rafmacalaba/datause-encoder-data huggingface dataset:
# **3. Uganda**

# **Economic and financial environment**

<mark>Uganda is a low-income country with a GDP per capita (</mark> PPP) of about $1,300 in 2011 <mark>and population of more than 35 million inhabitants. About 40 percent of the population lives under the poverty line of 1.25 USD a day according to the latest figures available (2009).</mark> Since 1990, economic reforms have ushered in an era of solid economic growth in Uganda based on continued investment in infrastructure, improved incentives for production and exports, lower inflation and better domestic security. Uganda is one of the fastest and most consistently growing countries in Africa, with substantial natural resources including copper, gold, and other minerals, and recently discovered oil.

Agriculture is the most important sector of the economy, employing over 80 percent of the labor force. About 90 percent of the population was live in rural areas. The remoteness limits the poor population’s access to produce markets and financial services. Few banks cater to poor or rural consumers in Uganda—it is expensive to build branches in rural areas and it is difficult to make money serving customers who only have a few dollars a month to save or spend. As a result, only one in five Ugandans are banked and one in two lack access to any financial services at all.<sup>15</sup>

# **Telecommunications environment**

<mark>Uganda’s communications sector is one of the fastest growing sectors in the country. As in the rest of the Africa, this is largely due to the rapid expansion of mobile telephony. According to statistics from Uganda Communications Commission, the number of mobile subscribers had reached 16.7 million in 2011 (about half of the population) and m</mark> ost adults either own a mobile phone or have access to one. While the number of mobile subscribers is soaring, fixed line

> 15 Grameen Foundation AppLab (http://www.grameenfoundation.applab.org/challenges.html).

11
# **References:**

Albala-Bertrand, J. 1993. Political Economy of Large Natural Disasters. New York: Oxford University Press.

Anthoff, D., and R.S.J. Tol. 2008. “The Impact of Climate Change on the Balanced Growth Equivalent.” Working Paper 228. Economic and Social Research Institute, Dublin, Ireland.

Burton, K., R. Kates and G. White. 1993. The Environment as Hazard, 2<sup>nd</sup> Edition. New York: Guilford Press Hope, C. 2006. “The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment Model Incorporating the IPCC’s Five Reasons for Concern.” Integrated Assessment 6(1): 19–56.

Horwich, G. 2000. Economic Lessons From the Kobe Earthquake. Economic Development and Cultural Change. 48, 521-542.

Hughes, Gordon. 2009. The Economics of Adaptation to Climate Change: Global Estimates for Infrastructure. Washington, DC: World Bank Kahn, Matthew. 2005. The Death Toll From Natural Disasters: The Role of Income, Geography, and Institutions. The Review of Economics and Statistics, 87(2) 271-284.

King, Elizabeth and Andrew Mason. 2001. Engendering Development. World Bank Policy Research Report. January Nordhaus, William and Joseph Boyer. 2000. Warming the World: Economics Models of Global Warming. Cambridge, MA: MIT Press.

Oxfam International. 2008. Rethinking Disasters: Why Death and Destruction is not Nature's Fault but Human Failure. South Asia Regional Centre, Oxfam (India) Trust, New Delhi.

26
24 More work is needed to improve data on costs, impacts and financing. An urgent priority is to obtain some project cost data for breastfeeding promotion (none is available). More work on the impact of project design could fruitfully be done using standardized project proposals or reports (in particular WFP proposals, USAID PVO Child Survival data and USAID Outreach grant data seem promising). Work could be done on project scale using service-delivery-unit level data (anghanwadi level data for the ICDS in India might be promising). More work could be done disaggregating components of costs (in more detail than the food/non-food breakdown used here). Again, WFP proposal documents might be a useful starting point. Many existing agencies could improve their record keeping, both with respect to standardized cost treatment, and with regard to recording estimates of impact. Better cost data need not involve much additional resources, but better impact data would. Nevertheless, better data are important so that important nutritional interventions are not de-emphasized in child survival initiatives relative to health interventions whose benefits are more easily quantified.
# Table 3: Correlates of Annual Equity Returns by Investment Vehicle (1990-2023)

Dependent variable is annualized TVPI. All macroeconomic variables are annual changes over the duration of the investment cycle. Size is the initial contribution, and duration is the investment holding period measured in years. The base category for the interaction is direct investment. The ”Funds” dummy indicates the investment was in a fund (“collective vehicle” in IFC nomenclature). Bootstrapped errors in parenthesis. * 10%, ** 5% and *** 1%.

