rafmacalaba/datause-encoder-data huggingface dataset:
# **Fiscal Management** in Federal Democracies: Argentina and Brazil

## William Dillinger and Steven B. Webb World Bank

Decentralizing public finances has many advantages but also raises potential problems, especially in the area of fiscal managemnent. One problem arises mainly during and soon after the transition to fiscal decentralization, when the central govenmnent, unless it reduces spending or increases its own tax resources, tends to have higher deficits as it shifts fiscal resources to subnational govemments, via transfers, revenue sharing, or delegation of tax bases. Reducing spending is hard not only because cuts are always hard but also because subnational govemments might not take on the tasks anticipated, leaving the central govemment with a legal or political obligation to continue spending for certain services. The second problem arises after decentralization, when the local or state government faces popular pressures to spend more and tax less, creating the tendency to run deficits. This universal tendency can be especially problematic if subnational governments and their creditors expect or rely on central govemrnment bailouts. Econometric evidence from 32 large industrial and developing countries indicates that higher subnational spending and deficits lead to greater deficits atthe national level (Fornasari, Webb, and Zou 1998; see also Treisman 1998).

This report investigates how these problems arose in Argentina and Brazil as they sought to decentralize in the 1980s and how each country dealt with them. We seek to understand the institutional arrangements-fiscal and political-that contributed to success or failure.

Argentina and Brazil have two of the most decentralized public sectors in Latin America and, along with Colombia and India, are the most decentralize **d** emocracies in the developing world. Subnational governments (SNGs) account for about half of public spending and are vigorous democracies in most jurisdictions, especially the largest. In both countries in the 1980s, the return to democracy revived and strengthened long-standing federal practices while weakening macroeconomic performance, resulting in unsustainable fiscal deficits, high inflation, sometimes hyperinflation, and low or negative growth. See table 1. The occasional stabilization plans each failed within a few years. Then in the 1990s-Argentina in 1991 and Brazil in 1994-both countries introduced stabilization plans that have had some success.' While national-level issues played the largest role first in preventing and then in bringing about macroeconomic stabilization, intergovernmental fiscal relations and the fiscal management of subnational governments were influential as well. State deficits and federal transfers often were out of control in the 1980s, contributing to the overall macroeconomic problems of both countries. Similarly, stabilization programs in the 1990s have sought to establish control and self-control over subnational spending and borrowing.

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EApop�� P�EA���� ∑���� EApop�� _PEA_ (1 1)+ _PEA_ (2 1)+ _PEA_ (3 1)+ ....+ _PEA_ (n j)= 1 for each stratum within a pre-war region P�EA��� is a probability of selection for an EA in a specific stratum (Urban or rural) and pre-war regions (e.g. rural Bari), _i_ is an EA number, _j_ is the regional stratum type. EApop�� is the population within an EA in a specific regional stratum, EApop�� is the sum of EAs’ population within a regional stratum, _n_ is the number of EAs within a regional stratum.

# **2.7 Comparison between Manual PESS urban EAs and our automated approach Urban EAs**

In UNFPA’s 2014 PESS, no Rural EAs were created, and the urban EAs were manually digitized based on high-resolution Google Earth Imagery. We compared the results of our automated methodology against those manually digitized EAs in urban areas in both Mogadishu and Hargeisa cities. Since the boundary of PESS urban EAs and their household size are confidential, we were unable to publish a complete comparison. Instead, we compared the distribution of population size and EA area between automatically and manually generated Rural EAs. The population and area were also computed for PESS urban EAs based on our high-resolution gridded population data sets and the populations and area of EAs within Hargeisa and Mogadishu cities were compared.

# **3. Results: Somalia case study**

# **3.1 Rural EAs**

# **3.1.1 Description of results from our split and merge algorithm**

A total of 253,833 polygons were created in Somalia rural areas after the splitting process. After applying the region merging technique in a rural area, 113,367 EAs were created, with population size ranging from 0 to 750 and a maximum area of 9 km2 (Figure 3a). In addition, the probability of EA selection proportional to the population size and area were also computed for each EA. Furthermore, the probabilities of selection were summed up in each regional stratum type and is equal to 1 (Figure 3b).

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**| PoHcy Research**

# **Education and Employment** WPS 862

This paper -a product of the Education and Employment<sup>Division, Population and Human Resources</sup> - Department is part of a larger effort in the department<sup>to build a knowledge base on higher education</sup> issues and reforms. Copies of the paper<sup>are available frec from the World Bank, 1818 H Street NW,</sup> Washington, DC 20433. Please contact<sup>Jcgajothy Vythilingam, room S6-220, extension 33677 (40 pages).</sup> February 1992.

