rafmacalaba/datause-encoder-data huggingface dataset:
ZAMBIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Benchmarking water and sanitation indicators**

||Unit|Resource<br>rich|**Zambi**|**a**<br>Middle-income<br>countries|
|---|---|---|---|---|
|Access to piped water|% pop|12.0|**18.3**|52.1|
|Access to standposts|% pop|12.6|**15.6**|18.9|
|Access to wells/boreholes|% pop|49.0|**46.9**|6.0|
|Access to surface water|% pop|23.7|**19.0**|13.0|
|Access to septic tanks|% pop|1.6|**18.1**|40.8|
|Access to improved latrines|% pop|6.4|**1.6**|1.4|
|Access to traditional latrines|% pop|54.8|**53.1**|30.4|
|Open defecation|% pop|27.6|**27.0**|14.3|
|Domestic water consumption|liter/capita/day|90.3|**80.7**|187.6|
|Urban water assets in need of rehabilitation|%|42.0|**42.0**|25.0|
|Revenue collection|% sales|69.7|**68**<sup>*****</sup>|100|
|Distribution losses|% production|43.6|**44.9**|27.4|
|Cost recovery|% total costs|55.6|**65.4**|80.6|
|Total hidden costs as % of revenue|%|270.4|**236.4**|<br>855.2|
|US cents per m<sup>3</sup>|**Zambia**|Scarce water re|sources|Other developing regions|
|Residential tariff|**48**|60|||
|Nonresidential tariff|**59**|120||3.0–60.0|

_Source:_ Banerjee and others 2008. Derived from AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data _Note:_<sup>*****</sup> Average of three largest utilities.

# **Challenges**

The share of the population without access to safe solutions is increasing over time (figures 5 and 6). Despite doing well at the high end of the coverage spectrum, Zambia does not fare much better than its peers when it comes to the percentage of the population relying on surface water or practicing open defecation. A full 19 percent of Zambia’s population continues to rely on surface water and as much as 27 percent of the population continues to practice open defecation. Moreover, trends in household access to WSS services from successive household surveys show that the share of the population living in these insanitary conditions continues to increase. An additional 0.8 percent of the population each year relies on surface water and an additional 0.4 percent of the population practices open defecation. The high health risk associated with these practices makes this a very troubling finding.

Zambia’s water utilities have relatively high levels of hidden costs due to inefficiencies (figure 7). First, utilities recover only about two-thirds of the total cost of service provision (when full capital costs are taken into account). Second, utilities are collecting only about 70 percent of the revenues owed by their customers. Third, about 45 percent of water produced is lost in distribution due to technical and nontechnical factors. This poor performance is not atypical of water utilities in other resource-rich countries in Africa, even if it lags far behind the performance of middle-income countries in the region.

13
This is also consistent with Proposition 4, which showed larger trade responses at lower levels of trade costs. These exercises are similar to those conducted in Johnson and Moxnes (2013), who also found relatively small magnification effects at current levels of trade but larger effects when trade costs were lower.

# **5.5 Calibrated model with assembly**

As an extension in section 4.4 we added a capstone assembly sector to the model. Our quantitative investigation of this extension focuses on the implied changes in the parameterization of the model that occur in this new structure. We treat assembly as a discrete additional stage and rework our indices ( _D_ , _DX_ , _N_ , etc.) to incorporate it. As before, we calibrate coordination costs, transaction costs, productivity and trade costs ( _τ_ = 1 _._ 36) so that the model replicates the data for upstreamness (variable _DXi_ ), gross-output-to-value-added ratios, wages and the overall trade-to-gross-output ratio. In addition, we assume that each final good has two components with equal cost shares in assembly ( _ηik_ = 0 _._ 5) and we assume that labor costs account for 10 percent of assembly costs ( _β_ = 0 _._ 1). Conditional on our estimate of trade costs, this value is chosen to be sufficiently low so that no country imports both components used to assemble a variety and then re-exports the final good.

