rafmacalaba/datause-encoder-data huggingface dataset:
capital-to-asset ratio of the major banks to 4.5 in 1991. In several rounds of recapitalisation, the capital-to-asset ratio of Hungarian banks was brought to 4% by mid-1994. Through the issue of subordinated debt a further increase to 8% is planned by the end of the year. In Poland, the nine commercial banks which took over the former monobank's assets in 1989 were recapitalised to more than 8% in 1993.28 In contrast, in Russia the problem of non-performing loans has been reduced through hyperinflation (Claessens and Pohl (1994)). The broad money supply contracted from 80% of GDP in 1990 to 16% in 1993 and credit to the enterprise and household sector shrunk to a mere 12% of GDP (chart 2). Even if problem loans as a share of total loans were large, their size in relation to GDP would be modest. Moreover, the contraction of the overall balance sheet of the banking system was accompanied by a large change in its structure. The share in total assets of the formerly dominating specialised banks has declined to about a third. For example, Sberbank, which continues to hold most of the country's savings, now accounts for only _5%_ of the total<sup>29</sup> . Two thirds of total banking assets are held by the about 2000 newly-created banks, of which about 20 to 30 are relatively large (chart 3). In sum, both in central Europe and in Russia past problem loans have been provided for and therefore are no longer the most important issue. However, uncertainty with respect to the likelihood of future bail-outs remains and continues to provide many banks with bad incentives (see below).

<!-- Start of picture text --> Chart 2<br>Financlal disintermedlatlon In Russia<br>80<br>70<br>60<br>X 50<br>. 40 Broad money<br>0<br>a 30<br>Credit to enterprises and households<br>20<br>10<br>0<br>1990 1991 1992 1993<br><!-- End of picture text -->

# _State ownership and influence_

At the beginning of the reforms all banks were owned by the state. State-owned banks are generally bad at providing effective corporate control because of political interference in lending decisions and the direct dependency of management on government. Even in the absence of direct interference state banks are likely to give more weight to employment and political considerations than to narrow financial returns. For banks to play a role in corporate control, therefore, their links to government need to be severed through privatisation.

28 However, the situation of some other banks, in particular in the cooperative sector, and that of BGZ and the savings bank remains precarious.

29 Of the old state banks, only Vneshtorgbank (which holds most of the foreign exchange) and Rosselkhozbank (which channels subsidised credit to agriculture) remain of some importance.

19
## **4.1.1 Calculating conditional mean clearance times**

Because of its non-linearity the model’s estimated parameters do not offer an intuitive representation of the quantitative effects of border policies on time in customs. In order to make the quantitative implications of the model transparent we use the estimated model parameters to conduct counterfactual analyses so that fitted and counterfactual values of the outcome variables can be compared. We ask, if the policies were changed in each country, how much would the expected time in customs change? To do this we calculate fitted values of the conditional expected mean duration over the existing data, and then replace the policy data with values that are consistent with best practice, and recalculate the expected values under the new policies.

The conditional mean duration given _x_ is calculated as where _λ_<sup>_d_</sup> ( _t|x_ ) is defined in Equation 3 for the logit discrete-time transition model. We use the difference of conditional mean duration given different covariates of _x_ to indicate the impact of changing _x_ on the expected time required to clear customs.

# **4.2 Multiple Imputation**

As noted above, one of the significant estimation challenges we face is the large amount of policy data that is missing in the OECD trade facilitation indicator data.<sup>25</sup> Data collection in an international setting is always challenging. In this case, where the goal of the collection effort was to provide detailed information on a topic that had not been widely studied, it is understandable that the data are incomplete. The difficulty of the collection effort nonetheless leaves us with important estimation challenges.

A common response to missing data on the right hand side is simply to exclude it, thereby restricting inferences to the sample of observations with complete data. The standard approach is highly problematic in this case, because the sample size is already small and the missing policy data are scattered idiosyncratically but not randomly across the sample. The sub-sample of countries with complete policy data is not representative of the whole.<sup>26</sup> What is more, for many countries

> 25The trade infrastructure variables we employ in robustness checks also have missing data.