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|||Quan|tile Regre|ssion||
||0.1|0.25|0.5|0.75|0.9|
|GDP growth|0.479|0.328|0.434***|0.352*|0.674*|
||(0.696)|(0.372)|(0.131)|(0.195)|(0.403)|
|RER|-0.568|-1.035***|-0.808***|-0.973***|-0.893***|
||(0.385)|(0.206)|(0.073)|(0.108)|(0.223)|
|Ln(Size)|0.051***|0.019***|0.005**|0.001|-0.020***|
||(0.012)|(0.006)|(0.002)|(0.003)|(0.007)|
|Ln(Duration)|0.304***|0.133***|0.009|-0.036***|-0.143***|
||(0.031)|(0.017)|(0.006)|(0.009)|(0.018)|
|Funds_×_GDP growth|0.257|-0.125|-0.333|0.092|-0.210|
||(1.480)|(0.791)|(0.279)|(0.415)|(0.857)|
|Funds_×_RER|-0.127|0.681|0.662***|0.630***|0.495|
||(0.861)|(0.460)|(0.162)|(0.241)|(0.498)|
|Funds_×_Ln(Size)|-0.041*|-0.013|0.002|0.006|0.012|
||(0.025)|(0.013)|(0.005)|(0.007)|(0.014)|
|Funds_×_Ln(Duration)|-0.263***|-0.117***|-0.002|0.027|0.088**|
||(0.060)|(0.032)|(0.011)|(0.017)|(0.035)|
|_R_<sup>2</sup>|0.171|0.061|0.023|0.038|0.109|
|Observations|1668|1668|1668|1668|1668|

Table 4 investigates whether these associations between macroeconomic variables and returns are heterogeneous by sector. IFC sectors are grouped into three main categories. FIG: collective investment vehicles and finance and insurance. MAS: Manufacturing, Agribusiness and Services. INFRA: electric power; information; oil, gas and mining; primary metals; transportation and warehousing; and utilities. FIG is the base category in the results presented in table 4. For this sector, annual returns are positively associated with GDP growth, especially in the higher percentiles: results are statistically significant for the 75th and 90th percentiles of annualized returns. RER depreciation is negatively associated with annual returns in the FIG sector. For MAS, GDP growth is not associated with higher annualized returns except for returns in the 90th percentile, where this association is negative. RER, on the other hand, is not associated with returns for sectors grouped under MAS. Even though returns in the INFRA sector are not statistically associated with GDP growth, RER depreciation is associated with lower returns in the INFRA sector. This is likely due to the local currency generation in the associated sub-sectors. Any real depreciation of the local currency then translates into lower USD returns. Table B3 in the appendix suggests that 19
# **The Role of Green Financial Sector Initiatives in the LowCarbon Transition: A Theory of Change**

Irene Monasterolo<sup>1</sup> , Antoine Mandel<sup>2</sup> , Stefano Battiston<sup>3</sup> , Andrea Mazzocchetti<sup>4</sup> , Klaus<sup>5</sup> Oppermann, Jonathan Coony<sup>5</sup> , Stephen Stretton<sup>5</sup> , Fiona Stewart<sup>5</sup> , Nepomuk Dunz<sup>5</sup> JEL Codes: E44, E52, G01, 016, Q51, Q54 Keywords: Green Finance, Greenhouse Gas Accounting, Green Finance Mechanisms, Macroprudential Policy, Monetary Policy.