Table 8: Calibration with Assembly

||Baseli|ne calibr|ation|Assem|bly calibr|ation||
|---|---|---|---|---|---|---|---|
|Country| ̄_Ai_| ̄_θi_|_γi_| ̄_Ai_| ̄_θi_|_γi_|_X_<sup>_asm_</sup><br>_i_<br>�<br>_i _<sup>_Xasm_</sup><br>_i_|
|US|1.000|1.172|0.490|1.000|0.893|0.511|0.000|
|Singapore|4.783|0.619|0.262|5.122|0.436|0.202|0.000|
|Japan|1.627|1.044|0.446|1.708|0.731|0.430|0.000|
|Taiwan|4.933|0.703|0.289|5.142|0.533|0.254|0.000|
|Korea|4.442|0.812|0.359|4.535|0.631|0.345|0.009|
|Malaysia|14.585|0.731|0.333|15.018|0.572|0.318|0.001|
|Thailand|17.993|0.761|0.393|16.563|0.659|0.461|0.019|
|Indonesia|17.850|1.159|0.726|3.519|2.236|2.671|0.050|
|China|25.432|0.793|0.339|14.228|1.121|0.691|0.884|
|Philippines|41.306|0.807|0.480|14.095|1.402|1.551|0.036|

The structural parameters from the calibrated assembly model are reported in Table 8 along with each country’s exports of assembled goods ( _Xi_<sup>_asm_</sup> ) as a share of the world total. Crosscountry homogeneity of labor productivity in the assembly activity means that the assembly 51
40

|Marg.Propensityto Cons.|Kilimanjaro||Ruvuma||Gansu||Inner Mongo|lia|
|---|---|---|---|---|---|---|---|---|
||OLS|hh FE|OLS|hh FE|OLS|hh FE|OLS|hh FE|
|Earned income in kind|0.000|-0.002|-0.001|-0.001|-0.006|-0.009|-0.007|-0.015*|
||(-0.102)|(-0.813)|(-0.320)|(-0.525)|(-0.830)|(-1.014)|(-0.874)|(-1.659)|
|Unearned income in cash|0.026**|0.005|0.071*|0.002|0.017|0.024|0.043|0.047|
||(2.186)|(0.264)|(1.719)|(0.037)|(1.195)|(1.544)|(1.252)|(1.600)|
|Unearned income in kind|0.005|0.033|0.136|0.248**|0.03|0.02|0.026|0.077|
||(0.186)|(1.159)|(1.609)|(2.421)|(0.717)|(0.274)|(0.139)|(0.438)|
|**_Gifts given_**|||||||||
|Earned income in cash|0.000|0.000|0.003***|0.002**|0.013|0.005|0.007|0.005|
||(0.887)|(0.160)|(4.443)|(2.530)|(1.484)|(0.318)|(1.315)|(0.857)|
|Earned income in kind|0.000|0.000|0.001|0.001|-0.005|-0.008*|-0.014**|-0.011|
||(-0.628)|(-0.634)|(1.643)|(1.193)|(-1.488)|(-1.834)|(-2.127)|(-1.493)|
|Unearned income in cash|0.001|-0.003*|0.014**|0.007|0.062**|0.080**|0.335***|0.322***|
||(0.363)|(-1.755)|(2.124)|(0.966)|(2.291)|(2.434)|(3.774)|(3.563)|
|Unearned income in kind|0.014**|0.014**|0.036**|0.052**|-0.001|0.022|-0.165**|-0.159*|
||(2.352)|(2.077)|(2.883)|(3.181)|(-0.042)|(0.470)|(-1.982)|(-1.707)|

