rafmacalaba/datause-encoder-data huggingface dataset:
which implies that shadow rent for water is twice the actual payment made to water. Under these findings we added this shadow rent to the payments made to irrigation water as a factor of production.

# **4 Empirical Results**

Simulations reported in this study are selected according to their relevance and importance in water management issues, as well as agricultural policies of Turkey in the medium to long run. The first set of simulations involve the effects of changes in world agricultural prices; the second set of simulations examine the impacts of rural to urban water reallocation within each region; and the last set of simulations analyze the impact of climate change on agriculture.

World agricultural prices began to rise in 2005 (OECD and FAO, 2008). The level of the price increase has caused international concern, because of its effect on those with low income. International institutions, including the OECD, EU, FAO, IFPRI, United Nations and the World Bank have called attention to the adverse affects of the increases in prices. The recent surge of prices in agricultural commodities has immediately affected low income consumers in Turkey. The share of food expenditures in total expenditures is near 40 percent in the lowest quintile (TurskSTAT, 2006). Although the prices are expected to decline from peaks achieved in 2007, the real prices of basic staples are expected to be higher than historical averages in the medium run. This situation may stimulate agricultural production and increase the income for farmers. It may also change the cropping pattern and increase the pressure on water resources. Consumers on the one hand have been and will continue to be adversely affected by the increase in prices. These interdependencies ask for a modeling structure which will take into account the economy wide effects of the increase in prices.

33
aggregate effect of electrification conditional on outages reported in the sample is associated with a 16 percent increase in expenditure for households in the bottom quantile but a 6.1 percent increase for those in the top quantile. The results are robust in models with and without controls for power outages (columns 2 and 1, respectively). This result suggests that the return to electrification is higher for poorer households in India. The distribution of the marginal effects of electrification on households’ expenditure by per capita expenditure quantile (when outages are controlled for) is further shown in figure 3. The marginal benefit of electrification gradually decreases as one moves from the 10th to the 50th quantile. From the 50th quantile onwards, the marginal benefit drops rather quickly and even becomes statistically insignificant.

This finding is somewhat different from those reported by Khandker et al. (2014). Using cross‐sectional data from the IHDS for 2005, Khandker et al. (2014) find that a larger share of the benefits from rural electrification accrues to wealthier households rather than to poorer ones. Because we use panel data from the IHDS that span seven years, we report longer‐term effects of electrification on the same households. The difference in findings suggests a possibility that over time the rate of return from electrification has declined among richer households while poorer households have caught up by diversifying their use of electricity.

# **VII. Conclusion**

Universal access to modern energy services is one of the three objectives of the Sustainable Energy for All (SE4ALL) program launched by the United Nations in 2011. Access to electricity not only brings better‐quality lighting; it also is a key driver of social and economic development. Using household‐level panel data from a nationally representative survey conducted in 2005 and 2012, this study estimates the impact of rural electrification on a broad range of household welfare measures in India.

We find that electrification is associated with substantial improvements in households’ income, expenditure, employment, and educational achievement. But the size of the effects depends importantly on the reliability of electricity service. For example, an additional hour of outages per day is associated with a 0.5 percent reduction in income on average. Gaining access to the grid combined with reliable electricity service is associated with a 17 percent increase in households’ income, but when access comes with frequent power outages, income rises by only 11 percent.

About 240 million people in India still lack access to electricity (IEA, 2015). Findings from our analysis suggest that connecting all these people to electricity would lead to a cumulative income gain of about 17
**Table 1. Number of conflicts and conflict‐countries by macroeconomic variable and definition**

|Variable|Definition 1|Definition 2|Definition 3|Definition 4|
|---|---|---|---|---|
|Government consumption as % of|64 conflicts|24 conflicts|36 conflicts|39 conflicts|
|GDP (PPP)|60 countries|23 countries|33 countries|36 countries|
|Public debt as % of GDP|32 conflicts|8 conflicts|22 conflicts|24 conflicts|
||32 countries|8 countries|20 countries|22 countries|
|Annual inflation rate (%)|46 conflicts|17 conflicts|30 conflicts|32 conflicts|
||43 countries|16 countries|26 countries|27 countries|

Source: Authors’ calculations based on data from UCDP/PRIO Armed Conflict Dataset. See text for details.

