rafmacalaba/datause-encoder-data huggingface dataset:
|**Table A25: International Budge**|**t Proj**|**ect Op**|**en Bu**|**dget In**|**dex (**|**OBI)**|||
|---|---|---|---|---|---|---|---|---|
|**Data Provider**<br>International Budget Project|||||||||
|**Description**<br>Nongovernmental organization<br>civil society capacity to influen|based<br>ce gove|in Was<br>rnment|hington<br>budget|, Unite<br>proces|d States<br>ses.|, devot|ed to d|eveloping|
|**Website**<br>www.internationalbudget.org<br> <br>|||||||||
|**Data Source**<br>Open Budget Index|||||||||
|**Type**<br>Expert assessment|||||||||
|**Respondents**<br>Local experts recruited by th<br>|e Intern|ational|Budge|t Proje|ct subj|ect to|anonym|ous peer|
|review|||||||||
|**Frequency**<br>First release in 2006, intended<br>**Coverage**<br>Global country sample|annual f|requen|cy||||||
|**Public Access**<br>Yes|||||||||
|**Description**<br>The Open Budget Index is bas<br>various dimensions of the avai<br>documents. Note that the 2006|ed on a<br>lability,<br>index r|questio<br>timelin<br>efers to|nnaire<br>ess and<br>data re|with 12<br>qualit<br>flecting|2 mult<br>y of ce<br>condit|iple cho<br>ntral go<br>ions in|ice que<br>vernme<br>2005.|stions on<br>nt budget|
|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**<br>Open Budget Initiative<br>X|X|X|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**<br>NA<br>..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**<br>NA<br>..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**<br>NA<br>..|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||
|NA<br>..|..|..|..|..|..|..|..|..|
|**Control of Corruption**<br>NA<br>..|..|..|..|..|..|..|..|..|
|**Country Coverage**<br>**59**|**59**|**59**|**..**|**..**|**..**|**..**|**..**|**..**|
|**Year of publication**<br>**2006**|**2006**|**2006**|**..**|**..**|**..**|**..**|**..**|**..**|

63
(1990), the United Nations (1989a) reports automobiles per 1000 people in developing country cities are generally less than fifty, as compared to between two and five hundred in developed countries. However, all developing countries reporting vehicle-kilometerstraveled in 1977 and 1987 experienced significant increases. In another United Nations study (1989b), it is reported that vehicle ownership is growing tremendously in many developing countries. In Indonesia vehicle ownership tripled from 1970 to 1981, in Brazil and Lagos it more than doubled, while in Nigeria vehicle ownership quintupled. In some cities, the problem may be critical in the near future. Vehicle ownership in Greater Bangkok is projocted to increese from less than six-hundred thousand in 1989 to two million by the year 2006 (Jimenez and Es:eland, 1990).

The pollution problems attributable to mobile sources are particu.Lar.Ly pronounced in some cities. Nuccio, Ornelas, and Restrepo (1990) report that atmospheric pollution in Mexico City is a grave problem. While industry contributes twenty percent of the annual total of atmospheric contaminants to the Metropolitan Zone of Mexico City -- approximately 393,000 tons of sulphur dioxide, 130,000 tons of hydrocarbons, 114,000 tons of carbon monoxide, 91,000 tons of nitric oxide, and 383,000 tons of diverse particulate matter -- these tctals are dwarfed by the contribution from mobile sources, which account for over eighty percent of the tonnage emitted. The serious air pollution problem in Mexico City is the result of several factors: the growth in the number of 4
The first element is understanding the policy. This requires agencies to set out policy descriptions that explain the intent of the policy (including the target groups and outputs) and the main components that need to be costed, i.e. the most significant cost drivers. The second and third elements relate to the two categories of appropriations within the Australian budget framework: departmental items (a department’s operating expenses) and administered items (the program expenses that a department administers on behalf of government). The ‘standardized costing model’ is applied to departmental expenses for NPPs. This is to provide ‘reasonable funding for salary and other marginal costs’ based on standard salary rates updated to incorporate the latest public sector remuneration surveys and inflation indexation of other on-costs. The costing of administered items, which relate principally to activities governed by eligibility rules established by the parliament, is expected to isolate implementation assumptions, for example about target group behavior to estimate volume projections. These are also discussed in more detail in Part 5.

