rafmacalaba/datause-encoder-data huggingface dataset:
believed to be more accurate. Indeed it is consumption inequality, based on the new data, which is reported in Figure 1 on Poland and Russia used to introduce this paper.<sup>5</sup> The data in Table 2 are also helpful in illustrating the evolution of inequality over time and decomposing its sources by household groups in countries that are deemed broadly representative of four clusters in the region, viz., Poland, Hungary and Latvia for the EU-8 countries, Romania for South Eastern Europe, Kazakhstan and Russia for the middle income CIS countries and Georgia, Moldova and Tajikistan for the low income CIS countries. Table 3 presents key data on the Gini index of inequality for those countries.

**Table 3.  Representative countries: inequality indices (Gini) for comparable per capita** **<u>consumption</u>**

|Country|**Georgia**|**Hungary**|**Kazakhst**<br>**an**|**Latvia**|**Moldova**|**Poland**|**Romania**|**Russia**|**Tajikistan**|
|---|---|---|---|---|---|---|---|---|---|
|Initial year/|1999/|**_1993/_**|2001/|**1998/**|**1998/**|**1998/**|**1998/**|**1997/**|1999/|
|End year|2002|**_2002_**|2003|**2002**|**2002**|**2002**|**2002**|**2002**|2003|
|Gini index, initial year|**0.397**|**_0.232_**|**0.346**|_0.336_|**0.371**|**0.296**|**0.274**|**0.353**|**0.289**|
|Gini index, end year|**0.391**|**0.250**|0.318|**0.350**|**0.328**|**0.320**|**0.289**|**0.338**|**0.327**|

Source: discussion in text Having consistent data is the first step towards understanding the drivers for its increase and attempting to predict future evolution. The next section starts with two stylized “explanations” for the inequality increase often used to contrast various countries experience.

# **IV. MAIN DRIVERS OF INEQUALITY IN TRANSITION**

To what extent can one appeal to the literature on inequality in transition for guidance on explanations of these disparate trajectories of growth and inequality? That literature suggests that the principal determinants of inequality in transition were:

- Wage decompression and growth of the private sector

- Restructuring and unemployment, reverting to subsistence economy

- Fiscal adjustment affecting Government expenditure and taxation, corruption

- Price liberalization, inflation and arrears

- Asset transfer, growth of property income

- Technological change, increased mobility and globalization **.**

> 5 The data used in Figure 2 for China come from Ravallion and Chen (2004).

11
- **Newbury, M. Catherine** , “Ebutumwa Bw’Emiogo: The Tyranny of Cassava A Women’s Tax Revolt in Eastern Za ̈ıre,” _Canadian Journal of African Studies / Revue Canadienne des Etudes_<sup>_ ́_</sup> _Africaines_ , 1984, _18_ (1), 35–54.

- **Northrup, David** , “The Ending of Slavery in the Eastern Belgian Congo,” _The End of Slavery in Africa_ , 1988, pp. 462–82.

- **Nunn, Nathan and Diego Puga** , “Ruggedness: the Blessing of Bad Geography in Africa,” _The Review of Economics and Statistics_ , 2012, _94_ , 20–36.

- **O’Mealia, T.** , “Violence, Protection, and the Political Economy of Security Provision in Ongoing Conflicts.” Phd dissertation, University of Michigan, Ann Arbor, MI 2022.

- **Pourtier, Roland** , “D ́esorganisation des transports et spirales du sous-d ́eveloppement au Za ̈ıre,” in Chantal Blanc-Pamard, ed., _Dynamique des syst`emes agraires: politiques agricoles et initiatives locales: adversaires ou partenaires_ , ORSTOM, 1993, pp. 49–69.

- **Raleigh, Clionadh and H ̊avard Hegre** , “Population size, concentration, and civil war: A geographically disaggregated analysis,” _Political Geography_ , 2009, _28_ (4), 224–238.

- **, Roudabeh Kishi, and Andrew Linke** , “Political Instability Patterns are Obscured by Conflict Dataset Scope Conditions, Sources, and Coding Choices,” _Humanities and Social Sciences Communications_ , 2023, _10_ (1), 1–17.

- **Rogall, Thorsten** , “Mobilizing the Masses for Genocide,” _American Economic Review_ , 2021, _111_ (1), 41–72.

- **Ross, Michael L.** , “Oil, Drugs, and Diamonds: The Varying Roles of Natural Resources in Civil War,” in Karen Ballentine and Jake Sherman, eds., _The Political Economy of Armed Conflict: Beyond Greed and Grievance_ , Boulder: Lynne Rienner, 2003, p. Specific page range if available.

