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- McCarten, W. J. (2003): “The challenge of fiscal discipline in the Indian states,” _Fiscal decentralization and the challenge of hard budget constraints_ , 249–286.

- Minguez, R., F. Navarro, and J. Torre (2012): “Fund For Financing Payments to Suppliers,” .

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- ——— (2005b): “And the last shall be first: federalism and soft budget constraints in Germany,” _Unpublished paper. Department of Political Science, Massachusetts Institute of Technology_ .

- Schmitt-Grohe, S. and M. Uribe (2003): “Closing Small Open Economy Models,” _Journal of International Economics_ , 61, 163–185.

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- Sorribas-Navarro, P. (2011): “Bailouts in a Fiscal Federal System: Evidence from Spain,” _European Journal of Political Economy_ , 27, 154–170.

- Stehn, S. J. and A. Fedelino (2009): “Fiscal incentive effects of the German equalization system,” _IMF Working Papers_ , 1–29.

46
Abebe, Girum, Stefano Caria, Marcel Fafchamps, Paolo Falco, Simon Franklin, and Simon Quinn (2016). “Curse of Anonymity or Tyranny of Distance? The Impacts of Job Search Support in Urban Ethiopia” Mimeo.

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Bell, Brian, Richard Blundell, and John Van Reenen (1999) “Getting the Unemployed Back to Work: The Role of Targeted Wage Subsidies.” _International Tax and Public Finance_ , 6(3): 339–60.

Bertrand, Marianne and Bruno Crépon (2016) “Teaching Labor Laws: Results from a randomized control trial in South Africa”, Mimeo.

Bruhn, Miriam and David McKenzie (2009) “In Pursuit of Balance: Randomization in Practice in Development Field Experiments.” _American Economic Journal: Applied Economics_ , 1(4): 200-232.

Cohen, Alex (2016) “Constraints on Labor and Land and the Return to Microcredit and Microinsurance”, Mimeo.

De Mel, Suresh, David McKenzie, and Christopher Woodruff (2008). “Returns to Capital: Results from a Randomized Experiment.” _Quarterly Journal of Economics_ 123(4): 1329–72.

De Mel, Suresh, David McKenzie, and Christopher Woodruff (2009) “Are Women more Credit Constrained? Experimental Evidence on Gender and Microenterprise Returns.” _American Economic Journal: Applied Economics_ 1(3): 1-32.

De Mel, Suresh, David McKenzie, and Christopher Woodruff (2010) “Wage Subsidies for Microenterprises.” _American Economic Review Papers and Proceedings_ , 100(2): 614-18.

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29
**Table 4.2 Characteristics of Select Port Channels**

|**Country**|**Port**|**Entrance**<br>**Channel**<br>**Depth(m)**|**Entrance**<br>**Channel**<br>**Length(m)**|**Impediments**<br>**to Deepening**|**Impediments**<br>**to Port**<br>**Expansion**|
|---|---|---|---|---|---|
|Antigua and Barbuda|St. John’s|11|5,557|Yes|Yes|
|Dominica|Woodbridge Bay/Roseau|—|—|No|No|
|Grenada|St. George’s|9|1,300|No|No|
|St Kitts and Nevis|Basseterre|—|—|No|Yes|
|.|LongPoint|—|—|Yes|Yes|
|St. Lucia|Castries|13|1,100|Yes|Yes|
||Vieux Fort|9|—|No|Yes|
|St. Vincent and the|Campden Park/|||||
|Grenadines|Kingstown|—|1,112|No|Yes|

_Source:_ Authors’ own elaboration on primary data. _Note:_ m = meters. — Not available.

# **Because of inadequate maintenance and refurbishment, the infrastructure and equipment in most**

**ports—including the newer ports of Campden Park, Nevis, and Vieux Fort—are aging very quickly.** This problem may be observed both among mobile equipment and permanent structures. In Antigua the port has decaying surfaces. The pavement in the container storage yard has completely disappeared; it is now a dust surface that is becoming a health hazard for port employees and users. The ports of Long Point and St. John’s have silting problems that will require dredging. Long Point has experienced silting to the point that the depth is now critical, and container vessels that have not been able to first unload cargo in Antigua can no longer dock in the port. Basseterre needs to make substantive investments in the surface of its pier and in cranes. Reach stackers and straddle carriers are aging in Vieux Fort and St. George’s.

