rafmacalaba/datause-encoder-data huggingface dataset:
- 56 -

# Box 3: CASE STUDY: SUBNATIONAL FINANCES IN THE NORTHEAST REGION OF BRAZIL

The following Indlcators were used by Rezende to analyze the financial situation of 9 States and 90 Municipalitles of the Northeastern Region of Brazil over the period 1979-1984:

- RC: Resource Commitment: Current Expenditures + Interest Payments + Amortization/Total Recelpts

- Borrowing

- ROR: Rellance on Own Revenues: Own Revenues/Total Receipts - Borrowing IER: Investment/Expenditure Ratio: Investments/Total Expenditures PCI: Per Capita Investment: Investment/Total Resident Population

- SIR: Savings/investment Ratio: Total Receipts - Borrowing - Current Expenditures - Interest - Amortization/investment

- DTT: Dependence on Total Transfers: Total Transfers/Total Recelpts - Borrowing DET: Dependence on Earmarked Transfers: Earmarked Transfers/Total Receipts - Borrowing OUT: Dependence on Untied Transfers: Untied Transfers/Total Receipts - Borrowing ONT: Dependence on Negotiated Transfers: Negotiated Transfers/Total Receipts - Borrowing DST: Dependence on State Transfers: State Transfers/Total Receipts - Borrowing (Municipalities Only)

In 1983 the situatlon of the 9 States was as shown below:

|States|RC|ROR|<br>IER|PCI|SIR|DTT|DET|DUT|DNT|DST|
|---|---|---|---|---|---|---|---|---|---|---|
|Plaul|0.56|0.29|0.22|24,769|1.82|0.71|0.05|0.27|0.38|.28|
|Maraihao|<br>0.46|0.30|0.22|13,171|2.67|0.64|0.08|0.39|0.17|.32|
|Sergipe|<br>0.38|0.47|0.24|43,510|2.86|0.53|0.03|0.25|0.26|.32|
|R.G. Norte|0.57|0.49|0.13|15,157|3.12|0.51|0.06|0.28|0.17|.41|
|Paraiba|0.69|0.52|0.11|13,394|2.23|0.48|0.03|0.29|0.15|.50|
|Ceara|0.76|0.62|0.01|11,201|15.65|0.38|0.02|0.28|0.08|.57|
|Pernambuco|0.48|0.68|0.13|16,083|4.23|0.32|0.04|0.15|0.13|.64|
|Alagoas|0.56|0.70|0.03|5,113|12,08|0.30|0.03|0.22|0.05|.66|
|Bahla|0.48|0.73|0.08|14,897|5.11|0.27|0.05|0.14|0.08|.63|

Observations:

- (a) only Bahli, Alagoas, and Pernambuco can meet their resource commitments with Internally generated revenues, as shown by the difference RC-ROR.

- (b) some States are very dependent upon transfers, with negotiated (discretionary) transfers representing a very high proportion of the total, followed by mandatory transfers. Earmarked transfers represent onJy a minor contribution everywhere.

- (c) States receiving more transfers Invest the most In per capita terms, Irrespective of their poor coverage of recurrent expenditures through Internally generated revenues.

- (d) tax receipts are highest In the more developed States (Bahia, Pernambuco, Alagoas) because of the greater yields from the value added tax (ICM). Non-tax receipts supply little Income, except In Sergipe and Rio Grande do Norte.
(World Value Surveys, 1981-2009). Moreover, identification of firm responses is facilitated by the unexpected and ―quasi-experimental‖ nature of the crisis and its severity, the high flexibility of Indonesian labor markets at the inception of the crisis, and the richness of the Indonesian manufacturing census dataset that not only provides detailed information on employer characteristics, but also gender disaggregated employment information by broad occupational categories (e.g white- and blue-collar workers).

