rafmacalaba/datause-encoder-data huggingface dataset:
# **6 Conclusion**

The United States plays an important role in global trade. It accounts for about 11 percent of global goods trade and is the largest export destination for one-fifth of the world’s countries. U.S. multinationals account for about 30 percent of the employment and sales of the world’s largest 100 non-financial multinational companies. U.S. arm’s-length trade growth has slowed steeply relative to intra-firm trade in the aftermath of the global financial crisis, from high pre-crisis rates. During the 2010-14 recovery, trade between non-affiliated firms grew at about half the pre-crisis rate. Intra-firm trade growth also slowed but to a considerably lesser degree.

The sharp slowdown in arm’s-length trade growth stems from a number of factors. A high share of arm’s-length exports is conducted with EMDEs, where growth has slowed sharply from elevated pre-crisis rates. In addition, firms trading at arm’s-length are more concentrated in sectors that grew particularly rapidly pre-crisis and sectors that benefited from the pre-crisis commodity price boom, which boosted pre-crisis trade but have languished since the crisis. In addition, arm’s-length trade is more sensitive to changes in demand and real exchange rates. For example, the income elasticity of arm’s-length U.S. exports is about one-fifth higher than that of intra-firm exports. For the United States, these effects account for a significant part of the post-crisis growth gap between arm’s-length and intra-firm trade.

Other factors have also been at play. Among these, the characteristics that make firms engaged in arm’s-length trade less resilient to the severe demand and financing shocks of the global financial crisis contributed to the post-crisis weakness of arm’s-length trade. Firms engaged in outsourcing tend to be smaller, less productive, less efficient in inventory management, and have more restricted access to finance than firms integrated vertically. Such factors may have accelerated the exit of firms trading at arm’s-length during the global financial crisis and its aftermath. Heightened financial risks and policy uncertainty may also have discouraged arm’s-length transactions.

While the post-crisis environment has favored multinationals that focus on intra-firm transactions, their activities can also raise policy challenges. For example, multinationals may have an incentive to adjust their transfer pricing—the prices assigned to intra-firm transactions—to raise the value of goods and services produced in countries with low corporate income taxes and reduce the value of those produced in countries with higher taxes. Policies to promote FDI and trade therefore have to be carefully calibrated to protect fiscal revenues. A number of global initiatives have been introduced since the global financial crisis to make global tax practices more transparent (IMF/OECD/UN/World Bank 2011, 2016). In addition, large and internationally active firms also tend to be better able to absorb the 16
# **Regulating Water and Sanitation Network Services**

# **Accounting for Institutional and Informational Constraints**<sup>**1**</sup>

## Daniel Camos

The World Bank

## Antonio Estache

ECARES, Université libre de Bruxelles JEL Codes : L25, L32, L33, L51, L95, L97 Keywords : Water and sanitation utilities, Regulation, Institutions.

> 1 We are grateful to E. Auriol, S. Bertomeu, R. Damania, S. Saussier and R. Schlirf for discussions on the ideas built-in the paper. We are the only ones to blame for mistakes or misinterpretations. None can be blamed on the organizations we are affiliated with or its members.

> 2 This paper will be a chapter of the forthcoming World Bank publication Uncharted waters: the economics of water scarcity and variability.
reform effectively (or the ability to deliver the subsidy ex-post), and his/her political views. We first show that in January 2011 - before implementation – the level of information about the reform, the expectations on the ability of the government to deliver and political priors help explain most of the overall satisfaction rate. On average, around 70 percent of the variance of the dependent variable is captured by our main regressors. Second, we show that the increase in the satisfaction rate over time is essentially driven by the ability of the government to deliver the subsidy. Throughout the five surveys following April 2011 the significance and magnitude of the coefficient identifying the above effect progressively increases. Finally, we show a non-marginal effect of political partisanship in the perception of the reform not only before the reform was implemented but also throughout the entire period of analysis.

Overall our findings suggest that the level of satisfaction with the reform could potentially have been affected by actions to increase the information of individuals. It is important to stress that such efforts could have played a role without necessarily modifying the content of the reform. In this sense the findings of our paper point to issues that go beyond the political economy of reform as it is often understood, i.e., in the sense of identifying winners and losers. Our paper suggests that exploring factors that may affect why an individual considers himself to be a winner or a loser is an under-studied yet worthwhile effort for understanding the success or failure of policy reforms.