Note: Total household expenditure on each expenditure category as dependent variable. T-values are in between brackets. All regressions include time varying village fixed effects. Regressions for Kilimanjaro and Ruvuma (Tanzania) also include loans, (lagged) livestock, members of households holding elected office, gender, education and age of household head, household size, dependency ratio and female share of household labor as covariates. Observations with earned income, unearned income, loans, (lagged) livestock in the top 1% are excluded in all regressions for Tanzania. In each regression for Tanzania, observations with the dependent variable in the top 1% are excluded. Regressions for China (Gansu and Inner Mongolia) also include loans, (lagged) financial assets, (lagged) value of livestock, whether business household or not, whether household rural cadre, gender, education and age of household head, household size, dependency ratio, female share of household labor, number of disabled adults aged 1860.
41 Flyvbjerg, B. M. Skamris Holm and S. Buhl, (2003a), “How common and how large are cost overrun in transport infrastructure projects”, _Transport Review,_ Vo. 23, 1, 71-88 Flyvbjerg, B, N. Bruzelius and W.Rothengatter (2003b), **Megaprojects and Risk: An Anatomy of Ambition,** Cambridge University Press Flyvbjerg, B. M. Skamris Holm and S. Buhl, (2002), “Cost underestimation public works projects: error or lie?:, J _ournal of the American Planning Association, 68, 279-295_ Friends of the Earth (2001), Dirty Water: The environemtal and social record of four multinational water companies, London Global Corruption Report (2004), available at http://www.globalcorruptionreport.org/ Guasch, J.L. (2004), **Granting and Renegotiating Infrastructure Concessions: Doing it Right** , WBI Development Studies Kaufman, D. , A. Kraay and M. Mastruzzi (2003), Governance matters III: Governance Indicators for 1996-2002”, June 30 draft, World Bank, available at <u>www.worldbank.org/wbi/governance</u> Khalil, F. and J. Lawarree (2001), “Catching the agent on the wrong foot: ex-post choice of monitoring”, _Journal of Public Economics_ , vol.82, pp.327-347 Hall, D. and E. Lobina (2002), “Water Privatization in Latin America 2002” Public Service International Research Unit, University of Greenwich Laffont, J.J. and M Meleu (1999), “A positive theory of privatisation for sub-Saharan Africa”, _Journal of African Economies_ , 8: 30-67.

Laffont, J.J., A. Faure-Grimaud and D. Martimort (1999), "The Endogenous Transaction Costs of Delegated Audition", _European Economic Review_ , vol. 43, 1999, p. 1039-1048 Laffont, J.J. and T. N’Guessan (1999), “Competition and corruption in an agency relationship”, _Journal of Development Economics_ , 60, 271-295 Laffont, J.J. (2000), **Incentives and Political Economy** , Oxford University Press Laffont, J.J. and D. Martimort (2001), **The Theory of Incentives: The Principal-Agent Model,** Princeton University Press Laffont, J.J. (2004), **Regulation and Development** , Cambridge University Press (forthcoming) Lanyi, A. (2004), “Measuring the economic impact of corruption”, The IRIS Discussion Paper series on Institutions & Development, University of Maryland, Paper 04/04 Lovei, L. (1998): “Gas Reform in Ukraine: Monopolies, Markets, and Corruption”, _Public Policy Journal_ ,169, available at: <u>http://rru.worldbank.org/PublicPolicyJournal/Summary.aspx?id=169</u> Lovei, L and A. McKechnie (2000), “The Costs of Corruption for the Poor: The Energy Sector”, _Public Policy Journal,_ 207, available at: <u>http://rru.worldbank.org/PublicPolicyJournal/Summary.aspx?id=207</u> Mitlin, D. (2002), “Competition, regulation and the Urban Poor”, Center on Regulation and Competition, CRC, Working Paper No.37, University of Manchester Nwankwo, S. and D. C. Richards (2001), “Privatization: The myth of free market orthodoxy in SSA”, The International Journal of Public Sctor Management, Vol. 14(2), pp165-179
# **Short-Term Effects of India’s Employment Guarantee Program on Labor Markets and Agricultural Productivity**