> 1 EDHEC Business School, EDHEC-Risk Climate Impact Institute (ERCII) 2 Paris School of Economics and CLIMAFIN

> 3 Ca' Foscari University of Venice, University of Zurich and CLIMAFIN

> 3 Ca' Foscari University of Venice, University of Zurich and CLIMAFIN 4 Ca' Foscari University of Venice

> 5 The World Bank
# **List of Boxes**

|Box 1. The Effect of Education on Other Assets............................................................... 8|
|---|
|Box 2. Relationship Between Formal and Informal Institutions ....................................... 9|
|Box 3. The Importance of Assets and Opportunity Structure to Effective Political Choice|
|in India .............................................................................................................................. 11|
|Box 4. School Council Empowerment—Variables ......................................................... 23|
|Box 5. Household and Community Empowerment—Variables...................................... 23|
|**List of Annexes**|
|Annex 1. Approaches to Measuring Empowerment........................................................ 35|
|Annex 2. Intervention-Level Indicators of Empowerment.............................................. 43|
|Table 1. Empowerment Indicators, Ethiopia Women’s Development Initiatives<br>Project ............................................................................................................ 43|
|Table 2. Empowerment Indicators, Nepal Rural Water and Sanitation Project .......... 49|
|Table 3. Empowerment Indicators, Honduras Community Based Education Project. 55|
|Table 4. Empowerment Indicators, Mexico Lifelong Learning Project ...................... 59|
|Annex 3. Empowerment Themes and Strategies in Selected PRSPs............................... 59|
|Annex 4. National-Level Indicators of Empowerment.................................................... 62|
|Table 1. Intermediate Indicators of Empowerment: Agency....................................... 63|
|Table 2. Intermediate Indicators of Empowerment: State Domain ............................. 65|
|Table 3. Intermediate Indicators of Empowerment: Market Domain.......................... 73|
|Table 4. Intermediate Indicators of Empowerment: Society Domain ......................... 75|
|Table 5. Direct Indicators of Degrees of Empowerment............................................. 77|
|Annex 5. Draft National Survey Empowerment Module ................................................ 83|
|Table 1. Summary of Survey Module and Institutional Mapping Themes and Tools. 86|

3
Section 5. Section ?? presents empirical results and a concluding section presents the main findings and limitations of current research.

The paper concludes that decentralization (localization) has significant negative effect on the incidence of corruption in the majority of our settings. Empowering local governments reduces frequency of bribery and amount of bribes paid to government officials both by households and by firms. Political decentralization matters even when fiscal side of decentralization is controlled for. The empirical results presented here further demonstrate that voice (local accountability) matters more than exit (interjurisdictional competition) in combating corruption.

# 2 How decentralization affects corruption: A brief survey of of the theoretical and empirical literature

A brief review of the conceptual and empirical literature is presented in this section to serve as a background for the empirical framework results subsequently reported in this paper.

## 2.1 Theoretical perspectives

Various authors have presented diverse perspectives on the role of localization in combating corruption. First we review various arguments advanced to support the view that decentralization may worsen corruption. Then we turn to the arguments for decentralization as a tool to restrain bureaucrats. Refer to Bardhan and Mookherjee (2005) for detailed literature survey of the topic.

### 2.1.1 Localization breeds corruption

It has been argued that localization brings officials in close contact with citizens promoting personalism, and a higher degree of discretion leading to safeguard of individual citizens needs at the expense of public interest. It also leads to weakening of monitoring, controls and audits by central agencies thereby creating opportunities for corruption (Tanzi (1995), Prud’homme (1994)). Treisman (1999) has argued that fiscal decentralization leads to overgrazing by police forces reporting to various orders of government and by regional politicians as they yield strong influence over central institutions of accountability in governance. Several authors have argued that political decentralization promotes higher incidence of corruption through the involvement of a larger number of officials in dealing with potential investors e.g. feudal lords and oligarchs (Shleifer and Vishny (1993), Blanshard and Schleifer (2000)) and by interest group capture where feudals and oligarchs dominate local political scene (Shah (1998)).

