rafmacalaba/datause-encoder-data huggingface dataset:
in place of the wood and paper industry. The remainder of this section analyzes Brazil’s GVC performance in these four GVC‐intensive sectors.

The growth in domestic value added embodied in exports for Brazil has been greatest in the automotive sector, together with chemicals but lags behind some of Brazil’s peer countries. Figure 3 presents the average annual growth rate of domestic value added embodied in gross exports between 1995 and 2011 in each of the four GVC sectors. Although Brazil’s average annual growth rate is high in automotive (14.2%), it lags behind some of its comparators. Turkey, Indonesia and Thailand have much higher growth rates than Brazil. Turkey is a relatively strong performer in automotive industry. The good performance of Turkey is likely to be linked to its proximity to the large GVC market, namely the EU. Similarly, for Mexico which is relatively close to the US market of the automotive GVCs, growth rates are high, though not as high as Brazil’s. GVC participation is doing relatively well compared to comparator countries in agribusiness (10.9%) and chemicals (13.6%), where Brazil has seen high growth rates of domestic value added embodied in gross exports.

The growth rates are much higher in Brazil than Canada, a country that shows very low levels of growth in the GVC sectors for which Brazil is competitive. In agribusiness, Brazil shows high growth rates together with Indonesia while Canada is showing low levels of value‐added growth in this sector. Canada has higher growth rates in basic metals which are somewhat closer in line with Brazil.

**Figure 3: Growth of domestic value added embodied in gross exports, by sector (1995‐2011)**

<!-- Start of picture text --> Agribusiness Metals<br>BRA 10.9 BRA 10.3<br>IDN 13.4 COL 24.6<br>MEX 10.4 IDN 19.2<br>ARG 9.2 THA 15.6<br>COL 7.5 MEX 11.5<br>TUR 6.8 ARG 10.9<br>CAN 6.0 TUR 9.2<br>THA 5.8 CAN 7.9<br>ZAF 3.2 ZAF 5.7<br>Automotive Chemicals<br>BRA 14.2 BRA 13.6<br>IDN 20.7 THA 16.3<br>THA 18.2 IDN 14.2<br>TUR 16.7 ARG 9.6<br>ARG 12.4 TUR 8.0<br>COL 12.4 COL 7.8<br>ZAF 11.6 CAN 6.6<br>MEX 9.4 MEX 6.1<br>CAN 0.2 ZAF 5.3<br><!-- End of picture text --> 6
|Bolivia|12|7|0|7|0|-0.083|-0.091|-0.10|-0.11|(-2.29)<br>-0.003<br>(-0.08)|
|---|---|---|---|---|---|---|---|---|---|---|
|Brazil|113|44|44|0|0|0.032|-0.010|0.04|0.01|-0.091<br>(-3.79)|
|Chile|14|14|0|14|0|-0.118|-0.102|-0.11|-0.10|-0.061|
|||||||||||(-5.90)|
|Colombia|17|2|2|0|0|0.060|-0.088|-0.01|-0.04|-0.173<br>(-2.64)|
|Costa|2|0|0|0|0|-0.025|-0.044|||-0.062|
|Rica||||||||||(-1.24)|
|Ecuador|40|8|0|8|0|0.030|-0.075|||-0.012|
|||||||||||(-0.44)|
|El|12|12|0|12|0|0.137|0.116|||-0.020|
|Salvador||||||||||(-0.64)|
|Mexico|8|0|0|0|0|-0.030|-0.011|||-0.035|
|||||||||||(-4.40)|
|Paraguay|7|0|0|0|0|0.088|0.042|0.09|0.04|-0.043<br>(-3.76)|
|Peru|64|24|0|24|0|0.007|-0.001|0.04|-0.01|-0.151<br>(-6.05)|
|Uruguay|8|0|0|0|0|-0.037|-0.063|-0.05|-0.08|-0.081<br>(-9.53)|
|Venezuela|36|23|0|0|23|-0.029|-0.282||-0.33|0.036<br>(0.97)|

Notes: t-ratios obtained from heteroskedasticity-consistent standard errors are in parentheses. PC = price cap; HR = hybrid regimes; RoR = rate of return; H = household prices; I = industrial prices; LP = labor productivity.

Panama was excluded from the analysis since there is data for only one period.

All KWh of electricity prices were converted into 2001 price levels using the US Consumer Price Index and expressed in terms of purchasing power parity.