The paper is organized as follows. The next section reviews the related literature and provides background information on the Indonesian crisis. The data are discussed in Section 3, which also provides a static overview of sorting patterns by gender and a bird‘s eye view of employment and wage adjustment during the crisis. Section 4 assesses sorting effects by examining whether firms that employed proportionately more women were more likely to exit and by analyzing how, among continuing firms, employment growth was correlated with the initial gender composition of the workforce. Next, Section 5 examines the evidence for differential treatment by assessing whether women were proportionately more likely to be fired than men in firms that adjusted the size of their workforce. It not only assesses whether men and women were treated differently at the firm-level, but also whether the crisis led to a change in such discriminatory practices/differential treatment of men and women within firms.<sup>4</sup> We therefore test how employment adjustment and wage setting during the crisis differed from non-crisis periods. Section 6 examines the evolution of the gender wage differences to assess whether women suffered more severe wage cuts than men, and the extent to which there is gender-specificity in employment-wage tradeoffs. Conclusions are presented in Section 7.

The appendix validates our firm-level analysis with an analysis of the evolution of wages and employment using data on manufacturing employees from SAKERNAS, a nationally representative labor force survey in Indonesia. We cannot tell from this labor force survey the characteristics of the employer, but the labor force data enable us to control for individual characteristics and the comparison with the firm-level data highlights the extent to which the patterns for the larger formal manufacturing sector were mirrored in the broader population of manufacturing workers.

> 4 For example, if women are always more likely to be laid off when a firm is forced to downsize, one might also expect them to be more likely to be laid off in times of crisis. Alternatively, it could be the case that women are only discriminated against during times of extreme economic stress, but not in less turbulent times. An interesting question in this regard is whether women suffer disproportionately during the crisis relative to other times. We address this by examining how differential treatment during the crisis differed from differential treatment during non-crisis times.

4
<!-- Start of picture text --> Inter-state migrants and per capita NSDP in 2011 by state<br>22 ‘e Uttar Pradesh<br>=<br>2<br>a @Bibar<br>eiE<br>3°<br>cibl ® Madhy sinsthen al @ Uttarakhand<br>Ss<br>Ge3<br>*<br>o 50000 100000 150000<br>Rupees<br><!-- End of picture text --> <!-- Start of picture text --> Intra-state migrants and per capita NSDP in 2011 by state<br>4<br>22 @ Uttar Pradesh<br>a =<br># Madhya Pradesh<br>eEN @Kamataka<br>a @ Rajasthan<br>3eS @ Bihar HESee acOD a RERSNE<br>S eonssa ory " acl<br>ES @ Assam om FRA M ona Del<br>9 50000 100000 150000<br>Rupees<br><!-- End of picture text -->
- 25 -

# **V. DISCUSSION**

# **_Hydrological forecasts_**

What does the above mean from a hydropower project perspective? First, it means that future hydrological series may be different from what one envisages at the project preparation stage. In ex ante assessing a hydropower project, it is broadly common that the 90 percent dependable hydrological level—which is referred to as a baseline hereinafter—is used as a forecast of water flow available in the future. It is a very conservative approach, which is high-principled for cautious project preparation purposes. However, it is worth recalling that this is just one of the univariate nonparametric point estimates, leaving most hydrological information unused.<sup>8</sup> As shown in Figure 8, the hydrological forecasts in 2025 based on our empirical results in fact look very different from data in the conventional 90 percent dependable year. The figure includes two types of forecasts: One is the dynamic forecasts, which are calibrated from the last observation in the sample, following the estimated system of equations. The other is onestep-ahead projections, which are calculated by fitting a set of values for the estimated equations to obtain the predicted values in the next period. In this regard the IPCC forecasts presented in Table 2 are adopted as a reference point.<sup>9</sup> In both Vishnugad Pipalkoti and Thac Mo cases, the rainy season would have higher levels of water than the baselines.<sup>10</sup> However, in the lean season water resources may become even more limited. For the Upper Kotmale project, the river would have a greater flow of water almost all the year around. Our dynamic forecasts look more prone to be greater than the one-step-ahead projections. The reason is that the long-run calibration is very sensitive to

> 8 It is a separate question whether the past hydrological time series contain useful information.

> 9 The dynamic forecasts tend to have much large standard errors, due to the nature of the estimation method. Since we are computing the 240-period-ahead projections, the standard errors are amplified quickly.