> 26For example, the mean time for clearance through customs in countries for which data exist for the first policy indicator, Provision of Information, is 3.2 days, while the mean for countries with this policy missing is 4.3 days.

16
# 1 Introduction

Import demand elasticities are crucial inputs into many ex-ante analyses of trade reform. To evaluate the impact of regional trade agreements on trade fl ows or customs revenue, one needs to fi rst answer the question of how trade volumes would adjust. To estimate ad-valorem equivalents of quotas or other non-tariff barriers one often needs to transform quantity impacts into their price equivalent, for which import elasticities are necessary. Moreover, trade policy is often determined at much higher levels of disaggregation than existing import demand elasticities.<sup>1</sup> This mismatch can lead to serious aggregation biases when calculating the impact of trade interventions that have become more of a surgical procedure. Finally, to compare trade restrictiveness and its associated welfare losses across different countries and time, one would need to have a consistent set of trade elasticities, estimated using the same data and methodology that, ideally, would be consistent with trade theory. These do not exist. The closest substitute and the one often used by trade economists is the survey of the empirical literature put together by Stern et al. (1976). More recent attempts to provide disaggregate estimates of import demand elasticities (although not necessarily price elasticities, but Armington or income elasticities) have been country speci fi c and have mainly focused on the United States. These include Shiells, Stern and Deardorff (1986), Shiells, RolandHolst and Reinert (1993), Blonigen and Wilson (1999), Marquez (1999, 2002, 2003), Broda and Weinstein (2003) and Gallaway, McDaniel and Rivera (2003).

The primary objective of this paper is to fi ll in this gap by estimating import demand elasticities for over 4,625 goods (at the six digit of the Harmonized System (HS)) in 117 countries. We use a methodology that is consistent with trade theory (i.e., imports are a function of prices and factor endowments), and data sources are identical for all countries and goods. We then use these estimates to construct theoretically sound measures of trade restrictiveness for 88 countries, based on Feenstra’s (1995) simpli fi cation of Anderson and Neary’s (1992, 1994, 2003) trade restrictiveness index (TRI).<sup>2</sup> The TRI is the uniform tariff that would maintain welfare at the current level under the observed tariff structure. Finally, we used the constructed TRI to calculate the overall

> 1 Trade policy is (almost by de fi nition) often determined at the tariff line level. To our knowledge the only set of estimates of import demand elasticities at the six digit level of the Harmonized System (HS) that exist in the literature are the one provides by Panagariya et al. (2001) for the import demand elasticity faced by Bangladesh exporters of apparel and the elasticities of substitution across exporters to the US by Broda and Weinstein (2003).

> 2 For the other 29 countries, we had no tariff schedules available.

2
system, namely the credibility of the budget. Figure 5 also signals that, given the importance of external financial support in many developing countries, donor practices have a significant impact on the performance of the PFM system (Box 2).

**Figure 5: PFM Performance Framework**

<!-- Start of picture text --> C. Budget Cycle D. Donor Practices<br>Policy-based<br>budgeting<br>Predictability and A. PFM Out-Turn<br>External Scrutiny Control in Budget<br>and Audit Execution Budget<br>Credibility<br>B. Cross-Cutting<br>Comprehensiveness<br>and Transparency<br>Accounting,<br>Recording,<br>and Reporting<br>Source: PEFA (2005).<br><!-- End of picture text --> We now turn to these six dimensions of the PFM framework. The key message, again, is that good PFM practices should by and large be applied to the security sector as well.

The first dimension, **PFM out-turns** , is very much related to the issues of fiscal sustainability discussed above. Another important aspect is the gap between budgets approved by the executive and legislature on the one hand and actual expenditures on the other. Systematic deviations, often with security institutions spending much more (at least as a share of the total budget if not in absolute terms) than other sectors, are a sign of poor or deceptive budgeting. Such deviations reduce the credibility of the budget, hence weakening its role as a policy tool. On the contrary, even where security requirements change unexpectedly, the resource implications should be managed through the budget process.