- **Ruggeri, Andrea, Han Dorussen, and Theodora-Ismene Gizelis** , “On the Frontline Every Day? Subnational Deployment of United Nations Peacekeepers,” _British Journal of Political Science_ , 2016, _48_ (4), 1005–1025.

- **Sayers, Michael W., Thomas D. Gillespie, and William D. O. Patterson** , “Guidelines for Conducting and Calibrating Road Roughness Measurements,” Technical Paper 46, World Bank, Washington D.C. 1986.

- **Schouten, Peer** , “The Materiality of State Failure: Social Contract Theory, Infrastructure and Governmental Power in Congo,” _Millennium_ , 2013, _41_ (3), 553–574.

- , _Roadblock Politics: the Origins of Violence in Central Africa_ , Cambridge University Press, 2022.

33
|With|23.6|29.8|10.7 53.1|12.9 21.8|2.3|9.1|12.7|21.8|2.5|10.8|<br>9.9|19.2|1.5|10.2|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Without|55.7|70.2|9.5<br>46.9|46.2 78.2|22.8|90.9|45.5|78.2|20.6|89.2|41.6|80.8|13.5|89.8|
|Drugstore|||||||||||||||
|With|29.5|37.2|11.2 55.3|18.3 31.0|5.4|21.7|18.0|30.9|5.3|22.9|14.8|28.7|3.2|21.3|
|Without|49.8|62.8|9.0<br>44.7|40.8 69.0|19.6|78.3|40.2|69.1|17.8|77.1|36.8|71.3|11.9|78.7|
|Doctor's office|||||||||||||||
|With|22.7|28.6|12.6 62.3|10.1 17.1|0.6|2.2|9.8|16.8|0.8|3.5|7.0|13.5|0.4|2.3|
|Without|56.6|71.4|7.6<br>37.7|49.0 82.9|24.5|97.8|48.4|83.2|22.3|96.5|44.6|86.5|14.7|97.7|

Source: Own computations using DHS. Note: *community characteristics are available in 2000 not in 2005 in the DHS. Results are computed using sample weights.

22
_Ingo Borchert and Mattia Di Ubaldo_ World Input-Output Database,<sup>6</sup> and find that deeper agreement result in both more goods and services trade, with the latter generally being more affected by deep PTAs. A crucial difference among these studies, however, is that Mulabdic et al. (2017) and Dhingra et al. (2021) measure depth with a count of legally enforceable provisions in each PTA, whereas Dhingra et al. (2018) go beyond aggregate effects and identify services, investment and competition provisions to be those most effective at lowering trade barriers<sup>7</sup> , especially for sectors such as transport and storage, that facilitate supply chain activity.

This paper’s contribution is to identify with a great deal more precision which aspect of the structure and content of services PTAs affects cross-border services trade. Compared to Dhingra et al. (2018), who assess the effect of the presence of a services chapter, in this paper we examine services PTAs only and distinguish between particular configurations of services provisions. This analysis is made possible by the availability of detailed data on the design and content of services PTA from the World Bank Deep Trade Agreement Database 2.0 ( _vertical depth_ ). Another important contribution of this paper is to exploit the most comprehensive data to date on services trade, the ITPD-E data described below, covering a great deal more countries and sectors than the WIOD data.

We also contribute to the literature linking the presence of trade agreements to valuechain activity. Production along global value-chains (GVC) also stands to benefit from services liberalization, as services have various roles in GVCs. Transport, communication and logistics link manufacturing activities across countries, while R&D, design, and

> 6Despite the similarity of approach, these three papers differ in their focus, with Mulabdic et al. (2017) analysing the differential impact of PTA depth for UK trade, Dhingra et al. (2018) narrowing the focus on the effect of provisions related to services, investment and competition, and Dhingra et al. (2021) taking the reduced form estimates to a general equilibrum framework to quantify welfare effects of deep PTAs.

> 7Dhingra et al. (2018) find that these provisions account for about 60% of the overall effect of trade agreements on exports.

