rafmacalaba/datause-encoder-data huggingface dataset:
# **ECONOMICS OF IRRIGATION WATER MANAGEMENT: A Literature Survey with Focus on Partial and General Equilibrium Models***

**Hasan Dudu**<sup>******</sup> **Sinqobile Chumi**<sup>*******</sup>

Key Words: Irrigation Management, Water Allocation, Water Pricing, Water Markets, Partial Equilibrium Models, Computable General Equilibrium Models, Water scarcity, Irrigation.

> * This paper is a product of the project  “Macro-Micro Feedback Links of Irrigation Water Management”, funded by the Research Committee of World Bank and managed by DECRG-RU. We are indebted to Erol Cakmak, Rashid Hassan and Ariel Dinar for their advice and comments.

> **Ph.D. Candidate Department of Economics, Middle East Technical University, Ankara, and a consultant to the World Bank (dudu@metu.edu.tr).

> ***PhD Candidate, Department of Agricultural Economics, Center for Environmental Economics and Policy in Africa (CEEPA), University of Pretoria, Pretoria (sinqochumi@yahoo.com.)
tures are reviewed; overspending, because huge disbursements in future years are hidden; conservatism, because incremental changes do not open up large future vistas; and parochialism, because programs tend to be viewed in isolation rather than in comparison with their future costs in relation to expected revenue.” At the basis of an MTEF is a commitment by the budget actors to a medium-term, typically two to four years, ...scal trajectory. Thus, it can be seen either as an ex ante constraint on the government, similar to a balanced budget requirement (Alesina and Perotti 1996), or as a "contract approach" to centralizing the budget process through a broad-based political agreement, as opposed to a "delegation approach" where the executive receives enhanced powers (von Hagen and Harden 1995). While the theoretical underpinnings of this institution are well understood, the empirical evidence on its impact is scarce. The main obstacle has been the shortage of data on MTEF adoption. An additional impediment has been the lack of sources of exogenous variation in national budget institutions (Acemoglu 2005).<sup>3</sup> This paper reports on the ...rst large-sample empirical study of the MTEFs’impacts on ...scal performance. As part of a larger World Bank (2012) study, we collect and analyze MTEF adoption data for a panel of 181 countries over the period 1990-2008, the most comprehensive dataset to date on worldwide MTEF adoption. Following World Bank (2012) we classify MTEFs into three phases, based on their level of sectoral disaggregation, and generate testable hypotheses about the e¤ects of each phase on multiple dimensions of ...scal performance.

The rich time variation in the data allows us to model the dynamics of the ...scal adjustment process, as well as address the endogeneity of MTEF to ...scal performance. We use a Di¤erence Generalized Method of Moments (D-GMM) approach to estimate dynamic panel data models of ...scal performance. These models are designed for "small T large N " panels and, when correctly applied, generate valid internal instruments that hold the promise of overcoming identi...cation issues due to the absence of strong external instruments, a typical hurdle with country-level data.<sup>4</sup> When an MTEF is implemented well we should observe (i) spending that is limited by resource availability (...scal discipline), (ii) budget allocations that re‡ect spending pri-

> 3One way to circumvent econometric identi...cation issues has been to study similar institutions operating at sub-national levels of government. See Besley and Case (2003) for a review of the literature that has employed U.S. state-level data.

> 4The D-GMM approach was ...rst proposed by Holtz-Eakin, Newey, and Rosen (1988) and later developed by Arellano and Bond (1991). Recent re...nements include Windmeijer (2005) and Roodman (2009).

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_PoLtcy_ RESiK-k1Rc i WORkUNG PAPR I 1927

# Summary **findings**

Using a household budget survey for 1992, Hassanshows the poor revenue performance and distribuLional impact of BuLgaria's personal income tax system. He explores the implications for revenue and income distribution of two alternative tax systens - a flat. tax and a progressive but simpler three-brackets tax system.

He demonstrates that simpler tax structures with lower tax rates could achieve at least equal revenue and distributional objectives and are superior in terms of efficiency anid equity. (The findings are robust when Bulgaria's significant tax evasion is included.) But tax changes since 1992 have, if anything, moved Bulgaria even further from a simple income tax system: the number of rates and brackets increased from 7 to 10, and **the** levels of exemption remain unchanged. (Complex, higher rates complicate administration and enforcement and provide inicentives for tax evasion. And in the alternative systems Hassan explores, the poor are protected with higher exemnptions.) Fortunately, the country's personal income tax structure began to move toward less nominal progressivity after Bulgaria's 1997 tax reform program. The tax rate in the top income bracket was reduced from 52 percent to 40 percent, the number of tax brackets was halved, and the exemption level was increased 20 percent (reducing tax burdens on the poor).

