rafmacalaba/datause-encoder-data huggingface dataset:
- 39 -

approach seems like a promising way to test for effects of irreversibility, particularly if used with disaggregated data. It does not, however, explicitly deal with effects of changes in risk.

In a recent working paper (1986), I performed some very simple nonstructural tests for the importance of risk. I used data on the stock market, on tne grounds that when product markets become more volatile, we would expect stock prices to also become more volatile, so that the variance of stock returns will be larger. This was indeed the case, for example, during the recessions of 1975 and 1980, and most dramatically during the Great Depression. Thus the variance of aggregate stock returns should be correlated with aggregate product market uncertainty.

Stock returns themselves are also a predictor of aggregate investment spending. My concern was whether the variance of stock returns also has predictive power with respect to investment, and whether that predictive power goes beyond that of stock returns themselves, as well as other variables that would usually appear in an empirical investment equation. I conducted two related exploratory tests. First, I tested and was able to accept the hypothesis that the variance of stock returns Granger-cause the real growth rate of investment. Specifically, the variance of returns is a strong predictor *of investment growth, but investment growth does not predict the variance of returns.22 22To say that X causes Y, " two conditions should be met. First, X should help to predict Y, i.e. in a regression of Y against past values of Y, the addition of past values of X as independent variables should contribute significantly to the explanatory power of the regression. Second, Y should not help to predict X. (If X helps to predict Y nd Y btlps to predict X, it is likely that one or more other variables are in fact "causing" both X and Y.)
This version: 15 Feb 2024 18 <mark>expanding.</mark><sup>8</sup> <mark>Our own analysis of recent data (</mark> **<mark>Figure 4)</mark>** <mark>confirms these trends, showing that since 1970 absolute income inequality has increased fourfold despite the fall in relative income inequality. Rapidly increasing absolute income inequality is likely a source of discontent for many Malaysians.</mark><sup>9</sup> <mark>Furthermore, according to official statistics, in urban areas, non-monetary deprivations in living standards -- crowded living spaces (proxied by the average number of people per bedroom) and access to health care facilities -- remain serious challenges. However, consistent with the official data on absolute income poverty, the overall</mark> incidence of multidimensional poverty is low at 2.6 percent in 2019 (DOSM 2020).

Following Ravallion and Chen (2003) and Ravallion (2019b), we examine the overall changes in income distribution by estimating the growth incidence curve (GIC) for the period 1984-2019 ( **Figure 5** ). This helps us understand the evolution of propoor growth. Reassuringly, the poor benefited the most -- the growth rate in average household income per capita was as high as 3.30% at the 10th percentile while the lowest value was at the very top. Income growth was positive at all quantiles which is consistent with the notion of broad-based reductions in poverty regardless of the poverty line chosen. Higher growth rates for poorer percentiles also helped reduce relative income inequality during the 2000s ( **Figure 4** ).

> 8 A simple example helps illustrate this. If everyone’s income increases by 10 percent, relative inequality is unchanged, but absolute inequality increases because that 10 percent increase represents a larger income increment for richer households.

> 9 The literature shows that about half of survey respondents perceive inequality in relative terms while the other half perceive it in absolute terms. See, for example, Amiel and Cowell (1992), Harrison and Seidl (1994) and Ravallion (2020a).

18
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|---|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Dakar Diamniadio Toll Highway<br>Eric R. Lancelot|
|Guangxi Integrated ForestryDevelopment and Conservation ProjectJin Liu|
|Guangxi Integrated ForestryDevelopment and Conservation ProjectJin Liu|
|Guangxi Integrated ForestryDevelopment and Conservation ProjectJin Liu|
|Guangxi Integrated ForestryDevelopment and Conservation ProjectJin Liu|
|Guangxi Integrated ForestryDevelopment and Conservation ProjectJin Liu<br>|
|Guangxi Integrated ForestryDevelopment and Conservation ProjectJin Liu|
|Guangxi Integrated ForestryDevelopment and Conservation ProjectJin Liu|
|Guangxi Integrated ForestryDevelopment and Conservation ProjectJin Liu|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|Technical and Vocational Education and Training<br>Ana Ruth Menezes|
|JSDF Grant: Law and Justice for the Poor(TF051227)<br>Robert Buergenthal|
|Philippines: Northwind Bangui BayProject<br>William Nicholas Bowden|
|Pensions System Administration Investment Project(Montenegro) Rajna Cemerska-Krtova|
_Table 7: Regression estimates of the probability of having skilled assistance at delivery_