# **_III. C. Descriptive evidence_**

Figures 2 and 3 (with panels a, b and c) cover two historical episodes of armed conflict and compare the macroeconomic dynamics of a conflict‐afflicted economy to an unafflicted neighboring economy. These figures illustrate the intuition behind the difference‐in‐difference estimator and anticipate some of the main econometric results discussed further below.

# **_Example 1. Nicaragua and Costa Rica in the late 1970s_**

Between 1978 and 1979, the Sandinista National Liberation Front led an insurrection in Nicaragua that ousted the 43‐year old Somoza family rule in the country. Costa Rica, Nicaragua’s neighbor with a long history without armed conflict, celebrated in 1978 both presidential and legislative elections followed by a peaceful political transition after two consecutive National Liberation Party governments.

From the early 1970s to the early 1980s, Costa Rica maintained the GDP share of government consumption (at current purchasing power parities) stable at around 18% (Figure 2a). In Nicaragua, despite having also a stable share of government consumption of around 4% during the years before the Sandinista revolution, it began to increase in 1977, a year before the onset of an armed conflict, reaching almost 20% of the GDP by 1983, five years after the onset of the conflict.

Regarding public debt (as percentage of GDP), both Costa Rica and Nicaragua experienced increases during the late 1970s and early 1980s (Figure 2b). However, the rise in public debt five years before the onset of the conflict in Nicaragua was notably steeper than in Costa Rica. From the pre‐ to the post‐onset period, Nicaragua’s public debt increased dramatically from 62% in 1977 to 116% in 1979.

8
The firms are profit maximizer. The profit of firm _i_ is where _ti_ is the net transfer that the firm gets from the government (subsidy minus tax and franchise fee).

The government is utilitarian and maximizes the sum of consumer and producer surpluses minus the social cost of transferring public funds to the firm(s). The transfer to the firm(s) can either be positive (i.e. a subsidy), or negative (i.e. a tax). The government’s objective function is where _λ_ is the opportunity cost of public funds.<sup>18</sup> The opportunity cost of public funds, _λ_ , drives the results of the paper. This opportunity cost, which can be interpreted as the Lagrange multiplier of the government budget constraint, measures the social cost of a transfer from the government to the firm. For _λ_ close to 0 _,_ the government maximizes the net consumer surplus; for larger _λ,_ the government puts more weight on transfers. The opportunity cost of public funds is positive because transfers to regulated firms imply either a decrease in the production of public goods, such as schooling and health care, or an increase in distortionary taxation. Each dollar that is transferred to the regulated firm costs 1 + _λ_ dollars to society. In developed economies, _λ_ is mainly equal to the deadweight loss accrued to imperfect income taxation. It is assessed to be around 0 _._ 3 (Snower and Warren, 1996). In developing countries, low income levels and difficulties in implementing effective taxation programs are strong constraints on the government budget, which leads to higher values of _λ_ . As a benchmark case the World Bank (1998) suggests an opportunity cost of 0 _._ 9. The value is much higher in countries that are heavily indebted.

> 18The analysis and the results of the paper are consistent with a less optimistic view of government objectives. It is just a matter of interpretation of _λ_ . With a non benevolent government, _λ_ is the weight the government puts on the transfers it can get out of the firms (e.g., bribes).

10
for “Political Stability”<sup>37</sup> , “Public Accountability”<sup>38</sup> , “Structural Reform”<sup>39</sup> and “Human Capital”<sup>40</sup> .

**Table 2. Private Investment to GDP**

(Increase with an improvement in)

||**Structural**<br>**Reforms**(a)|**GOV**(a)|**_QA _**|**_PA_**|**_PS_**|**Human**<br>**Capital**(b)|**Total**<br>**Contributions **(c)|
|---|---|---|---|---|---|---|---|
|**1980**|2.3|3.5||||0.4|6.1|
||||_1.4_|_1.1_|_0.9_|||
|**1990**|3.0|3.4||||0.5|6.8|
||||_1.4_|_1.2_|_0.8_|||

_<u>Source</u>_ <u>: Authors’ calculations</u> Notes: (a) Direct impact on private investment calculated from equation (9), Table 1.

(b) Indirect impact on private investment calculated from equations (9) and (10), Table 1. (c) Sum of direct and indirect impact.

