rafmacalaba/datause-encoder-data huggingface dataset:
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA

**Figure 9. Ratio of water and wastewater charges at different levels of consumption**

<!-- Start of picture text --> 30<br>25<br>ONEA<br>20 AWSA<br>15 NWASCO<br>KIWASCO<br>10<br>Walvis Bay<br>5 ONAS/SDE<br>0<br>4m3 10m3 40m3<br>Charges<br>Ratio of Water to Wastewater<br><!-- End of picture text --> _Source:_ AICD WSS Survey Database, 2007.

# Cross-subsidies for metered and unmetered consumers

The high prevalence of metering implies a low incidence of unmetered rates, and is imposed as a fixed charge. The downside of the fixed charge is that households consuming a low volume of water end up paying a higher price, and consumers of high volumes of water pay a price that does not adequately reflect the true cost of the water used. For instance, in Ghana, consumers who survive on the minimum subsistence level of 4 m<sup>3</sup> /month pay $0.77/m<sup>3</sup> , while those at the higher end of 40 m<sup>3</sup> /month pay only $0.08/m<sup>3</sup> (figure 10). This is also the case with other utilities in Nigeria that levy a fixed charge. Comparing metered and unmetered rates at the same levels of consumption suggests that, at the lower end, unmetered consumers are paying more. In Kaduna, for instance, the unmetered price at the subsistence consumption level of 4 m<sup>3</sup> is 14 times that of the metered rate. At the higher levels, the metered consumers pay more. At the high-end consumption level of 40 m<sup>3</sup> , unmetered consumers pay slightly more than metered consumers only in Kaduna WB and LWSC.

**Figure 10. Ratio of unmetered and metered rates at different levels of consumption**

<!-- Start of picture text --> 18<br>16<br>14<br>12<br>4 m3<br>10<br>10 m3<br>8<br>6 40 m3<br>4<br>2<br>0<br>GWCL FCT WB Kaduna Katsina SWSC LWSC NWSC<br>prices<br>Ratio between un-metered and metered<br><!-- End of picture text --> _Source:_ AICD WSS Survey Database, 2007.

# Cross-subsidies of small and large consumers

Average tariffs across different consumption levels in African utilities reveal the high price that small consumers are paying for water. To test whether small consumers pay lower prices than large consumers, we computed the water price per cubic meter at several consumption levels. The effective price sharply 18
<!-- Start of picture text --> Al<br>Economic bd<br>recession ©<br>e<br>ey ~o-- ee ee<br>eee<br>ie<br>= Weak state ; e bd<br>E © of the economy . e 8 e<br>5 e<br>2lie lll. mela©)hChULD<br>xX Strong state 2 C e ° = e<br>& of the economy e<br>Fs \ ° °<br>>°e3e .<br>°v e 0<br>e e<br>o<br>1984 1988 1992 1996 2000 2004 2008 2012<br>Year<br><!-- End of picture text -->
is the subjective nature of the innovation measurement, we examine the extent to which the different types of bias that occur when collecting subjective data play a role in innovation measurement.

Results show that in many cases self‐reported innovations in the short and long questionnaire are different, even when the same person is interviewed in the two waves or when explanatory cards with examples or definitions to improve the comprehension of the innovation questions are shown. More importantly, for self‐reported innovations that can be verified we find that the short questionnaire provides an incorrect answer in a significant number of cases. We find that issues of framing explain some of this unreliability, while cognitive problems do not appear to play a significant role. Overall, we found that some of the questions by which innovation is measured in current surveys are not appropriately designed to measure innovation in developing countries. This is especially important when using short surveys and, therefore, we recommend not to use short innovation modules with only a few questions in firm‐level questionnaires that do not provide detailed verification questions.

The paper makes several contributions to the discussion on how to measure innovation. First, to the best of our knowledge this is the first paper that builds on the literature on subjective data to empirically analyze the potential sources of bias that may affect respondents’ behavior in innovation surveys. Second, the paper makes use of a unique effort conducted by the World Bank Group to increase the coverage of innovation surveys. The data used are the most comprehensive set of comparable cross‐country surveys on innovation in developing countries available so far. Finally, the paper reports on a unique experiment carried out to analyze methodological issues affecting data collection efforts and to assess the robustness of the Oslo manual framework in the context of developing countries.