The second dimension, **comprehensiveness and transparency** , is of particular relevance to the security sector. Comprehensiveness means that the security sector is fully incorporated in the budget and budget process. Comprehensiveness is sometimes challenged by real or alleged confidentiality issues (see next paragraph). Another challenge is the role of the international community, which often supports the sector financially but without using normal budget systems (see Box 2). Comprehensiveness also means that all sectors (including security) must compete on a level playing field for funding during budget planning and formulation. This does not imply a mechanical or “equity” based allocation of resources, but rather that different sectors contest for resources during the budget formulation process on an equal footing based on their role in the national strategy and (for sub-sectors) within the corresponding sector strategy.<sup>14</sup> Transparency in the security sector is contentious given the nature of the sector, which in some specific cases requires confidentiality leading to a culture of secrecy. Nevertheless, traditional arguments in favor of transparency apply to the security sector as well: (i) sharing 14 This dimension also encompasses good management of fiscal risks, which includes sound oversight of state-owned enterprises. As noted above, in theory security sector institutions should not oversee public enterprises, but in practice this is frequently the case. The problems associated with such a situation should not be compounded by lack of transparency and monitoring of the concerned SOEs.

21
**14**

increased land productivity in agriculture to be more important in Asia and Latin America where labor and food markets are better developed than in Africa.

More recent evidence by Dorosh and Haggblade (2003) applying both fixed price semi input-output (SIO) models as well as fully price endogenous CGE models to eight SSA countries confirms the existence of sizeable growth linkages from investments in agriculture—as before the indirect effects of investment induced growth prove to be about as large as the direct effects. Moreover, fixed price (SIO) multipliers from investments in export and food crops typically exceed the manufacturing multipliers, consistent with the literature, though they also find that this is no longer the case when prices of the non-tradables are endogenized (as in the CGE).

The methodologies (fixed price (semi) input-output models and price-endogenous CGE models) underpinning this micro evidence are structural in nature and data intensive. While this constitutes the strength of these approaches providing a lot of insights in the nature and extent of the linkage effects, it also constitutes their weakness as the results (partly) depend on the validity of the structural assumptions. To complement the empirical insights from the micro data we follow Bravo-Ortega and Lederman (2005), who focus on Latin America, and explore whether there exists evidence of a causal relationship between agricultural and non-agricultural output (in the Granger, 1969, sense)<sup>11</sup> by applying dynamic panel data estimation techniques to international cross country data. In doing so, we do not for example have to assume supply flexibility or fixed prices in the non-tradables sector, in effect observing a ‘reduced form’ of the full general equilibrium outcome. The downside of this “reduced form” approach is obviously that it does not provide much insight in the mechanisms of the linkage effects.

Bravo-Ortega and Lederman (2005) find evidence of a positive causal link (in the Granger, 1969, sense) between (lagged) agricultural growth and non-agricultural growth, also for poor countries, though the effect is smaller for Latin America. The reverse effect is also discernable— lagged effect of non-agricultural output on agriculture, though this is negative for low-income

> 11 The concept of Granger causality holds that a variable Yai Granger causes Yni if Yni can be better predicted using lagged values of Yai than without them.
Policy Research Working Paper 7384

# **Abstract**

This paper estimates the impacts of secondary school on human capital, occupational choice, and fertility for young adults in Kenya. The probability of admission to government secondary school rises sharply at a score close to the national mean on a standardized 8th grade examination, permitting the estimation of causal effects of schooling in a regression discontinuity framework. The analysis combines administrative test score data with a recent survey of young adults to estimate these impacts. The results show that secondary schooling increases human capital, as measured by performance on cognitive tests included in the survey. For men, there is a drop in the probability of low-skill self-employment, as well as suggestive evidence of a rise in the probability of formal employment. The opportunity to attend secondary school also reduces teen pregnancy among women.