The costing process for departmental expenses generally apply the ‘Cost = Input Quantity x Input Price’ principle. The total cost is determined by breaking down a policy costing into cost elements, conducting a price-based and quantity-based analysis of each element, and summing the cost subtotals to calculate an overall cost estimate. This approach is also accompanied by a number of conventions:

- Costings should be calculated on a current price basis.

- Costings should be prepared in accrual and cash terms (so that both the impact on the fiscal balance and underlying cash balance are calculated).

- Costings should take account of direct behavioral responses (or first round effects), but will generally not incorporate second round effects.

- The assumptions used in costings should be transparent and, to maintain consistency, assumptions used in one policy costing will generally be used again for costing similar policies.

Similar to the Australian approach, the Canadian _Guide to Costing_ is based on commonly accepted practices and sets out a generic costing process that is intended to be used for all costing exercises:

- Cost purpose – what purpose will the cost information be used for?

- Cost object – what is being costed, such as an activity, output or service?

- Cost base – which costs are relevant to the purpose and object?

24
|||(0.286)|||
|---|---|---|---|---|
|**Current expenditures**|||0.6495||
||||(0.577)||
|**Investment expenditures**|||0.5714**||
||||(0.281)||
|**Research and extension**||||8.9864**|
|||||(4.128)|
|**Infrastructure**||||0.6776*|
|||||(0.352)|
|**Support and development**||||0.0562|
|||||(0.991)|
|**Administration**<br>**and**<br>**procedures**||||0.2547|
|||||(0.860)|
|**Chuquisaca**|0.3885***|0.3702***|0.3995***|0.3799***|
||(0.108)|(0.112)|(0.107)|(0.110)|
|**Cochabamba**|0.0951|0.0825|0.1042|0.0782|
||(0.092)|(0.091)|(0.092)|(0.092)|
|**Oruro**|0.1316|0.1024|0.1437|0.1226|
||(0.100)|(0.100)|(0.099)|(0.100)|
|**Potosí**|0.3162***|0.3089***|0.3178***|0.3080***|
||(0.096)|(0.097)|(0.096)|(0.097)|
|**Tarija**|-0.3516***|-0.3640***|-0.3485***|-0.3470***|
||(0.036)|(0.032)|(0.040)|(0.035)|
|**Santa Cruz**|-0.2899***|-0.2934***|-0.2830***|-0.2975***|
||(0.060)|(0.058)|(0.061)|(0.057)|
|**Beni**|-0.3164***|-0.3148***|-0.3124***|-0.3339***|
||(0.059)|(0.057)|(0.062)|(0.053)|
|**Pando**|-0.3711***|-0.3674***|-0.3750***|-0.3742***|
||(0.034)|(0.032)|(0.033)|(0.033)|
|<br>**Observations**|327|327|327|327|
|**R2**|0.217|0.223|0.217|0.226|
|***** p<0.01, ** p<0.05, * p<0.1**|||||

Note: Marginal effects reported; standard errors reported in parenthesis. Source: Authors from World Bank (2010) APER database.

The results from both estimations do not provide a clear direction in the relationship between public spending in agriculture and vulnerability. It is possible—in fact, desirable—that public spending allocations are guided by levels of past vulnerability. Hence, endogeneity may be biasing the results.<sup>10</sup> Some categories of public spending, such as infrastructure, could be 10 We investigate this relationship by regressing the impact of 2003 VAM levels on agricultural spending in 2007, at the municipal level. Annex 3 reports the results, which confirm that an endogenous relationship may well underlie 15
several important issues which emerged as crucial in preparing medium-term forecasts. The first of these was the role played by changes in inventories and by changes in inflation in the formation of commodity price movements. Although exchange rate changes began to influence commodity prices more notably after 1972, these rates, together with taxation policies were found to have an important impact not only on revenue earnings of exporting countries but also on producer responses. In the case of rubber, these policies were found to influence longer-term market equilibrium. Longer-term adjustments were also captured in the model by quantifying substitution effects between synthetic and natural rubber. Total elastomer demand was first determined and then natural rubber demand derived using market share analysis. Petroleum prices, which suddenly became an important factor in synthetic rubber competition, also were included in the analysis.