- - This paper a product of the Poverty Reduction and Economic Management Sector, Europe and Central Asia Region is part of a larger effort in the region to analyze the social and revenue dimensions of tax reforms in transition economies. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Alison Panton, room Hl 1-033, telephone 202-458-5433, fax 202-477-0816, Internet address apanton@ecworldbank.org. The author may be contacted at fhassan@worldbank.org. June 1998. _(25_ pages)

_The Policy, Research Working Paper Series disseminates the findinigs of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers can-y the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its E-xecutive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
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43 markedly, from AS 34.6 billion in 1989 to AS 51.3 billion in 1994. Tax evasion, amounting to at least 80% in 1988 _(before_ the introduction of the 10%-withholding tax) and discrimination against honest taxpayers came to an end. Given that tax evasion is progressive, the application of the withholding tax actually resulted in a more equitable distribution. In addition, administrative and compliance costs were reduced.44 Finally, neither capital outflows increased nor savings were reduced after the introduction of the final withholding tax in 1993/94.45 However, the label ,,dual income tax" cannot be attached to Austrian reforms. As shown above, probably the most important feature of the dual approach is to apply a uniform, low tax rate on capital income. In Austria, the treatment of different types of capital income is (relatively) uniform; however, the composite tax burden on capital income resembles the highest personal income tax rate rather than a reduced rate.

# **Section 4 - Toward a Reform of Capital Income Taxation in Hungary**

This section discusses the options that are available to improve the current system of taxation of capital income in Hungary. The essential objectives to be pursued are that the tax regime should not distort incentives to increase in domestic savings (i.e., to minimize allocative distortions) and should facilitate the establishment of a 'level playing field' in financial markets. Moreover, in designing a new tax structure, the impact of inflation, as well as distributional and administrative constraints must be accounted for.

Hungary's per capita GDP in 1993 was 37% of the EU average and two thirds of the per capita GDP of Greece, the least wealthy EU member. Therefore, in the context of EU integration, the central issue for future generations will be the speed of the convergence of the Hungarian per capita GDP to the EU average level. To narrow the gap with other EU countries, accelerated growth in Hungary, requiring high rates of savings, is necessary. Table 10 (taken from Sachs & Warner) compares per capita growth rates and rates of saving and investment in Hungary, Poland, and the Czech Republic with those of the less wealthy Member States of the EU and some fast-growing 43 Recall that in 1989, interest income was taxable under the PIT. A 10% withholding tax was credited against the assessed income tax liability.

4 Compliance costs for taxpayers either fell to 0, or, when the filing option was chosen, remained unchanged. At the same time, the reduction of the number of people filing a tax return reduced administrative costs as well.

45 This experience was not shared by West Germany, which had to abolish a withholding scheme in July 1989 **alf** a year after introduction) due to massive capital outflows.
- Anderson, J.O., J.G.  Thundiyil and A. Stolbach (2012). Clearing the Air: A Review of the Effects of Particulate Matter Air Pollution on Human Health. Journal of Medical Toxicology, Vol. 8, Issue 2,  pp. 166–175.

- Attfield, M.D. and N. S. Seixas (1995). Full Prevalence of pneumoconiosis and its relationship to dust exposure in a cohort of U.S. Bituminous coal miners and ex-miners. American Journal of Industrial Medicine. Vol. 27, Issue 1, pp. 137–151.

- Attfield, M.D., E.D. Kuempel (2008). Mortality among U.S. underground coal miners: A 23-year follow-up. American Journal of Industrial Medicine. Vol. 51, Issue 4. pp. 231–245.

Baker, P.J. and D.G. Hoel (2007). Meta-analysis of standardized incidence and mortality rates of childhood leukaemia in proximity to nuclear facilities. Cancer Care, Vol. 16, Issue 4, pp. 355–363.

British Petroleum (2017). BP Energy Outlook 2017, BP. London.

Peter Burgherr and Stefan Hirschberg (2014) Comparative risk assessment of severe accidents in the energy sector, Energy Policy, Vol. 74, Supplement 1, pp. S45-S56.

Coggon and Taylor (1998). ‘Coal mining and chronic obstructive pulmonary disease: a review of the evidence’ Thorax 398.

- Coggon, D. and A.N. Taylor (1998). Coal mining and chronic obstructive pulmonary disease: a review of the evidence. Thorax. Vol.53, pp.398–407.

- Cohen, AJ, M. Brauer, R. Burnett, HR Anderson, J. Frostad, K. Estep, K. Balakrishnan, B. Brunekreef, L. Dandona, R. Dandona, V. Feigin, G. Freedman, B. Hubbell, A. Jobling, H. Kan, L. Knibbs, Y. Liu, R. Martin, L. Morawska, CA Pope III, H. Shin, K. Straif, G. Shaddick, M. Thomas, R. van Dingenen, A. van Donkelaar, T. Vos, CJL Murray, M H Forouzanfar (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet, Vol. 389, pp. 1907–18.