# **I. Introduction**

India’s Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) is, with a budget of US$ 7.8 billion in 2011/12 alone, the world’s largest workfare program. It offers unskilled employment in works to provide local productivity-enhancing infrastructure for up to 100 days per year to households who expressed interest in such work by acquiring a ‘job card’. Wages are statutorily set and equal between men and women yet, it is hoped, not attractive enough to prevent self-targeting. MNREGS can affect agricultural productivity in three ways. First, although MNREGS is designed to minimize impacts on agricultural labor markets by not offering employment in the main growing season, it is likely to directly or indirectly affect labor supply and demand. In the short term, this would affect wage rates and employment levels while in the medium to long term investment in mechanization may alter capital/labor ratios. Second, by providing implicit insurance against downside risk, resource transfers and predictable wage payments may allow poor farmers to increase investment or adopt crop portfolios with higher risk and expected return. Finally, local infrastructure (e.g. for water management or roads) constructed under MNREGS can directly increase returns to land and labor, the main assets of small farmers and the rural poor.

Not surprisingly given its size and ambition, MNREGS has attracted considerable scholarly interest. While non-random roll-out poses challenges, many studies explore program impacts on consumption smoothing, wages, welfare, and socioeconomic outcomes. By and large these studies find that resource transfers by the program significantly increased consumption and welfare by the poor, especially in the dry season, leading to wage growth and female empowerment. Impacts of newly established productive infrastructure on agricultural productivity, although a defining characteristic of MNREGS as compared to, say, a simple cash transfer scheme, have been less well studied. In fact, critics argue MNREGS may displace existing jobs rather than generate new ones; that poor quality of assets built under the program may undermine its contribution to higher productivity; and that program-related labor demand may push up agricultural wage rates, undermine mobility, and encourage premature mechanization. In a worst case scenario, this could depress rural labor demand and create dependency on the program not as an employer of last resort in crises but as a permanent feature of rural India’s institutional landscape.

To explore short-term effects of MNREG on labor markets, risk taking, and agricultural productivity, we use unique national panel data from 1999/2000 and 2007/08 that offer three advantages. First, as they cover the same households in 1999, i.e. before the program had been conceived, and in 2007/08 when some but 2
With major hydropower projects coming on stream in the next few years, Bhutan’s macroeconomic environment is expected to change radically in a short period of time. This will potentially exacerbate the already existing imbalances between public and private sector. The three hydropower projects currently under construction in Bhutan together have a cost of 150 percent of the country’s GDP. Already today, hydropower contributes 30 percent of the Royal Government of Bhutan’s domestic revenues and this figure is expected to reach 56 percent by 2024/25. While Bhutan’s hydropower developments are a huge opportunity for the country’s development, they also carry risks. In particular, they are expected to lead to an appreciation of real exchange rates through higher wages and land and real estate prices. This “Dutch disease” phenomenon will make it ever harder for the private sector to compete in the tradable sector. Furthermore, because the hydropower revenues will enter Bhutan through the government budget, the incentives to expand the size of the public sector and increase public sector pay and benefits are likely to rise. This might limit opportunities for the private sector even in the non-tradable part of the economy.

# **3. Data, Analytical Strategy and Econometric Model**

# **3.1. Data**

The analysis relies on data from the 2014 edition of the Bhutan Labour Force Survey (BLFS), described in detail in MoLHR (2015). The BLFS is a household survey that is conducted annually by the Department of Employment of Bhutan’s Ministry of Labour and Human Resources (MoLHR). The BLFS covers a nationally representative sample of around 6,000 households in all 20 _dzongkhags_ of Bhutan. As Bhutan’s urban residents are more heterogeneous than its rural population and economic activities in urban centers are more diverse, urban households are oversampled. In 2014, the BLFS covered 1,479 rural and 4,440 urban households for a total sample of 5,919 households. On average, sampled households contained about four household members resulting in a total sample size of 23,587 individuals.

