rafmacalaba/datause-encoder-data huggingface dataset:
By

# **Parallel Systems and Human Resource Management in India’s Public Health Services A View from the Front Lines**

Gerard La Forgia<sup>a</sup> , Shomikho Raha<sup>b</sup> , Shabbeer Shaik<sup>c</sup> , Sunil Kumar Maheshwari<sup>d</sup> and Rabia Ali<sup>e</sup> aHealth, Nutrition & Population Unit, East Asia and Pacific Region, World Bank, Washington DC, USA bSocial Development Department, World Bank, Washington DC, USA cCenter for Good Governance, Hyderabad, India dIndian Institute of Management, Gujarat, India eEducation Sector Unit, East Asia and Pacific Region, World Bank, Washington DC, USA JEL Classification: I1, H7, H83

**Key words:** India, health systems, governance, human resource management, public management.

# **Acknowledgements** :

The authors are grateful for comments on earlier versions received from Rajeev Ahuja, Sofi Bergkvist, Peter Berman, Jishnu Das, Ariel Fizbein, Nick Manning, Somil Nagpal and Vikram Rajan. This research was supported by a generous grant from DFID/India. The paper draws on field research, data, and transcripts prepared by the Center of Good Governance (CGG), Hyderabad, India and Wone Management Systems, Ltd. The CGG team consisted of Mr. Varada Raju, Radhakrishnam Raju, Manas Ranjan Kar, and Pavan Kumar B. The Wone team consisted of Vinita Satija and Puneet Gaur.
shows the results of one such analysis, using the common rule of thumb is that household spending higher than 5% of household income would represent a hardship, and thus would not be “affordable”. This analysis indicates that **_if all households were connected to the utility service_** and only O&M costs were fully recovered, a sizeable fraction of households, about one-third in South Asia, would suffer hardship except in LAC. The impact of reducing subsidies to a point that they recover a sizeable share of capital costs is very dramatic with more than 65% of households in India and Sub-Saharan Africa facing affordability problems.

**Table 3.4  % of Households Paying More Than 5% of Income on Utilities in Alternative Cost Recovery Scenarios**

|Country/Region|LAC|East Asia|Sub-Saharan<br>Africa|India|
|---|---|---|---|---|
|Tariffs covering O&M (US$6 per month)|<5%|20%|35%|35%|
|Tariffs covering O&M plus substantial Share<br>of Capital Costs (US$13 per month)|15%|45%|65%|65%|

Source: Derived from Foster and Yepes (2005)<sup>11</sup> This evidence gives a clear rationale for the widespread use of WSS and electricity subsidies at least in low-income countries. A significant number of utility customers would suffer if those subsidies were substantially abolished. However, these numbers can be deceptive, since they are based on the hypothetical case that all potential users are connected to the system. A substantial share of the lower income groups may not be connected, and thus do not benefit from any such subsidy. These households would therefore not be affected at all by an increase in user charges, except insofar as better cost recovery might permit utilities to expand services. As such, this analysis overstates the number of households that would actually be affected, and abstracts from the very real divide between low income households that benefit from connections and the subsidies that accompany those connections and those that do not. However, even correcting for this coverage gap still suggests that moving to full cost recovery could have widespread enough impacts that most policymakers would hesitate to move too far or too quickly in that direction.

> 11 Calculations are based on simulation of the cost of a minimum subsistence consumption bundle of both WSS and electricity services provided at the respective tariffs. Distribution of income data needed to estimate percentage of affected households was derived from household surveys.

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Table 1: Parameter Values

|Denition|Parameter|Value|Units|
|---|---|---|---|
|Capitals Share in Income|_α_|0_._3|-|
|Standard Deviation of the Productivity Shock|_σ_<sup>_a_</sup>|0.75|Percent|
|Returns to Education|_λ_|0_._07|-|
|Base Year Capital Stock|_K_0|0.2|Trillion US $|

on the other hand, calculated by using a capital output ratio of 2.55, which is close to the estimations of IMF Article IV Sta Appraisal Report.

Table 2 shows the macroeconomic and scal assumptions that apply throughout the projections with the exception of the baseline TFP growth. In the medium term, the TFP growth picks up from the current low levels as the economy recovers from the ongoing economic slowdown. In the longer term, as capacity utilization approaches its equilibrium level, the TFP growth also converges to its long-term level of 1.2 percent.