This paper is a product of the Human Development and Public Services 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 author may be contacted at oozier@worldbank.org.

_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
**Table 4: Impact of PBF on health provider outcomes**

||**Baseline**<br>**Mean in**<br>**PBF**<br>||**PBF**<br>|**p-**<br>||
|---|---|---|---|---|---|
|**Variable**|**districts**|**Trend**|**impact**|**value**|**N**|
|**Panel A: General provider outcomes**||||||
|Monthly income (Salary+PBF bonus)|635.0|69.6|437.6***|0.000|2084|
|Number of days absent in past 30 days|0.3|0.0|0.3|0.361|2126|
|Number of hours worked in past week|22.4|-0.2|5.3|0.878|2126|
|Number of patients seen in past day|3.8|-2.2|4.9*|0.065|2125|
|Satisfaction score<sup>a</sup>|0.486|0.015|0.183**|0.019|2126|
|**Panel B: Providers knowledge measur**|**ed by clinic**|**al vignett**|**es**|||
|Severe dehydration vignette:||||||
|Correct diagnosis|0.245|0.081|0.260|0.288|2095|
|Proportion of history taking|0.442|0.020|0.096*|0.093|2095|
|Proportion of exam procedures|0.516|-0.077|-0.005|0.932|2095|
|Pneumonia vignette:||||||
|Correct diagnosis|0.355|0.030|0.043|0.672|2095|
|Proportion of history taking|0.421|0.021|0.088|0.107|2095|
|Proportion of exam procedures|0.345|-0.034|0.037**|0.040|2095|
|Acute respiratory infection vignette:||||||
|Correct diagnosis|0.173|0.194|0.066|0.383|2094|
|Proportion of history taking|0.556|0.091|0.023**|0.020|2095|
|Proportion of exam procedures|0.377|-0.017|0.060*|0.056|2095|
|Malnutrition/Anemia vignette:||||||
|Correct diagnosis|0.640|0.004|-0.003|0.608|2094|
|Proportion of history taking|0.470|0.024|0.087|0.119|2095|
|Proportion of exam procedures|0.316|-0.097|0.014**|0.034|2095|
|Moderate cardiovascular risk vignette:||||||
|Correct diagnosis|0.315|0.017|0.135|0.395|2095|
|Proportion of history taking|0.336|-0.022|0.117*|0.066|2095|
|Proportion of exam procedures|0.373|-0.088|0.132**|0.040|2095|
|<br>High cardiovascular risk vignette:||||||
|<br>Correct diagnosis|0.230|0.034|0.265**|0.021|2095|
|Proportion of history taking|0.385|-0.016|0.089*|0.079|2095|
|Proportion of exam procedures|0.371|-0.112|0.142**|0.011|2095|

Note: data from interviews of health providers in both rural health centers and health houses. All regressions are the difference-in-differences specification with facility fixed effects. Standard errors are clustered at the district and survey round level with wild bootstrapping. All regressions include controls for facility type, exposure to the collaborative quality improvement and citizen scorecards interventions, age and gender of providers, position, a binary indicator for experience of over 20 years and an indicator for being born in the district. Standard errors are clustered at the district and survey round level with wild bootstrapping. a Proportion of categories about which providers reported to be satisfied. The individual categories are listed in Appendix Table A4. b The definition of all the indicators related to the clinical vignettes indicators are specified in Appendix 5. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 26
# **6. References**

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- Ardanaz, Martín, and Alejandro Izquierdo. 2017. "Current expenditure upswings in good times and capital expenditure downswings in bad times?: New evidence from developing countries." _IDB Working Paper_ (IDB Working Paper No. IDB-WP-838) No. IDB-WP838.

- Auerbach, Alan J., and Yuriy Gorodnichenko. 2011. "Fiscal Multipliers in Recession and Expansion." _National Bureau of Economic Research_ no. w17447.