Egypt's policy on higher education, Richards argues, must take account of the realities<sup>of</sup> declining government budgets and employment and increasing reliance on the private sector, which must become more competitive intemationally. Education in Egypt must increase Egyptians' ability to cope with economic disequilibria: to respond quickly and effectively to changing technological and market opportunities.

The Government of Egypt's strategy<sup>for</sup> achieving this goal is to stabilize the number<sup>of</sup> university students and raise the quality<sup>of</sup> instruction. This fundamentally sound strategy, pursued since the mid-1980s, has required considerable courage of policymakers.

Policymakers are struggling to correct a longtime, inequitable misallocation<sup>of educa-</sup> tional resources - including an overenrolled university system combined with a persistently high rate of illiteracy.

The Nasser regime greatly expanded higher education and guaranteed jobs to university graduates. As a result of rapidly growing<sup>enroll-</sup> ment in the 1970s and 1980s, the quality of education deteriorated seriously. Classes<sup>are too</sup> big and resources are scarce for anything<sup>but</sup> professorial salaries (which remain low),<sup>so</sup> learning amounts to little more than memorization and repetition. The system does not foster the development of synthesizing, problemsolving, or creative thinking abilities. And with tertiary institutions overenrolled, academic success requires the use of tutors, whose fees<sup>are</sup> beyond the reach of students of modest<sup>means.</sup> The Government is trying to improve conditions by stabilizing university enrollments, expanding the role of two- and four-year technical institutes, increasing the use of pedagogical materials in university instruction, and promoting several innovative interdisciplinary<sup>programs</sup> that stress problem-solving and applied work.

The job guarantee has effectively been suspended since the early 1980s, but<sup>students</sup> have responded slowly to changing signals<sup>from</sup> the labor market, because being a university graduate is socially prestigious and presumed<sup>to</sup> increase one's chances of marrying well.

ThePolicy Research Working PaperSeriesdisseminates<sup>thefindingsofwork under way in theBank. Anobjectiveoftlh series</sup> is to get these fimdings out quickly, even if<sup>presentations are less than fully polished. The findings, interpretations, and</sup> conclusions in these papers do not necessarily represent<sup>official Bank policy.</sup> Produced by the Policy Research Dissemination<sup>Center</sup>
Figure 5. Country Cases: Intensifying Severity (Rows Ranked by Medium, Lower and Low Peak Severity) <!-- Start of picture text --> Brazil Chile Mexico Peru<br>50<br>40<br>30<br>20<br>1<br>1 1<br>10 1<br>0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Bolivia Colombia Russian Federation Saudi Arabia<br>10.0<br>2<br>7.5<br>3<br>5<br>5.0<br>4<br>2.5<br>0.0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Bangladesh Egypt, Arab Rep. India Iraq<br>2.5<br>2.0<br>13<br>1.5 20 16<br>1.0 20<br>0.5<br>0.0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Nigeria Pakistan Sudan South Africa<br>2.5<br>2.0 13 11 12<br>1.5<br>20<br>1.0<br>0.5<br>0.0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization. Note: The daily severity ratio is the daily equivalent of the cumulative severity ratio. Horizontal line shows nth cause of death above or around the maximum daily severity ratio. Cut-off date for all data is May 23.

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underlying obligation and market factors, such as liquidity and the efficiency in executing transactions. Ideally, these elements should be disentangled. CDS spreads reflect the cost of insuring that a particular firm or institution defaults on its bond or loan payments and therefore reflect the market’s perception on credit risk. Bid-ask spreads denote the price difference between a quote of a market maker for immediate sale (bid) and an immediate purchase (ask). The level of the bid-ask spread is a common measure of the liquidity of the market. In developing countries, the availability of CDS spreads for individual companies<sup>11</sup> may be subject to limitations, while the accuracy of the bid-ask spread as an indicator for market liquidity depends on the efficiency of financial markets.<sup>12</sup>

- _Volatility indices_ **.** Volatility indices are common indicators of the general level of risk aversion in markets. Most volatility indices calculate implied volatility on the basis of option prices. The Chicago Board Options Exchange Volatility Index (VIX index; also known as the _fear index_ ) represents a measure of the market's expectation of volatility over the next thirty-day period.

- _Market turnover data._ These constitute a basic indicator of overall liquidity conditions for a particular financial asset or asset class.

- _Risk mitigants._ These include safeguards in the market: both legal (collateral, guarantees, netting) and institutional (central counterparty, regulation/supervision).

# **_The Real Economy_**

- _Financial losses for households and nonfinancial corporations_ : Relevant indicators include the amount of uninsured deposits and possible shortfalls in the deposit insurance fund (which may need to be borne by taxpayers, depositors, and others). The expected pay-out time for the deposit fund is also relevant, as negative wealth effects may be amplified in case depositors lose access to their accounts for a prolonged period. In case of a crisis involving financial markets, households and nonfinancial corporations may be facing losses due to ownership of financial assets. This includes not only direct ownership but also indirect ownership: for example, through participation in life insurance and pension funds.