11
**Figure 4. Reasons cited for not having a mobile phone (respondents can select multiple options)**

<!-- Start of picture text --> 80 Africa Asia Latin America<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>No mobile coverage No electricity at home Can't afford it<br>Note: Individual weights are applied in the calculation.<br>Around 70 percent of those who own a mobile phone only have a basic or feature phone. 10 Overall,<br>smartphone penetration is higher in Latin American and Asian countries than in African countries. For<br>instance, among those who own a mobile phone, the percentage with a smartphone is above half in all Latin<br>American countries in the sample, and close to half in Asian countries (48.5 percent in Cambodia, 51.8<br>percent in Nepal, and 47 percent in Sri Lanka). That a smartphone is not necessary and not affordable are<br>the two most cited reasons for not having one (figure 5). The affordability obstacle is especially severe in<br>Mozambique, Rwanda, and Tanzania.<br>Figure 5. Main reason cited for not choosing a smartphone among owners of a basic mobile phone<br>(respondents can choose only one main reason)<br>Africa Asia Latin America<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>No necessity Can't afford Too complicated Other<br>having a mobile phone)<br>Percentage points (% of adults not<br>phone)<br>own a basic phone or a feature<br>Percentage points (% of those who<br><!-- End of picture text --> _Note:_ Individual weights are applied in the calculation.

Around 70 percent of those who own a mobile phone only have a basic or feature phone.<sup>10</sup> Overall, smartphone penetration is higher in Latin American and Asian countries than in African countries. For instance, among those who own a mobile phone, the percentage with a smartphone is above half in all Latin American countries in the sample, and close to half in Asian countries (48.5 percent in Cambodia, 51.8 percent in Nepal, and 47 percent in Sri Lanka). That a smartphone is not necessary and not affordable are the two most cited reasons for not having one (figure 5). The affordability obstacle is especially severe in Mozambique, Rwanda, and Tanzania.

**Figure 5. Main reason cited for not choosing a smartphone among owners of a basic mobile phone (respondents can choose only one main reason)**

_Note:_ Individual weights are applied in the calculation.

10 Annex 2 contains detailed country-level information on smartphone ownership and smartphone uptake rates.

8
**_Note:_** _EC_ indicates emission coefficient; _ECS_ denotes energy mix; _EI_ describes energy intensity; _PCG_ ( _P C G_ 2 ) stands for per capita GDP (squared); and _P_ is population size.

# **6. Conclusions**

In this study, we measured the role of various factors that have driven CO2 emissions over the past 35 years at the global level and different groups of countries by their current income level (i.e., high income, upper-middle income, lower-middle income, and low income). We employ data from 110 countries in two different techniques: Index decomposition technique (identity analysis) and an econometric technique. We also examined if a factor has influenced CO2 growth differently during different time intervals: early period of the study horizon (1980-2015) and late interval of the study horizon (2005-2015).

The results from our identity and econometric analysis reveal that income (measured in terms of per capita GDP) growth and population growth are the major drivers for the increased CO2 emissions during the period. Improved energy efficiency (i.e., decreasing energy intensity of GDP) has slowed down the growth; otherwise, CO2 emissions would have increased even further.

Improvement of energy efficiency has contributed in all groups of countries to slow down their CO2 emission growth. This factor is more prominent in the highincome and upper-middle-income groups of countries. The fuel mix factor or the increasing share of low carbon (e.g., natural gas) or no carbon (e.g., renewable) fuels in the total primary energy supply mix contributed to slow down CO2 growth in the highincome group of countries. It is income growth that is responsible for CO2 growth in the upper-income and middle-income countries. Population growth is more responsible for CO2 emission growth in low-income countries, although they have a very small role in the growth of global CO2 emissions.

The analysis provides some important policy insights. Although one could argue that upper-middle-income countries’ per-capita emissions are lower than that of highincome countries and therefore pressure should still be on high-income countries, our 24
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Tanzania > Telecom investments > Macro results**

||||Reduction in<br>h bli|T|elecom<br>I|Fi|I|
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|<br>expenditure|VAT|<br>duties|<br>aid|<br>tax|
|Ym|Aggregate household income|7,925.60|0.23|0.30|0.31|0.32|0.30|
|EV|Equivalent variation||0.22|0.30|0.31|0.33|–0.08|
|s|Wage|1.00|0.25|0.46|0.49|0.51|0.44|
|yg|Government income|668.13|0.52|4.73|4.73|4.73|4.73|
|g|Total government expenditure|516.07|–2.84|2.61|2.61|2.61|2.61|
|ye|Firm income|2,293.57|0.71|0.44|0.40|0.37|0.46|
|It|Total private investment|1,228.04|1.53|1.04|0.73|0.30|0.82|
|GDP|GDP|7,582.43|0.45|0.45|0.45|0.45|0.45|
|e|Nominal exchange rate|1.00|0.36|0.15|–0.64|–1.87|0.18|

