rafmacalaba/data-use-mentions-extended huggingface dataset:
countries with large fiscal space is up to 1 percentage point higher than in countries with small fiscal space, on average.<sup>25</sup>

# **Figure 4. Effects of public investment on output conditional on fiscal space**

## **A. Large fiscal space B. Small fiscal space**

<!-- Start of picture text --> Percent Baseline response Percent Baseline response<br>2.5 2.5<br>2.0 2.0<br>1.5 1.5<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>-0.5 -0.5<br>-1 0 1 2 3 4 5 -1 0 1 2 3 4 5<br>Year Year<br><!-- End of picture text --> _Source:_ Authors’ estimates.

_Note:_ Responses of real GDP (cumulative change in year _t_ relative to year _t_ = -1, in percent) to a public investment shock equivalent to 1 percent of GDP, conditional on fiscal space. _t_ = 0 is the year of the shock. High-efficiency and low-efficiency samples are based on the top and bottom quartiles of the IMF (2021b) public infrastructure efficiency index, which ranges from 0 to 100 (the values above 81 and below 47, respectively).

Fiscal space influences the output effects of public investment through two channels. The first is associated with the effects on private sector, as additional public spending in countries with weak fiscal positions may lead to lower disposable income of liquidity-constrained households, as well as increased tax burdens for the private sector in the future (which may also be anticipated). The second channel relates to the interest rate effect, as scaling up of government expenditures in countries with high levels of debt may lead to higher international interest rate spreads, on account of
For this we use the newly available foreign export supply elasticities provided by Nicita, Olarreaga and Silva (2015). Since there are no estimates of the export supply elasticities faced by MERCOSUR as a block, we proxy for the change in market power due to the customs union formation by first calculating the minimum of the export supply elasticities faced by the four MERCOSUR members, and then measuring the change in the inverse export supply elasticity, from that of the policy-imposing country to the inverse of the minimum

> 25 Export data from WITS are available for most countries starting around 1990. We exclude Uruguay because its export data start only in 1994.

> 26 For example, Kennan and Riezman (1990) show the existence of a tariff externality arising under a customs union. When a country imposes a tariff, the terms of trade of the other member improves when it is also an importer of that good, and this externality is internalized under a customs union because tariffs are set jointly. This tariff coordination effect means that, by coordinating their tariffs as one larger country, the members will want to raise their external tariffs to shift their terms of trade in their favor. Krugman (1991) shows that external tariffs will rise after the formation of a customs union because its members will want to take advantage of the increased size of the bloc to improve their terms of trade. See also Bond and Syropoulos (1996) and Syropoulos (1999).

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Hussain and Siddiqi (2013) analyze the cyclicality of government expenditure in six South Asian countries using data from 1980 to 2010. While they find evidence that fiscal policy was procyclical during this period, they find no evidence that the strength or quality of political systems or institutions affected the procyclicality. Similarly, Zakaria and Junyang (2015) study the cyclical properties of fiscal policy in seven South Asian economies and explore possible factors explaining fiscal cyclicality. They find strong procyclicality of government expenditure and argue that limited access to domestic and international borrowing and a wider dispersion of political power are contributing to procyclicality.

The empirical evidence on the size of fiscal multipliers in developing countries is relatively scarce, but it suggests that multipliers are quite small. Using a Panel VAR model and quarterly data for a large set of countries, Ilzetzki, Mendoza and Végh (2012) find that the government spending multiplier in developing countries is insignificant for consumption spending, but 0.6 on impact and 1.6 in the longrun for investment spending. In addition, they show that multipliers tend to be larger in advanced economies, in countries with fixed exchange rates, in more closed economies, and in economies with lower levels of public debt. Huidrom et al. (2016), using an Interactive Panel VAR model, conclude that fiscal multipliers depend on fiscal positions. They find that multipliers tend to be larger when government debt and deficits are low. Using a large sample of developing countries, Kraay (2012, 2014) obtains an average government spending multiplier somewhere between 0.4 and 0.5.

Hayat and Qadeer (2016) estimate fiscal multipliers for Bangladesh, India, Pakistan and Sri Lanka over the period from 1982 to 2014 using a Panel VAR model. They find an initial impact close to 0.4 and a surprisingly large long-run effect. Tax multipliers, on the other hand, are statistically insignificant. Yadav et al. (2012) estimate the impact of fiscal shocks on the Indian economy using quarterly data from 1997 Q1 to 2009
.001]||[0.002]|
|Currency overvaluation (t-1)|||0.000|0.147***|
||||[0.001]|[0.026]|
|Currency mismatch (t-1)|||-0.001|-0.000|
||||[0.001]|[0.001]|
|FDI (t-1)|||0.001|-0.019|
||||[0.031]|[0.030]|
|Constant|-3.150**|-3.962***|-3.321***|-2.796***|
||[1.532]|[0.362]|[0.643]|[0.625]|
|No. of observations|1,634|2,085|1,325|1,271|
|No. of countries|103|92|88|88|
|Estimated on annual data 1985-2018 ex<br>inflation and reaction shocks reflect per<br>2-year U.S. bond yield driven by a react<br>percent, from 2.4 percent to 6.2 percent.<br>*** p<0.01, ** p<0.05, * p<0.1|cluding 2009. St<br>cent not percenta<br>ion shock raises<br>|andard errors in b<br>ge point. For exam<br>the probability of|rackets. The coeffic<br>ple, a 25-basis-poi<br>a currency crisis by|ients on the real,<br>nt increase in the<br>over 100|