29
<!-- Start of picture text --> Plc,Plannlng, anid Research I<br>Wrld Development Report I<br><!-- End of picture text --> In many African nations, the fiscal balance is extremely fragile, so fiscal restraint is necessary for stabilization and adjustment efforts to succeed.

Governments tend to increase public expenditures when export commodity revenues are - high and maintain public spending even after revenues fall. This is a formula for building unmanageable debt. Botswana and Cameroon, which exercised restraint during boom times, are exceptions to the rule in Africa.

# growth, and low generation of revenue.

Fiscal deficits drove real exchange rates up in the late 1970s and early 1980s - and fiscal policy should now facilitate real depreciation adjustment by pulling deficits down.

External aid has often supported unsustainable fiscal policy. Far from lessening the need for strictural adjustment, it should now be used for rehabilitation and infrastructure development.

Inflation, driven by public spending, has serious consequences such as the erosion of the real value of taxes, adding to public deficits. However, attempts to reduce inflation through fiscal austerity may have undesirable side effects, if austerity is pursued in the wrong way when public sector real wages are driven down below subsistence, productivity is reduced.

Low public sector productivity results in poor delivery of human services, the deterioration of infrastructure, hindering economic Market-oriented exchange rates are not a substitute for fiscal restraint, but a complement. Flexible rates alone failed in Zambia and Sierra Leone. Combined with prior fiscal restraint, however, they succeeded in Ghana.

This is a background paper for the 1988 World Development Report. Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rhoda Blade-Charest, room S 13-060, extension 33754.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these fndings out quickly, even if prcsentations are less than fuly polished. The findings, interpretations. and conclusions in these papers do not necessarily represent official policy of the Bank.

Copyright ) 1988 by the International Bank for Reconstruction and DevelopmentlThe World Bank
associated with household ownership of a bicycle, as this is actually an indicator of relative poverty (Hanlon and Smart, 2008).

# **4.3 Revised poverty outcomes: 1996/97-2002/03-2008/09**

Combining the results presented in Sections 3 and 4, we calculate revised poverty figures using the revised PLs and extrapolating from the 1996/97 model (see Section 2.3). Table 7 (see Appendix) compares the revised poverty figures with the official statistics. In Figure 5, below, we group poverty figures by consolidated urban and rural areas in the Northern, Central and Southern macro-regions.

This portion of the analysis yields three important conclusions. First, at the national level the revised figures show a different trend than that indicated by the official statistics. Rather than recording a rapid reduction in poverty during the first period (1996/97-2002/03) and stagnation in the second (2002/032008/09), essentially an L-shaped trend, we observe a change in the gradient of poverty reduction: from 2002/03 to 2008/09 poverty reduction slowed but did not stop (see also van den Boom, 2010). As shown by the confidence intervals in the last row of Table 7, the revised values for poverty at the national level in 2002/03 and 2008/09 are statistically different and indicate a declining trend during the period.

Second, the official and revised poverty levels across regions are similar for 1996/97 (with the South being significantly richer than the Central and Northern regions) but differ substantially in the later surveys. According to the official figures, in 2002/03 poverty was heavily concentrated in the South: rural Maputo is ranked as the poorest rural area, and urban Maputo is the second-poorest urban area. The rest of the country, with the exception of the rural North, shows poverty rates of below 50 percent. In 2008/09, the official poverty figures prompt a sharp re-ranking of regions, and the Central provinces, together with the Maputo area, become the poorest in the country.

Our revised figures, by contrast, show that in 2002/03 the Southern provinces, both urban and rural, seem to be better-off than the Central and Northern regions, with urban areas consistently recording lower poverty rates than rural areas. This pattern is confirmed in the revised figures for 2008/09. Poverty is lowest in urban areas and in the South, and highest in rural Central and Northern Mozambique.

Third, the official and revised poverty rate trends also diverge quite strikingly across regions. According to the official figures the poverty ratio over the entire period (1996/96-2008/09) drops substantially only in Niassa and Cabo Delgado, both urban and rural, while there is a moderate reduction in the rural and urban South. Poverty rates stagnate in rural Nampula, while increasing in the Central region, both in urban and rural areas, and in urban Nampula.