The open economy modeling environment makes it necessary to specify global rates of return to capital in the long run. We assume that gross returns to capital remain stable in the long term. In the meantime, the interest rate dierential between the Bulgarian and global economy decreases from about 2.5 percentage points in the 2012 to about 0.1 percent by the end of projection horizon. This transition is determined by several factors including the current risk aversion of global investors, and economic and institutional convergence of Bulgarian economy with the rest of the EU, which should reduce the risk premium. Finally, we assume that the current unemployment rate will gradually converge to its long-term level of 5 percent just before 2040.

On the scal side, we impose a unitary elasticity of non-tax revenues, therefore, their share remains constant in GDP in the medium and long term. The only adjustment comes from a reduction from 4.8 percent in 2012 to 3.6 in 2015, which represents the expectations of the authorities as reported in the Medium Term Fiscal Framework of Government of Bulgaria (2012). Direct tax revenues are, on the other hand, projected to exhibit a gradual and modest improvement over time, increasing from 5.4 percent of GDP in 2012 to 6.5 percent in 2050. This improvement is in line with a progressive tax system: as the average income increases, tax payments will be made at higher brackets. Finally, the share of grants, which mainly comprises the EU grants, is estimated to rst increase in the medium term, then decrease to about 3 percent as Bulgaria moves up the per capita income ladder.

15
<u>Table 3. Baseline Teacher Survey: Description and Balance</u>

||||Treatment-|
|---|---|---|---|
||Control|Treatment|Control|
|Gender (1=male)|0.92|0.88|-0.048|
||(0.27)|(0.33)|(0.04)|
|Age|31.9|30.68|-1.219|
||(9.08)|(7.93)|(1.20)|
|Teacher is permanent|0.6|0.63|0.024|
||(0.49)|(0.49)|(0.08)|
|Teaching experience (months)|29.78|24.88|-4.908|
||(30.15)|(23.93)|(4.23)|
|Perc of teacher with O-level|0.02|0.01|-0.011|
||(0.14)|(0.09)|(0.02)|
|Perc of teacher with A-level|0.22|0.24|0.023|
||(0.42)|(0.43)|(0.07)|
|Per of teacher with university|0.3|0.38|0.088|
||(0.46)|(0.49)|(0.07)|
|Per of teachers, other education|0.47|0.37|-0.1|
||(0.50)|(0.48)|(0.08)|
|Commute (in minutes)|30.18|28.49|-1.69|
||(27.47)|(26.84)|(4.54)|
|Teacher walk to schools (perc)|0.56|0.53|-0.036|
||(0.50)|(0.50)|(0.08)|
|Teacher has a plan for semester (perc)|0.32|0.33|0.007|
||(0.47)|(0.47)|(0.09)|
|Teachers has a lesson plan (perc)|0.24<br>(0.43)|0.2<br>(0.40)|-0.04<br>(0.08)|
|Salary (UGX; approx. 2500 UGX=1 US$)|123049.5|143865.6|20816.041**|
||(70244.63)|<br>(68420.92)|(12289.05)|
|Max. number of observations|106|121|227|
|Date||Dec 2010 / April|2011|
|Test for jointly significance||||
|Chi2|||11.664|
|Prob>Chi2|||0.473|

Note. This table presents the coefficient of the OLS regression of the treatment indicator (equal 1 if treated; 0 otherwise) against each of the listed baseline characteristics, as reported by teachers 20
**Table 3: Comparison of import customs duties in Tunisia and Libya**

