rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6997

# **Abstract**

To many, reducing water losses is seen as key to more sustainable water management. The arguments to reduce water losses are compelling, but reducing water losses has turned out to be challenging. This paper applies a panel data analysis with fixed effects to determine the major drivers of non-revenue water, which is define as the volume of water losses per kilometer of network per day. The analysis uses data from the International Benchmarking Network for Water and Sanitation Utilities, covering utilities in 68 countries between 2006 and 2011. The analysis finds that non-revenue water is driven by many factors. Some of the most important drivers are beyond the control of the utility, such as population density per kilometer of network, the type of distribution network, and the length of the network, which are largely the result of urbanization and settlement patterns in the localities that the utility serves. The opportunity costs of water losses are also key in explaining what drives non-revenue water. The paper finds that very low opportunity costs of water losses have an adverse effect on the reduction of non-revenue water. Country fixed effects turn out to be important, meaning that the environment in which the utility operates has an important impact on non-revenue water levels. An important conclusion is that the design of non-revenue water reduction programs should study the main drivers of non-revenue water to provide utility managers with a better understanding of what can be achieved in terms of non-revenue water reduction and whether the benefits of these reductions exceed their costs.

This paper is a product of the Water Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cvandenberg@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings 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 carry 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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
for this group in the initial year (2001): their headcount is equal to 44.1% more than 2 percentage points below the 46.2 level<sup>36</sup> calculated for all 2001 (stayer and potential mover) agricultural households. This lower _level_ of poverty implies that moving households are on average poorer than those who remain in agriculture. Accordingly, the _changes_ in P0 are 12.1 instead of 13.7 percentage points. In 2015, about 15 percent of the population still live in agricultural households.<sup>37</sup> The agricultural expansion following trade liberalization has only a minor effect on agricultural employment, by far not enough to offset the reduction in agricultural employment from the BaU. Accordingly, the change of the share of agricultural households due to trade liberalization is only minor, in particular for the Doha scenario. Yet, when translated in actual migrating individuals, this small share change means that almost four hundred thousand individuals – who would have become members of non-agricultural households in the BaU – in the full liberalization scenario remain in agricultural households. Despite the fact that these “potential mover households” are on average poorer than the typical “stayer household”, as we illustrate below, poverty among agricultural households decreases compared to the BaU. The poor stayers hence gain under both trade scenarios although this gain is almost negligible for the Doha scenario.

_<u>Table 15: Poverty impact of trade, agri stayers</u>_

||2001 levels <sup>BaU 2001-</sup><br>15 changes<br>Doha % of<br>BaU<br>change<br>Households remaining in ag|Full % of<br>BaU<br>change<br>ri|
|---|---|---|
|P0|44.1<br>-11.7<br>101.7|109.5|
|P1|20.0<br>-7.0<br>102.4|108.5|
|P2|11.7<br>-4.6<br>102.3|108.2|
|Population %|14.9<br>100.4|101.5|

Source: Authors’ calculations.

As could be indirectly inferred from the analysis of the stayers, the group of the movers should experience the largest welfare gains. Indeed as illustrated in Table 16, in the BaU agricultural households who become non-agricultural households record a 22.4 percentage points reduction in their headcount index, down from a considerably high, especially in comparison to the stayers group, initial level of 56.6 percent.

> 36 Shown in Table 5.

> 37 The initial poverty levels among those who stay in agriculture under the trade scenarios are almost identical to the initial levels among the BaU stayers, so we decided not to report them. The same holds for the movers, for whom we report results later.

45
space by offering a higher payment to ride in the public space. This was flagged in the app to make the price difference salient (Figure 1b).<sup>24</sup> Participants proceeded through a common fixed sequence of rides with varying price differentials ranging from $0 to $0.20, as graphed in Figure 2 and detailed in Table A2.<sup>25</sup> To limit framing in these first phase rides, our exit survey does not include questions on potential experiences of harassment during these rides.

# **5.2 Reduced-Form Results**

We use data from the crowdsourcing app to estimate the effect of assigning an opportunity cost to ride in the reserved space on riders’ demand for the reserved space. We then present results about key demand shifters (presence of men and risk perception). Lastly, Section 8 investigates and rules out journey planning, avoiding other crimes, sorting of perpetrators and congestion as alternative demand mechanisms.