- Consonni, D., A. C. Pesatori, A. Tironi, I. Bernucci,  C.  Zocchetti and P. A.  Bertazzi (1999). Mortality study in an Italian oil refinery: Extension of the follow-up. American Journal of Industrial Medicine, Vol. 35, Issue 3, pp. 287–294.

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In short, aggregate and OECD FCS in China did not outgrow those in Latin America between 1997 and 2003. Even for the U.S. stocks, we find that some countries like Mexico and El Salvador accumulated more FCS than China since 1997. Only in the manufacturing sectors China accumulated more capital than most Latin American countries, although these FCS data were significantly smaller than those reported for the entire period. Latin American countries have in general performed better than China relative to their lack of relative growth. India, in contrast, continued to accumulate FCS faster than our sample of Latin American economies between 1997 and 2003.

# **6. Conditional relative stocks**

As noted above, relative FCS trends differ across source countries, thus suggesting that bilateral characteristics may be important in determining the allocation of FDI. Consequently, this section analyzes trends in FCS while controlling for distance and source-country characteristics. To deal with these issues, we use the OECD and UNCTAD data to estimate cross sectional regressions for each year with source and host country dummies and the bilateral distance between source and host countries<sup>14</sup> . In each regression we exclude the dummy for China as a host country, and then interpret the dummy-variable coefficients of the other host countries as the effect of each host country relative to China. We then repeat the exercise excluding the dummies for China plus Hong Kong (China) and India. This econometric approach is consistent with existing literature on the determinants of FDI in developed and developing countries, which

> 14 Since we are controlling for source country fixed effects, we include all source and host countries that are available in the dataset, but exclude small host countries with populations below 500,000 people. The bilateral distance is measured in miles and was taken from Rose (2004).

15
been profound, as the rate of maintaining student-teacher contact dropped to an estimated 17% among households with school-aged children.

Presently, it is not possible to reliably predict the trajectory of SARS-CoV-2 and its impacts within any given country. The precise mortality and disease burden, and the economic benefits and drawbacks of different approaches to controlling the virus, remain unknown. We present some of the first results of the economic burden of the global pandemic in low-income countries, where data have proven particularly scarce. The need to understand the contemporaneous socioeconomic impacts of the pandemic and the related restrictions on households, individuals, and children will continue to be important for evidence-based policy making.

# **References**

1. WHO, Coronavirus disease (COVID-19) Weekly Epidemiological Update. (2020); <u>https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200921-weeklyepi-update-6.pdf?sfvrsn=d9cf9496_6.</u>

2. World Bank. _Global Economic Prospects, June 2020_ . (World Bank, Washington, D.C., 2020); DOI: 10.1596/978-1-4648-1553-9.

3. M. Mbow, _et al._ , COVID-19 in Africa: Dampening the storm? _Science_ **369** , 624-626 (2020).

4. D. Laborde, W. Martin, J. Swinnen, R. Vos, COVID-19 risks to global food security. _Science_ **369** , 500-502 (2020).

5. P.G.T. Walker, _et al_ ., The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. _Science_ **369** , 413-422 (2020).

6. H. Morsey, L. Balma, and A.N. Mukasa, ‘Not a good time’: Economic impact of COVID-19 in Africa. (African Development Bank Group, Working Paper Series #338, 2020).

<mark>7. W.G. Cochran,</mark> _<mark>Sampling Techniques</mark>_ <mark>, 3</mark><sup>rd</sup> <mark>ed. (Wiley, New York, 1977).</mark> <mark>8. K</mark> . Himelein, Weight Calculations for Panel Surveys with Subsampling and Split-off Tracking. _Statistics and Public Policy_ **1** (1): 40 – 45 (2014).

9. Y, Tillé. _Sampling Algorithms_ (Spring, New York, 2006).

10. Greene, W.H. _Econometric Analysis_ , 8<sup>th</sup> ed. (Pearson, New York, 2017).

11. S. Hsiang, _et al_ ., The effects of large-scale anti-contagion policies on the COVID-19 pandemic. _Nature_ **584** , 262-267 (2020).