Continuing efforts by the Bank to analyze buffer stock stabilization were continued in the econometric tin market modeling effort by Chhabra, Grilli and Pollak (1978). The International Tin Agreement, initially ratified in 1956, was at that time unique in that it employed a combination of buffer sutck and export controls to keep prices within agreed price stabilization bands. The construction of the tin model which incorporated a buffer stock mechanism explained important price fluctuations not only by including the ITC buffer stock and industry stock holding but also by including stocks held by the U.S. General Services Administration. While this feature enabled the modelers to simulate long term cyclical movements in tin prices and quantitites, underlying trend movements were also captured by incorporating substitution effects and producers' responses to market prices, the latter considered relative to long term 24
percent of the sample. Although we include regional dummy variables in the regressions that follow, the regional preferences for certain lending types should be kept in mind when interpreting results.

_Dependent variables._ The key dependent variable in our analysis of profitability is the financial self-sufficiency (FSS) ratio, a measure of an institution’s ability to generate sufficient revenue to cover its costs.<sup>9</sup> Values below one indicate that it is not doing so. We prefer the financial self-sufficiency ratio to other measures of financial performance because the data are adjusted as described above, and because it offers a more complete summary of inputs and outputs than standard financial ratios such as return on assets or equity. For robustness, however, we also use as dependent variables an unadjusted measure of operation self-sufficiency (OSS) and a measure of adjusted return on assets (ROA).<sup>10</sup> Many of the explanatory variables that we use also contain extreme values as summarized in Table 1. The correlations between FSS, OSS, and ROA are positive and significant, but not perfect (ranging from .59 to .90). The three measures are also similarly correlated with a number of our explanatory variables. For example, the age and size of institutions are significantly correlated with all three performance measures (Table 2). Regression analyses allow us to investigate the strength of those correlations after controlling for region, lending type, and other relevant covariates.

> 9 The financial self-sufficiency ratio is adjusted financial revenue divided by the sum of adjusted financial expenses, adjusted net loan loss provision expenses, and adjusted operating expenses.  It indicates the institution’s ability to operate without ongoing subsidy, including soft loans and grants.  The definition is from _MicroBanking Bulletin_ (2005), p. 57.

> 10 The operational sustainability ratio is financial revenue divided by the sum of financial expenses

> (unadjusted for implicit subsidies), net loan loss provision expenses, and operating expenses.  It indicates the institution’s ability to operate without subsidy and without drawing down capital to pay for operating expenses.  Unlike the financial self-sufficiency ratio, the OSS is not adjusted.  Return on assets is measured as adjusted net operating income (net of taxes) divided by adjusted average total assets.  Definitions are from _MicroBanking Bulletin_ (2005), p. 57.

9
and road grants; 0.02 between maize potential and road grants; and -0.04 between soy bean potential and road grants. Following figures visually represents the lack of correlation between potential output and road grants in Uganda. Regarding coffee, the Kitgum district has the second highest potential output, but receives less than the three lowest potential districts, Mukono, Wakiso, and Tororo, who each have the potential of only 1,000,000 kilograms of coffee.

# **Figure 4:  Road condition compared to road maintenance funds of selected districts in Uganda**

<!-- Start of picture text --> 45 800<br>40<br>40 699 700<br>36<br>35<br>34<br>35<br>32 600<br>30<br>481 480 500<br>25<br>400<br>20<br>300<br>15 239<br>211 216<br>180 183 190 200<br>10 145<br>5 3 3 2 2 100<br>1<br>0 0<br>District<br>None mayor constraints (%)<br>Road Maintenance<br>Ntungamo Nebbi Mpigi Lira Mbarara Adjumani Ssembabule Kapchorwa Kamwenge Pader<br>Uganda Shillings (1,000,000)<br>Percentage of Non- Major Constraints (%)<br><!-- End of picture text --> Source: Ministry of Finance, Planning and Economic Development. Data sorted by percentage of roads none considered as a major constraint by household interviewed.