We estimate a simple difference-in-differences specification as follows, where _di,t_ indicates that rider _i_ was assigned a positive opportunity cost to ride in the reserved space at period _t_ : In all ride-level specifications, we weigh observations by _N_ <u>1</u> _i_<sup>,theinverseofthenumberof</sup> rides taken by the individual rider throughout the revealed preference exercise; this accounts for variation in participation frequency, which was not fully controlled by the app. Crucially in interpreting these results, we show that reporting through the app is uncorrelated to treatment assignment; hence, attrition is unlikely to bias our estimates across phases of the study (Appendix D). Zero opportunity cost rides are the omitted category. We include individual rider fixed effects _γi_ , such that the effect of the opportunity cost is identified from within-rider variation across rides; as commuters have fairly rigid commuting habits,

> 24Note that, if anything, adding salience on the price difference in this case provides a conservative measure of willingness to pay for the reserved space.

> 25We randomize whether the reserved or public space option is offered on top in the app. Table A7 shows that this order does not the results.

18
Table F.6: Productivity and Technology Sophistication Controlling for ln(K/L) and ln(Labor Cost/L), GBF and SSBF EXT

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|ln(VAPW)|ln(VAPW)|ln(VAPW)|
|ln(K/L)|0.14***|0.14***|0.14***|
||(0.03)|(0.03)|(0.04)|
|ln(Labor Cost/L)|0.68***|0.68***|0.69***|
||(0.04)|(0.04)|(0.04)|
|GBF EXT|0.65**<br>(0.29)|||
|GBF EXT<sup>2</sup>|-0.08*|||
||(0.04)|||
|GBF MOST||0.93**||
|||(0.45)||
|GBF MOST<sup>2</sup>||-0.16*||
|||(0.09)||
|GBF GAP||0.12||
|||(0.09)||
|GBF EXT*AGRI|||-0.22<br>(0.18)|
|GBF EXT*MANF|||0.24***|
||||(0.07)|
|GBF EXT*SVC|||0.14*|
||||(0.08)|
|SSBF EXT*Agriculture|-0.23|-0.20|0.10|
||(0.23)|(0.23)|(0.26)|
|SSBF EXT*Food Processing|0.25|0.26|0.22|
||(0.16)|(0.16)|(0.16)|
|SSBF EXT*Apparel|0.12|0.10|0.06|
||(0.09)|(0.09)|(0.09)|
|SSBF EXT*Retail and Wholesale|0.10|0.08|0.09|
||(0.12)|(0.12)|(0.12)|
|SSBF EXT*Other Manufacturing|0.02|0.02|-0.01|
||(0.05)|(0.05)|(0.05)|
|SSBF EXT*Other Services|0.15|0.17|0.17|
||(0.13)|(0.13)|(0.14)|
|Var(ABF EXT)|0.27***|0.30***|0.31***|
||(0.10)|(0.09)|(0.09)|
|Firm Characteristics|_~~√~~_|_~~√~~_|_~~√~~_|
|Observations|2,496|2,496|2,496|
|R-squared|0.64|0.64|0.64|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors in parentheses. The regressions controlled for observable firm characteristics including size (i.e., small, medium, and large), sector (i.e., agriculture crops, livestock, food processing, wearing apparel, pharmaceutical, moto-vehicle, wholesale and retail, finance, land transport, hospital, other manufacturing, and other services), age (i.e., 0-5, 6-10, 11-15, and 16 or more), export, and multinational status.

99
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- Fafchamps, Marcel, Christopher Udry, and Katherine Czukas. 1998. "Drought and Saving in West Africa: Are Livestock a Buffer Stock?" _Journal of Development Economics_ 55 (2): 273-305.

- Hermida, Priscila. 2010. "The Long-Term Effect of Natural Disasters: Health and Education in Guatemala after the 1976 Earthquake." Unpublished manuscript.

- Hoddinott, John. 2006. "Shocks and Their Consequences across and within Households in Rural Zimbabwe." _Journal of Development Studies_ 42 (2): 301-21.

- Hoddinott, John and Bill Kinsey. 2001. "Child Growth in the Time of Drought." _Oxford Bulletin of Economics and Statistics_ 63 (4): 409-36.

- Jalan, Jyotsna and Martin Ravallion. 1999. "Are the Poor Less Well Insured? Evidence on Vulnerability to Income Risk in Rural China." _Journal of Development Economics_ 58 (1): 61-81.