**Dominica’s Port of Roseau also requires heavy reinvestment in infrastructure** . The main pier, constructed in 1976, is supported over water using iron tube pilings that now show advanced rust and other damage from wear and tear, and hurricanes. The condition of the pier cannot support the weight of a mobile crane. Between 150 meters and 240 meters of essential pier length are in need of urgent repair to prevent collapse. Recent estimates put the repair costs at $12 million.

**Disruptions in both St. Lucia ports, due to aging mobile cranes, are taking a toll on trade.** A 2012 breakdown of the ports’ sole mobile crane (dating back from 1993 and now a discontinued model) resulted in a one-year outage. The shipping line CMA CGM transferred operations to Trinidad, while the volumes of Bernuth Lines (now King Ocean) dropped to one-fourth of previous levels. The port has had to absorb 50 percent of the extra labor costs incurred by the outage, along with 50 percent of the increased labor costs caused by a breakdown of the spreader used to lift containers with the crane. Before the breakdown of the crane, the port was experiencing the highest annual volume of throughput since 1996.

27
We also used ICW to develop two _Classroom Observation subscales_ . For each round, we partition the underlying variables into two groups according to their pre-specified domains. The first group includes variables related to _Structure, Engagement, and Supervision,_ while the second group includes variables related to _Literacy and Numeracy, Problem Solving, and Fine and Gross Motor Activities_ . We then use ICW to aggregate over the variables within each group, to generate two subscale scores that were used in Appendix Table 8.

Finally, we used _Principal Component Analysis_ (PCA) to identify the latent orthogonal factors underlying the classroom observation index. We run PCA separately by round using all available classroom observation variables at each round, and then extract the first two principal components. These principal components are then standardized per round (baseline, 18-, and 36-month follow-up) using the control group’s mean and standard deviation. Impacts on these principal components are also presented in Appendix Table 8.

64
# **Appendix A: Definition of Variables:**

# **Dependent Variable:**

Work: 1 if main occupation is work, zero otherwise

# **Explanatory Variables:**

**Individual characteristics:** Female: 1 if female, zero otherwise Age: Age Age Squared: Age squared Child of HHhead: 1 if child of household head, zero otherwise Disable: 1 if disabled, zero otherwise

# **Household characteristics:**

Socio economic group of household head dummies: 1 if as stated below,<sup>zero otherwise:</sup> ("public or semi-public sector employee" is reference group) Private Formal: private sector employee (formal) Private Informal: private sector employees (informal) Self-employed, Agr: own account worker (agriculture) Self-employed, Non-Agr: own account worker (non agriculture) Unemployed: unemployed or non active Other: other or unknown Owns land: 1 if household operates land, zero otherwise Owns Cattle: 1 if household owns cattle, zero otherwise Owns Sheep: 1 if household owns sheep, zero otherwise Quintile: household wealth quintile (households are weighted according to various poverty predictors, e.g.,<sup>how frequent they</sup> get meat to eat, whether the household uses toothpaste, etc - see Fofack<sup>(1998) for</sup> details).

# **Community characteristics:**

Urban location: 1 if urban community (1984 population>5000) Primary: nearest primary school (minutes) Secondary: nearest secondary school (minutes) 18
[Houthakker and Tobin (1952), Madden (1991), Huffman and Johnson (2000), Moschini and Rizzi (2007), Ramadan and Thomas (2011)].

The paper is organized as follows. Section 2 describes the data and issues related to the valuation of subsidized and rationed goods in the Iraqi context. Section 3 details the methodological approach of the Mixed Demand Model. Section 4 discusses estimations and results including welfare analysis, and Section 5 concludes.