||Import tax b<br>|urd<br>|en in Tunisia<sup>20</sup><br>|<br>|Import tax burd<br>|en in Libya<br>|<br>|<br>||
|---|---|---|---|---|---|---|---|---|---|
|Product|Custom D<br>Tunisia|uty|Custom<br>Service Fee|Other<br>(in<br>TND)|Custom Duty<br>Libya|Custom<br>Service Fee|Other<br>(in<br>equivalent<br>TND)|<br>Total Tunisia|Total Libya|
|Bananas|36%||3%|0.500/kg|5.25%|0.75%|100|39%<br>+<br>0.5<br>TND/kg|6% + TND 100|
|Apples|36%||3%|0.200/kg|5.25%|0.75%|100|39%<br>+<br>0.2<br>TND/kg|6% + TND 100|
|Cheese|36%||3%|1.200/kg|5.25%|0.75%|100|39%<br>+<br>1.2<br>TND/kg|6% + TND 100|
|Tea|36%||3%||5.25%|0.75%|100|39%|6% + TND 100|
|Roasted coffee|36%||3%||5.25%|0.75%|100|39%|6% + TND 100|
|Juice|36%||3%||5.25%|0.75%|100|39%|6% + TND 100|
|Chocolate|36%||3%|3.800/kg|5.25%|0.75%|100|39%<br>+<br>3.8<br>TND/kg|6% + TND 100|
|Tires|27%||3%||5.25%|0.75%|100|30%|6% + TND 100|
|Carpets|30%||3%||5.25%|0.75%|100|33%|6% + TND 100|
|Clothing|30%||3%||5.25%|0.75%|100|33%|6% + TND 100|
|Shoes|30%||3%||5.25%|0.75%|100|33%|6% + TND 100|
|Refrigerators|30%||3%||5.25%|0.75%|100|33%|6% + TND 100|
|Air conditioners|30%||3%|10/1,000 BTU|5.25%|0.75%|100|33% + 10/1,000<br>BTU|6% + TND 100|
|TVs|30%||3%||5.25%|0.75%|100|33%|6% + TND 100|

Sources: Authors' calculations, WTO, OTEXA

> 20 Flat fee of 50 TND per cargo are not included in the table since this is due to a local agreement.

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funds have to date flowed mostly in, not out, implying that some markets may have been subject to extrapolative price behavior; that is, high prices leading to more buying by investment funds, in turn leading to even higher prices, and so on. In fact, that may have been the case with the sovereign wealth funds, whose revenue comes from commodities, which in turn it is invested in commodities. Second, index funds invest on the basis of fixed weights or past performance cri‐ teria, and hence investment often behaves differently from what market funda‐ mentals would dictate. Third, the large size of these funds compared to commod‐ ity markets may exacerbate price movements. Or, as Soros (2008: 3) characteristi‐ cally put it “... the institutions are piling in on one side of the market and they have sufficient weight to unbalance it.” What conclusion can be derived from of all this in the context of excess li‐ quidity and speculation? Was investment fund activity at least partly responsible for the recent commodity boom? Any commodity‐related activity on the financial side is unlikely to alter long‐term price trends, which will ultimately be deter‐ mined by market fundamentals. But, such activities can induce higher price va‐ riability in the sense of exacerbating the length and the amplitude of price cycles, as they most likely did during the ‘perfect storm’ of 2007/08.

# **_Dietary changes and income growth in middle­income countries_**

Typically agricultural price booms are linked to supply shocks such as weather events or animal diseases that disturb normal production patterns. Supply shocks were no exception in the recent boom. Droughts played a major role in the reduction of dairy exports from New Zealand. Australia’s grain production was severely affected by the three droughts experienced during 2002/08 (a highly unusual weather pattern, often linked to global warming). The 2009 drought in South America affected the oilseeds market for more than a year. But the magni‐ tude of these events, although cumulative and coming after a long period of normal weather patterns, falls short of explaining the extent of the food price spike.

Could a demand shock offer a more plausible explanation, as has often been suggested? For this to have been the case, such a shock should have taken place either unexpectedly and suddenly or through a rapid shift in long‐term ex‐ pectations about food demand patterns. It has often been argued that a structural shift has taken place in the demand for grain by emerging countries, including China and India, and especially during the past decade when these two countries experienced high income growth. The June 2009 issue of _National Geographic_ , for example, noted that “... as countries like China and India prosper and their people move up the food ladder, demand for grains has increased.” Similar ar‐

**_Page 9_**
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# **1. Introduction**

The greatest damages from climate change are predicted to be in the agricultural sector in Sub-Saharan Africa. Agriculture is predicted to be especially vulnerable in this region because it already endures high heat and low precipitation, is a large fraction of the economy, and relies on relatively basic technologies (Pearce et al. 1996; McCarthy et al. 2001). Despite this dire prediction, relatively few economic studies have tried to quantify the damages to African agriculture using African data (Kurukulasuriya & Rosenthal 2003). What little information there is available from agronomic studies (such as Rosenzweig & Parry 1994) suggests that warming would have large effects. Other notable exceptions include some economic analyses of specific crops in regions within selected countries (Molua 2002; Gbetibouo & Hassan 2005; Deressa et al. 2005) and limited agronomic studies (such as Rosenzweig & Parry 1994). These studies suggest that warming would have large effects. But they do not have the scope of this study and cannot completely capture how farmers might respond to warming and thus what the overall economic impacts might be.