In each household covered in the BLFS, all questions are answered by one respondent, often the household head. Respondents are questioned about variables concerning the household as a whole and about socio-demographic and work-related characteristics of all household members. Together with items on labor force participation, the core of the BLFS questionnaire encompasses questions related to the household members’ current work status and employment history. An additional module

- 6 -
**Table 3. Trade Itensity Ratios for Mercosur Countries in Intra-Trade and for Trade with North America.**

||||||Trading Partner||||
|---|---|---|---|---|---|---|---|---|
||||Me|rcosur|||North America||
|Exporter|Year|Argentina|Brazil|Paraguay|Uruguay|Canada|Mexico|USA|
|Argentina|1979-81||7.4|86.8|27.5|0.3|6.0|0.7|
||1986-88||14.0|40.5|49.2|0.2|2.8|0.8|
||1994|--|39.2|92.7|89.4|0.1|1.5|0.7|
|Brazil|1979-81|9.0|--|80.7|19.0|0.6|3.2|1.4|
||1986-88|9.9||56.0|31.6|0.6|1.4|1.7|
||1994|21.7|--|71.5|36.7|0.3|2.1|1.3|
|Paraguay|1979-81|41.7|11.2|--|45.1|0.0|3.4|_0.5_|
||1986-88|48.6|41.9||48.8|0.0|0.2|0.3|
||1994|25.2|67.2|--|27.7|0.0|0.4|0.4|
|Uruguay|1979-81|22.7|14.6|52.2|--|0.4|0.6|0.7|
||1986-8|8<br>36.0|33.1|22.2||0.2|1.7|0.8|
||1994|44.7|43.7|36.8||0.2|2.1|0.4|

Source: Trade intensity indices computed from UN COMTRADE statistics.
in country c to destination country j, depends upon a set of variables Xcikj: Speci...cally, we model the hazard function of a trade relationship as a multiplicative function between an unspeci...ed time-dependent baseline hazard function, and an exponential function of country and sector ...xed e¤ects, an interaction term between our measure of risk with the level of ...nancial development, a set of controls and the unobserved e¤ects. In the Cox PH model (Appendix 8:J), the inclusion of ...xed e¤ects, result in a shift of the baseline hazard function. We further allow, for the shape of the baseline hazard function h(t) to vary across products–HS8-digit–by ...tting a strati...ed Cox PH model. Strati...cation according to the product indicator variable �k with 698 the number of agri-food products, adds more ‡exibility to the model and allows to estimate the e¤ect of the Xcikj on the hazard rate within-product. Thus, the empirical model we estimate is as follows: where FDc is the level of ...nancial development of country c, sanitary_riskk the risk index of product k, �i is a ...rm ...xed e¤ect, �j is a destination country ...xed e¤ect, � is an exporter � HS2 sector ...xed e¤ect<sup>10</sup> , and "cikj is an unobserved e¤ect. To capture the di¤erential e¤ect of ...nancial development across products, we interact the country level of ...nancial development with the product level of ...nancing needs (FDc � sanitary_riskk) and control for product and country ...xed e¤ects. This allows to isolate the impact of ...nancial development on product survival after controlling for omitted variable bias at the country and product level. The level of ...nancial development is taken at the initiation of the sample period for each exporting country<sup>11</sup> . That is the year we ...rst have export data. All other explanatory variables take value at the initiation of the trade relationship. Our vector of Controls includes various product and ...rm characteristics as well as traditional bilateral gravity variables. The productrelated variables include the value of export in US dollars in the initial year of the trade relationship in logs, initial_exportcikj. This re‡ects the level of con...dence importers have in the pro...tability of their trading partner (Rauch and Watson, 2003; Albornoz et al., 2010). Additionally, we include the total number of destination markets served by ...rm i in country c with product k in the initial year of the trade relationship, NDestinationscik, in log terms. This allows to control for the experience the ...rm has in supplying the world market with product k. We control for the degree of export

> 10Sector is de...ned at HS2 level.