In addition, to comparing the weight of the agricultural potential to the allocation of road maintenance grants, comparison of agricultural potential value is also possible, using international prices, provided by the Uganda Export Promotion Board, local prices, 35
36 FERNANDES-KLENOW-MELESHCHUK-PIEROLA-RODR ́ıGUEZ-CLARE when exports from a given origin to a given destination are high, exports per firm are responsible for, on average, at least 40% of the high exports. This finding is robust to looking at all destinations or only the largest destinations, including all firms or ignoring very small firms, including all country pairs or only ones for which more than 100 firms export, and to disaggregating across industries. When we look at average exports by percentile of exporting firms (rather than average exports per firm), we find the intensive margin is more important the higher the percentile.

Although variation in fixed trade costs across country pairs can make the Melitz-Pareto model fit the intensive margin in the data, such fixed trade costs would need to be negatively correlated with distance. Moreover, variation in fixed trade costs does not reproduce the pattern of a steadily rising intensive margin across exporter percentiles. Allowing firms to export multiple products or taking into account granularity does not reverse these implications.

In contrast, moving away from a Pareto distribution and assuming that the productivity distribution is lognormal resolves the puzzles. Specifically, we estimate a Melitz model with lognormally distributed firm productivity and idiosyncratic firm-destination demand shifters and fixed costs. We estimate this model using likelihood methods on the EDD firm-level data. Our estimated Melitz-lognormal model is consistent with the positive intensive margin overall and with the intensive margin rising by exporting firm percentile. This specification also implies Since the trade elasticity is no longer a constant in the full Melitz-lognormal model, one would expect from the analysis in Arkolakis et al. (2012) that the welfare effects of a trade cost reduction would be different from those in the Melitz-Pareto model (see Melitz and Redding, 2015). However, extending exact the hat algebra approach popularized by Dekle et al. (2008) to our estimated full Melitz-lognormal model, we find that the Melitz-Pareto model provides a remarkably good approximation for the welfare effects of trade liberalization.

Looking ahead, moving from Pareto to lognormal firm productivity may matter more when taking into account how domestic firms can learn from firms selling or producing in the domestic market. In Alvarez et al. (2014), Buera and Oberfield (2015), and Perla et al. (2015), trade liberalization boosts the level or growth rate of technology through such learning spillovers. The size of this dynamic gain should depend on whether the distribution of firm productivity is Pareto vs. lognormal, as it interacts with how trade alters the distribution of producer and seller productivity. For example, trade liberalization induces more entry of marginal exporters under Pareto than under lognormal — as illustrated by the unchanging exports per exporter under Pareto (zero intensive margin elasticity, unit extensive margin elasticity) versus the sizable intensive margin and weaker extensive margin under lognormal.
# **_E. Firm dynamics, export unit values (prices) and distance to export markets_**

Having established some stylized facts about firm dynamics and export growth in Costa Rica, we now examine how export unit values, the ratio of export values in real dollars per kilogram of merchandise, evolved as a consequence of firm and product dynamics. The relevant calculations are presented in Table 10.

For each year in the sample, Table 10 presents the number of new firm-product combinations (FPs), the number of incumbent (i.e., that existed in the previous year) firmproduct combinations, as well as the ratio of unit values of entrants over incumbents.<sup>6</sup> Ratio 1 is the mean unit value of entrants over the average unit value of incumbents; ratio 2 is the median unit value of entrants over the median unit value of incumbents. For each unit value ratio, Table 10 presents the average ratio for each year as well as the median ratio, the latter being less sensitive to outliers.

The data on the number of new and incumbent firm-product combinations confirm our previous conclusion that there was a very high rate of renewal of firm-products. Indeed, the number of new FPs exceeds the number of incumbent FPs in every year. Regarding unit value ratios, however, the data need to be interpreted with caution. The mean ratios would suggest that new FPs tended to export at higher unit values on average than incumbent FPs. The median ratios, in contrast, suggest that the typical new FP exported at unit values that were lower than those of incumbent FPs. Because new FPs tended to dominate Costa Rican exports in the long run (i.e., by 2007 since 1997), the fact that the typical new FP tended to export products at lower unit values than incumbent FPs should manifest itself in a downward trend in the overall median unit value of Costa Rican exports. This is, indeed, what happened – see Figure 2 – although this finding and graph need to be interpreted carefully but literally as the median unit value in dollars per kilogram derived from a sample of qualitatively vastly different products.