> 10 Again, the baseline is a conservative assumption, which is the 10th percentile estimate. On the other hand, our dynamic estimate is, roughly speaking, calculated on a mean basis, as usual.
<!-- Start of picture text --> Figure 11(c): Vietnam: Return to skill by sector over time (Tertiary and<br>above)<br>1<br>0.8<br>0.6<br>0.4<br>Agriculture/Minin<br>0.2 g<br>Manufacturing<br>0<br>Trade/Services<br>-0.2 1992 1998 2004 2006<br>-0.4<br>Return<br><!-- End of picture text -->

**Year**

**Figure 11(d): Vietnam: Proportion of skilled workers by sector over time (Tertiary and above)**

<!-- Start of picture text --> 25<br>20<br>15 Agriculture/Mining<br>10 Manufacturing<br>Trade/Services<br>5<br>0<br>1990 1998 2004 2006<br>Year<br>% skilled<br><!-- End of picture text --> 52
while capital incomes and rentiers have not disappeared from the top. This asymmetry is not found at the top of the distribution (top 5% and upwards). Our results are robust to treating negative incomes and capital gains dierently, and to how we allocate income from self-employment and closely-held corporations to capital and labor.

We examine the association in two ways. We begin by decomposing the top 1% income share by factor incomes, which is frequently done with standard inequality measures, but has not been applied to top income shares. The inequality in total income is decomposed into the labor share in total income, the top 1% share within the distributions of capital and labor incomes, and the alignment coecient, which captures the association. The alignment coecient, like the Pearson correlation coecient, is aected by monotone transformations in the marginal distributions. In the second part of the paper, we thus use a rank-based measure of association that is more general and invariant to such transformations. Specifically, we analyze the association matrices between labor and capital, which are a discrete approximation to the copula density and equivalent to transition matrices in the study of economic mobility. The literature on mobility also provides us a test of increasing association based on cumulative association matrices.

This remainder of the paper is structured as follows. Section 1 describes the data. In Section 2, we develop and estimate a decomposition of top income shares by factor incomes. Section 3 reports the results from the association matrices. Section 4 concludes and discusses how the changes in the association may be related to top marginal tax rates. The Appendix provides additional results and several robustness checks.<sup>1</sup>

# 1 The data

We use the Statistics of Income public use les (PUF) by the US Internal Revenue Service over the period 1962 to 2006, which are (nearly) annual public-use samples based on federal

> 1A discussion of our US results after the mid-1990s in comparison with Norway can be found in an unpublished note by Aaberge et al. (2017).

4
26 project provinces and 12 being health support project provinces. The DHS thus covers 30 project provinces and 11 nonproject provinces.<sup>16</sup> The outcome we explore is, as already mentioned, infant mortality. We have data at the level of the individual child indicating when the child was born, if the child is still alive, and if not when it died.<sup>17</sup> We classify a child as an infant death if it died within its first year. We focus on children who were born at least a year before the interview (this ensures we have a full year of exposure to the risk of death), and who were born not more than five years before the interview (this ensures that the births in the 2002 survey we use occurred after the start of the two projects). In the two years combined, we have 4568 children born between 1 and 5 years prior to the survey.

We use government health spending data at the provincial level from the years 1997 and 2003, and convert them to a per capita basis and to 1997 prices using the GDP deflator for Vietnam.<sup>18</sup> In addition to government health spending, _X_<sup>1</sup> , we include as our _X_<sup>2</sup> vector a vector of wealth quintile dummies, the expectation being (and, as it turns out, broadly consistent with the data) that the survival prospects of newborns will tend to be better in more wealthy households.<sup>19</sup> The DHS does not include income or consumption, and like others (cf. e.g. Filmer and Pritchett 2001) we proxy household wealth through a

> 16 One province—Dac Lac—appears on both project lists. See footnote 8.

> 17 One limitation of the DHS (World Bank 2006) is that it collects birth histories only from (ever) married women.