# **2. Data and valuation of ration items**

The mixed demand model is estimated using the Iraq Household and Socio Economic Survey (IHSES) for 2012. IHSES covers roughly 25,000 households and is designed to be representative at the provincial or governorate level. The survey collects detailed data on all aspects of household income and expenditure and a wide variety of socio-economic indicators. These include household composition and demographic information for each individual member; housing; health; education; job search, past and current employment; wages, earnings and income; loans and assistance; consumption of food and non-food items; ownership of durable goods; time use, anthropometrics; access to justice; household shocks and coping strategies, migration; life satisfaction; and consumption and receipts of PDS or ration items.

There are 13 ration products: rice, brown wheat flour, white wheat flour, children's food, powdered milk, vegetable fat, vegetable oil, dry white beans, chick peas, lentils, sugar, salt and tea. These ration items differ in terms of their importance: for instance, 36% of total ration expenditure is spent on brown wheat flour compared to almost 0% for salt and tea. The other highly consumed ration products are: sugar (26% of total ration expenditure), vegetable oil (22% of ration expenditure) and rice (14% of ration expenditure) (Figure 3). Taken together, brown wheat flour, rice, vegetable oil and sugar represent almost 98% of total ration expenditures. Hence, our analysis focuses on the consumption of these four ration items and their free market counterparts.

Information about PDS items is collected in two separate modules: the rations module and a 7-day diary for food purchases. The former collects information about the quantity of ration items received, consumed, bartered, sold or given away by the household during the last 30 days. In the rations module, households are also asked how much they would pay in the open market to purchase each PDS or ration item. The diary records all purchases of food including ration items over the last 7 days (expenditures and quantities). To estimate a household’s consumption of PDS items, we follow the methodology for the construction of the official welfare aggregate for estimating poverty [World Bank (2013)]. Any purchases of PDS items recorded in the diary over the 7-day recall period are multiplied by a factor of 30/7 to obtain 30 day equivalents, and these quantities are added to the consumption of PDS items over the last 30 days as recorded in the rations module. In practice, purchases of PDS items (recorded in the diary) are few and far between.

4
**Table 6: Average leverage by sector, based on all identified data (%)**

||**Leverage 1 – M**|**arket based**|**Leverage 2 – Acc**|**ounting based**|
|---|---|---|---|---|
|**Year**|**Infrastructure**|**Services**|**Infrastructure**|**Services**|
|1991|26.2|37.7|21.6|53.5|
|1992|3.6|44.2|16.9|59.7|
|1993|19.3|31.2|37.2|48.1|
|1994|15.3|20.7|50.9|40.8|
|1995|25.4|33.4|41.6|47.8|
|1996|23.2|22.4|36.6|38.7|
|1997|38.1|62.7|36.5|53.0|
|1998|41.1|60.6|45.5|62.0|
|1999|41.1|54.7|31.4|67.6|
|2000|46.8|47.4|23.9|55.1|
|2001|36.5|57.3|18.9|46.5|
|2002|13.8|58.2|10.9|50.5|

Source: Datastream and authors’ calculations.

The key results for each sector can be summarized as follows.

- _Transport services_ —transport service providers in developing countries appear to be characterized by relatively high levels of leverage. When the market value of equity is used in the calculations, there is evidence of a gradual upward trend over the past decade. The average leverage(1) has increased from below 40% in 1991 to 58% in 2002. When leverage is measured by the book value of equity, the average leverage appears to fluctuate at around 50%, without any clear upward or downward trend. However, a dip is observed in this measure of leverage after 1999. This is not only high when compared to other sectors, it is also high compared to similar sectors in developed, countries where leverage rates, as defined, here tend to be around 30%.

- _Transport infrastructure_ —the increase in average leverage up to 2001 was more pronounced in this sector. When measured with leverage(1) in Figure 3.1, the average leverage has increased from close to 0% in 1992 to close to 40% in 2001. However, a significant decrease is observed from 2001 to 2002. However leverage(2) does not exhibit a similar upward trend in the mid–1990s. Although the average leverage(2) seemed to have increased from 20% to 50% between 1991 and 1994, this has been followed by a steady decline to 10% in 2002.