This study is based on a cooperative research effort among 11 African countries: Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, Kenya, Niger, Senegal, South Africa, Zambia, and Zimbabwe. Its purpose is to understand how climate affects current African farmers. Using empirical data about current farmers, the study intends to predict how climate change will be likely to affect future farmers in Africa. The sample of farmers was distributed across many different climate zones so that there would be a great deal of climate variation among the participants. After data cleaning, 9064 surveys of individual farmers were useable (see Table 1).

This study uses the Ricardian method to measure how climate affects net revenues. This method is a cross-sectional technique that measures what determines net revenues to farmers (Mendelsohn et al. 1994; Mendelsohn & Nordhaus 1996; Mendelsohn & Dinar 2003). It has been applied to selected countries in the low latitudes, namely Brazil and India (Sanghi 1998; Mendelsohn et al. 2001), using district level data, and Sri Lanka and Cameroon (Kurukulasuriya & Ajwad 2006; Molua 2002), using household level data, but never across a continent using household level data.

Section 2 briefly reviews the theory behind the Ricardian method and discusses its potential advantages and disadvantages and the empirical specification. Section 3 then discusses the 4
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# TABLE 3 (CCNT.), Page 3

SXJICES: Chocng Yorg Ahn, "Financing Local and Regional Development: The Case of the Republic of Korea," Paper presented to the Workshop on Financing Local and Regional Development (Nbgoya: UND, 1984).

Roy BEhl, "Intergovernental Crants in Bangladesh," Metropolitan Studies Proram Occasional Paper No. 87, The Maxwell Schoxl (Syracuse, NY: Syracuse University, May 1984).

Ved P. Gandhi, "Tax Assign¢ent and Revenue Shring in Brazil, India, Malaysia and Nigeria," in Tax Assi'ermets in Federal Countries, ed. by Charles McLAre, Centre fcr Researdh cn Federal Financial Relations (Canberra: Australian National University, 1983), pp. 328-359.

DELvid Greytak and Victcr Mendez, "'fl' Inpct of Intergpverwmsital Grants on Local Govwrnrents in Febudor: A Study of FCoAPAR," Metropolitan Studies Prc aam Occasional Paper No. 106, The Maxwell Schcol (Syracuse, NY: Syracuse University, September 1986).

M*,haned Zakri bin Abdul Rashid, "Structure and Utilization of Itergovwernntal Fiscal Trnsrfers in Malaysia," Paper presented to Meeting on Firnging Lnwcal and Regional Development (Nagpya: UN RD, 1986).

Larry Scroeder, Raral Develcpnant Grarts to Loal Govnts in Asia, Metropolitan Studies Prcuan M aiph No. 19, The Mawell Sdol (Syracu, NY: Syuse University, Mawch 1987).

Larry Sftoeder, "nglaesh: Urban Gover t Finance and Mariagenat Lmsues and Oppprtunities," Repcrt No. 5790-ED, Urban and Water Supply Division, South Asia Projects Department (Washington, DC: The Warld Pank, Jurie 1985).

_Larry_ Schroeder and Janmes WozWy, "Finacin Rrval **Local** Pandcayats in Nepal," Metropolitan Studies Pro8an Oocasional Paper No. 109, Ihs Maell School (Syracuse, NY: Syracuse University, August 1987).

Warld BEnk, "Financing Urban Services in India," South Asia Urban and Water Supply Depertment (mlmeo) (1984).

Warld Bank, "Financing Pakistan's Provinmial Services: Trends and Issuss," Soth Asia Promrams Depert2mnt (mineo) (1986).

Wcrld Bank, 'Mexico: Finaneing States and Municipalities, lhends, Issues and Reommendaticns," Mexico Prograns Division, Urban Projects Division, Latin America and the Caribbean Regional Office (ineo) (1987).
where c and co are variable costs with and without restrictions, respectively, P and Q are price and quantity with restrictions, β is the estimated coefficient for the impact of a change in the STRI.

# 3. Results and Caveats

## **3.1 Results**

The results for the 103 countries and 11 sectors of the Services Trade Restrictiveness Indices that we have calculated based on the World Bank database and the Australian methodology are presented in table 12. Our AVEs are presented in table 13. To assist with interpretation, in table 14, we present the STRIs and AVEs by sector, but aggregated to three levels of development. In table 15, we show our STRIs and AVEs by sector, aggregated by geographic region. Full details of the calculations are available online.<sup>22</sup> Focusing on high income countries, table 14 shows that professional services are among the sectors with the highest AVEs in high income countries. But high income countries have uniformly lower estimated AVEs than transition countries, developing countries or least developing countries.