||**IV-PROBIT**|**PROBIT**|
|---|---|---|
|Years of schooling|0.3067|0.1324|
||(0.0319)**|(0.0081)**|
|Log of household consumption per capita (Rp.'000000)|-0.0008<br>|0.0022<br>|
||(0.0010)|(0.0006)**|
|Age at childbirth|0.0241<br>|0.0447<br>|
||(0.0131)|(0.0111)**|
|**_Parity (reference group: first birth)_**|||
|Low parity: 2nd or 3rd birth|0.1295|0.0433|
||(0.0879)|(0.0983)|
|Medium parity: 4th or 5th birth|0.4473|-0.0493|
||(0.1619)**|(0.1139)|
|High parity: 6th birth or higher|0.4678|-0.1844|
||(0.2134)*|(0.1400)|
|Outcome of previous pregnancy: alive|0.1529|-0.1768|
||(0.1192)|(0.0908)|
|Urban area of residence|0.2075|0.7043|
||(0.1755)|(0.0539)**|
|Time to nearest health centre or midwife - hrs|0.0016<br>(0.0806)|-0.2097<br>(0.0881)*|
|**_Year of childbirth (reference group: 1985-89)_**|||
|1990-1994|0.0284|0.1576|
||(0.0743)|(0.0695)*|
|1995-1997|0.2986|0.6662|
||(0.1675)|(0.1091)**|
|1998-2000|0.3203|0.6627|
||(0.1793)|(0.1436)**|
|**_Region fixed effects_**|||
|Sumatera|0.2206|0.452|
||(0.1544)|(0.1362)**|
|Java|0.2505|0.2976|
||(0.1189)*|(0.1248)*|
|Jakarta|0.6353|0.8821|
||(0.2024)**|(0.1809)**|
|Bali|1.3032|1.5349|
||(0.2300)**|(0.1804)**|
|Nusa|-0.1494|-0.3183|
||(0.1622)|(0.1664)|
|Kalimantan|-0.0129|-0.1384|
||(0.1661)|(0.1837)|
|**_Cohort of birth fixed effects_**|||
|Cohort 1: 1-6yrs in 1974|0.8915|0.7712|
||(0.6941)|(0.7938)|

47
# **Appendix 1: Descriptive Statistics Couple-Level Data**

||Mean|Std dev|
|---|---|---|
|Religion<br>|||
|Fraction of Muslim couples|0.78||
|Men’s sibling composition at time of first child birth|||
|Fraction of men who have at least one living brother|0.80||
|Men’s average total number of living siblings|3.97||
|Year of first child birth|||
|Fraction of couples with first birth in 1975-1979|0.039||
|Fraction of couples with first birth in 1980-1984|0.10||
|Fraction of couples with first birth in 1985-1989|0.14||
|Fraction of couples with first birth in 1990-1994|0.18||
|Fraction of couples with first birth in 1995-1999|0.20||
|Fraction of couples with first birth in 2000-2004|0.22||
|Fraction of couples with first birth in 2004-2007|0.11||
|Fertility preferences and actual outcomes|||
|Women’s ideal proportion of sons|0.42|[0.21]|
|Women’s ideal number of sons|1.23|[0.86]|
|Women’s ideal number of daughters|1.22|[0.85]|
|Women’s ideal number of children|2.96|[1.29]|
|Men’s ideal proportion of sons|0.41|[0.24]|
|Men’s ideal number of sons|1.28|[1.02]|
|Men’s ideal number of daughters|1.18|[0.94]|
|Men’s ideal number of children|3.14|[1.47]|
|Couple’s actual proportion of ever-born sons|0.52|[0.36]|
|Couple’s actual number of ever-born children|2.54|[1.80]|
|Couple’s actual proportion of living sons|0.52|[0.36]|
|Couple’s actual number of living children|2.33|[1.57]|
|Other controls|||
|Fraction of couples residing in urban area|0.41||
|Women’s average education level|2.65|[1.34]|
|<br>Men’s average education level|2.82|[1.34]|
|Women’s average age|33.1|[8.06]|
|Men’s average age|37.1|[8.29]|
|Couple’s average wealthquintile|2.89|[1.47]|