These simulations show quite interesting results (see Table 2). A first set of conclusions concerns the potentially significant impact of improved governance institutions in the region. An amelioration of one standard deviation of the “Administrative Quality” would have increased private investment by 1.4 percent of GDP during the 1980s and the 1990s. This augmentation is of 1.1 to 1.2 percent of GDP in the case of “Public Accountability” and of 0.8 to 0.9 percent of GDP for an amelioration of the “Political Stability”. In total, private investment could have been higher by 3.4 to 3.5 percent of GDP if governance institutions had been reformed in an appropriate way.

Our calculations also point out that governance deficiencies have not been the only reasons of low private investment performance in MENA. Reforming the economy in other dimensions is necessary to boost private investment in the region. This has been the case over the whole period, but more importantly during the 1990s, where private investment could have been increased by 3 percent of GDP if structural reforms had been improved by one standard deviation (2.2 percent in the 1980s).

33
# Child Undernutrition in Pakistan: What Do We Know?

Dhushyanth Raju Ritika D’Souza _JEL codes_ : I15, O15, J13 _Keywords_ : Pakistan, children, undernutrition, stunting, wasting, underweight

> Raju, World Bank, <u>draju2@worldbank.org; D’Souza, World Bank, rdsouza@worldbank.org. We thank Francisca</u> Ayodeji Akala, Harold Alderman, Deepika Nayar Chaudhery, Silvia Kaufmann, Iftikar Malik, Nkosinathi Vusizihlobo Mbuya, Rekha Menon, Quynh T. Nguyen, Lucian Bucur Pop, E. Gail Richardson, Mohammad Farhanullah Sami, <mark>Albertus Voetberg, and M</mark> elissa Williams for valuable comments, suggestions, and support; and Naihan Yang for research assistance.
## **Figure 2.1 Turkey 2004, budgetary cycle diagram**

## - <u>Turkey Public Financial Programming</u>

<!-- Start of picture text --> Current CG Budget Investment Budget<br>Consolidated and Annexed Budget Agencies State Economic Enterprises<br>May MoF, Treasury and SPO<br>--> Budget Circular Drafting (I)<br>June High Planning Council (Council of Ministers)<br>--> Budget Circular Approval (II)<br>Prime Minister<br>--> Budget Call (III)<br>--> Current Budget Guide Distribution MoF --> Investment Prepa SPO ration Criteria Distribution (IV)<br>July Central Government Agencies SEEs (V)<br>--> Current Budget Proposal --> Investment Budget Proposal --> Annual Programs Preparation<br>August Treasury<br>MoF and CG Agencies SPO and CG Agencies --> Internal technical revision of SEEs (VI)<br>--> Current Budget Negotiations --> Investment Budget Negotiations<br>annual programs<br>September Treasury, SEEs, High Audit Board,<br>MoF, Treasury and SPO<br>--> Aggregate Budget Allocations Line Ministries, PA (VII)<br>--> SEE Targets' Actualization<br>October Treasury, SEEs and SPO<br>High Planning Council (Council of Ministers) --> Budget Allocation Endorsement --> Borrowin--> Transfers revisiong requirement estimation (VIII)<br>Prime Minister and Council of Ministers<br>--> Annual Program/Investment Financing Decree Inception (IX)<br>November Treasury<br>Turkish Grand National Assembly<br>--> Budget Approval --> Principles and Procedures for annual (X)<br>program implementation by SEEs<br>December President<br>--> Budget Law (XI)<br>January SPO<br>--> List of Investment Projects as an Annex to the Annual Program Decree (XII)<br><!-- End of picture text --> _Source: World Bank (2005)_

# Qualitative template at the operator level

The purpose of the qualitative template at the operator level is to provide a schematic evaluation of the incentives and regulatory framework determining the ― ̳commercial‘‖ behavior of the providers financed by public funds.

29
Malaysia and Thailand have the highest<sup>levelof relianceon bank lending.In Malaysia's</sup> case, however, there is greater reliance<sup>on equities (137 percent of GDP, which is not</sup> much below the percentages for the U.S.<sup>and someEuropean countries.)</sup> Corporate bond markets, on the other hand, are<sup>very poorlydeveloped acrossthe</sup> region although Korea's market is considerably<sup>larger than that of the others (except for</sup> Japan's). Japan's financing source profile<sup>(in terms of loans,equities and corporate</sup> bonds) is more characteristic of other Asian<sup>countries and some European countries</sup> (especially Germany) than it is of the U.S.<sup>167In fact,thereisa well-recognized</sup> difference between the bank-centered<sup>model,of which Germanyand Japan 168are</sup> representative, and the Anglo-Saxon model, of<sup>whichthe GreatBritain and theUnited</sup> States are representative.'69 Table 5.9