The paper is structured as follows. The next section discusses the conceptual framework for the analysis. Section 3 presents the data used and the experiment conducted in data collection. Section 4 describes the extent of firm level innovation in the two surveys and analyzes the main differences. Section 5 empirically explores the determinants of these differences. The last section concludes.

# **2. Conceptual Framework**

## **2.1 Measuring firm‐level innovation outcomes**

Innovation requires the transformation of knowledge capital or innovation inputs into innovation outputs, such as the introduction of new products or processes, increased quality of existing products or processes, marketing or organizational changes, and patented intellectual property. Firms invest in knowledge capital and innovation inputs in order to increase their capabilities to create innovative outcomes. In 4
**Figure 1: Traditional and Non-traditional per capita Exports, 1950-2010**

(in US dollars of 2010) <!-- Start of picture text --> 1000<br>900<br>Traditional<br>800<br>Non-traditional<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>1950 1960 1970 1980 1990 2000 2010<br><!-- End of picture text --> Given this dynamism, it is likely that Latin American economies are reducing their well-known traditional lag in terms of openness (e.g. the relationship between the size of the trade flows and total economic activity) in comparison to other regions. In this regard, Table 2 shows the ratio of exports to GDP by decade and by country. The figures reveal that Peru has improved its ratio by almost 10 percentage points in the last decade compared to the 1990s (22.6% versus 13.4%), standing out as one of the countries with the largest improvements, along with Argentina, Bolivia and Chile.

**Table 2: Exports of Goods and Services from Selected Latin American Countries, 1961-2008**

(percent of GDP)

||**1961-1970 **|**1971-1980 **|**1981-1990 **|**1991-2000 **|**2001-2008**|
|---|---|---|---|---|---|
|**Argentina**|6.3|7.3|9.3|9.0|23.5|
|**Bolivia**|n.d.|25.7|22.8|20.3|32.8|
|**Brazil**|6.7|7.5|10.1|8.7|14.4|
|**Chile**|13.7|19.3|27.5|28.8|40.5|
|**Colombia**|12.5|15.5|15.0|16.6|17.1|
|**Costa Rica**|25.1|29.4|31.8|40.6|46.1|
|**Ecuador**|16.3|24.8|25.7|28.9|30.3|
|**Mexico**|7.7|9.1|17.2|24.9|27.3|
|**Peru**|18.2|16.6|16.7|13.4|22.6|
|**Uruguay**|14.3|15.7|22.6|18.4|26.6|
|**Venezuela**|25.3|26.0|25.5|28.2|32.6|

_Source: Own calculations using WDI primary data._ To complement this analysis, Figure 2 presents the deviations of the forecasted versus actual export/GDP ratio for a sample of Latin American countries for two years (2000 and 2008), in which 6
borrowers in the region are financing public consumption (e.g. goods and services, compensation of employees). In other words, they might be looking for budget support. Finally, there are no considerable differences in the growth rate of investment between light and heavy borrowers — i.e. it fluctuates between 3.6 and 3.7 percent per year. However, total investment grew at 4.1 percent per year among heavy borrowers. The findings from Figures 20 and 21 show that heavy borrowers in the region are experiencing a debt-fueled inefficient investment.

**Figure 21. Growth of aggregate demand components across types of borrowers in SubSaharan Africa, 2012-17**

<!-- Start of picture text --> Private Consumption Growth Public Consumption Growth Investment Growth<br>6 4 5<br>5<br>4<br>3<br>4<br>3<br>3 2<br>2<br>2<br>1<br>1<br>1<br>0 0 0<br>Light Moderate Heavy Light Moderate Heavy Light Moderate Heavy<br>Percent per year, 2012-17 (group medians) Percent per year, 2012-17 (group medians) Percent per year, 2012-17 (group medians)<br><!-- End of picture text --> _Source: World Bank, World Development Indicators. Elaboration: AFRCE Staff estimates._ The rate of growth in the components of aggregate demand across types of borrowers in the region depicted in **Figure 21** conceals the great degree of heterogeneity across countries within each group. Additional insights are provided about the cross-country behavior of investment growth among heavy borrowers and that of public consumption growth among moderate borrowers.