Source: Indonesian 2007 Demographic and Health Survey (DHS). Note: Education level is defined as 0=no education, 1=incomplete primary, 2=complete primary, 3= incomplete secondary, 4=complete secondary, 5=higher.

38
labor, and capital markets.<sup>2</sup> We discuss how the PRC’s international integration has accelerated domestic reform, raised per capita GDP, and elevated its economic stature on the global scene. In implementing domestic integration, the PRC has been successful in concentrating resources and production in the coastal regions and allowing for rural–urban migration. People have moved from low-productivity sectors such as agriculture to high-productivity sectors such as manufacturing and services, as well as from rural poor areas to major cities. Product markets became more integrated over time, as regional trade increased and product prices were increasingly similar throughout the country. Capital markets, though still dominated by the force of the state, have been improving over time, and informal mechanisms arose to improve the state-dominated financial system. We propose that the mainstay of a nationally harmonious development strategy consists of measures to facilitate agglomerations that deliver scale of economies, rapid growth, and universal provision of basic amenities and social services that ensure a comparable level of living standards across the country.

The rest of the paper is organized as follows. Section II analyzes various dimensions of domestic integration, including urban agglomeration; rural–urban migration; and integration on the labor, product, and capital markets in the PRC. Section III assesses the PRC’s international openness, through SEZs and WTO accession sequentially. Section IV provides concluding remarks.

# **II.  Domestic Integration**

The past 3 decades has witnessed remarkable level of domestic integration in the PRC, more so in some markets than in others. People have moved from low-productivity sectors such as agriculture to high-productivity sectors such as manufacturing and services. The population of coastal and rich regions is increasing at the expense of that in the inland and poor regions. The sharp line drawn between urban and rural and state and nonstate has become more blurred over time. Capital markets, though still dominated by the force of the state, have been improving over time, and informal mechanisms have emerged to compensate the inadequacy of the state-dominated financial system.

> 2Given that this survey largely draws from several key surveys, the list of references is not as comprehensive as many other surveys. Interested readers are encouraged to read the mentioned surveys directly, and the references therein.

3
of media by country-year, television programs lead worldwide as the primary source of news, followed by radio, print, and remotely, by online sources. This empirical distribution confirms previous findings suggesting that especially in developing contexts with low internet penetration and literacy rates, online and print media mostly addresses urban elites, whereas TV and radio are the most politically consequential media platforms because they can reach the broadest and most diverse news consumers ( <mark>Keefer and Khemani 2014). Accordingly, b</mark> ased on individual surveys on news consumption and insights from the theoretical literature, we created an ordinal variable ranging from 1 to 4, with lower values indicating a larger audience reachable by the outlet. TV is coded as 1; radio as 2; printed newspapers and magazines 3; and online 4. As an identification strategy, we take into account the variation in country-year specific audience sizes for each media type and use it as an instrumental variable for our reach measure. We employ several two least square regression (2SLS) analyses with the actual country-specific survey results regarding the audience size for each type of media. The instrument is very strong, with a large F-statistic, and the results of the 2SLS regressions hold across multiple specifications, confirming that the audience size drives up the severity of censorship via _Media reach_ as an endogenous regressor. The Online Appendix also includes robustness checks with media supply used jointly with demand to construct the instrument.