Our revised figures indicate a very different pattern (see Figure , below). Already evincing a lower level of poverty at the outset of the period, urban areas in the South show a considerable reduction in 22
across the activities on the phone and how to press start and stop a given activity and were given time to practice. It was also emphasized that the recording needed to be continuous over the study period. After the initial orientation on day 2, the interviewer check-ins for the smartphone arm were conducted on days 3, 6, 9 and 11 to reinforce the basic tenets of the training provided on Day 2; download and review the time use data from respondents’ devices; identify and discuss potential anomalies in the downloaded data; and answer questions/concerns from the respondents.<sup>8</sup> Figure 1. Overview of field organization and visits to different treatment arms In the 24-hour recall diary arm, the interviewers visited the respondents three times during the study period (on days 6, 9 and 11 in each EA) to elicit their activities during the previous day, in between the time respondents woke up and when they went to sleep — during which the interviewer used the “time stamps” revealed by the respondent over the course of their conversation to code responses into the diary that was

> 8 On the first check-in, the enumerator revisited in depth the entries between the time the enumerator left the household on day 2 and when they returned on day 3. This was done together with each respondent to make sure that he/she understood (a) the icons that are corresponding to each featured activity; (b) the idea of stopping each activity after starting it; and (c) the ability to record simultaneous activities. In this respect, the enumerator/supervisor was able to reinforce the training that was administered on day 2. On the subsequent check-in days, the enumerator and respondent also reviewed the entries to make sure that any anomalies (e.g., activities that were forgotten to be stopped) were addressed using the admin function on the app only accessible to the enumerator – see section 3.2 for a discussion. Any other queries that the respondents had were also addressed.

7
can be defined as the “provision of assistance in the form of cash to the poor or those who face a probable risk, in the absence of the transfer, of falling into poverty” (Tabor, 2002)<sup>5</sup> .

# **3.1    The Context of Malawi**

Malawi is one of the poorest countries in the world, ranking 160 out of 188 countries in the 2007 Human Development Index (UNDP 2007). Eighty-five percent of the population million lives in rural areas (entire population =14.2 million). As an agricultural society, most Malawians are dependent on smallholder farming and fishing. In 2008, gross domestic product (GDP) was USD 800 per capita, with the percentage share of household income in the poorest 10% of households at 2.9% compared to 32% in the wealthiest 10%. According to the World Bank's Malawi Social Protection Status Report (2007) the national poverty line of Malawi is set at MK16,165/year corresponding to MK44.3 or USD 0.50 per person per day, significantly below the standard USD l per day per person. The average poor household subsists on an income of around MK36.4 - 17.8 percent below the MK44.3 daily poverty line while the ultra poor subsist on MK26.40, on average.

According to the 2005 Malawian Integrated Household Survey (IHS), 52% of households fall below the poverty line, and of these, 22% fall below the ultra poverty line, so that approximately 7 million people living in an estimated 1.3 million households are absolutely poor and 3 million people living in 550,000 households are ultra poor (NSO 2005).

Vulnerability is defined as inability of households to deal with shocks to their livelihoods. Rising vulnerability implies both an increasing likelihood of shocks taking place and a declining ability to overcome shocks without experiencing livelihood collapse. Vulnerabilities affecting Malawi include (Malawi Government, 2007):

- agricultural vulnerability (i.e. erratic rainfall, land constraints, lack of livestock and constrained access to fertilizers, inputs and credit);

- economic shocks and processes (undiversified livelihoods, weak markets, interactions between transitory shocks and chronic poverty);

> 5 As pointed out by Hulme (2009), in the case of Sub-Saharan Africa social assistance programmes have taken the lead and dominated the most recent social protection policies, in contrast with North Africa where social insurance is the main approach.

7
**Table 1. Answers to income and consumption adequacy** **<u>questions</u>**

||<br>**Percent**|**age of respo**|<br>**nses:**|
|---|---|---|---|
||**less than**<br>**adequate**|**adequate**|**more than**<br>**adequate**|
|Total income|68.7%|30.6%|0.7%|
|Food consumption|46.6%|51.4%|2.0%|
|Clothing|52.7%|46.9%|0.3%|
|Housing|58.8%|41.0%|0.1%|
|Schooling|52.6%|47.1%|0.3%|
|Health care|52.0%|47.9%|0.1%|
|Number of observations|3317|||
# **Appendix D: Difference across regions**