12. <mark>World Bank, Ethiopia - High-Frequency Phone Survey on COVID-19 2020. (World Bank, Washington, D.C., 2020); https://microdata.worldbank.org/index.php/catalog/3716.</mark> 13. <mark>World Bank, Malawi - High-Frequency Phone Survey on COVID-19 2020. (World Bank, Washington, D.C., 2020); https://microdata.worldbank.org/index.php/catalog/3766</mark> <u>.</u> 14. <mark>World Bank, Nigeria - High-Frequency Phone Survey on COVID-19 2020. (World Bank, Washington, D.C., 2020); https://microdata.worldbank.org/index.php/catalog/3712.</mark> <mark>15. World Bank, Uganda - High-Frequency Phone Survey on COVID-19 2020. (World Bank, Washington, D.C., 2020); https://microdata.worldbank.org/index.php/catalog/3765.</mark> 9
indirectly estimate the village population density using the HIES survey weights, under the assumption that the survey weights reflect the inverse probability of housing unit being selected for the sample. The steps used to estimate village level population density from the HIES are described in Appendix A.

# **2.2 Publicly available areal estimates of population density**

We validate several publicly available gridded population data sets against each other and with the census, and compare them with our survey-based population density predictions. We examine the following five sources: (1) Landscan for the year 2010; (2) WorldPop for 2010 and 2015; (3) Facebook HRSL for 2015; (4) Gridded Population of the World v3 (year 2010) and v4 (year 2015), and (5) GHSL for 2015. All sources employ a top-down approach using a combination of areal interpolation techniques, including basic dasymetric approaches in conjunction with ancillary data, and statistical modeling methods to distribute census population to smaller grids. While GPW redistributes data based on an equal weighting technique, other sources use measures such as built-up area, road count and density, elevation, slope, and light intensity (“covariates”) to proportionally redistribute population. Facebook, WorldPop 2015, GPW 2015, and GHSL estimates are based on the 2012 Sri Lankan census; WorldPop 2010 and GPW 2010 are based on the 2001 census; and Landscan uses mid-year population of the country of the past year calculated by the Geographic Studies Branch, US Bureau of Census (Oakridge National Laboratory, 2018).<sup>11</sup>

# **2.3 Satellite data**

We use various sources of geo-spatial data in our models. The low-resolution public indicators are from three sources: (1) Night time lights from the Visible Infrared Imaging Radiometer Suite (VIIRS) at a resolution of 750 m per pixel. We use the maximum and mean intensity of two months, namely March and September, 2014; (2) Global Forest Change data based on Hansen et al. (2013), from which we use mean tree cover in 2000, and gain and loss in forest area between 2000 and 2014, at a resolution of 30 meters per pixel; (3) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)’s elevation and slope data at a resolution of 30 meters per pixel.

We also use publicly available built-up area measures based on higher resolution imagery. The Global Urban Footprint (GUF) (year 2012) and Global Urban Footprint plus (GUF+)

> 11 Detailed information on the input population, ancillary data, and the redistribution methodology for each source is presented in the supplementary Table S1.

6
We have GIS data on the entire transmission and distribution network in Uganda as of 2016.<sup>16</sup> With few exceptions, respondents who report having a connection live close to the distribution network (Figure 5). Households located far from the network do not have electricity connections. However, there are still many households close to the network without a connection (Figure 6).<sup>17</sup> For each enumeration area in the survey, we calculate the straight-line distance to the nearest distribution line and the nearest transmission line. Households in enumeration areas located within 500 meters of the distribution network are much more likely to have a connection (Figure 7). For households in the highest expenditure quartile living within 500 meters of the network, the proportion with a connection exceeds 50 percent. The probability of a connection declines rapidly at greater distances from the grid, with almost no households more than five kilometers from the grid observed to have a connection. At all distances, households in the highest expenditure quartile are much more likely to have a connection.

# **4.3 Energy prices**

Umeme is the major electricity distributor in Uganda, with more than 90 percent of the total number of electricity customers. There are nine small distribution companies (or cooperatives in rural areas) covering 13 service territories. Some are operated by private operators and others by the Uganda Electricity Distribution Company Ltd. (UEDCL). Quarterly data on residential electricity prices for each distribution company are from the Electricity Regulatory Authority.<sup>18</sup> Umeme has a two-tier, increasing block tari↵for electricity. The consumption quantity for the first tier is 15 kWh per month. Of the households for which we observe monthly consumption, 86 percent have consumption higher than 15 kWh. Given the low proportion of households on the first tier, we do not explicitly model the nonlinear price structure. Instead, we assume that all Umeme households face the second tier price.

The regulator does not report information on connection charges. We collected connection charge information from the websites of the distribution companies, with historical

> 16The Ugandan Energy Sector GIS Working Group makes these data publicly available: `http://data. energy-gis.opendata.arcgis.com/` .

> 17The finding that many households close to the grid are unconnected matches the findings of Lee et al. (2016b). They undertake a census of all structures within a 600-meter radius of 150 electricity transformers in rural Kenya. Despite the availability of the distribution network in their communities, only 5 percent of households and 22 percent of businesses have an electricity connection.

> 18 `http://era.or.ug/index.php/statistics-tariffs/tariffs/distribution-tariffs/ 2014-10-14-10-24-55` .

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