For print media in particular, we also generated a measure of the frequency of publication (daily, weekly, biweekly, triweekly, monthly, bimonthly and quarterly). Other independent variables of interest include the gender of the journalist/actor being censored. In addition to our event-specific variables, a large number of country-level controls are included in the analysis: the economic concentration of the media market; the legal environment in which the media operates; the level of democracy; ethno-linguistic fragmentation; public sector corruption; the composition 30
|**Sector and Subsector**|**N**|**Share of total sample**|
|---|---|---|
|Financial services including insurance|49|<br>2.0%|
|Utilities|32|1.3%|
|Other professional, scientific, and technological|20|0.8%|
|<br>Real estate|18|0.7%|
|Hospitality|16|0.7%|
|<br>Telecom|13|0.5%|
|Media|9|0.4%|
|Health|7|0.3%|
|Arts and recreation|6|0.2%|
|Education|5|0.2%|
|Scientific research and development services|5|0.2%|
|Water supply and waste management|3|0.1%|
|<br>Residential care and social work|2|0.1%|
|Machinery and equipment|1|0.0%|
|<br>Metals and metal products|1|0.0%|
|<br>Public administration and defense services|1|0.0%|
|Other or unclassified services|357|14.7%|

_Source_ : Computation based on the 2019 GIC Survey.

_Source country_ : Overall, nearly 80 percent of respondents report having foreign owners from the East Asia & Pacific and Europe & Central Asia regions (see Table 2).<sup>3</sup> The vast majority of respondents (87 percent) report having foreign owners from high-income countries.

**Table 2. Respondents by region of foreign owner**

|**Host**<br>**country**||**Decline**<br>**to**<br>**answer**|**East**<br>**Asia**<br>**&**<br>**Pacific**|**Europe**<br>**&**<br>**Central**<br>**Asia**|**Latin**<br>**America**<br>**&**<br>**Caribbean**|**Middle**<br>**East &**<br>**North**<br>**Africa**|**North**<br>**America**|**South**<br>**Asia**|**Sub-**<br>**Saharan**<br>**Africa**|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|
|Brazil|n|0|39|147|15|2|45|2|0|250|
||%|0|15.6|58.8|6|0.8|18|0.8|0|100|
|China|n|6|142|61|3|0|36|2|0|250|
||%|2.4|56.8|24.4|1.2|0|14.4|0.8|0|100|
|India|n|2|50|124|2|6|56|3|7|250|
||%|0.8|20|49.6|0.8|2.4|22.4|1.2|2.8|100|
|Indonesia|n|0|203|38|1|2|10|3|1|258|
||%|0|78.68|14.73|0.39|0.78|3.88|1.16|0.39|100|
|Malaysia|n|0|160|54|1|4|25|5|1|250|
||%|0|64|21.6|0.4|1.6|10|2|0.4|100|
|Mexico|n|1|41|94|21|0|89|3|0|249|
||%|0.4|16.47|37.75|8.43|0|35.74|1.2|0|100|
|Nigeria|n|0|18|55|0|15|23|13|40|164|
||%|0|10.98|33.54|0|9.15|14.02|7.93|24.39|100|

3 Regions are defined according to World Bank Group classifications, as outlined here: <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups</u> 6
where Y<sup>�</sup> ��� denotes estimated average baseline school readiness for the communities mapped to the students in the school. A non-zero estimate of β��� implies that imbalance exists between the school readiness treatment and control group students using this method of mapping. Equation (10) is also estimated using gender, the TEHCI home activity index, exposure to the CLRW intervention, and attrition as dependent variables, to measure imbalance in these variables as well.