- Kazianga, Harounan and Christopher Udry. 2006. "Consumption Smoothing? Livestock, Insurance and Drought in Rural Burkina Faso." _Journal of Development Economics_ 79 (2): 413-46.

- Maccini, Sharon and Dean Yang. 2009. "Under the Weather: Health, Schooling, and Economic Consequences of Early-Life Rainfall." _American Economic Review_ 99 (3): 1006-26.

- Menon, Nidhiya. 2009. "Rainfall Uncertainty and Occupational Choice in Agricultural Households of Rural Nepal." _Journal of Development Studies_ 45 (6): 864-88.

- Porter, Catherine. 2012. "Shocks, Consumption and Income Diversification in Rural Ethiopia." _Journal of Development Studies_ 48 (9): 1209-22.

- Pörtner, Claus. 2008. "Gone with the Wind? Hurricane Risk, Fertility and Education." Unpublished manuscript.

- Premand, Patrick and Renos Vakis. 2010. "Do Shocks Affect Poverty Persistence? Evidence Using Welfare Trajectories from Nicaragua." _Well-being and Social Policy_ 6 (1): 95-129.

- Skoufias, Emmanuel and Agnes R. Quisumbing. 2005. "Consumption Insurance and Vulnerability to Poverty: A Synthesis of the Evidence from Bangladesh, Ethiopia, Mali, Mexico and Russia." _The European Journal of Development Research_ 17 (1): 24-58.

- Vu, Linh and Paul Glewwe. 2011. "Impacts of Rising Food Prices on Poverty and Welfare in Vietnam." _Journal of Agricultural and Resource Economics_ 36 (1): 14-27.

- Webb, Patrick and Thomas Reardon. 1992. "Drought Impact and Household Response in East and West Africa." _Quarterly Journal of International Agriculture_ 31 (3): 230-46.

- Yamano, Takashi, Harold Alderman, and Luc Christiaensen. 2005. "Child Growth, Shocks, and Food Aid in Rural Ethiopia." _American Journal of Agricultural Economics_ 87 (2): 273-88.

Zimmerman, Frederick J. and Michael R. Carter. 2003. "Asset Smoothing, Consumption Smoothing and the Reproduction of Inequality under Risk and Subsistence Constraints." _Journal of Development Economics_ 71 (2): 233-60.

24
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# **1. Introduction**

Economic growth in Sub-Saharan Africa has been slow for decades (Easterly and Levine, 1997; Ndulu and O'Connell, 2007). According to data from the Penn World Table (Heston et al., 2009), Sub-Saharan African real income per capita grew at less than one percent per year over the past half century. In this paper we examine whether and to what extent policy induced distortions to agricultural and non-agricultural production are responsible for Sub-Saharan Africa's dismal growth performance. The average share of GDP from agriculture in Sub-Saharan African countries during the past half century has been more than one-third (WDI, 2011). Even in recent years agricultural production in Sub-Saharan Africa has constituted about a quarter of total GDP and more than half of total employment (Sandri, Valenzuela and Anderson, 2007). Given these large shares, the question of how much policy induced distortions to relative agricultural prices have slowed the region’s economic growth is economically relevant -- both for the academic debate on the determinants of Africa's growth tragedy as well as for the longstanding debate in the development economics literature on whether the net positive growth externalities from industrial activity exceed those from agriculture.

Our estimation strategy to identify the causal effects that policy distortions to relative agricultural prices have on economic growth in Sub-Saharan African countries is based on a twostep estimation approach. In the first step, we estimate the response of these policy distortions to economic growth, using plausibly exogenous variations in rainfall and international commodity price shocks as instrumental variables.<sup>1</sup> The instrumental variables approach enables us to examine how distortions to relative agricultural prices respond to exogenous changes in GDP per capita growth. Importantly, beyond informing the political economy debate on the determinants of policy distortions, the results from this first step provide useful information on the extent to which these policy distortions are endogenous to changes in Sub-Saharan African countries' GDP per capita

> 1 We thus build on the prior literature that has shown that rainfall and international commodity price shocks have a significant effect on real GDP per capita growth of Sub-Saharan African countries. See for example Miguel et al. (2004), Barrios et al. (2010), or Brückner and Ciccone (2010, 2011).