# **Tanzania > Telecom investments > Sectoral results**

||||Reduction in<br>||Telecom<br>|||
|---|---|---|---|---|---|---|---|
||||other ublic||Imort|||
|Variable|Sector|Base|p<br>expenditure|VAT|p<br>duties|Foreign aid|Income tax|
||Crop agriculture|1,964.70|0.37|0.16|0.20|0.20|0.04|
||Export agriculture|1,311.08|0.51|0.20|0.12|0.04|0.17|
|Va|Mining and gas|141.43|0.32|0.26|0.27|0.29|0.27|
|Value added or|Industries|1,242.16|0.46|–0.02|0.20|0.44|0.22|
|output|Construction|780.66|1.76|1.50|1.22|0.93|1.57|
||Private services|1,672.10|0.61|0.55|0.52|0.51|0.50|
||Public services|470.30|–2.12|1.53|1.56|1.70|1.53|
||Crop agriculture|1.02|–0.11|0.16|0.11|0.07|–0.05|
||Export agriculture|1.02|–0.20|0.09|0.27|0.07|–0.18|
||Mining and Gas|1.06|0.39|0.43|0.39|–1.06|0.13|
|pq<br>Market prices|Industries|1.10|0.15|0.69|0.53|–0.51|0.08|
||Construction|1.00|0.67|0.74|0.46|–0.03|0.57|
||Private services|1.00|–0.26|–0.09|–0.17|–0.31|–0.28|
||Public services|1.00|0.18|0.40|0.37|0.23|0.28|
||Crop agriculture|1.00|0.27|0.19|0.27|0.29|0.01|
||Export agriculture|1.00|0.34|0.05|–0.04|–0.14|0.00|
|r<br>|Mining and gas|1.00|0.93|0.19|0.37|0.74|0.36|
|Rental rate of<br>capital|Industries|1.00|0.61|–0.11|0.35|0.84|0.33|
||Construction|1.00|2.80|2.30|1.56|0.80|2.47|
||Private services|1.00|0.16|0.21|0.16|0.13|0.05|

43
# Do Value-Added Estimates Add Value? Accounting for Learning Dynamics<sup>_∗_</sup>

Tahir Andrabi Jishnu Das Asim Ijaz Khwaja Tristan Zajonc<sup>_†_</sup>

## 1 Introduction

Models of learning often assume that children’s achievement persists between grades—what a child learns today largely stays with her tomorrow. Yet recent research highlights that treatment effects measured by test scores fade rapidly, both in randomized interventions and observational studies. Jacob, Lefgren and Sims (2008), Kane and Staiger (2008), and Rothstein (2008) find that teacher effects dissipate by between 50 and 80 percent over one year. The same pattern holds in several studies of supplemental education programs in developed and developing countries. Currie and Thomas (1995) document the rapid fade out of Head Start’s impact in the United States, and Glewwe, Ilias and Kremer (2003) and Banerjee et al. (2007) report on education experiments in Kenya and India where over 70 percent of the one-year treatment effect is lost after an additional year. Low persistence may in fact be the norm rather than the exception, and a central feature of learning.

Low persistence has critical implications for commonly used program evaluation strategies that rest heavily on assumptions about or estimation of persistence. Using primary data on public and private schools in Pakistan, this paper addresses the challenges to value-added evaluation strategies posed by 1) imperfect persistence of achievement, 2) heterogeneity in

> _∗_ An earlier version of this paper also circulated under the title “Here Today, Gone Tomorrow? Examining the Extent and Implications of Low Persistence in Child Learning”.

> _†tandrabi@pomona.edu,_ Pomona College. _jdas1@worldbank.org,_ World Bank, Washington DC and Center for Policy Research, New Delhi; _akhwaja@ksg.harvard.edu,_ Kennedy School of Government, Harvard University, BREAD, NBER; _tzajonc@fas.harvard.edu,_ Kennedy School of Government, Harvard University. We are grateful to Alberto Abadie, Chris Avery, David Deming, Pascaline Dupas, Brian Jacob, Dale Jorgenson, Elizabeth King, Karthik Muralidharan, David McKenzie, Rohini Pande, Lant Pritchett, Jesse Rothstein, Douglas Staiger, Tara Vishwanath, and seminar participants at Harvard, NEUDC and BREAD for helpful comments on drafts of this paper. This research was funded by grants from the Poverty and Social Impact Analysis and Knowledge for Change Program Trust Funds and the South Asia region of the World Bank. The findings, interpretations, and conclusions expressed here are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent.