# **Concluding remarks**

In this study we compare the relative performance of public and privatized Latin American electricity distribution utilities for the years 1994 to 2001. The labor productivity analysis gives the following main conclusions: (i) privatized firms are more labor efficient than their public counterparts; and (ii) there is evidence of labor productivity growth during the period. The results are significant in economic terms— private firms use about 30% to 45% less labor to produce a given bundle of outputs than public firms, and they are relatively robust to controlling for the potential endogeniety of ownership that could arise if governments privatized those utilities with the highest labor productivity first.

23
<!-- Start of picture text --> Cobb-Douglass Function<br>°2<br>3<br>3 8<br>Nu<br>°cy°<br>rybe<br>3<br>°53<br>0.70 0.75 0.80 0.85 0.90 0.95 1.00<br>2002<br><!-- End of picture text --> <!-- Start of picture text --> TransLog Function<br>°<br>SSE 2<br>geQ<br>a<br>n3<br>©<br>so<br>0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00<br>2002<br><!-- End of picture text --> <!-- Start of picture text --> TransLog Function<br>r<br>* ~<br>° fe” {A oy \<br>oO8a80TM — 5<br>2a3<br>rynN3<br>°R3<br>0.80 0.85 0.90 0.95<br>2002<br><!-- End of picture text --> <!-- Start of picture text --> Cobb-Douglass Function<br>2 By)<br>° a (67<br>oO8 .<br>a<br>°2°<br>ry2°<br>0.90 0.92 0.94 0.96 0.98 1.00<br>2002<br><!-- End of picture text --> <!-- Start of picture text --> TransLog Function<br>°=<br>ZEN<br>® ier) ) ]<br>° @ Wy<br>ef Ld<br><4<br>°°<br>©<br>¢<br>0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00<br>2002<br><!-- End of picture text --> <!-- Start of picture text --> Cobb-Douglass Function<br>°2<br>8<br>°<br>°o<br>a<br>8<br>°<br>rynN3<br>°53<br>0.70 0.75 0.80 0.85 0.90 0.95 1.00<br>2002<br><!-- End of picture text -->
figure for all other irrigation channels in Punjab is 0.044 (6.3% of mean tail DPR).<sup>24</sup> Breaking this down by year, Figure 5 shows that, despite their selection on specific criteria, our sample channels track non-sample channels quite closely; in both sets of channels, tail shortage appears to be trending downward over time. An indicator for whether _DPR_<sup>_T_</sup> _/DPR_<sup>_H_</sup> _<_ 0 _._ 5 _,_ a measure of extreme tail shortage in the channel, averages 0.073 in the sample across years, which is to say that, on about 7% of channels, tail outlets receive less than half the volume of irrigation relative to their allotment than head outlets.<sup>25</sup> So as to more closely match the timing of the outlet-level land data, collected in 2016, our empirical analysis of DPR is limited to the last 4 years of currently available data (20112014). Over this period, tail shortage averages 0.047, or 7.3% of mean tail DPR.

# **5 Estimation**

## **5.1 Empirical strategy**

### **5.1.1 Econometric specification**

For the sake of exposition, suppose that we have data on an outcome _Ypc_ at position _p_ = _H, T_ of channel _c_ . Letting _Hpc_ = **1** ( _p_ = _H_ ), our basic estimating equation is where _Zc_ is a vector of channel level controls, _μc_ is a channel-level fixed effect (absorbing the constant term) and _εpc_ is an idiosyncratic error. Notice that for _βH_ = _βT_ = _λ_ = 0, the 1 fixed effects estimator _β_<sup>ˆ</sup> 0 = �( _YHc − YTc_ ) is the average head-tail outcome differential _NC_

> 24To maintain comparability, we exclude all 1,053 channels that were covered by the irrigation management reform that began in the mid-2000s (see Jacoby et al. 2018).

> 25Although the corresponding average for non-sample channels is similar (0.080), Figure 5 indicates a substantial discrepancy for years prior to 2010. To reiterate, this is not an issue of sample representativeness.

23
# **INTRODUCTION**

In the past two decades, aid organizations, multilateral institutions and national governments have increasingly sought to involve local communities in development. The assumption is that mobilizing poor communities and involving them in all aspects of project design and implementation leads to more effective and equitable service delivery (WDR 2004). Billions of dollars have been spent on such community-driven anti-poverty interventions (Mansuri and Rao 2012). However, the scale and complexity of these interventions make such programs vulnerable to a number of challenges and considerable variations in local capacity (Woolcock 2009). Even if projects are able to build small islands of success in their first phase, they are unable to sustain the intensity and momentum required to induce participation as they scale-up (Mansuri and Rao 2012).