30
aggregate were, unsurprisingly, very often positive. In the absence of data on returns on less liquid assets, this means the specification can only be run with M1.3 Since we are concerned only with the direct effect of depreciation on money demand through the interest rates, cointegration based estimation methods were not appropriate since they generate the total impact elasticity through all variables in the system.<sup>4</sup> Although the literature on estimating interest response of money demand is long and contentious, as we will see, the precision of these estimates does not appear critical to the results. The threshold value is scaled by the money multiplier, so as to make it consistent with the ratio of reserves to Ml, rather than base money.

Finally, unlike the European subsample, the Latin American countries adopted a target zone or peg at different times within our sample period. Further, in the case of Argentina, the deceleration of inflation in the early part of the stabilization plan introduced a high degree of both real exchange variance, interest rates and other variables that was unrelated to the sustainability of the peg per se. We therefore begin the sample in 1992:1 when inflation was falling to levels below 50%. The effect in both cases is to generate an unbalanced panel.

As a preliminary test of the model, table la presents the thresholds calculated first using the GARCH and then the moving average specifications of the variance, as well as the level of RIMl for the entire sample, and table lb the same information for several countries experiencing crises in both Europe and Latin America. As is immediately evident, on average, the reserve ratio 3 This also raises questions about the interpretation of the interest rate coefficient in empirical tests of monetarist models of the exchange rate that employ M2, and use these same interest rates as the opportunity cost of holding it. Estimates available on request.

4 Johansen(1995) and Lutkepol (1994) argue that the coefficient from cointegrating regressions cannot be interpreted as the necessary
panel of 75 countries around the world, Esfahani and Ramı́rez (2003) finds that the contribution of infrastructure services to GDP is substantial and, in general, exceeds the cost of provision of those services.

> 3 Munnell (1992) is the earliest review of infrastructure and economic growth issue; since the literature was at its infancy level, the study only highlights the importance of further research in this area.

> 4 This paper is also similar to Munnell (1992) but it provides more details on the methodological framework used to analyze the issue.

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, NSOs in the region face even more challenges. Only 16 microdata sets, out of a potential 140 that ideally would have been collected, and 81 that have been collected, are downloadable from NSO websites. Consequently, and depending of the definition used, only 10%-20% of the expected microdata are available to the public on NSO websites. Within the price and health data categories none of the NSOs makes microdata publicly available.

> 15 Microdata available nationally may also be on the platforms of other national agencies besides the NSO. If this is the case, we examine the website of the national agency as well.

> 16 For the <mark>2015 Tunisia Budget survey,</mark> it is important to note that not all variables are included in the microdata set available for immediate download.

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TABLE 3. Percentage of Censuses Conducted during the Middle of each Census Round

|**Census round**|**Censuses by birthplace**|**Censuses by nationality**|
|---|---|---|
|**1960**|78|66|
|**1970**|71|71|
|**1980**|78|59|
|**1990**|80|58|
|**2000**|84|57|

_Source:_ Authors‘ calculations based on data described in text.

38
well as at least nine months of repayment history for each loan<sup>9</sup> . The information contained in each loan application can be grouped into the following categories, corresponding to the sections of the Lender’s standard application format: (1) basic client information including a detailed description of the client’s business, (2) documents and verification (3) balance sheet and (4) income statement. In addition, participants in the experiment had access to three types of background checks for each applicant: (5) a site visit report on the applicant’s business, (6) a site visit report on the applicant’s residence and (7) a credit bureau report, available for 66% of all applicants. We limit our attention to uncollateralized small business loans to self-employed individuals, with a ticket size between Rs 150,000 (US$ 3,000) and Rs 500,000 (US$ 10,000).<sup>10</sup> Loan file summary statistics are reported in Table III. We consider only term loans to new borrowers, many of whom are first-time applicants for a formal sector loan.<sup>11</sup> The median loan in our database has a tenure of 36 months, a ticket size of Rs 283,214 (US$ 6,383) and a monthly installment of Rs 9,228. (US$ 208).