Table 12. Estimates of equation (10) testing balance for the baseline sample

|dependent variable:|baseline<br>community<br>TEHCI<br>score (SD)|home<br>support|female|CLRW|
|---|---|---|---|---|
|_1 year of school readiness interv_<br>|_ention sample_<br>|_(starting 1st_<br>|_grade in 201_<br>|_6)_<br>|
|probability of being from a|‐0.06|‐0.02|0|0.08|
|treatment community|(0.08)|(0.01)|(0.01)|(0.09)|
|constant|‐0.01|0.78***|0.41***|0.58***|
||(0.04)|(0.01)|(0.01)|(0.05)|
|observations|1214|1214|1214|1214|
|r‐square|0.00|0.01|0.00|0.00|
|_2 years of school readiness interv_|_ention sample_|_(starting 1st_|_grade in 20_|_17)_|
|probability of being from a|‐0.09|‐0.02|0.01|0.11|
|treatment community|(0.07)|(0.01)|(0.02)|(0.09)|
|constant|0|0.78***|0.47***|0.47***|
||(0.03)|(0.01)|(0.01)|(0.05)|
|observations|1290|1290|1289|1290|
|<sup>r‐square</sup>|0.01|0.01|0.00|0.01|

Table presents estimates of equation (10) for the listed dependent variables at baseline. Non‐zero coefficients of the probability of being from a treatment community imply imbalance between students from treatment and non‐treatment communities as a result of the community‐school mapping. Standard errors included in parentheses. Statistical significance at the 1, 5 and 10 percent levels are denoted by ***, **, and *, respectively.

35
Table 5 Aggregate Income Per Family Member by Types of Workers: At Current Prices - Ukraine

|Family typos|1980<br>|1985<br>|1986|1987|1988|1989<br>|1990<br>|1991<br>|
|---|---|---|---|---|---|---|---|---|
|All families|~<br>123|~l~-<br>141|-<br>145|148|149 ||I<br>162|NNNNN<br>182|N<br>349|
|||||Worker F|<br>amilies||||
|Industry|132|151|152|154|152|174|196|377|
|State farm|103|120|125|127|134|143|163|318|
|Collective farm|96|125|129|131|136|151|171|323|
|||||Pensione|r Families||||
|Blue & White Collor|74 ||_97<br>||891|92] |||_|__<br>106I|116|<br>227|
|Collective farm|78 ||101|106 ||105|11S|132J|<br>155|<br>33|

Table 6 Per Capita Income of State and Collective Farm Workers and Pensioner Families as a Percentage of Industrial Worker Families