### 2.1.2 Localization limits opportunities for corruption

A growing body of conceptual literature, on the other hand, argues that localization offers potential for greater accountability by moving the decision making closer 3
- Kaufmann, Daniel, and Shang-Jin Wei, 1999. "Does 'Grease Money' Speed Up the Wheels of Commerce?" Policy Research Working Paper #2254, World Bank, Washington DC.

- Knack, Stephen and Omar Azfar, 2000. "Are Large Countries Really More Corrupt?" Policy Research Working Paper #2470, World Bank, Washington DC.

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- Lederman, Daniel, Norman Loayza, and Rodrigo Reis Soares, 2001. "Accountability and Corruption: Political Institutions Matter." Mimeo, World Bank, Washington DC.

- Leff, Nathaniel, 1964. "Economic Development through Bureaucratic Corruption." _American Behavioral Scientist_ 8 (3), 6-14.

- Li, Wei, 1999. "Corruption and Resource Allocation under China's Dual-Track System." Mimeo, University of Virginia, Charlottesville, VA.

- Li, Hongyi, Lixin Colin Xu, and Heng-Fu Zou, 2000. "Corruption, Income Distribution, and Growth." _Economics and Politics_ 12 (2), 155-82.

- Li, Wei, and Lixin Colin Xu, 2001. "Liberalization and Performance in the Telecommunications Sector around the World." Mimeo, World Bank, Washington DC.

- Lui, Francis T, 1985, "An Equilibrium Queuing Model of Bribery." _Journal of Political Economy_ 93 (4), 760-8 1.

- Mauro, Paolo, 1995. "Corruption and Growth." _Quarterly Journal of Economics_ 110 (3), 681712.

- Megginson, William L. and Jeffry M. Netter, 2001. "From State to Market: A Survey of Empirical Studies on Privatization." _Journal of Economic Literature_ 39 (2), 321-389.

- Mookherjee, Dilip, and I.P.L. Png. 1995. "Corruptible Law Enforcers: How Should They Be Compensated?" _Economic Journal_ 105 (428), 145-159.

- Murphy, Kevin M., Andrei Shleifer, and Robert W. Vishny, 1991, "The Allocation of Talent: Implication for Growth." _Quarterly Journal of Economics_ 106 (2), 503-30.

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Myrdal, Gunnar, 1968. _Asian Drama Vol. II._ Random House, New York, NY.

- Oldenberg, Philip, 1987. "Middlemen in Third-World Corruption." _World Politics_ 39 (4), 50835.

27
unskilled workers employed in production, these ratios (together with the capitaloutput ratios mentioned earlier) capture fairly well the view that the country considered is poor and endowed with a relatively abundant supply of labor (with only part of it educated), while facing at the same time a relative scarcity of physical (particularly public) capital.

In equation (20), σ, the intertemporal elasticity of substitution, is set at 0.4. This relatively low value is consistent with the evidence indicating that the intertemporal elasticity of substitution tends to be low at low levels of income (see Ogaki, Ostry and Reinhart (1996) and Agénor and Montiel (2007)), a result that may reflect either short planning horizons or liquidity constraints, as discussed for instance by Agénor (2004, Chapter 2).<sup>25</sup> In the resource constraint of the private sector, equation (21), private consumption is set at 93.3 percent of output. This value is quite sensible for many low-income countries, where limited private resources are allocated to savings and investment. It corresponds to the value observed for Haiti in 2005. The tax rate on (factor) income, τY, is calculated as 0.042, whereas the tax rate on consumption is calculated as 0.057, both of which correspond to the values observed for Haiti in 2005. The first value is in line with actual ratios for many low-income countries, where taxation (which is essentially indirect in nature) provides a more limited source of revenue than in higher-income countries. The coefficient θO in that equation, which measures the share of other current government spending allocated to transfers to households, is calculated as 0.009, which corresponds to the value observed for Haiti in 2005.

Coefficients ρH and ρS in the discount rate function (equation (22)) are set at 0.01, respectively. The estimate of the first coefficient is based on the results in Lawrance (1991), who identified an (inverse) relationship between the rate of time

> 25Of course, using even lower values of the intertemporal elasticity of substitution would "flatten" the response of consumption to shocks. However, they would not affect the direction of the effect discussed below.