**Figure 22** reports the annual average growth rate of gross capital formation across 18 heavy borrowers in Sub-Saharan Africa. Three countries in this group experienced a collapse in investment growth (Equatorial Guinea, Chad and Sierra Leone). The collapse in investment might be attributed partly to adverse commodity price shocks (e.g. plunge in the price of oil and iron ore) as well as idiosyncratic shocks (fragility and conflict, and health shocks). On the other hand, 14 countries in this group exhibit positive investment growth, with a median of **7.4** percent per year. This growth rate fluctuates from **1.3** percent per year (Gabon) to **17** percent (Zimbabwe).

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24 Table 2: Additional descriptive statistics for 27 NCMS pilot counties, 2005

|||NCMS ‘|model’<br>Inpatient|Inpatient||
|---|---|---|---|---|---|
|||Inpatient|expenses|expenses|All 4|
||Inpatient<br>expenses<br>only|<br>expenses<br>and<br>catastrophic<br>outpatient<br>expenses|<br>and<br>pooling<br>account<br>for<br>outpatient<br>expenses|<br>and<br>household<br>account<br>for<br>outpatient<br>expenses|<br>‘models’<br>combined|
|Number of counties operating NCMS model|4|2|12|9|27|
|Number of reimbursement episodes per<br>NCMS member|0.02|0.04|1.25|0.29|0.76|
|_of which:_inpatient|100.0%|66.4%|3.4%|9.0%|4.1%|
|_of which:_above county hospital|25.9%|12.9%|0.3%|1.0%|0.5%|
|County hospital|46.6%|36.2%|1.2%|3.5%|1.6%|
|THC|25.9%|12.9%|1.7%|3.1%|1.7%|
|Delivery (any level)|1.6%|4.4%|0.2%|1.3%|0.3%|
|Outpatient|0.0%|33.6%|96.6%|91.0%|95.9%|
|_of which:_above county hospital|N/A|5.2%|1.1%|0.0%|1.0%|
|County hospital|N/A|18.1%|3.7%|2.4%|3.5%|
|THC|N/A|10.3%|32.0%|32.3%|32.1%|
|Village clinic|N/A|0.0%|59.8%|56.3%|59.2%|
|Average reimbursement per episode||||||
|Inpatient|1,150.0|1,436.0|458.0|744.0|728.0|
|Delivery|298.0|72.0|354.0|121.0|211.0|
|Outpatient||58.0|9.0|11.0|14.0|
|Composition of total reimbursements||||||
|Inpatient|100%|98%|66%|84%|80%|
|_of which:_above county hospital|48%|33%|15%|29%|26%|
|County hospital|41%|57%|29%|34%|34%|
|THC|11%|7%|19%|19%|17%|
|Outpatient|N/A|2%|34%|16%|21%|
|_of which:_above county hospital|N/A|0.5%|0.3%|0.1%|0.2%|
|County hospital|N/A|1.2%|3.3%|0.5%|2.0%|
|THC|N/A|0.6%|12.3%|6.4%|9.0%|
|Village clinic|N/A|0.0%|17.7%|9.0%|12.7%|
# **Table 7.1. Estimated annual use values per hectare of forest in India** **<u>(Billion Rs. except where indicated)</u>**

||Low|High|
|---|---|---|
|**Direct**|||
|Timber|17.2|17.2|
|Non timber values|21.0|21.0|
|Fodder|94.4|188.8|
|Ecotourism|51.2|51.2|
|Carbon sequestration|266.8|339.5|
|Total direct|450.6|617.7|
|Per hectare, Rs.|6,471.3|8,871.2|
|**Indirect**|||
|Soil erosion|15.5|15.5|
|Water recharge|6.4|6.4|
|Total indirect|21.9|21.9|
|Per hectare, Rs.|314.5|314.5|
|**Total use values**|472.5|639.6|
|**Total per hectare, Rs.**|6,785.9|9,185.7|

_Source:_ Staff estimates applying secondary data from GAISP (2005-2006), FAO (2009), Gundimeda (2005), Haripriya (2001), Pearce et al (1999), 3rd National Report on Implementation of UN Convention to Combat Desertification (2003), World Bank (2006), World Bank (2012), data from indiastat.org and www.indg.in.