But why does the intensity of community participation decrease as projects expand? One important reason is what we might call ‘implementation failure.’ Expanding the scope of the project from a few communities to a large number puts pressure on the project’s implementation unit, which in turn weakens its intended impact. Several papers and project documents highlight this phenomenon (Mosse 2004, Barron et al. 2011, Swidler and Watkins 2015). Little scholarly attention, however, has been paid to identifying the processes and mechanisms that constitute implementation failure during scale-up, and few studies have compared ‘successful’ and less successful implementation _within_ the same project.

In this study, we draw on qualitative data that cover four years (2011–15) in the life of one of the largest World Bank-assisted, anti-poverty projects in India. We observed the project at a unique juncture in its trajectory, when it was scaling-up from its first phase (covering 400,000 households) to its second phase (covering 800,000 households). Impact evaluations found stark differences in outcomes in the first phase (Datta 2015) and the second phase (Hoffmann et al. 2017). Our own qualitative work found that the first phase was akin to a mini-social movement that challenged traditional structures of power and patriarchy (Sanyal et al. 2015). In this paper, we examine the changes in the project as it expanded into the second phase in order to explain the difference in outcomes. We use a combination of in-depth interviews, focus groups, and 2
-9-

the CPEs have performed reasonably well. We shall expect increased price competitiveness rather than greater quality differentiation for exports with low market shares.9/ Finally, we shall assume that reduction in export price will not provoke retaliations of importing countries' governments by adopting antidumping measures. Whether this makes our approach an over-simplification or not depends on whether the importing countries' governments perceive the CPEs' exports a real threat or not. Clearly,there is enough evidence to argue that CPEs have traditionally behaved as if they were not constrained by this threat. Even thounh anti-dumping measures have been applied in the West against CPEs. it has so far been extremely difficult to prove cases of dumping against CPEs.10/ Moreover,our point is that the CPEs' exports of manufactures tend to be of lower quality,and the price at which these exports are sold must be correspondingly lower to make them saleable. This also implies that enporters of these products are typically not competitors with domestic producers in strictu sensu.

# VI. DATA

The CFEs are defined in this paper as the socialist countries of Eastern Europe. They include Bulgaria, Czechoslovakia, GDR, Hungary, Poland, Romania and the Soviet Union. These socialist countries are treated here as a group rather than individually, which limits our analysis. Unfortunately, Individual treatment of each CPE was not possible due to budgetary and time constraints. The disadvantage of this approach is that we aggregate data across different. socialist countries and the data are, therefore, affected by the weights of individual countries. In addition, we have aggregated developed and developing countries into two separate groups to provide the basis of our comparisons. Export prices were estimated on the basis of unit value indices. We have used detailed EEC trade data base, which is defined according to the EEC classification. The calculation of unit value indices was carried out at sixdigit level of disaggregation. The indices were calculated for two years: 1982 and 1984. The data on NTBs have been taken from the data bank of the World Bank which provides a detailed description of the methodology.

9/ This relationship between market shares and export prices constitutes our hypothesis. Clearly, other relationships could be hypothesized but they were not tested due to data limitations. As we shall see further below, our own test is rather crude since the data base was not amenable to the appropriate econometric treatment.

10/ See, for example,Brown (1987).
|**Ta**|**ble 1: Sou**<br>|**th Asia Labor**<br>_PPP Internati_|**Productivi**<br>_onal Dollar_|**ty Levels by**<br>_s per worke_|**Sector, 200**<br>_r_<br>Ratio|**5**<br>Ratio|
|---|---|---|---|---|---|---|
||Total|Agriculture|Industry|Services|(3)/(2)|(4)/(2)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Bangladesh|3,319|1,390|6,208|4,679|4.5|3.4|
|Bhutan|8,940|4,841|19,366|8,938|4.0|1.8|
|India|4,540|1,597|7,479|8,901|4.7|5.6|
|Maldives|10,271|8,474|7,533|12,790|0.9|1.5|
|Nepal|2,596|1,513|1,716|5,552|1.1|3.7|
|Pakistan|7,952|3,556|10,439|11,829|2.9|3.3|
|Sri Lanka|8,990|4,968|8,906|11,856|1.8|2.4|
|**Addendum**|||||||
|China*|7,230|2,021|14,853|9,608|7.4|4.8|
|Thailand|12,647|3,335|24,948|14,818|7.5|4.4|
|Malaysia**|30,593|17,544|49,270|25,439|2.8|1.5|
|Korea|40,013|17,199|60,012|34,556|3.5|2.0|

* Data for China is from 2004.