> 18 The provincial government health expenditure data cover spending in facilities operated by the provincial government or lower levels of government, however financed. In addition to including spending financed from the provincial government’s own budget and transfers from central government (whether general or specifically for socalled ‘national’ programs), it also includes spending financed by local ‘people’s committees’, foreign and domestic donors, payments by Vietnam’s social health insurance scheme, and user fees levied at government facilities. It excludes spending in central government facilities in a province, such as national teaching hospitals. In 2000, spending in central government facilities accounted for only 17% of total government health spending.

> 19 The exclusion of other household-level variables—such as mother’s education—means that the wealth quintile coefficients likely pick up multiple determinants of child survival that are correlated with the household wealth index. The focus of the estimation is on the coefficients on government spending, so not including these other factors explicitly ought not to matter.
Americans while others should reduce it, with little net effect. Low trust in people, rightof-center ideology, and beliefs that the poor are lazy and can escape poverty are all significantly associated with lower support for aid. Only 36% of Americans agree people can be trusted most of the time, lower than in 7 of the other 8 countries. The U.S. ranks first in agreement that poor people are lazy and lack willpower; it ranks third in agreement that poor people can escape poverty, and in being right-of-center ideologically. However, it also ranks at the top – by a wide margin – in church attendance, and above average on political interest and TV watching. Those three variables are all associated with stronger support for aid giving.

Satisfaction with one’s financial situation in the WVS analysis, and trust in NGOs and international institutions in the GI analysis, are also associated with greater support for aid. The U.S. ranks in the middle among donor countries on these variables, so they cannot account for Americans’ low support for aid.

It is possible that the implications of some of these attitudinal and behavioral variables differ across countries. For example, interest in politics and TV consumption may increase Americans’ knowledge of foreign affairs and development issues less than it does in smaller countries. Exploring these nuances is beyond the scope of this paper, however.

Foreign aid helps recipient countries develop infrastructure, strengthen institutions, and address humanitarian crises while providing an important foreign policy tool for donor nations. Paul Collier, in his recent book on foreign aid, argues that improving the effectiveness of aid requires that donors be more willing to accept risks and hence a higher rate of failure. But, “at present, the powerful force of public opinion is driving agencies in precisely the opposite direction. They cannot afford failure....aid agencies are not run by fools; they are full of intelligent people severely constrained by what public opinion permits” (2007: 184). Where support for aid is shallow and citizens are ill-informed about foreign affairs and development issues, “vociferous lobbies” may influence opinion and distort aid policies. Better understanding the factors that influence public support for foreign aid can therefore only advance the quality of distributed foreign aid in the future.

20
and are freely mobile between the traded and non-traded-goods sectors. The relative price of traded goods is then given by the labor productivity differential between traded and non-traded goods. To see this more formally, let _MP_ T denote the marginal (physical) product of labor in the traded goods sector and let _MP_ N denote the corresponding marginal product in the production of non-traded goods. Also let _P_ T and _P_ N denote the prices of traded and non-traded goods while _W_ T and _W_ N are the corresponding wage rates. Under standard assumptions (including competitive, profit-maximizing, producers) we have _W_ T= _P_ T _MP_ T and _W_ N= _P_ N. _MP_ N. With perfect labor mobility, we have _W_ T= _W_ N in equilibrium. The key relationship generating the BalassaSamuelson effect is then immediate, namely that _P_ T/ _P_ N= _MP_ N/ _MP_ T.

In using the Balassa-Samuelson model to explain why PPPs tend to be lower (relative to market exchange rates) in poorer countries, it is assumed that the more developed the country the higher its labor productivity in traded goods, but that productivity for non-traded goods does not vary systematically with level of development. A higher marginal product of labor in traded goods production comes with a higher wage rate, which is also binding on the non-traded goods sector (given that labor is freely mobile), implying a higher price of non-traded goods in more developed countries and thus a higher overall price level. By the same reasoning, low real wages in poor countries entail that non-traded goods tend to be cheaper. The ratio of the purchasing power parity rate to the market exchange rate will thus be an increasing function of income.