The geographical disaggregation for transport companies is less meaningful because of the smaller sample size. The main distinction that can be made is between Asian companies and companies from other parts of the world. This is reported in Table 7 below. For Asian companies, the trend in leverage has been broadly similar, regardless of whether market or book values of equity are used, although, pre-1996, the level of leverage(2) remained higher than that of leverage(1). Both leverage measures experience a substantial jump after 1996 and peak in 1998, but have been decreasing steadily since then.

10
Table 5 – Strong Predictors of Insurance Function Growth<sup>47</sup>

||Life|Papers|P&C|Papers|
|---|---|---|---|---|
|**Economic**<br>|||||
|Inflation|-|All relevant studies|+|Feyen et al|
|Income|+|All studies|+|All studies|
|Unemployment|-|Mantis and Farmer,<br>Outreville (1980),<br>Beenstock et al,<br>Lenten and Rulli|||
|Education|+|All relevant studies|+|All relevant studies|
|**Structural**|||||
|Population|+|Mantis and Farmer,<br>Feyen et al|+|Nakata and Sawada,<br>Millo and Carmeci,<br>Feyen et al|
|Population<br>density|+|Feyen et al|||
|**Social/**<br>**Cultural**|||||
|Muslim|-|All relevant studies|-|Feyen et al, Park and<br>Lamaire|
|Private v. gov’t<br>control|+|Feyen et al|+|Feyen et al|
|Monopoly|-|Outreville (1996)|-|Outreville (1990a,<br>1990b, 1992)|
|Openness|+|Arena, Curak, Avram<br>et al, Chen et al|||

47 Health insurance is largely policy driven and has not been modeled.

34
resonates with findings from other studies that report that food related issues explain little variation in malnutrition related indicators in Pakistan.

**Table 6: Weight-for-age Z-score (Pakistan)**

|||**Model 1**<br>|**- Without**<br>|**Controls**<br>||
|---|---|---|---|---|---|
|VARIABLES|OLS|Unco<br>|nditionalqu<br>|antile regr<br>|ession<br>|
|||10th|25th|50th|75th|
|Per capita kcal|-0.002|-0.017|0.023|0.024|-0.013|
||(0.033)|(0.054)|(0.043)|(0.050)|(0.049)|
|1-SSR|0.026|-0.059|-0.001|0.096*|0.056|
||(0.038)|(0.067)|(0.053)|(0.051)|(0.053)|
|1 - SSEXR|0.007|0.058|0.011|0.015|-0.010|
||(0.036)|(0.066)|(0.054)|(0.049)|(0.049)|
|HDDS|-0.035|-0.004|-0.035|0.047|-0.057|
||(0.036)|(0.067)|(0.052)|(0.048)|(0.049)|
|Per capita expenditure|-0.006|-0.080|-0.087|0.022|0.017|
||(0.042)|(0.070)|(0.055)|(0.056)|(0.057)|
|Food share|0.003|-0.049|0.037|-0.012|-0.009|
||(0.037)|(0.061)|(0.052)|(0.051)|(0.052)|

**Model 2 - With Controls**

|VARIABLES|OLS|Uncon|ditionalqu<br>|antile regre<br>|ssion<br>|
|---|---|---|---|---|---|
|||10th|25th|50th|75th|
|Per capita kcal|0.002|-0.038|-0.022|0.027|-0.009|
||(0.036)|(0.066)|(0.061)|(0.054)|(0.051)|
|1-SSR|-0.012|-0.150**|-0.058|0.061|0.064|
||(0.044)|(0.074)|(0.065)|(0.057)|(0.061)|
|1 - SSEXR|0.005|-0.011|-0.036|-0.025|0.020|
||(0.044)|(0.075)|(0.068)|(0.057)|(0.059)|
|HDDS|-0.054|-0.118|-0.080|0.002|-0.049|
||(0.046)|(0.077)|(0.069)|(0.059)|(0.062)|
|Per capita expenditure|-0.004|-0.119|-0.140*|-0.021|0.025|
||(0.052)|(0.088)|(0.076)|(0.065)|(0.067)|
|Food share|0.036|-0.038|0.063|0.047|0.038|
||(0.042)|(0.065)|(0.062)|(0.055)|(0.057)|