**Figure D.1: (Pro)cyclicality in SSA, Resource Rich vs. Other Countries**

**Figure D.1.a: (Pro)cyclicality in SSA countries, separating for resource-dependent countries**

<!-- Start of picture text --> 0.34<br>0.33<br>0.33<br>0.32<br>0.31<br>0.31<br>0.30<br>0.29<br>0.29<br>0.28<br>0.27<br>0.26<br>0.25<br>SSA Resource‐dependent Others SSA<br>country<br><!-- End of picture text -->

**Figure D.1.b: (Pro)cyclicality in SSA countries, separating for resource-rich countries**

<!-- Start of picture text --> 0.34<br>0.33<br>0.33<br>0.32<br>0.31 0.31<br>0.30<br>0.29<br>0.28<br>0.28<br>0.27<br>0.26<br>0.25<br>SSA Resource‐rich country Others SSA<br><!-- End of picture text --> 47
<!-- Start of picture text --> Ll \'—e— Urban LGAs - capital districts 1'—4— Urban LGAs - non-capital districts '<br>1--e-- Rural LGAs - capital districts ' --#-- Rural LGAs - non-capital districts '<br>na 1 ! oor te = '<br>a5 wesc ee Been —<br>g 8ge H M@ <<------- 1 ae > 1 iia eee = ar om aeSeas” pee<br>a Z| 1 ' ' iiatetel el<br>5¢ f ' ! '<br>. 2S 09 ' ' 1 1<br>2% 1 ' ' 1 1<br>2 I ' ' i '<br>== ' ' ' ' '<br>2<br>0.8 t t t<br>' Restriction | State of | State of Emergency | Ramadan starts ' Ramadan ends<br>| imposed , Emergency , extended H H<br>fi ' ' i i<br>Week of Week of Week of Week of Week of<br>16 Mar 23 Mar 30 Mar 20 Apr 18 May<br><!-- End of picture text -->

<!-- Start of picture text --> —e— Urban LGAs - capital districts —#— Urban LGAs- non-capital districts --e--Rural LGAs - capital districts --#--Rural LGAs - non-capital districts<br>Mt H H H<br>B=2| | NY. H H : }<br>Oozo ' oe 1hwo H 1 t of x fan<br>£o * 0-0 0-0-e/e" /- iM e > 0-9. Le a we ete<br>of 08 ' & x A aA IVA wPdetes pe iyiw +4<br>20 H A. 1 ed! \ wy H i; Ls. A. NY i|i yok A<br>Sog# !iH HHH H0 ‘a‘ “ HHH Nsx re ra ‘x7 %& > 4 / iyiigi |i f a®* ay<br>S&S 0. H i H i + \k<br>i Restriction H State of H State of Emergency i Ramadan starts i Ramadan ends<br>04. 18| Marimposed 27 Mar| Emergency3 Apr! extended 23 Apr{ 23{ May<br><!-- End of picture text -->
micro-enterprise).<sup>3</sup> Our survey has information on a uniquely broad range of variables: sociodemographic characteristics, current and initial business characteristics, business outcomes (sales, profits and costs), managerial practices, cognitive and non-cognitive skills of the entrepreneur, access to credit, growth expectations and obstacles, among others. The sample consists of 10,275 female micro-entrepreneurs. To our knowledge, our survey is the first one measuring both cognitive and non-cognitive skills, in addition to business characteristics and outcomes, for such a large sample of micro-entrepreneurs in Mexico.

We classify the women in our sample as ”opportunity” or ”necessity” entrepreneurs according to the self-reported reason for opening their business. A respondent is classified as opportunity entrepreneur if she reports opening her business either because (i) she wanted to become independent, (ii) she had money and found a good business opportunity or (iii) she wanted to practice her profession or develop her career profile. Conversely, necessity entrepreneurs are those who started their business because they could not find a well-paid or suitable job and needed a source of income. After excluding women who started their business out of family tradition or other reasons, we are left with a sample of 8,949 entrepreneurs (21 percent classified as opportunity and 79 percent as necessity).

In Table 1, we report the mean differences between opportunity and necessity entrepreneurs in the following characteristics: (i) Business performance measures (weekly profits, sales and sales per worker); (ii) business practices, as measured by a standardized Composite Business Practices score (CBP)<sup>4</sup> ; (iii) characteristics of the business and the entrepreneur (age, proportion with at least one worker and monthly salary expenses); (iv) cognitive skills (education, standardized scores of Raven and digit span recall tests); and (v) non-cognitive skills (standardized measures of agreeableness, conscientiousness, extraversion, intellect/ imagination and neuroticism<sup>5</sup> , impulsiveness, locus of control, willingness to take risks, self-confidence, self-efficacy, optimism, self-satisfaction and trust).