28
Regarding the private sector, “there is little data on investment activity in Afghanistan,” a gap this study seeks in part to redress (World Bank, 2015). The Integrated Business Environment Survey (IBES) in 2009 estimated approximately 400,000 firms operating in Afghanistan, in a variety of sectors. The vast majority of these firms (94 percent according to IBES) are small, containing less than 9 employees. However, firms with over 500 employees, which constitute just .17 percent of all firms, support nearly one-third of all industrial employment. The firm size category which contributes the least total employment is SMEs (here between 10-499 employees), suggesting a missing middle in the size distribution.

In sum, the Afghan economy presents a number of puzzles. Despite a long period of near double-digit growth, and massive inflows of foreign aid and military expenditure, the country has not yet found a sustainable growth path and has faced a sharp contraction since 2012. A range of serious challenges related to limited infrastructure, weak institutions, and related issues—all exacerbated by four decades of conflict—continue to impede progress in reducing national poverty levels.

# **2.2 Insecurity in Afghanistan**

Since the late 1970s, Afghanistan has experienced violence and political instability, laying the foundations for the current insecurity. A coup d’etat by the communist People’s Democratic Party of Afghanistan (PDPA) in April 1978 sparked internal conflict, and resulted in the invasion of Soviet troops in December 1979. The resulting geopolitical alignment consolidated the Afghan states dependence on the USSR as Western and Islamic states directed assistance to mujahidin resistance groups. This anti-communist resistance culminated with the withdrawal of Soviet troops in February 1989, though the client regime of Sayid Muhammad Najibullah maintained control until the USSR’s collapse in 1991. After mujahidin commanders took Kabul in 1992, rivalries emerged and a new conflict appeared along ethnic lines with the governments of Iran, Pakistan and Uzbekistan backing different factions.

In the early 1990s, the Taliban movement arose in the southern city of Kandahar and 8
# **I. Introduction**

Between 1975 and 1978, under the regime of the Khmer Rouge, Cambodia experienced a dramatic political shock: massive killings and starvation, large-scale population transfers, forced labor on collective farms, and an almost complete destruction of the school system.

The extreme atrocities endured by the Cambodians have been documented and identified as a genocide (Banister and Paige Johnson 1993; Chandler 1996; Kiernan and Boua 1982; Kiernan 1993; Kiernan 1996; Kiljunen 1984; Sliwinski 1995; Vickery 1984), however, few studies have quantified the extent or measured the long-term impact of these events. Using diverse micro-level representative surveys of Cambodia (Cambodia Socio-Economic Survey 1997, Demographic and Health Survey 2000, Labor Force Survey 2001), this paper investigates the long-term consequences of the Khmer Rouge period on a range of outcomes in today's Cambodia.

The aim of the analysis is to determine the legacy of the Khmer Rouge period on the current population structure, health status, and schooling levels in Cambodia. The excess mortality of the 1970s dramatically affected the population composition. Using data from siblings’ mortality in the Cambodia Demographic and Health Survey, I show that excess mortality was heavily concentrated in the 1974-80 period. I also show that adult males were most likely to die, indicating that violent deaths represented a large share of the excess mortality. Individuals with an urban or educated background were more likely to die, establishing that they were especially targeted. Infant and under-five mortality was also at very high levels around the Khmer Rouge period. For cohorts exposed to the risk of violence during the period, disability rates are much larger for males than for females and this is mainly due to disability caused by landmines and other weapons.

Fertility and marriage rates were very low under the Khmer Rouge but rebounded dramatically immediately after. Since the death toll was extremely heavy for adult males, the marriage market seems to have adapted by reducing the age difference between partners.

As the school system was ruined under the Khmer Rouge regime, one of the lasting legacies of the period is also that individuals, and in particular males, who were of schooling age at the end of the 1970s have a lower level of educational achievement than the preceding and subsequent birth cohorts.

1