|||||Warker F|amilies||||
|---|---|---|---|---|---|---|---|---|
|| Industry|100|100||100|100|100|100|100|
|State farm|78|79.5|82.2|82.5|82.7|82.2|83.2|84.4|
|Collective farm|72.7|82.8|84.9|85.1|84|86.8|87.2|85.7|
|||||Pensione|r Families||||
|Blue & White Collor|56.1|64.2<br>|58.6<br>|59.7 |||60.9|59.2|60.2|
|Collectivefarm|59.1|66.9[|<br>69.7|6.2|71|75.9|79.1|88.3|
|P005784|1982|1990|12 Closed|Yemen,Republic of|
|---|---|---|---|---|
|P005784|1982|1991|15 Closed|Yemen,Republic of|
|P005784|1982|1992|17 Closed|Yemen,Republic of|
|P005785|1983|1985|0 Closed|Yemen,Republic of|
|P005785|1983|1986|0 Closed|Yemen,Republic of|
|P005785|1983|1987|0 Closed|Yemen,Republic of|
|P005785|1983|1988|0 Closed|Yemen,Republic of|
|P005785|1983|1989|8 Closed|Yemen,Republic of|
|P005785|1983|1990|10 Closed|Yemen,Republic of|
|P005786|1983|1985|0 Closed|Yemen,Republic of|
|P005786|1983|1986|0 Closed|Yemen,Republic of|
|P005786|1983|1987|0 Closed|Yemen,Republic of|
|P005786|1983|1988|0 Closed|Yemen,Republic of|
|P005786|1983|1989|7 Closed|Yemen,Republic of|
|P005786|1983|1990|10 Closed|Yemen,Republic of|
|P005786|1983|1991|12 Closed|Yemen,Republic of|
|P005786|1983|1992|15 Closed|Yemen,Republic of|
|P005787|1983|1985|0 Closed|Yemen,Republic of|
|P005787|1983|1986|0 Closed|Yemen,Republic of|
|P005787|1983|1987|0 Closed|Yemen,Republic of|
|P005787|1983|1988|0 Closed|Yemen,Republic of|
|P005787|1983|1989|5 Closed|Yemen,Republic of|
|P005788|1983|1985|0 Closed|Yemen,Republic of|
|P005788|1983|1986|0 Closed|Yemen,Republic of|
|P005788|1983|1987|0 Closed|Yemen,Republic of|
|P005788|1983|1988|0 Closed|Yemen,Republic of|
|P005788|1983|1989|8 Closed|Yemen,Republic of|
|P005788|1983|1990|10 Closed|Yemen,Republic of|
|P005788|1983|1991|12 Closed|Yemen,Republic of|
|P005789|1984|1985|0 Closed|Yemen,Republic of|
|P005789|1984|1986|0 Closed|Yemen,Republic of|
|P005789|1984|1987|0 Closed|Yemen,Republic of|
|P005789|1984|1988|0 Closed|Yemen,Republic of|
|P005789|1984|1989|7 Closed|Yemen,Republic of|
|P005789|1984|1990|9 Closed|Yemen,Republic of|
|P005789|1984|1991|11 Closed|Yemen,Republic of|
|P005789|1984|1992|13 Closed|Yemen,Republic of|
|P005789|1984|1993|15 Closed|Yemen,Republic of|
|P005790|1984|1985|0 Closed|Yemen,Republic of|
|P005790|1984|1986|0 Closed|Yemen,Republic of|
|P005790|1984|1987|0 Closed|Yemen,Republic of|
|P005790|1984|1988|0 Closed|Yemen,Republic of|
|P005790|1984|1989|7 Closed|Yemen,Republic of|
|P005790|1984|1990|10 Closed|Yemen,Republic of|
|P005790|1984|1991|11 Closed|Yemen,Republic of|
|P005790|1984|1992|15 Closed|Yemen,Republic of|
|P005790|1984|1993|19 Closed|Yemen,Republic of|
remainder traceable to poor governance, red tape, and financing constraints. Infrastructure constraints in Uganda at that time were among the highest in the region. Inadequate power was the constraint that weighed most heavily on the country’s firms (figure 2b). Customs clearance processes before export was the second largest constraint hindering firms’ productivity. The average output loss owing to electricity supply problems is about 10 percent; a number higher than in most countries in Sub-Saharan Africa (World Bank 2005). However, since then, some improvements have been made including the formation of a common Customs Union under the umbrella of the East African Common Market.

**Figure 2. Infrastructure deficits erode firms’ productivity 2002-2006**

<!-- Start of picture text --> Mauritius<br>Kenya<br>Tanzania<br>Ethiopia<br>Uganda<br>Malawi<br>0% 50% 100%<br>percent of productivity deficit attributable to<br>sectors<br>Infrastructure Others<br><!-- End of picture text --> <!-- Start of picture text --> Mauritius<br>Tanzania<br>Uganda<br>Malawi<br>Kenya<br>Ethiopia<br>0% 20% 40% 60% 80% 100%<br>percent of productivity deficit attributable to sectors<br>Electricity Customs clearance Transportation ICT Water<br><!-- End of picture text --> _Source:_ Escribano, Guasch, and Pena 2010

# The state of Uganda’s infrastructure

This report begins by reviewing achievements and challenges in each of Uganda’s major infrastructure sectors, with the key findings summarized in table 1. Hereafter, attention turns to the problem of how to finance Uganda’s infrastructure needs.

6