To rule out vintage effects and ensure consistency in the initial screening standards applied to loans used in the experiment, we restrict our sample to loans originated in 2009 Q1 and 2009 Q2. Based on the Lender’s proprietary data on the repayment history of each loan, we then classified credit files into performing and non-performing loans. Following the standard definition, we classify a loan as delinquent if it has missed two monthly payments and remains 60+ days overdue, and as performing otherwise. To calculate profitability to

> 9More than 90% of all defaults
which may potentially serve as a guide to the desirability of expanding it further.

In the following sections, we first summarize the main features of Village Fund lending since 2004, using data from the Socio-Economic Surveys of 2004, 2009, and 2010, and show that the Village Fund remains important by any standard. We then describe how Village Funds, which are highly decentralized, actually operate at a practical level, drawing heavily on the findings of a survey of over 3,000 Village Funds undertaken in 2010. We then develop a model of Village Fund Behavior from which we derive a number of testable propositions. In a subsequent section we refine our measures of Village Fund performance, which we then try to explain using econometric models that are based on our theoretical discussion. The

> 1 The average exchange rate during 2001 was Bht44.51/$, which implies that a million baht are equivalent to $22,468.  The exchange rate as of mid-October 2006 was Bht37.57/$, which  would  value a million baht at $26,617.

2
Once qualitative information is transformed and standardized to isolate the various units of measure, it is combined in composite indices. The standardized indicators are multiplied by weights to create index scores. Weights may be selected by decision‐makers or determined by statistical methods. Subjective weighting should be based on deliberation with key stakeholders to reflect the relative importance of component indicators. The strength of this weighting approach is in structuring discussions on the relative importance of component indicators and policy goals. The risk, however, is in exposing the process to manipulation in pursuit of prevailing interests. As such, regardless of the weighting system employed, it is important that decision‐makers consider and decide the weighting, in advance of analysis, and record these decisions transparently.

In the cases of Vietnam and Panama, however, a statistical method, Principal Component Analysis (PCA) was used to determine the weights of each variable in the index’s additive function. One of the main characteristics of PCA is the ability to calculate coefficients based solely on the statistical relationship between variables. This is useful when there is an explicit preference to objectively assign weights and likelihood of redundancies in underlying data. In Vietnam, the Ministry of Planning and Investment expressed a desire to eliminate all subjectivity from the process, whereas the use PCA in Panama was practical and experimental, based on the desire to test the approach. Annex 6 includes more notes on PCA.

# _Social and Environmental Index (SEI)_

Infrastructure projects are meant to improve quality of life. A number of direct social and environmental benefits are associated with their implementation, including improved access to public services and job and income opportunities created during the construction and execution of investments. These benefits come at a cost, however. Engineering works frequently require clearing forested areas, polluting and endangering natural environments, and sometimes construction works involve the resettlement of families or communities. The IPF directly considers relevant social and environmental benefits and costs via the social and environmental index (SEI), whose sub‐components are dependent on
total assets and the share of savings deposits over total assets to thus control for the business orientation of the bank. Finally, we control for year and season-specific effects. Specifically, we introduce dummy variables for the different years and for the four quarters.

Since we work with quarterly data and the effect of the transformation process might not be immediately effective, we drop the two quarters following immediately the transformation date. However, as noted below, we test for sensitivity of dropping these two periods.

Our sample contains data for both consolidated and unconsolidated banks.

Specifically, we use unconsolidated balance sheet and income statement information for bank groups, which include former state-owned banks, while we use consolidated information for other banks in our sample. To control for a potential selection bias, we introduce a dummy variable that takes the value one for banks for which we use unconsolidated information, i.e. which belonged to a bank group at some point during the sample period. We also introduce an interaction term of this dummy variable with the log of total assets.<sup>39</sup> The quality of the data poses a challenge for estimation. Variation in the quality of reporting across banks and over time is exacerbated by the fact that our dataset was assembled from different databases of the Brazilian Central Bank. The mix of banks with consolidated and unconsolidated balance sheet information introduces another source of measurement error. All these factors bias our estimations against finding any significant relationship between the transformation process and bank performance. Following Cull, Matesova and Shirley (2002), we deal with this problem in two different ways. First, we restrict the sample

> 39 We also considered using the total assets of the group instead of mixing consolidated and unconsolidated assets.  However, this would introduce unnecessary noise since for privatized banks, total assets would jump in the period in which they are purchased by a large bank and thus bias the coefficient on Privatized and Privatized after Federalization towards zero.

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decision to treat capital remunerations as exogenous and hence losing some of the macromicro consistency conforms to the limitation of household surveys to capture incomes deriving from capital (see Szekely and Hilgert, 1999).