2
# **Figure B6: Lead**

<!-- Start of picture text --> Log scale<br>9<br>Log price Permanent component<br>8<br>7<br>6<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.0<br>Medium-term cycle Transitory component<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 47 —_
- 48-

shocks in TOT still affect growth even when it was acco'nmodated through real depreciation (as represer' 'ed by NPRI).

The two equations were estimated using Sudanese annual data from 1971-1988. The results are reported in equations (5.1) and (5.2) below.

R' = 0.56, R<sup>2</sup> = 0.29, DW = 2.02 (5.2) _(GDPag)_ = 0.44 -0.22 _NPR,_ -. 22 ( ) _f;DP_ (14.91) (-3.98) '(2.03) _GDPX_ + _0.57 T)_ -0.05 ( _AG.CON._ (1.87) _TOT (-1.19) G.CON. A IMP_ - 0.004 ( _mP_ (-1.02) _IMP_ RI = 0.73, R<sup>2</sup> = 0.62, D.W. = 1.74 t- statistics are in parenthesis.

The results show both measures of distortions to have - as expected - negative influences on the growth of real agricultural output (equation (5.1)); the influence of direct taxation is much stronger in terms of magnitude, however. Only indirect taxation affects the share of agriculture in
27

**Table** 13: **Changes in RCA indexes in machinery supplied to environmentally sensitive industries, 1980-90**

||RCAs<br>above I<br>1980|1990|RCAs<br>incrmased<br>in 1980-90|
|---|---|---|---|
||_____-|_number-e||
|**IzzduatrW**|_9_|9|0|
|Austia|a|9|5|
|**Fia'ad**|S|_5_|5|
|Norway|0|0|2|
|Sweden|9|**7**|1|
|**_Germany_**|9|9|0|
|Japan|6|7|3|
|US|7|**_S_**|1|
|Developing|**0**|1|8|

**Source:** United Nations COMTRADE **database.**

The likely positive impact of higher standards on competitiveness of supplier industries in Japan and in Europe is also confirmed by the direction of exports. High shares of exports to industrial countries suggest that the machines comply with their higher environmental standards. The share of industrial countries in the machine exports of Austria and Germany has increased from two-thirds to four-fifths over the past ten years. Japan also exports more environmentally sensitive machines to other industrial countries, contrary to its pattern in the sensitive goods. The US has maintained an export share of about 40% to developing countries with no major change over the period. Developing countries increasingly supply each other with machinery for environmentally sensitive industries.

# 5. Policy implications.

The lack of a systematic relationship between environmental standards/expenditures and trade performance in environmentally sensitive goods suggests that restricting trade to compensate for differences in environmental standards would do little to improve competitiveness in environmentally sensitive industries. As higher environmental expenditures
**-** **<u>Appendix table 1: First stage regression determinants of transfer rights</u>**

||**Rights without Approval**|**Rights with Approval**|
|---|---|---|
|Inherited from husband's family|-0.801***|0.579***|
||(13.17)|(10.11)|
|Inherited from wife's family|-1.150***<br>|0.852***<br>|
||(11.56)|(9.11)|
|Just walked-in|-0.442***|0.307**|
||(3.48)|(2.57)|
|Rented-in|-2.483***<br>|-0.221***<br>|
||(35.16)|(3.33)|
|Freehold and_mailo_tenure|0.367***|-0.432***|
||(3.88)|(4.85)|
|Freehold and_mailo_occupant/tenant|-0.127|0.039|
||(1.27)|(0.41)|
|Knowledge of land law at the hh level (index)|0.070|0.240***|
||(0.89)|(3.25)|
|No. of years possessed|0.001<br>|-0.001<br>|
||(0.36)|(0.40)|
|Distance from house|0.004***|-0.002***|
||(6.81)|(4.54)|
|Parcel area in acres|-0.015|0.021*|
||(1.13)|(1.71)|
|Parcel area squared|0.000<br>|-0.000<br>|
||(1.17)|(1.21)<br>|
|Good land quality|-0.063|-0.131***|
||(1.29)|(2.85)|
|Poor land quality|0.079|-0.033|
||(1.14)|(0.50)|
|Flat land|-0.092*|0.022|
||(1.88)|(0.47)|
|Steeply sloped land|0.132|-0.051|
||(1.41)|(0.58)|
|Irrigated land|0.029|-0.101|
||(0.12)|(0.44)|
|Swamp/Wetland|-0.088|0.090|
||(0.45)|(0.49)|
|Household head's age|0.019**<br>|-0.032***<br>|
||(2.14)|(3.89)|
|Head's age squared|-0.000|0.000***|
||(1.00)|(3.14)|
|Head's education in years|-0.015**|0.020***|
||(2.34)|(3.42)|
|Female headed household|-0.433***|0.298***|
||(6.51)|(4.76)|
|Household size|-0.008|0.015**|
||(1.09)|(2.07)|
|Value of all assets three years ago (Log)|0.093***|-0.022|
||(5.77)|(1.46)|
|Distance to the nearest district town in miles|<br>-0.022***|<br>0.016***|
||(3.95)|(3.12)|
|Public electricity accessible to households|<br>0.168***|<br>-0.216***|
||(2.79)|(3.82)|
|Constant|1.607***|1.027***|
||(7.54)|(5.12)|
|Observations|2185|2185|
|R-squared|0.49|0.20|