> 11We do this due to the short time span of our sample, and the poor reliability of the data for African countries. Additionally, we used the average level of ...nancial development over the sample period for each country, and ...nancial development measured at the beginning of each spell, results remain qualitatively the same.

11
(1) _RER_ = _Edc_ ⋅ [ _PMd_ ]α ⋅ [ _PXd_ ] (1 −α ) / _PNd_ where _Edc_ is units of domestic currency per unit of foreign currency, _PMd_ and _PXd_ are price indices of importables and exportables respectively, _PNd_ is domestic nontradables price index, and α is the share of importables in total tradables.

In the sections that follow the internal real exchange rate is measured using foreign wholesale price indices to proxy tradable prices and Tanzanian CPI index to proxy nontradables. We choose this measure for two reasons. The first is data availability and the second is that a careful analysis of the various measures of the REER showed that all suitable candidates were very strongly correlated over the sample and the empirical results that follow are not sensitive to various measures (Figure1 and Annex 1). We did, however, augment our preferred measure to allow for the parallel nominal exchange rate.

**Figure 1. Tanzania: Real Effective Exchange Rate Measures**

<!-- Start of picture text --> Real Effective Exchange Rate for Tanzania (2000=100) Real Effective Exchange Rate for Tanzania (2000=100)<br>Full Sample 130 Recent History<br>320<br>120<br>280 110<br>240 100<br>200 90<br>80<br>160<br>70<br>120<br>60<br>80 50<br>40 40<br>1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 1990 1992 1994 1996 1998 2000 2002 2004<br>REER (WPI-based) REER(WPI+ Comm. Price) REER (WPI-based) REER (WPI+Comm. Price) REER (Exp+Imp)<br>REER (2000=100) REER (2000=100)<br><!-- End of picture text --> <!-- Start of picture text --> Many LICs either Figure 2. Tanzania Parallel Market Premium<br>have had or still have 800<br>parallel markets for 700<br>600<br>foreign exchange,<br>where an exchange rate 500<br>determined by the 400<br>market (either legally 300200<br>or illegally) coexists 100<br>with a pegged or 0<br>managed exchange 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003<br>rate. Using the USD/TZA Shilling Parallel Market Rate Parallel Premium<br>officially managed<br>nominal exchange rate Source: IFS and Pick’s currency yearbook<br>only in the calculation<br>premium (%)<br>US $/TZA Shilling<br><!-- End of picture text --> of an REER could be a misleading indicator of economy-wide price incentives in the LIC as the official rate is most times overvalued. Tanzania has had a parallel exchange rate for much of it post-independence history, with the parallel exchange premium peaking at 4
<mark>this result here, but interpret it as capturing the political geography of media intimidation coming from terrorist groups, organized crime networks or paramilitary groups and militias.</mark> <mark>In terms of audience size and its political consequences we constructed two variables.</mark> _<mark>Media reach</mark>_ <mark>codes the outlets subject to censorship according to audience size, from TV (1), radio</mark> <mark>(2), print (3) to narrower segments of news consumers via online platforms (4).</mark> Model 2 in Tables 1, 2, and 3 shows that the size of the audience triggers different intensities of censorship. Mass (non-elite) media, such as television, a key medium in many developing countries, radio or newspapers are subject to significantly harsher censorship vis-à-vis more specialist or elite media outlets such as online news platforms. A change in type of media from TV to internet news reduces the chances of severe censorship by around 10% on average (Figure 6).

<!-- Start of picture text --> Predictive Margins of media reach with 95% CIs<br>1 2 3 4<br>Media reach (1-TV; 2-Radio; 3-Print; 4-Online)<br>.2<br>.15<br>.1<br>Pr(Censorship severity=1)<br>.05<br><!-- End of picture text --> Figure 6: Predictive margins of _Media reach_ on the probability of censorship severity Table 4 presents the results of two least squared analyses when the _Media reach_ variable is instrumented with the Pew survey data on outlet-specific news consumption.

36