- Baez, Javier E, Alejandro de la Fuente, and Indhira Santos. 2017. "Do Natural Disasters Affect Human Capital? An Assessment Based on Existing Empirical Evidence." _The World Bank Research Observer_ 33(2): 235–263. doi:10.1093/wbro/lkx006.

- Balassone, Fabrizio, and Manmohan Kumar. 2007. "Cyclicality of fiscal policy." In _Promoting fiscal discipline_ , by Manmohan S. Kumar and Teresa Ter-Minassian, 19-35. Washington D.C.: International Monetary Fund.

- Balassone, Fabrizio, and Maura Francese. 2004. _Cyclical Asymmetry in Fiscal Policy, Debt Accumulation and the Treaty of Maastricht._ Rome: Bank of Italy.

- Baldacci, Emanuele, Nazim Belhocine, Gabriela Dobrescu, and Iva Petrova. 2011. "Assessing Fiscal Stress." _IMF Working Papers_ (IMF Working Papers) (Working Paper No. 2011/100): 43.

- Baldacci, Emanuele, Sanjeev Gupta, and Carlos Mulas-Granados. 2011. "Fiscal Crises, Causes, Consequences, and Policy Responses." _IMF Working Paper WP/11/280._

- Barnichon, Regis, Davide Debortoli, and Christian Matthes. 2022. "Understanding the size of the government spending multiplier: It’s in the sign." _The Review of Economic Studies_ 89.1: 87-117.

- Caballero, Ricardo J., and Arvind Krishnamurthy. 2004. "Fiscal Policy and Financial Depth." _National Bureau of Economic Research Working Paper 10532._

- Calderón, César, William Easterly, and Luis Servén. 2003. "Latin America’s infrastructure in the era of macroeconomic crises." In _The Limits of Stabilization, Infrastructure, Public Deficits and Growth in Latin America_ , by William Easterly and Luis Servén, 21-94. Palo Alto, CA: Stanford University and Washington, DC: World Bank.

- Carneiro, Francisco, and Leonardo Garrido. 2016. "New Evidence on the Cyclicality of Fiscal Policy." _World Bank Policy Research Working Paper_ No. 7293.

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**Table 11: Relationship between firm exits and enforcement**

||||_Firm Exits_|||
|---|---|---|---|---|---|
||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|
|_Slump_|0.010<sup>***</sup><br>(0.003)|0.017<sup>***</sup><br>(0.006)|(0.000)|(0.000)|(0.000)|
|_Surge_|‐0.003<br>(0.003)|0.006<br>(0.005)|(0.000)|(0.000)|(0.000)|
|_Slump*Enforcement_||‐0.001<br>(0.001)|‐0.001<br>(0.001)|‐0.001<br>(0.001)|‐0.001<br>(0.001)|
|_Surge*Enforcement_||‐0.001<br>(0.001)|‐0.001<br>(0.001)|‐0.001<br>(0.001)|‐0.001<br>(0.001)|
|_Fixed effects_|Year|Year|Sector * Year|Sector * Year|Sector * Year|
|_Shock * LagEmp_|No|No|No|Yes|Yes|
|_Shock * Age_|No|No|No|No|Yes|
|_Observations_|328,324|328,324|328,324|328,324|328,324|
|_R2_|0.012|0.012|0.019|0.020|0.027|
|_Adjusted R2_|0.012|0.012|0.018|0.019|0.025|
|_Residual Std. Error_|0.246 (df = 32831|5) 0.246 (df = 3283|12) 0.245 (df = 3278|31) 0.245 (df = 3278|28)<sup>0.244 (df =</sup><br>327822)|

_Note:_<sup>*</sup> p<sup>**</sup> p<sup>***</sup> p<0.01; Errors clustered by Sector The specification includes the following variables: Slump*log(K/L), Surge*log(K/L) which were omitted for brevity.

28