** The utilities industry in Malaysia is included in services rather than industry. _Source_ :  Bosworth and Maertens, 2009.

**<u>Figure 3: Sectoral Contributions to National Labor Productivity in</u>**

<!-- Start of picture text --> %<br>100<br>90<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>70<br>60<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>1990s<br><!-- End of picture text --> <!-- Start of picture text --> 2000- 1990s<br>04<br>China<br><!-- End of picture text --> <!-- Start of picture text --> 1990s 2000-<br>06<br>Sri Lanka<br><!-- End of picture text --> <!-- Start of picture text --> 1990s<br>Service<br><!-- End of picture text --> <!-- Start of picture text --> 06<br>Bangladesh<br>Industry<br><!-- End of picture text --> <!-- Start of picture text --> 06<br>Pakistan<br>Agriculture<br><!-- End of picture text --> <!-- Start of picture text --> Reallocation<br><!-- End of picture text --> _Source_ : World Bank 2008a.

_Note_ : Labor Productivity is calculated as average annual growth in real output per worker. The contribution from resource reallocation is calculated by subtracting each sector's growth rate, weighted by the sector's share in value added at the outset of each sub-period, from the total economy's growth rate. The figure is restricted to only positive contributions to labor productivity growth.

Services in South Asia have much higher productivity as compared to agriculture and industry (Table 1) and growth in service productivity has contributed the most to the 10
Deolalikar 1998, Sindelar and Thomas 1991, and Strauss and Thomas 1996 for discussions relating to poor countries). The DHS data cannot be used to test or correct for whether fever is recognized more systematically by mothers from richer households which would dampen a relationship between fever and poverty. The multivariate analysis, however, will control for mother's education, which would likely capture a large part of the self-selective nature of reporting.

The percentage of children in the DHS reporting any fever in the past two weeks is high (Table 3).4 The average for Western and Central African countries is 35 percent with a rural-urban differential on the order of 6 percentage points. Among these countries, Ghana has the lowest reported level of fever (29 percent) and Benin has the highest (55 percent). In the Eastern and Southern African countries the overall level is slightly higher, with a smaller rural-urban difference. The range is narrower, with Zimbabwe at the low end (31 percent) and Uganda at the high end (50 percent). While the average rural-urban differential is smaller than that in Western and Central Africa it is still large in some countries. For example in Malawi and Uganda it is about ten percentage points.

In Western and Central Africa roughly half of all fevers are unaccompanied by a cough, and this ratio does not vary substantially across urban and rural areas. There is some variation in this ratio across countries, mostly between 35 percent in Burkina Faso and 59 percent in Niger and Ghana. The exception is C.A.R. where only 14 percent of all **_5_** fevers are reported to be without a cough. In Eastern and Southern Africa roughly onethird of all fevers are unaccompanied by a cough, ranging from 24 percent in Rwancla to 43 percent in Mozambique.

# _Measuring household wealth rankings_

Studies analyzing inequalities in outcomes typically use consumption expenditures as a measure of long-run income (see discussion in Deaton 1997). Although the DHS include detailed health information, data on consumption expenditures are not collected. This paper uses an approach based on an index of assets owned by household members as well as housing characteristics advocated and applied in Filmer and Pritchett (1999, 2001) for the analysis of inequalities in education outcomes. A similar asset index approach has also been used by others to analyze health outcomes in DHS data, for 4. DHS surveys frequently use a weighting scheme to adjust sample to population averages. These weights are used in this analysis. See discussion below on cross-country weights.

5. C.A.R. has a slightly different questionnaire design. If treatment was sought then respondents were asked how much was spent on treatment. It is unlikely though that this would have affected the percentage reporting no cough with a fever.

6
# **1. Introduction**

Excessive credit provision by the financial system was one of the main sources of the 2007 _−_ 2008 global financial crisis.<sup>1</sup> When credit provision becomes excessive is judged against an unobserved benchmark known as equilibrium credit. One of the most challenging aspects of determining excessive credit provision is the estimation of equilibrium credit.

The Basel III regulatory framework proposed by the Basel Committee on Banking Supervision instructs macroprudential supervisors to estimate equilibrium credit by using the Hodrick-Prescott (HP) filter applied to the ratio of nominal credit to nominal GDP (henceforth, credit-to-GDP ratio).<sup>2</sup> Any _”significant”_ deviation of the credit-to-GDP ratio from its HP filtered trend then triggers accumulation of the counter-cyclical capital buffer.<sup>3</sup> Although such an approach could be seen as simple and transparent, its purely statistical nature disregards fundamental changes in equilibrium credit due to economic and Greater and more credit can financial development. financial deepening provision improve access to finance and economic growth (see, among others, Dell’Ariccia _et al._ , 2012, page 5). Excessively restrictive credit, on the other hand, especially in developing economies with increasing credit needs, is likely to result in underinvestment and slow economic growth. Therefore, a structural approach based on economic fundamentals, which accounts for the level of financial development of the economy, seems to be a more appropriate approach to estimate equilibrium credit.