Based on these figures total annual losses from degraded forest land and annual deforestation losses are presented in Table 7.2. The resulting losses are in the range of 0.1-0.3% of GDP. We should note that this is very likely an underestimate of total losses as it excludes non-use values loss. Gundimeda (2005) estimates that the non-use and bioprospecting values of forests could be as much as 6-20 times greater than use values. Due to the high uncertain nature of this estimate we did not use it in this study.

**Table 7.2. Estimation of annual forest** **<u>value loss, Rs. per hectare, except where indicated</u>**

|Losses<br>% loss|Low|High|
|---|---|---|
|**Direct va**<br> <br>|**lues**<br>||
|Timber<br>80-<br>100%|198|248|
|Non timber<br>values<br>20-<br>100%|60|301|
|Fodder<br>0%|1,356|2,712|
|Ecotourism<br>100%|51|51|
|Carbon<br>sequestration<br>20%|766|975|
|Total direct|2,432|4,287|
|Average % loss|42%|53%|
|Total direct , Rs Bn.|60.5|106.7|
|**Indirect values**|||

27
changes in access to service. Although there are endogeneity problems with this specification, we still present it for information. Results of our estimation are provided in Table A.1 in the Annex.

# **3.2 Results**

As expected, the regressions show that both initial household characteristics and access to services are important in explaining real income growth in rural Vietnam. Households that have larger families initially experienced higher income growth. In addition, the education level of the household head has an increasingly positive impact on growth. The better educated the household head the higher is the income growth. Surprisingly, the age of the household head seemed to negatively impact the growth. Although, age is generally thought to be capturing the experience in this case, experience seemed to have an inverse impact. The households with a greater initial capital stock (i.e. farm implements, livestock, and transport related durables) experienced a higher growth. This is not surprising as these households are more likely to be in a better position to respond more positively to the reforms, thus reaping more of the benefits<sup>29</sup> . Also the households where the household head’s occupation is not agriculture had significantly higher growth. The initial income level is significant and negative, indicating a strong catch up effect (β-convergence).

Although the share of cash crop income turned out to be not significant, it is not so unexpected as the growth in cash crop is limited to specific regions (specifically, Central Highlands, and Southeast), and has been the driving force for growth<sup>30</sup> only in these regions. More importantly, this may be due to the fact that in most of Vietnam the expansion of rice trade acted as a cash crop which also shows that the income growth can be triggered by the expansion of basic food commodities, if the comparative advantage can be generated and sufficient price increase can take place.

> 29 Lopez, Nash and Stanton (1995) find that farmers with more productive assets (farm implements, vehicles etc.) are more likely to respond positively to price changes, using Mexican farm survey data. 30 In fact when we estimate our model for only Central Highlands and Southeast, we find that the initial share of cash crop income is positive and significant.

21
Figure 5. UK and US historical inequality extraction ratios (elasticity of the social minimum with respect to mean income = 0.5) <!-- Start of picture text --> 90<br>80<br>England/UK<br>70<br>USA<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1650 1700 1750 1800 1850 1900 1950 2000 2050<br>Years<br><!-- End of picture text --> Source: See notes to Table 1.