> 3Fixed and semi-fixed stands on the street, and dwellings in which the woman was found selling or producing something (or had a sign indicating so) also qualified as enterprises and were included in the survey.

> 4The CBP score is an index that measures how well entrepreneurs in our sample manage their business and is constructed considering measures of marketing, keeping stock, record keeping and financial planning, following Fafchamps and Woodruff (2014). 5These personality traits are referred to as ”the big five” in the literature.

4
# References

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Chamley, C. (1985), "On a Simple Rule for the Optimal Inflation Rate **in** Second Best Taxation", Journal 2f Public Economics, 26, 1, pp. 35-50.

Chamley, C. And Q. Hussain (1989), "The Removal of Taxes on Financial Assets in Thailand, Indonesia and the Philippines: A Quantitative Evaluation", The World Bank PPR Working Paper.

Diamond, D.W. (1984), "Financial intermediaries and Delegated Monitoring", Review of Economic Studies, 51, pp. 393-414.

Diamond, D.W. And P.H. Dybvig (1983), "Bank Runs, Deposit Insurance and Liquidity", Journal of Political Economy, 91, pp. 401-19.

Diamond, P.A. And J.A. Mirrlees (1971), "Optimal Taxation and Public kroduction", American Economic Review, 61, pp. 8-27 and 261-78.

Easterly, W. (1989), "Fiscal Adjustment and Deficit Financing During the Debt Crisis", World Bank PPR Working Paper 138.

Englund, P. (1989), "Monetary Policy and Bank Regulations in an Economy with Financial Innovations", Economica, 56, 224, pp. 459-72.

Englund, P. And L.E.O. Svensson (1988), "Money and Banking in a Cash-inAdvance Economy", International Economic Review, 29, 4, pp. 681-706.

Fama, E.F., "What's Different About Banks?" (1985), journal of Monetary Economics, 15, pp. 29-40.

Gelb, A. And associates (1989), Oil Windfalls: Blessing or Curse? (Oxford University Press).

Gillis, M. (1987), "The VAT and Financial Services", The World Bank DRD Discussion Paper 220.

Giovannini, A. And M. de Melo (1989), "Government Revenue from Financial Repression", The World Bank, mimeo.

Hoffman, L.A., S.N. Poddar and J. Whalley (1987), "Taxation of Banking Services under a Consumption-type, Destination Basis VAT", National Tax Journal, 40, pp.<sup>54 7</sup> -<sup>54</sup> .

Honohan, P. (1990a), "Price and Monetary Convergence in Currency Unions: the Franc and Rand Zones", World Bank PRE Working Paper.

35
which are very clearly visible for the case of Bolivia.

<!-- Start of picture text --> 1980 1985 1990 1995 2000 2005 2010 2015<br>Raw Data: AGR Adj Data: AGR Raw Data: SER Adj Data: SER<br>80<br>60<br>40<br>Share of Employment (%) 20<br>0<br><!-- End of picture text --> Figure A.1: Employment Shares: Raw vs. Adjusted (Bolivia) The first step involves identifying the LRS. There are two major data sources that are free from any of the limitations. These refer to the official estimates for 1980 – 1988 and the estimates from household/labor surveys for 2004 – 2009. As the official estimates are available for more periods, they serve as the LRS for Bolivia. The maximum annual change in sectoral share within the LRS happened for Services during 1985 – 1986 when Services realized a 3.4 percentage points expansion in its share. To add data points from other sources to the LRS, I begin with the year closest to the LRS which is 1989. The deviation in sectoral share of Agriculture from 1988 to 1989 exceeds 45 percentage points. This is much higher than the maximum annual change of 3.4 percentage points observed in the LRS and hence, the raw data from 1989 is dropped. Similarly, data points from 1989 to 1997 are all excluded as the average annual change for each of them exceed 3.4 percentage points. However, the average annual change for all sectors from 1988 (the nearest data point that has passed the selection criteria) to 1999 is lower than 3.4 percentage points. Therefore, raw data from 1999 is appended to the LRS and serves as a reference point for 2000. Likewise, the process moves forward for the remaining data points.

The hollow circles and squares represent the subset of data points that pass the selection process for Agriculture and Services respectively. The dashed lines represent the path of selected data points 46