The existing literature has studied equilibrium credit provision by estimating longrun credit demand functions that also allow for short-run dynamics. Typically, the focus has been on modelling credit demand with two different dependent variables. For example, Cottarelli _et al._ (2005), Boissay _et al._ (2005), Kiss and Vadas (2007) and Coudert and Pouvelle (2010) use the credit-to-GDP ratio, while the ratio of nominal credit to the GDP Deflator (henceforth, real credit) is used in, Calza _et al._ (2001), Hofmann (2004), Calza _et al._ (2003), Brzoza-Brzezina (2005), Durkin _et al._ (2009), Coudert and Pouvelle (2010) and Eller _et al._ (2010).

Defining the dependent variable in a credit demand model to be either the credit-toGDP ratio or real credit imposes strong a priori restrictions on the statistical model that is used, which may not be supported by the empirical data and observed economic behavior. Namely, such restrictions implicitly assume a unit elastic relationship between credit demand and GDP and the GDP deflator. That is, a one-percent increase in GDP

> 1See, for example, Loayza and Ranciere (2006).

> 2See page 13 of BCBS (2010).

> 3 See Step 3 on pages 13 _−_ 14 in BCBS (2010) on how exactly deviations from equilibrium are tied to increases in the capital buffer, risk weighted assets and what the term _”significant”_ means in relation to percentage points away from the HP filtered trend. How macroeconomic factors can be incorporated in the risk weighting of assets over different phases of the business cycle is described in Buncic and Melecky (2013).

3
times as effective in reducing poverty in low income SSA as 1 percentage point of GDP growth outside agriculture.

To the best of our knowledge there are only a few CGE-based studies analyzing the implications of drought in SSA. Pauw, Thurlow and van Seventer (2010) study the impact of droughts on sectoral GDP growth in Malawi. The authors find that drought with the probability of occurrence of every 5 years reduces overall agricultural GDP by 1 percent. The more severe the drought is, the larger the estimated loss of GDP. A drought that occurs every 25 years could destroy over 20 percent of agricultural GDP. Since forty percent of Malawi’s GDP originates in agriculture, such a severe drought could reduce GDP volume by 10 percent.

In our stress-test, we look at the implications of a widespread drought that would occur in two waves. The first wave would hit Cameroon, Cote d’Ivoire, Ghana, Nigeria, Senegal, Ethiopia, Kenya, Tanzania and Uganda over 2013-2015, the second wave of drought would hit all the remaining SSA countries over 2016-2018. In line with previous studies we model drought as a shock to productivity in agriculture. Consistent with similar historical shocks, we choose the level of productivity shock that would reduce agricultural output initially by around 10% compared to the pre-shock level. This corresponds to reducing agricultural productivity in the first year by 15%. Subsequently, productivity recovers to its pre-shock level over the next 2 years. Other assumptions, such as current account developments or productivity growth in other sectors, remain as in the baseline scenario.

Following the drop in agricultural output, prices of agricultural products and food increase much faster than the equivalent baseline numbers. For Nigeria, Ethiopia or Zambia the initial shock results in an increase in agricultural and food prices by an additional 15%. Imports of food products increase significantly to replace domestic output. As productivity drops and food product prices rise, more workers are needed in an attempt to raise production as part of a general-equilibrium effect. Since unskilled workers in rural areas are mainly employed in agricultural production, their wages increase much faster than in the baseline. In the case of Nigeria, for example, wages of unskilled workers jump by 16% in 2013 or during the first year of the negative productivity shock in agriculture.

Households will bear the burden of higher food prices. Even though wages of unskilled workers increase, the increase of income is slower than the increase of agri-food prices. Since agri-food expenditures constitute a high share of household budgets, without government or international intervention real consumption would decrease substantially. The initial loss in household consumption for SSA as a whole would amount to 2.3% in 2013. As agricultural output recovers by 2025, total SSA consumption would be still 1.2% lower than in the baseline. In several countries such as Nigeria where the share of food imports in consumption is relatively high, prices increase substantially while domestic income does not go up as much.

14