Figure 6. Inequality possibility frontier (left) and the inequality extraction ratios (right panel) for China and Russia <!-- Start of picture text --> IPF<br>China<br>China 2011<br>2011 1990<br>2009 2009<br>1990<br>Russia Russia<br>1988 1988<br>300 10002000 5000 20000 300 1000 2000 5000 20000<br>GDI per capita GDI per capita<br>100 100<br>70 70<br>60 60<br>Gini 50 50<br>40 40<br>Inequality extraction ratio<br>30 30<br>20 20<br>0 0<br><!-- End of picture text --> 14
future living standards. GB credit is targeted to landless households in poor villages. Some of their sampled villages were not eligible for the program and within the eligible villages, some households were not eligible, namely those with land (though it is not clear how well this was enforced). The authors implicitly use an unusual _DD_ design to estimate impact.<sup>42</sup> Naturally, the returns to having land are higher in villages that do not have access to GB credit (given that access to GB raises the returns to being landless). Comparing the returns to having land between two otherwise identical sets of villages — one eligible for GB and one not — reveals the impact of GB credit. So the Pitt-Khandker estimate of the impact of GB is actually the impact on the returns to land of taking away village-level access to the GB.<sup>43</sup> By interpretation, the “preintervention baseline” in the Pitt-Khandker study is provided by the villages that have the GB, and the “program” being evaluated is not GB but rather having land and hence becoming ineligible for GB. (I return to this example below.) The use of different methods and data sets on the same program can be revealing. As compared to the study by Jalan and Ravallion (2002b) on the same program (Argentina’s _Trabajar_ program), Ravallion et al. (2005) used a lighter survey instrument, with far fewer questions on relevant characteristics of participants and non-participants. These data did not deliver plausible single-difference estimates using PSM when compared to the Jalan-Ravallion estimates for the same program on richer data. The likely explanation is that using the lighter survey instrument meant that there were many unobservable differences; in other words the conditional independence assumption of PSM was not valid. Given the sequence of the two evaluations, the key omitted variables in the later study were known — they mainly related to local level connections (as evident in memberships of various neighborhood associations and length of time living in the same barrio). However, the lighter survey instrument used by Ravallion et al. (2005) had the advantage that the same households were followed up over time to form a panel data set. It would appear that Ravallion et al. were able to satisfactorily address the problem of bias in the lighter survey instrument by tracking households over time, which allowed them to difference-out the miss-matching errors arising from incomplete data.

> 42 This is my interpretation; Pitt and Khandker (1998) do not mention the _DD_ interpretation of their design. However, it is readily verified that the impact estimator implied by solving equations (4a-d) in their paper is the _DD_ estimator described here. (Note that the resulting _DD_ must be normalized by the proportion of landless households in eligible villages to obtain the impact parameter for GB.)

> 43 Equivalently, they measure impact by the mean gain amongst households who are landless from living in a village that is eligible for GB, less the corresponding gain amongst those with land.

35
each of the variables remain unchanged. The parameter estimates on the terms-of-trade and the trade liberalization variables decline, while the estimate on the aid inflows variable increases somewhat. A similar pattern is observed with the FM-OLS estimators.

**-** **<u>Table 1. Tanzania: Long Run Cointegration Estimation Results</u>**

|Variable|OLS<br>(1)|OLS<br>(2)|OLS-<br>Parallel<br>Rate<sup>1/</sup><br>(3)|FMOLS-<br>(4)|FMOLS-<br>Structural<br>Break<br>(5)|FMOLS-<br>Parallel<br>Rate<sup>1/</sup><br>(6)|
|---|---|---|---|---|---|---|
|Log (ToT)|0.68***<br>(0.23)|0.73***<br>(0.13)|0.30***<br>(0.09)|0.80***<br>(0.17)|0.84***<br>(0.15)|0.31***<br>(0.08)|
|Log (Open)|-0.80***<br>(0.17)|-0.73***<br>(0.12)|-0.44***<br>(0.08)|-1.08***<br>(0.16)|-0.95***<br>(0.15)|-0.57***<br>(0.07)|
|Log (Invest)|0.11<br>(0.15)||||||
|Log (PROD)|-0.00<br>(0.48)||||||
|Log (Aid)|-0.29**|-0.30***|-0.43***|-0.26**|-0.31**|-0.46***|
|_Cons|(0.14)<br>4.71***|(0.09)<br>4.41***|(0.06)<br>5.77***|(0.13)<br>5.18***|(0.15)<br>4.69***|(0.06)<br>6.24***|
||(1.46)|(0.90)|(0.62)|(1.17)|(1.23)|(0.53)|
|No. of obs<br>|36|36|36|36|31|36|
|Adjusted R<sup>2</sup>|0.77|0.77|0.80||||
|D-W Stat.|1.23|1.22|1.97||||
|Residual ADF|-3.92|-3.89|-5.74||||
|test|||||||
|LC ( P-value)||||0.09|>=0.20|0.11|
|MeanF (P-<br>value)||||<=0.01|0.09|<=0.01|
|SupF (P-<br>value)||||<=0.01|<=0.01|<=0.01|

