University Library of Munich, Germany.

Teulings, C., and R. Baldwin, eds. 2014. Secular Stagnation: Facts, Causes and Cures. London: CEPR Press.

United Nations. 1959. World Economic Survey 1958. New York: United Nations.

World Bank. 2009. East Asia and Pacific Economic Update: Transforming the Rebound into Recovery. November. Washington, DC: World Bank.

World Bank. 2010a. Middle East and North Africa Economic Update: Recovering from the Crisis. April. Washington, DC: World Bank.

World Bank. 2010b. South Asia Economic Update: Moving Up, Looking East. June. Washington, DC: World Bank.

World Bank. 2016. Global Economic Prospects: Spillovers amid Weak Growth. January. Washington, DC: World Bank.

World Bank. 2020. Global Economic Prospects: Slow Growth, Policy Challenges. January. Washington, DC: World Bank.

Zarnowitz, V. 1992. Business Cycles: Theory, History, Indicators, and Forecasting. Chicago: University of Chicago Press.

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**for human capital accumulation** . Using data from Spain, De La Roca and Puga (2017) show that workers living in bigger cities see faster growth in earnings over the life cycle, consistently with the view that they acquire more valuable experience; this conclusion is supported by the evidence in Eckert et al. (2022), which leverages the quasi-random allocation of refugees across local labor markets in Denmark. In the context of developing countries, Monge-Naranjo et al. (2018) argue that the formation of slums (a key driver of urban growth in many countries) has accelerated human capital accumulation and provided a stepping stone to low-income households.

**At the same time, both developing and developed countries face several challenges in ensuring that cities remain environments where human capital can easily form and thrive.** The living conditions and standards of urban services are still very low in the developing world, which might constitute a barrier to skill accumulation and participation in urban labor markets. Another key issue is represented by the growth in residential segregation by income, which is amplified by and contributes to differences in educational opportunities across neighborhoods (Fogli and Guerrieri, 2019). Tackling these issues is crucial for urbanization and human capital growth to continue to be as intertwined as they have been in the recent past.

# **5 Conclusions**

**The research presented in this paper provides some important lessons for the role of human capital for economic development. First, the accumulation of human capital is a dynamic and multifaceted process, which involves different types of investments across all stages of the life cycle, as well as the accumulation of a wide variety of skills and traits.** Individuals in developing countries face significant obstacles in all these dimensions, calling for a holistic approach when designing human capital policy for economic development. Increasing educational attainment is only one of the many possible margins for policy action in this context, and likely not very effective if not associated by improvements in educational quality and the relaxation of barriers to the accumulation of other complementary forms of human capital.

**Second, there is growing evidence that the way human capital is utilized in practice shapes the extent to which its accumulation is reflected in a stronger economic performance.** The accumulation of different forms of human capital represents both a cause and a consequence of structural changes associated with economic development, such as the reallocation of labor across sectors, occupations, and firms, as well as the adoption of new technologies. Barriers to these processes, such as those often encountered in developing countries, partially undermine the potential gains associated with human capital growth by preventing an efficient utilization of talent. This underscores important complementarities between policies targeting human capital accumulation and those aiming at removing obstacles to firm growth and labor mobility.

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disappointment with financial sector reforms have often led to calls for renewed government intervention in the financial sector, either by re-establishing government-owned financial institutions or by a “tighter” financial safety net. While this would be a step back, it underlines the necessity for government to take a more “pro-active” role by considering specific bottlenecks that prevent private agents to provide financial services even if macroeconomic stability, contractual and informational efficiency and free entry into financial intermediation have been achieved. It underlines the role of government in enabling and creating markets, as discussed in section 3.3; a discussion, however, that is just starting and that will be informed by trials and errors.

# **5. Challenges for financial systems in a global economy**

One important aspect of liberalizing financial systems is liberalizing financial flows across borders. This implies both free entry of foreign providers and the possibility for agents to purchase financial services abroad. It implies free capital flows in the form of debt and equity between foreign and domestic agents across borders and within countries. In the following, I will touch briefly on some aspects of international financial liberalization as they refer to the financial sector and financial sector policies; for broader discussions of these issues, see Clarke et al. (2003) and Bekaert and Harvey (2003).

More and more developed and emerging economies allow the entry of foreign financial service providers, with so diverse countries as Botswana, Czech Republic, Hungary and Mexico and Poland seeing foreign-owned banks take more than 80% of the banking market.<sup>10</sup> In many developing countries, foreign bank entry has mostly occurred through the privatization process of failed government-owned or private domestic banks, with the hope that foreign banks bring in

> 10 Data are for end 2001; source: Barth, Caprio and Levine (2006).

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government spending drove the large impact on the economy. This is in sharp contrast with the procyclicality of subnational government spending in many advanced economies, such as the United States and the European Union. It is important to note that from a political economy point of view, these “federal” systems, which function within widely different political systems, are not strictly comparable. While the United States is formally a federal country, the European Union, which has a monetary union, does not yet have a fiscal union as decisions about taxes and spending remain at the national level, though some members, such as Germany, have a federal system. China, on the other hand, is a unitary state, even though in several important ways it behaves as a fiscal federalism and its fiscal system shares some of the key features of fiscal systems in federal countries (Bahl and Martinez-Vazquez 2003). In particular, fiscal decentralization has been a fundamental aspect of China’s transition to a market economy, and the country has made substantial progress in moving away from its highly centralized fiscal management system and tax sharing system, though the current fiscal system requires further reforms, particularly in the areas of sub-provincial expenditure assignment, dealing with widening fiscal disparities and intergovernmental transfer system (Shen, Jin and Zou 2012).

Quasi-conventional wisdom holds that at the central government level, fiscal policies are acyclical in industrial countries and procyclical in developing countries (Arreaza, Sorensen, and Yosha 1999; Seitz 2000; Talvi and Vegh 2000). In general, government spending in developing countries closely follows—and hence reinforces—the business cycle, because of imperfections in international credit markets that prevent developing countries from borrowing in bad times and the political economy of the strong incentive to pursue expansionary fiscal policy in good times (Gavin and Perotti 1997; Caballero and Krishnamurthy 2004; Talvi and Vegh 2005). Using a quarterly data set on 49 countries for the period 1960–2006, Ilzetzki and Vegh (2008) document the procyclicality of government spending, establishing a causal relation between output and government consumption in developing countries.

The revenues of subnational governments are strongly procyclical in most advanced economies and developing countries (box 2). Countries whose subnational governments are most constrained by the basic fiscal contract to rely on an inflexible income-elastic tax base often display greater procyclicality (Rodden and Wibbels 2010).The decline in local own-source revenues is often exacerbated by cuts in tax revenues shared with the central government. Stimulus transfers from the central government often fail to fully offset the procyclicality of subnational fiscal position (IMF 2012b). As a result, in the absence of substantial borrowing or 14