||Finan<br>|cingSourcesfor<br>199<br>**(%of**|SpecifiedCou<br>8<br>**GDP)**|ntries||
|---|---|---|---|---|---|
||BankClaims<br>onPrivate|||||
|Country|<br>Sector<sup>1</sup>|TotalEquities|Total Bonds<sup>2</sup>|Govt.Bonds<br>|CorporateBonds<br>|
|Indonesia|68.9|24.0|1.8|0.0|1.1<br>|
|Korea|82.5|35.7|86.5<br>|55.6<br>|30.9<br>|
|Malaysia|104.1|136.9|52.5|27.4<br>|21.0<br>|
|Philippines|50.3|54.3|14.2|12.1|1.9<br>|
|Thailand|127.8|30.9|18.6|8.6<br>|3.1<br>|
|Japan|133.3|65.9|137.7|97.7|40.0<br>|
|France|77.1|68.1|83.2|50.3|32.9<br>|
|Germany|125.9|51.5|94.5|40.8|53.7<br>|
|Italy|62.4|48.0|133.2|102.5<br>|30.7<br>|
|The Netherlands|124.0|159.6|64.4|52.8<br>|11.7<br>|
|UK|120.8|169.7|61.0|33.2|27.8|
|USA|63.6|158.0|164.2|94.0|70.2|
|<br>Bankdaimsarethe<br>asequitysecurities.<br>**2**Bondfigures forthe<br>Bondfiguresfordeve|<br>closestavailablep<br>Claimsonthepriva<br> developingcountri<br>lopedcountriesare|<br>roxyforbank<sup>loans</sup><br>tesector,<sup>therefore,</sup><br>es<sup>includeonlydeb</sup><br> debt<sup>securitiesof </sup>|<sup>,althoughclaims</sup><br> <sup>shouldapproxima</sup><br><sup>t securities within</sup><br><sup>allmaturities,notj </sup>|<sup>couldalsoinclude</sup><br><sup>te to loans tothe</sup><br><sup>itialmaturitiesof</sup><br><sup>ustbonds.</sup>|<sup>otherclaims,such</sup><br><sup>privatesector.</sup><br><sup>atleastoneyear.</sup>|
|Source:Compiledfro|mEndo(2001:Tab|le<sup>5,245)</sup>||||

Financial structure in the region has changed<sup>over time as illustratedin the figures</sup> below, which show bank loans and corporate<sup>bonds, respectively,asa percentageof total</sup> external finance for eight selected countries<sup>for the years1990 to1999.Withthe decline</sup> in bank lending after the crisis came<sup>arise in corporatebondissuanceinthe crisis</sup> countries from $68 billion in 1997 to $130<sup>billion in 200017,mostly inKorea,but alsoto</sup> a lesser extent in Malaysia and Thailand.<sup>17'After the bankingsector problemsof the</sup> crisis, capital markets grew in importance<sup>formost of the crisis-affectedcountries.</sup> 69
**<u>Table 6: Impact of certification on perceived land tenure security: Marginal effects from probit model</u>**