This argument helped reinforce the (considerable) international statistical effort that has gone into the development of purchasing power parity exchange rates, led by the _International Comparison Program_ (although PPPs existed before Balassa-Samuelson). The PPP rate expresses a currency’s value in terms of its purchasing power over commodities, both traded and non-traded, relative to the numeraire currency (almost invariably the $US). The PPP is based on the prices actually paid for goods; the exchange rate does not directly enter into its calculation (though of course it will matter indirectly, via domestic prices and spending patterns).

The Balassa-Samuelson model offers a theoretical explanation for the Penn effect. Balassa (1964) found evidence that richer countries tend to have higher price levels in data for 12 countries. Since then most empirical tests of the Penn effect have used cross-sectional data from the ICP. Every round of the ICP appears to have confirmed the Penn effect.<sup>10</sup> Based on such

> 10 Amongst others, see Summers and Heston (1991), Heston, Nuxoll and Summers (1994), Rogoff (1996), World Bank (2008a) and Deaton and Heston (2010).

6
- 16 -

Of particular note, if the estimated covariance _ρ_ is not significantly different from zero, then the OLS provides an unbiased estimate of _α_ . Φ(•) and φ(•) are the standard normal cumulative and density distributions, respectively.

Moreover, it is worth noting that the decision of joint bidding may potentially be endogenous but irrelevant to the number of bidders _N_ , which is introduced to measure the conventional competition effect. In our setting, _N_ is assumed to be given. Hence, in some sense, our analysis will be partial, because the dynamics may not be taken into consideration between the reducing effect of several firms’ making a coalition on competition and the increasing competition effect of more firms’ participating by joint bidding.

As discussed above, we examine six types of bidding consortia: (i) joint bidding (regardless of local or foreign), (ii) joint venture including at least one local firm, (iii) joint venture composed of only local firms, (iv) joint bidding including at least one foreign firm, (v) joint venture composed of only foreign companies, and (vi) coalitional bidding by local and foreign firms.

# **IV. ESTIMATION RESULTS AND DISCUSSION**

First, the ordinary least squares (OLS) models are performed. Table 4 shows the results for road procurement auctions. It reveals that competition reduces the expected procurement price, since the coefficient associated with the number of bidders is significant and negative. This is one of the main findings in our companion paper (Estache and Iimi, 2008). Other implications are deferred to it.

Regarding the joint bidding effect, there is no strong evidence that joint bidding practices are pro-competitive, except for a few cases. In the road sector the coefficient associated with _JBID_ is estimated at 3.01 with a 10 percent level significance, implying that joint bidding
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 1. Infrastructure’s contribution to annual per capita economic growth, actual and potential**

a. Infrastructure’s contribution between 2001 and 2005 <!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>ICT Power Roads<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>ICT Power Roads<br>Percentage points<br>Percentage points<br><!-- End of picture text -->

## b. Potential contributions

_Source:_ Calderón 2009.

# The state of Angola’s infrastructure

Angola’s population of around 18.5 million is unequally distributed across the country. The most densely populated areas surround the capital Luanda and a handful of other major cities. Overall, the coast and the southern and eastern parts of the country are less populated than the interior highlands (figure 2a). The spatial distribution of population is influenced by the presence of vast natural resources and agricultural potential. Angola’s interior highlands (figure 2c), abundant in water resources, are well suited for agriculture. The south and southeast are dry savanna; the far north is covered by rain forest. Angola’s oil fields are located in the coastal region in the north and west. Angola is rich in various 4
In summary, our findings would suggest that the single focal episode we identified represented important monetary shock, and potentially that the bank lending played a crucial role in the monetary transmission mechanism.

<!-- Start of picture text --> Cumulative Forecating Error Base Money<br>0.02<br>0<br>Deq-97 Jan-9 Feb-98 Mar-98 Apr-98 May-98 Juq-98<br>-0.02 + \<br>-0.04<br>-0.12<br>< Figure 4 ><br>Cumulative Forecast Error l<br>0.05<br>De Jan-9 Feb-98 Mar-98 Apr-98 May-98 Ju, -98<br>-0.05<br>-0.15<br>-0.2<br>< Figure 5 ><br><!-- End of picture text --> 9