- _Restricted access to financial services_ : Relevant indicators include sectoral and regional lending concentrations of banks. The banking system’s capital adequacy ratio may also be taken in consideration, as this gives a sense about the financial capacity of banks to provide the real economy with loans.

- _Consumer and business confidence indicators._ These may give a notion about the risk that the financial disturbance causes economic agents to reappraise the economic outlook, with postponements of consumption and investment expenditure. In this

> 11 Sovereign spreads are usually available.

> 12 In inefficient financial markets, high bid-ask spreads may reflect high transaction costs rather than liquidity pressures. This should already be visible in non-crisis times.

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preference, it would be captured by _ηcFirstGirli_ , _ηsFirstGirli_ , and _ηtFirstGirli_ .

Given the fixed effects included in specification (1), our coefficients of interest are identified from (a) changes in expected dowry payments within state-caste groups over time, (b) differences in expected dowry payments across castes within a state-year, and (c) differences in expected dowry payments across states within a caste-year. The literature on dowry variation in India suggests a few potential sources of such variation. During our study period (1986-2007), the states of Andhra Pradesh (1986), Tamil Nadu (1989), Maharashtra (1994), and Karnataka (1994) amended the laws governing female inheritance rights for Hindus, Buddhists, Jains, and Sikhs, thereby generating variation in bequest-motive for dowry by state-year of birth-religion. Roy (2015) has shown that these amendments affected dowry payments. Another potential source of variation is macroeconomic changes such as those brought about by trade liberalization. Using an older (1999) round of REDS, Chakraborty (2015) has shown that tariff cuts resulting from the 1991 trade reforms in India also affected dowries differentially across districts and castes/ religions via effects of tariff cuts on within-caste wealth inequality across districts, consistent with the theory proposed by Anderson (2003). Chiplunkar and Weaver (2017), also using the 1999 REDS data, demonstrate that the dowry variation in India during 1975-1999 can be explained by greater differentiation in groom quality over time and marriage market competition within state-caste-religion for more educated grooms; this explanation analyzes dowry as groom-price. However, given the small size of our estimation sample, we do not explicitly model or utilize the dowry variation generated by these channels.

Any remaining threats to identification of the coefficient of interest, _β_ 1, as the causal effect stem from omitted caste-state-year specific factors that may be correlated with _Dowrycst_ and that differentially affect FG and FB parents. To address the former concern, we replace the main effect of _Dowrycst_ with caste _×_ state _×_ year fixed effects in specification (1) to estimate an even stricter specification that non-parametrically controls for everything that varies at the caste-state-year level and is correlated with household savings.

# **4 Effect on Savings**

In Table 3 we present results from equations (1) and (2) that estimate the impact of expected future dowry payments on parents’ current saving behavior. We expand the definition of the savings variable as we move from column (1) to column (6). In the first two columns, the outcome is annual flow of per capita saving in financial institutions; in the next two columns we add annual per capita flow of cash saving to the first outcome; and in the last two columns we add the annual per capita flow of interest earned by the household on its savings to the second outcome. Note that the expected dowry variable, unless otherwise mentioned, is in INR 10,000.

The coefficient of _Firstborn girl_ is negative and always insignificant, implying that, in the 12
Policy Research Working Paper 5560

# **Abstract**

This paper investigates the potential of and constraints to a rice Green Revolution in Sub-Saharan Africa’s large-scale irrigation schemes, using data from Uganda, Mozambique, Burkina Faso, Mali, Niger, and Senegal. The authors find that adequate irrigation, chemical fertilizer, and labor inputs are the key to high productivity. Chemical fertilizer is expensive in Uganda and Mozambique and is barely used. This is aggravated when water access is limited because of the complementarities between fertilizer and irrigation. Meanwhile, in the schemes located in four countries in West Africa’s Sahel region, where water access is generally good and institutional support for chemical fertilizer exists, rice farmers achieve attractive yields. Some countries’ wage rate is high and thus mechanization could be one solution for this constraint. Improvement of credit access also facilitates the purchase of expensive fertilizer or the employment of hired labor.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The corresponding author may be contacted at k.kajisa@cgiar.org. This study was supported by the Knowledge for Change Program, under the project title, “An African Green Revolution: Finding Ways to Boost Productivity.” The authors thank Derek Byerlee, Peter Hazell, Donald F. Larson and participants at a Nairobi workshop in April 2010 for suggested improvements to an earlier draft.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
First, consider employment. We see that for firms with 5 to 9 workers, the standard deviation of employment is about one-third of the mean. This coefficient of variation (the standard deviation divided by the mean), increases to 0.72 on average for firms with 10 to 100 workers and 1.25 on average for firms with more than 100 workers. The heterogeneity is much larger with regard to revenues – on average the cross-sectional standard deviation is 3.1 times the mean for firms with 5-9 workers, and 4.2 times the mean for firms with 10 to 100 workers. The potential for a large outlier to increase this dramatically is seen in a couple of cases where the coefficient of variation exceeds 10. Finally, only a minority of firms export, with quite a lot of variation across countries in this percentage.