||**Expect a**|**n increase**|**Expect a**|**decrease**|
|---|---|---|---|---|
|||**Chamberlain**||**Chamberlain**|
|Land use certificates issued|-0.135***|-0.135***|-0.093***|-0.095***|
||(-4.12)|(-4.13)|(-4.34)|(-4.43)|
|Relative land size|-0.106***|-0.106***|0.040***|0.041***|
||(-7.33)|(-7.31)|(4.11)|(4.26)|
|Share of good quality land|0.066**|0.070***|-0.055**|-0.055**|
||(2.52)|(2.66)|(-2.33)|(-2.35)|
|Number of dependents|-0.011|-0.001|0.014**|0.019|
||(-1.59)|(-0.05)|(2.38)|(1.57)|
|Number of adult male|-0.026**|0.008|0.018**|0.027|
||(-2.47)|(0.38)|(2.15)|(1.51)|
|Number of adult female|-0.019*|-0.033|0.013|0.025|
||(-1.70)|(-1.54)|(1.39)|(1.43)|
|Number of oxen|-0.004|0.001|0.011|-0.005|
||(-0.49)|(0.08)|(1.63)|(-0.52)|
|Value of other animals x 10<sup>-3</sup>(Birr)|0.006|0.008|-0.002|0.002|
||(1.21)|(1.45)|(-0.72)|(0.52)|
|Roof corrugated iron sheet|-0.066***|-0.019|0.052***|0.031|
||(-3.01)|(-0.56)|(2.90)|(1.04)|
|Age of household head (years)|-0.015***|-0.016***|0.006*|0.010***|
||(-3.73)|(-3.70)|(1.77)|(2.62)|
|Age of household head squared|0.000***|0.000***|-0.000|-0.000|
||(2.76)|(2.75)|(-1.30)|(-1.39)|
|Female headed household|-0.032|0.021|-0.049**|-0.062|
||(-1.16)|(0.35)|(-2.13)|(-1.44)|
|Household head can read and write|-0.023|0.013|0.032*|0.019|
||(-1.22)|(0.41)|(1.84)|(0.66)|
|Year = 2002|-0.093***|-0.098***|-0.012|-0.021|
||(-4.85)|(-4.96)|(-0.57)|(-0.96)|
|Year = 2004|-0.243***|-0.253***|-0.049**|-0.070***|
||(-14.70)|(-13.34)|(-2.38)|(-3.04)|
|Year = 2007|-0.320***|-0.333***|0.052*|0.012|
||(-17.17)|(-15.53)|(1.92)|(0.38)|
|Number of observations|3042|3042|3042|3042|
|Number of households|882|882|882|882|
|Log lik.|-1467.109|-1461.588|-1510.578|-1502.232|
|<br>Chi-squared|518.218|517.908|111.531|126.756|
|<br>rho|0.070|0.070|0.049|0.046|
|sigma_u|0.274|0.274|0.227|0.220|
|Lik.-ratio test of rho=0|-1469.227|-1463.689|-1511.736|-1503.235|
|Chibar2|4.237|4.204|2.315|2.006|

**Note:** The dependent variable is whether the household expects an increase or decrease in landholdings over the coming 5 years due to land redistribution and reallocation. The Chamberlain specification includes the mean value of the time-varying household level variables (Chamberlain 1980), coefficients for which are not reported. A constant term is included in all the regressions. Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

23
where the mother received prenatal care is the relevant service provider for the prenatal care and birth outcomes, since incentives and resources affect prenatal care and birth outcomes by improving clinic-level care. Information on the clinic the mother used for prenatal care was not coded in the birth record. Instead we used a number of databases to assign mothers to clinics. First, we were able to assign Plan Nacer beneficiaries to clinics using Plan Nacer administrative data. For the majority (83 percent) of nonbeneficiaries we were able to identify the clinic through pharmaceutical prescriptions from the REMEDIAR program.<sup>9</sup> For the remainder we used geographic coordinates to identify the clinic closest to where the mother’s home. The birth records contained addresses associated with each of the births, and information from SISA provided geographic coordinates for the health facilities.

3. _Identify the timing for incorporation of clinics into Plan Nacer._ While many clinics signed contracts with the Plan early on, most of them did not effectively implement it until much later. Once enrolled, clinics needed time to enroll beneficiaries and learn the program’s administrative and billing processes. As the effective start date of the program, we use the date on which a clinic submitted the first bill for payment to plan for services provided to Plan beneficiaries.<sup>10</sup> We lost about 3 percent of these observations due to missing beneficiary status or because we were unable to identify the clinic where the mother obtained her prenatal care; provinces did not appear to differ with respect to sample attrition. The actual number of observations used in any given analysis may have been smaller due to missing values for a specific outcome.

# **3.3. Outcome Indicators**

We analyze indicators of prenatal care use, prenatal; care quality, and birth outcomes (table 5). The indicators include the number of prenatal care visits, whether a pregnant mother that required a tetanus vaccine received that vaccine, whether birth was by cesarean, birth weight, low birth weight (< 2,500 grams), and neonatal mortality in the hospital. Delivery by cesarean section is an indicator of

> 9 We validated the clinic assignments using the REMEDIAR program and the geographic assignment with the beneficiary sample. The beneficiary sample has information from Plan Nacer on the specific clinic used.  The clinic assignments using REMEDIAR matched the Plan Nacer administrative data in well over 97 percent of cases.

> 10 As clinics usually bill Plan Nacer quarterly to the date of the first bill, we assume that the clinic began Plan-related activities in the quarter prior to the bill.

15