# **4.4 Tanzania**

For Tanzania, results for height-for-age and weight-for-age are presented in Tables 7 and 8, respectively. Looking at the OLS coefficients, it appears that in the parsimonious model, HDDS exhibits 24
- 28 -

Table 5 Tax Admunistraton in Trulstion Soci;at E3c9;lp

|Country|"Inverted|Centmalzed|
|---|---|---|
|Hungary||x|
|Poland||xi|
|Romania||x2|
|Bulgaria||x2<br>l|
|CSFR|x||
|Russia|XI||
|China|x|l|
|Vietnam|x||

# Notes:

1. In Poland, all national domesic taxes, and some local taxes and fes are colUected by treauy offices and then transferred to state and local budgets. Other local taxes and fees are paid directly to Gmias.

2. In Romania and Bulgaia, the local goverments do not have their own administrton. All local taxes are collected by local branches of the MOF.

3. The State Tax Service is in the proce of centalifzing.

100. liDcations for The Lonuer Tern Intergovemmenbl Svstom Although intergovernmental fiscal reform _is_ necessuily takig place in this strned macroconomic context and hence inevitably reflects changing short-term fiscal needs and psures, care must be taken to prevent the stabilition objective from fully dominating the redesign of the intergovemmental finances megime. A major challenge facing the trmnsitional economies is to develop an intergovernmental fiscal system which is an optimal combination of mles ad di - one dbt wiU be both flexible enough to be compatible with macroeconomc stabdilization and the major structural shifts which are taking place in the economy, and at the sam time provide a stable framework for the effective operion of both central and sub-national govemments in the lcnger termL 101. One solution along these lines that has been adopted in some market economies, for example, pivots around a system of intergovernmental transfers in which the totl is flexible with cbanging macreconomic circumstnces - for example, some percengta of total central taxe - while the distribution of this total among diffent subnational govemments is Oformula-driven", with the amount received by different localities depending on such factors as potential "fiscal capacity' and **assessed** need. This approach repreents a compromise, in which the center gives up some degree of control over its revenue but also insulate itself from ad hoc and possibly escalating demands from localities, while localities avoid discretionary cutbacks in local trasfers to meet stabilizaion objectives, with all the expenditure dislocations that this implies. In most of the countries under review, thus regularizing the overall size and distribution of the tanmsfer, like strengthening local finances by broadening the local tax base to improve local tax yields, represent high-priority reforms. This point is developed further below.
Overall, these firm-level results are consistent with tariff evasion through under-reporting of unit prices by connected firms.

How costly was tariff evasion by connected firms? Accurately quantifying the costs of tariff evasion is challenging, but one admittedly crude yet conservative method to answer this question is to assume that unit price differentials between connected firms and the median import price reported by other private sector firms importing the same product from the same country at the same time are due to evasion. By multiplying that price differential by the quantity imported by connected firms and the tariff rate one can arrive at an estimate of the _additional_ tax loss associated with being connected (private firms on average evade; this measure allows us to assess how much more connected firms evaded than other firms). Doing this, and summing over all import transactions by connected firms in our database for which tariff data exists and for which there is at least one counterpart declaration by a private firms suggests that in 2009 alone, connected firms evaded approximately 217 million US dollars’ worth of taxes _more_ than other firms would have. Over the period 2002-2009, they cumulatively evaded 1.2 billion US dollars’ worth of taxes more than other private firms would have. These estimates are very conservative. If instead of using median prices reported by other firms, we use the average price reported by other firms, the number rises to 2.6 billion USD. Also, this price discrepancy can only be calculated when tariff data are available and when at least one private sector firm is importing the product at the same time as Ben Ali firms; such observations account for less than half the value of all import transactions reported by connected firms between 2000 and 2009. Most importantly, this method only considers underreporting of prices, not other types of tax fraud; if Ben Ali firms were able to grant themselves exemptions, then they would not need to underreport, in which case they are likely to report higher prices than non29