Notes: Standard errors are in parentheses. * indicates significance at the 10% level, ** indicates significance at the 5% level and *** indicates significance at the 1% level.

1/ REER is calculated incorporating parallel exchange rate. The share of parallel varies with the size of parallel market premium.

A key result is that the inverse relationship between aid inflows and the REER observed in the bivariate context holds in the multivariate specification. The statistically significant inverse relationship between aid inflows and the equilibrium REER is somewhat unexpected, but the finding is not unique (Nyoni 1997: Tanzania, Ogun 1995: Nigeria, Sackey 2001: Ghana, Ouattara and Strobl: CFA Franc Zone). Nevertheless, we think this relationship deserves careful consideration before any policy conclusions are drawn. We consider three possible avenues that may be leading to a spurious relationship between aid inflows and Tanzania’s REER. The first is multicollinearity between aid flows and our measure of trade liberalization. The second is that the specification may not be adequately capturing the possibility that the aid inflows generate positive supply side responses in the economy that may overwhelm the expected demand side effects.

root of) parallel premium. While somewhat arbitrary, this definition does reflect discussions with Bank of Tanzania officials.

15
Notes 1 Tapscott (1995). 2 Statista.com. 3 Rosotto et al. (2018). 4 Coase (1937). 5 Freund and Weinhold (2004); Meltzer (2015). 6 Blinder and Krueger (2013); Dossani and Kenney (2007). 7 Wang and Mason (2005). 8 Alstyne, Parker, and Choudary (2016). 9 Freischlad (2018). 10 Haskel and Westlake (2018). 11 Brynjolfsson et al. (2008); Lee (2018). 12 Boyd (2018). 13 Parker and Van Alstyne (2005); Rochet and Tirole (2003); Weyl (2010). 14 Argenton and Prüfer (2012). 15 Argenton and Prüfer (2012); Eisenmann, Parker, and Van Alstyne (2006); Varadajan, Yadav, and Venkatesh (2008). 16 World Bank, World Development Indicators (database), http://databank.worldbank.org/source/world-development-indicators. 17 Corporaal and Lehdonvirta (2017); Dossani and Kenney (2007). 18 Corporaal and Lehdonvirta (2017). 19 Hallward-Driemeier and Nayyar (2017). 20 Brynjolfsson and Kahin (2002). 21Tencent Technology (2018). 22 Russell (2018). 23 Ling (2017). 24 Khan (2016). 25 Bork (1978). 26 Furman et al. (2019). 27 Brannon (2019). 28 Khan (2016). 29 Makovšek and Veryard (2016). 30 Jagasia (2017). 31 Farrell and Simcoe (2012). 32 Simcoe (2012). 33 Farrell (2012). 34 World Bank (2018a). 35 European Commission (2019). 36 Miller (2016); Parker (2016). 37 Lee (2019). 38 World Bank (2018b). 39 OECD (2013a). 40 OECD (2018b) 41 Meijer (2019). 42 HM Treasury (2018). 43 IMF (2019). 44 OECD (2019). 45 Shay (2019). 46 OECD (2018a). 47 OECD (2018b). 48 OECD (2018b). 49 AICPA and CIMA (2019). 50 Lowry (2019); Smart and Bird (2009). 51 Komsky (2017). 52 Cooper, LaSalle, and Wei (2015). 53 UNCTAD (2016). 54 UNCTAD (2018). 55 UNCTAD (2016). 56 Manyika et al. (2016). 57 Cory (2017).

25