However, we should note that the facts discussed above do not mean that new FPs entered at unit values that were below the median (or average) unit value of incumbent firms that exported the same product (at the 10-digit level of the Harmonized System). In fact, rough estimations of the evolution of the relative unit value of new FPs over the

> 6 Recall that the PROCOMER data measures the quantum of exports in kilograms for all products.

13
�G capture standard percentage changes (on the left-hand side, the change in Y is of course indexed by h because it is calculated for each horizon h). As in Owyang, Ramey, and Zubairy (2013), T controls for potential time trends. It is important to note that, in this approach, each time horizon in the IRF (captured by h) is obtained from a di¤erent individual equation. We thus obtain the IRF values directly from the �h estimated coe¢ cients. Unlike a VAR speci...cation, the estimated coe¢ cients contained in �(L) and �(L) are not used directly to build the IRF values but only serve as controls, “cleaning”the �h from the dynamic e¤ects of output and the e¤ects of past government expenditure changes. Following standard practice, spending multipliers are then constructed by multiplying the IRF values by the mean value of Y=G.

We then follow AG (2012) and compute non-linear ...scal multipliers in economic expansions and recessions for OECD countries (Sections 3.2 and 4). For this purpose, we modify our linear speci...cation (1) as follows: with Following AG (2012), I(:) is a transition function for each country that ranges between 0 (largest expansion) and 1 (deepest recession). xi;t is a normalized variable measuring the state of the business cycle which, using the 7-quarter moving average of the growth rate 9
23

# **Table 1**

India: Real Gross Fixed Investment (percent of real GDP) <!-- Start of picture text --> 1960/61-69/70 1970/71-79/80 1980/81-84/85 1985/86-89/90 1990/91-94/95<br>[<br>Aggregate Investment 17.46 17.70 18.98 19.84 J 20.72<br>Public Investment 7.88 7.62 9.45 9.49 8.18<br>Government 1.96 1.88 2.17 1.93 1.57 i<br>Enterprises 5.91 5.74 7.28 7.56 6.66 a/<br>Private Inmvestment 9.58 10.08 9.53 10.35 12.54<br>Households 7.22 8.39 5.99 6.71 6.13<br>Corporations 2.36 1.68 3.54 3.64 6.41<br><!-- End of picture text --> Note: a11990/91 to 1993/94 Source: Central Statistical Office and own calculations.
# **7  Conclusion**

Several studies have shown that the unbanked earn less and are more likely to belong to a marginalized occupational group than their banked counterparts. Most such studies are based on a limited regional sample, and do not account for geographical variation in access to banking services, which is significant because poorer and marginalized areas are likely to be thinly served by banks. In this paper, where we attempt to control for these region effects by comparing proximate banked and unbanked households across a nationally representative sample of bank customers, the analysis suggests that income and occupational differences can only account for a small part of the variation in the banking choices of neighbors.

The unbanked in Mexico are poorer than their banked neighbors, but the corresponding differences in wealth are larger, with education being another important correlate of the decision to open a bank account. We also find no evidence of prohibitively high bank account costs, and given that in our sample the distribution of income across the banked and the unbanked is similar, this suggests that a simple transactions cost-benefit calculation alone does not drive banking choice. While we cannot claim to measure their causal impact, our findings imply that education and other unobserved households traits play a significant role in the decision to use banks.

These correlations between bank accounts, wealth and education, as also our finding that banked households borrow more, should be interpreted in view of the endogeneity of the choice to open a bank account. For example, an inference consistent with these patterns is that opening an account, higher saving and borrowing are jointly driven by unobserved household traits. To the extent that education and related behavioral traits drive financial choices, simply getting people to open bank accounts will have little impact, while a policy targeting education and these attitudes will increase both bank accounts and the welfare-improving potential of access to banks.