A structuralist feature introduced in the model is the assumption of labor market segmentation. Some degree of labor segmentation is allowed between agricultural and non-agricultural sectors and, within urban areas, between formal and informal activities. The labor market segmentation assumption gives rise to wage differentials across labor market segments.<sup>12</sup> At the micro level workers are reallocated among agriculture, nonagriculture informal, and formal activities by means of a probit model where the probability of switching sectors is estimated as a function of several personal and household characteristics (see Annex 3 for the complete list of variables used in the model and its results). Workers are allowed to switch between the different labor market segments until the CGE-predicted labor allocation is achieved. For those workers who switch, a labor income is imputed on the bases of worker‘s observable characteristics and the return of them prevailing in the receiving labor market segment.

The top-down approach used here takes into account important sources of household heterogeneity such as the structure of income by labor segment and the composition of consumption by commodity—the various θ‘s in the above equation. In other words, although there are only a handful of variables linking the macro and the micro, these shocks will have a different welfare impact across households. Additionally, allowing for full heterogeneity means that in the new, _simulated_ , distribution, households, as well as individuals, can be identified according to the complete set of socio-economic characteristics recorded in the survey. It is thus easier to identify a specific characteristic – such as region of residence, employment status, gender, education, age, etc. – that may strongly correlate with larger than average losses from the Canal expansion and then use this information in targeting compensatory measures.

# **4. What If Panama Expands Its Canal? Macroeconomic and Distributional
the poor policies that adjustment lending targeted were both inefficient and biased against poor people. Nevertheless, the lack of specific attention to poverty issues led to progress involving damage to poor people and understandably attracted heavy criticism.

Although conditionality can support policy changes, it cannot persuade reluctant reformers. The number and frequency of conditions attached to adjustment loans and

> 51 William Easterly 2005

> 52  See Adjustment Lending Retrospective, June 15, 2001; Review of World Bank Conditionality, September 2005; Conditionality in Development Policy Lending, November 15, 2007, and  2009 Development Policy Lending Retrospective, October 27, 2009

38
is the appraisal of profiles by location and vulnerable group. Table 4 reports the profiling of multidimensional exclusion in these two countries for a cross-dimensional threshold of 33 percent (experiencing multiple social gaps in 4 indicators or more).<sup>10</sup> South Africa shows a higher Social Sustainability Index of 0.34, compared to 0.31 in Peru, explained by a 2- percentage point incidence gap (67 percent compared to 65 percent in Peru), and a 6-percentage point intensity gap (53 percent in South Africa, and 47 percent in Peru). This means that, on average, people in South Africa who experience multiple social gaps do so in eight indicators simultaneously, and in Peru in seven indicators.

However, this pattern is not uniform across vulnerable populations or locations within each country. By gender, we find that women vis-à-vis men in South Africa fare worse in their experiences of social gaps compared to women vis-à-vis men in Peru: something reflected by a 12-percentage point gap in social-gap rates in South Africa, and a 4-percentage point gap in Peru (Table 4). By ethnicity, Indigenous from the Amazonian regions (referred to as other indigenous) Quechuas and Aymaras in Peru and Black South Africans are especially fragile compared to non-native populations and Whites, respectively.<sup>11</sup> Yet the incidence gap between Black South Africans and White populations reaches 43 percentage points: far greater than any of the incidence gaps across ethnic groups in Peru (Table

> 9 For a discussion on the selection of indicators using statistical methods, see Ballon (2023).

> 10 For robustness purposes, we have also analyzed profiles of multidimensional exclusion for cross dimensional thresholds of 50 percent of joint exclusions. These are reported in section 4.3. The results presented here are robust to the choice of cross-dimensional threshold.

> 11 The ENAHO survey uses the main native language spoken by respondents as the criteria for ethnic classification. The survey includes the following groups: Aymara, Quechua,
. First, pre-primary budgets are grouped with primary education in Pakistan; therefore, the ECE budgetary allocations and expenditures are unknown. To address this, we used a part of the primary education budget proportionate to the number of grades at the primary level as the pre-primary education budget. However, this is not a robust way of estimating budgets. Second, the human resource and infrastructure figures are not disaggregated for ECE in the provincial EMIS and other data systems. For provinces where this data was unavailable, we made proportionate calculations like the ones mentioned above. Third, the enrollment data for private schools is not readily and accurately available in the EMIS/NEMIS or other data systems. Nor is data on children with special needs (population, enrollment, etc.) accurately available. Fourth, multigrading at the ECE level is not identifiable through EMIS systems. Lastly, there are inconsistent ECE policies within provinces: e.g., caregivers are a mandatory part of the public ECE system in Punjab but not in other provinces.

# **Future Directions**

This paper presents a preliminary status of ECE financing in Pakistan, outlining estimates not available before at the national or provincial level. However, the data systems in the country must be tailored to capture the planning and costing needs of the ECE sub-sector. Updating these estimates regularly and with more precise data would allow an opportunity to track progress and adjust as needed.

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