1
# **ABSTRACT**

This paper decomposes changes in inequality, which has in general been increasing in the transition economies of Eastern Europe and the former Soviet Union, both by income source and socio-economic group, with a view to understanding the determinants of inequality and assessing how it might evolve in the future. The empirical analysis relies on a set of inequality statistics that, unlike “official data”, are consistent and comparable across countries and are based on primary records from household surveys recently put together for the World Bank study “Growth, Poverty and Inequality in Eastern Europe and the Former Soviet Union: 1998-2003” [World Bank (2005b)].

The increase in inequality in transition, as predicted by a number of theoretical models, in practice differed substantially across countries, with the size and speed of its evolution depending on the relative importance of its key determinants, viz., changes in the wage distribution, employment, entrepreneurial incomes and social safety nets. Its evolution was also influenced by policy. This diversity of outcomes is exemplified on the one hand for Central Europe by Poland, where the increase in inequality has been steady but gradual and reflects, inter alia, larger changes in employment and compensating adjustments in social safety nets and, on the other for the Commonwealth of Independent States by Russia, where an explosive overshooting of inequality peaked in the mid-1990s before being moderated through the extinguishing of wage arrears during its post-1998 recovery.

The paper argues that the process of transition to a market economy is not complete and that further evolution of inequality will depend both on (i) transition-related factors, such as the evolution of the education premium, a bias in the investment climate against new private sector firms which are important vehicles of job creation and regional impediments to mobility of goods and labor, as well as increasingly (ii) other factors, such as technological change and globalization. The paper also contrasts key features of inequality in Russia in the context of other transition economies with trends in inequality observed in China where rapid economic growth has been accompanied by a steep increase in inequality. It argues that the latter’s experience is, to a large extent, a developmental, rather than a transition-related phenomenon deriving from the rural-urban divide and is, therefore, of limited relevance for predicting changes in inequality in Russia.

2
Korea, Vietnam and Pakistan, respectively, in terms of ton kilometers. Domestic navigation has supplanted rail as the dominant mode of freight transport in China over the period 1980 to 2006, and although the share of road is higher in 2006 compared to 1980, this share has been relatively stable in recent years (see Figure 3(a)). The use of aviation for freight transport remains negligible in China. In Korea, shipping is the main mode for freight transport from 1994 to 2000 (see Figure 3(b)). Shipping is also by the far the most important mode for freight transport in Vietnam, with a share above 80 percent for much of the 1990-2005 period, followed by road, and then rail (see Figure 3(c)). However, domestic navigation is not utilized for freight transport from 1990 to 2004 in Pakistan, where freight is overwhelmingly transported via road, which gained significant share from rail over this period (see Figure 3(d)).

Note that modal mix data in terms of transportation services are available only for four countries (i.e., China, Korea, Vietnam and Pakistan) out of the 12 countries considered in this study. Moreover, since these data are taken from each country’s national statistical agency, they are not comparable across countries due to the lack of international reporting standards for transportation data. For example, road transport activity indicated for China does not include public buses and taxis, whereas they are included in road transport activity for Korea. Furthermore, the temporal scope of the data also differs. Modal mix is available for China from 1990 to 2006, while it is only available for varying parts of that time frame for Korea, Vietnam and Pakistan. Hence, such data will not be useful in the decomposition analysis pursued later. Instead, we will 10
factor productivity and econometrically demonstrate the impact of the investment climate on productivity.

But this paper also goes a step further than previous earlier papers, and ranks the broad measures of the investment climate by their relative importance in explaining the variation in productivity across countries. This ranking technique can help policymakers use the results of the productivity analysis to design and implement policies for creating a pro-growth, and thus pro-poor, investment climate.

# **The Data**

The data used for this study were drawn from an investment climate survey conducted by the World Bank in 2003 across five transition countries: the Kyrgyz Republic, Moldova, Poland, Tajikistan, and Uzbekistan. The survey used an identical instrument in each country to conduct face-to-face interviews with firms’ managers and bookkeepers or accountants. The questionnaire was similar to that used in the Business Environment and Enterprise Productivity Survey conducted by the European Bank for Reconstruction and Development and World Bank in 2002. But it was amended to collect accounting data needed to estimate TFP and to improve comparability with the World Bank’s core productivity and investment climate survey instrument. The survey, known as the Business Environment and Enterprise Productivity Extension Survey, was wide ranging: in addition to the accounting data, it sought both quantitative and qualitative information on investment climate indicators, including managers’ perceptions of the business environment.

4