From table 15, we see that fixed line telephone services in South Asia and especially in Sub-Saharan Africa stand out as the sector and regions with the highest AVEs. There are 13 countries with AVEs of 915 percent, with 12 of them in Sub-Saharan Africa. Given that our calculated STRIs in fixed line do not appear dramatically higher than in mobile telephone services, the very high AVEs in fixed line may appear puzzling. The explanation is that the AVE is calculated based on an estimate of the quantity impacts of the barriers. In particular, it depends on the number of subscribers per 100 inhabitants (see equations 3 and 4). Given much higher penetration of mobile telephone subscribers than fixed line in many developing countries, especially in many Sub-Saharan countries, we will often get much higher AVEs in fixed line for comparable STRIs in fixed line and mobile telephone services. State owned telecom monopolies traditionally have typically done an extremely poor job of providing telephone service in SubSaharan Africa. For example, in Kenya and Tanzania, which are two of the countries with an estimated 915 percent AVE in fixed line, there is less than one fixed line subscriber per 200

> 22 Links for the STRI calculations are provided above. For the ad valorem equivalent calculations, see the folder “Ad Valorem Equivalents Calculations” at https://drive.google.com/?pli=1#folders/0B0V-t-Bs4hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2

14
# **2. Baseline Analysis**

# **_Macroeconomic assumptions_**

The assumptions underlying the projections in this analysis of fiscal sustainability are in line with the IMF’s most recent review under the Extended Credit Facility (March 2013). The main macroeconomic assumptions for the baseline are shown in Table 1.

**Table 1. Baseline Projections: Macroeconomic Assumptions**

||Act<br>2011|Prelim<br>2012|Proj<br>2013|2014|2015|2016|2017|Av.<br>2013-17|
|---|---|---|---|---|---|---|---|---|
|Real GDP growth rate (%)|4.2|4.0|4.5|5.1|5.5|5.5|5.5|5.2|
|Inflation (%)|14.3|15.4|11.1|7.8|6.0|5.5|8.3|7.7|
|Nominal exchange rate (LCU/ USD)|1,261.1|1,440.6|1,596.6|1,622.1|1,637.0|1,653.6|1,641.6|1,630.2|
|Coffee price (US cents per lb)|273.2|187.6|158.6|171.9|177.9|158.0|158.0|164.9|
|Oil(US$/barrel)|104.0|105.0|102.7|98.5|94.7|91.9|89.4|95.4|

_Source_ : IMF (March 2013) and Joint World Bank–IMF Burundi Debt Sustainability Analysis (January 2013).

**The macroeconomic scenario reflects improved external conditions, with moderate recovery in global growth, continued easing of international food and fuel prices, and strong regional economic activities.** Real GDP growth is expected to accelerate over the fiveyear projection period starting in 2013—from 4.0 percent in 2012 to 4.5 percent 2013, 5.1 percent in 2014, and 5.5 percent in 2015–17. Agricultural output (35 percent of GDP) will continue to dominate economic performance. Recent developments in agro-industry, the construction of the hydroelectric dam (kabu 16) in 2013, and the construction of several road projects in the following years will sustain moderate growth in the secondary sector. Ongoing integration with the East African Community will support growth in wholesale and retail trade while also spurring investments in the tertiary sector more generally.<sup>8</sup> Annual average inflation is expected to fall from 15.4 percent in 2012 to 11.1 percent in 2013 and to an average of 7.7 percent thereafter. The nominal exchange rate is expected to depreciate against the US dollar at an annual average rate of 2.7 percent.<sup>9</sup> The coffee price is expected to bottom out in 2013 and then to fluctuate around the medium term average of 165¢ per pound. The oil price is expected to fall throughout the period, from US$105 per barrel in 2012 to US$89.4 per barrel in 2017.

# **_Fiscal policy_**

**The government is expected to continue implementing fiscal and structural reforms.** Tax revenue is assumed to grow at 5.2 percent a year on average in real terms for the next five

> 8 Background information provided by EIU, CPIA, and IMF.

> 9 Check this is consistent with the inflation projection—depreciation rate is too slow compared to inflation rate (assuming, in effect, 5 percent inflation in the US?)

7