Absolute value of z statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1% 28
45 work locations are taken as given and could affect _stimates of trip generation in the first stage. Models featuring feedback to trip generation are needed to endogenize these decisions. The effect of changes in facilities on trip generation also cannot currently be modeled.

The classic transportation model is most explicit about the behavioral and engineering links from instruments to targets within the choice set covered by these models. However, its use for predicting emissions effects and changes for alternative scenarios is still problematical. Emissions, particularly of HC and CO, depend to a great extent on what has been termed "modal vehicle activities," such as acceleration, deceleration, idle, cruising, and engine starts and stops. This type of activity-level detail is not provided by the classic models. With ri search now underway to identify "modal emissions rates" to match modal activities, the classic model can, in theory, be modified to provide activity-level information for use with the emissions factors.

Concerning tractability, a major effort at applying a sophisticated classic transport model and analyzing its properties is still very expensive and time-consuming, although data requirements are not much more than with simple models. Such a major effort is the Santiago Transportation Study. A VAX 8600 was run over 24 hours to run the full system equilibrium with 260 zones and several modes (Ortuzar and Uillumsen, 1991). Models with many fewer nodes and links are available as packages (but without the source code). The best of these is EMME/2 developed by Michael Florian.

Turning now to the Ingram model, being an older model, it does not incorporate many of the innovations in the recent versions of the classic model; it uses a gravity model, for instance, and does not use discrete
even more drastic in states that had experienced a large housing boom, or in the rust belt where the relative size of manufacturing is important. Unemployment increased more for workers without qualifications, as they were dominating sectors hit by the crisis (construction, leisure and transportation) **.** Average unemployment duration is still increasing, and the share of long-term unemployed has reached unprecedented levels (Figure 14).

# **Figure 14: U.S. Unemployment Trends**

<!-- Start of picture text --> a) Unemployment by Sector b) Unemployment by Educational Level<br>30% 18%<br>16%<br>25%<br>14%<br>20% 12%<br>10%<br>15%<br>8%<br>10% 6%<br>4%<br>5%<br>2%<br>0% 0%<br>Construction Manufacturing Less than High School High School<br>Durable Goods Wholesale & Retail Less than College College<br>Hospitality National<br>Source: Bureau of Labor Statistics. Source: Bureau of Labor Statistics.<br>Note: Unemployment data not seasonally adjusted. Note: Unemployment data seasonally adjusted.<br>Unemployment rate Unemployment rate<br><!-- End of picture text -->

## **c) Long Term Unemployment**

<!-- Start of picture text --> 12% 50%<br>10% 40%<br>8% 30%<br>6% 20%<br>4% 10%<br>2% 0%<br>Jan 1950 Nov 1956 Sep 1963 Jul 1970 May 1977 Mar 1984 Jan 1991 Nov 1997 Sep 2004 Jul 2011<br>National (left axis) Share Unemployed more than 27 weeks<br>Source: Bureau of Labor Statistics.<br>Note: Unemployment data seasonally adjusted.<br>Unemployment rate Unemployment rate<br><!-- End of picture text --> 25. The resulting recession in the U.S. quickly spread to other advanced economies as well as to emerging markets and developing countries, triggering fears of a renewed depression. A few 18