# **2.3 Firms participating in SME projects are typically also relatively small in number and very heterogeneous.**

Last, it is useful to get a sense of the number of firms actually involved in major policy efforts to actively benefit firms in Africa, and what these policy efforts are. A typical World Bank loan to enhance private sector competitiveness in Africa has two main elements: a component desired to improve the business climate and reduce regulatory burden and a component which actively tries to enhance the productivity of firms through policies which work at the firm level. One of the most common forms of actively helping firms is through matching grant programs, whereby the government reimburses firms for 50 percent of the cost of business services like hiring a consultant, launching a marketing campaign, training workers, or attending a trade fair. The typical justification for such subsidies is a belief that firms underinvest in these services because of externalities to other firms: for example, firms might hesitate to train workers if there is a chance that the workers will then leave and start their own firms or go to work for competitors. A few of these programs focus solely on the export sector, with the aim of getting firms to diversify into other markets or other products, but most of these matching grant programs are open to a wide variety of sectors.

Table 3 uses data from World Bank project completion reports to highlight the number of firms participating in these projects. A typical project seems to involve giving out matching grants totaling $1-5 million to between 100 and 500 firms, and has a duration of around 5 years. Projects focusing on exports have fewer firms: with a range of 13 to 149 firms seen in the data. Within some of these programs there can be considerable heterogeneity in both the size of the 5
gional convergence. This result also holds when examining the regional convergence in the major sectors separately. We also see in column (2) that states with an initial high prevalence of poverty rate have experienced relatively higher productivity; this effect is considerably stronger in the agricultural sector suggesting the prevalence of poverty in agriculture-intensive regions. Note that the convergence rate in the services sector is about half when initial poverty is accounted for relative to the estimates when the industry and state fixed effects are included. A feature worth mentioning is the fact that after conditioning on departmental poverty rates or fixed effects, convergence appears to be significant and faster when the data is disaggregated by industries. This suggests that productivity convergence would have been even faster if industries were more “similar”.

Table 5: Growth of aggregate labor productivity, 2002-18

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Initial<br>labor<br>productivity|-0.024<sup>_∗∗∗_</sup>|-0.024<sup>_∗∗∗_</sup>|-0.023<sup>_∗∗∗_</sup>|-0.024<sup>_∗∗∗_</sup>|-0.023<sup>_∗∗∗_</sup>|-0.024<sup>_∗∗∗_</sup>|
|(log)|(0.0012)|(0.0012)|(0.0012)|(0.0012)|(0.0011)|(0.0012)|
|Initial poverty rate|-0.0006<sup>_∗∗∗_</sup><br>(0.0001)||||||
|Initial years of schooling (tot.||0.003<sup>_∗∗∗_</sup>|||||
|pop.)||(0.0007)|||||
|Initial working age popula-|||0.000||||
|tion (% of tot. pop.)|||(0.0002)||||
|Initial government tax col-||||0.004<sup>_∗∗∗_</sup>|||
|lected per capita (log)||||(0.0009)|||
|Initial HHs with access to|||||0.0002<sup>_∗∗_</sup>||
|electricity network (% total<br>HHs)|||||(0.0000)||
|Initial HHs with landline or||||||0.0002<sup>_∗∗∗_</sup>|
|mobile telephone (% of total<br>HHs)||||||(0.0000)|
|Constant|0.165<sup>_∗∗∗_</sup>|0.144<sup>_∗∗∗_</sup>|0.151<sup>_∗∗∗_</sup>|0.138<sup>_∗∗∗_</sup>|0.136<sup>_∗∗∗_</sup>|0.152<sup>_∗∗∗_</sup>|
||(0.0075)|(0.0068)|(0.0115)|(0.0071)|(0.0100)|(0.0068)|
|Industry FEs|No|No|No|No|No|No|
|<br>State FEs|No|No|No|No|No|No|
|Obs.|2,266|2,266|2,266|2,266|2,266|2,266|
|R<sup>2</sup>|0.306|0.301|0.294|0.301|0.296|0.302|

footnote: World Bank staff using RAIS. Note: Robust standard errors parentheses.<sup>_∗∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗_</sup> _p <_ 0 _._ 1.

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