rafmacalaba/datause-encoder-data huggingface dataset:
**Table 13. Deposit Compensation Cost for the Government**

||**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**1997**|**1998**|**Total**|
|---|---|---|---|---|---|---|---|---|---|
|**Bulearia**||||||||||
|Bonds issued|0.0|0.0|0.0|0.0|0.0|3.3|1.3|0.04|**_3.3_**|
|Interest payments|0.0|0.0|0.0|0.0|0.0|1.4|0.2|0.1||
|**Total**||||||||||
|**Macedonia**||||||||||
|Cash|0.0|0.0|0.0|0.0|0.0|0.0|0.2|0.0|**0.2**|
|Increase in government<br>debt2'|<br>0.0|0.0|0.0|0.0|22.5|0.0|0.0|0.0|24.3|
|**Total**||||||||||
|**Poland**||||||||||
||From 199<br>correspon<br>budget.|5 to 1998 th<br>ds to less th|e Deposit<br>an 0.01 pe|Protection<br>rcent of G|Fund paid $ DP per year|51.4 Mln<br>. The Fun|to deposit<br>d is partly f|ors, which<br>inanced by|the|
|**Latvia**<br>Cash|0.0|0.0|0.0|0.0|0.04|0.0|0.0|0.0|O|
|**Lithuania**<br>||||||||||
|Cash compensation and|0.0|0.0|0.0|0.0|0.0|0.3|0.0|0.0|**0.3**|
|write-off of governmen|t|||||||||
|deposits||||||||||
|Bonds issued|0.0|0.0|0.0|0.0|0.0|0.0|0.8|0.0|0.9|
|Interest payments|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0||
|Deposit compensation|0.0|0.0|0.0|0.0|0.0|0.0|0.1|0.0|0.1|
|from Deposit Insurance||||||||||
|<br>Fund paid by the||||||||||
|government||||||||||
|**Total**|||||||||*|
|**Kvr2yz ReDublic**||||||||||
|<br>Cash|0.0|0.0|0.0|0.0|0.0|0.0|0.3|0.0||

1/TotalCostisthenetpresentvalue(NPV)atend-1998oftheannualcostsduring991-98. The NPV is calculated using the relevant interest rate for each cost item.

2/ The government assumed the obligation for the repayment of households foreign currency deposits that were lost after the dissolution of the former Socialist Republic of Yugoslavia.

# **4.4 Total fiscal and quasi-fiscal costs of banking crises**

Table 14 gives the total fiscal and quasi-fiscal costs of banking crises in the twelve transition countries. These include the costs to the government and the central bank for restructuring banks, and the costs to the government for compensating depositors.<sup>32</sup> The bulk of these costs comes from bank restructuring costs incurred by the government, which are typically several times the size of direct payments to depositors or the costs incurred by the central bank.

The _CEEs_ incurred by far the largest costs, which ranged from around 7 percent of GDP for _Poland_ to 42 percent of GDP for _Bulgaria_ cumulatively over the period 1991-98. The costs incurred by the _CIS_ 32 See Appendix Table 6 on consolidated fiscal costs.

34
- Krugman, Paul. (1980), “Scale Economies, Product Differentiation, and the Pattern of Trade,” _The American Economic Review_ , 70(5), 950–959.

- Markusen, James R. (1989), "Trade in Producer Services and in Other Specialized Intermediate Inputs," _American Economic Review_ , Vol. 79, 85-95.

- Markusen, James R. (2002), _Multinational Firms and the Theory of International Trade_ , Cambridge, MA.: MIT Press.

- Markusen, James R., Thomas F. Rutherford and David G. Tarr (2005), “Trade and Direct Investment in Producer Services and the Domestic Market for Expertise,” _Canadian Journal of Economics_ , Vol 38 (3), 758-777.

- Markusen, James R. and Anthony Venables (1998), “Multinational Firms and the New Trade Theory,” _Journal of International Economics_ Vol. 46, 183-204.

- Melitz, Marc J. (2003): “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity,” _Econometrica_ , Vol. 71(6), 1695–1725

- Melitz. Marc J. and Stephen J. Redding (2015), “New Trade Models, New Welfare Implications,” _American Economic Review_ , Vol. 105(3), 1105-1146.

- Melo, Jaime de, and David G. Tarr (1990), "Welfare Costs of US Quotas in Textiles, Steel and Autos," _Review of Economics and Statistics_ , August, 489-497.

- Petri, Peter A., Michael G. Plummer, and Fan Zhai (2012), “Trans-Pacific Partnership and Asia-Pacific Integration: A Quantitative Assessment,” Washington, DC: Peterson Institute of International Economics.

- Ricardo, David (1817), On the Principles of Political Economy and Taxation. Available at: http://www.econlib.org/library/Ricardo/ricP.html.

- Rutherford, Thomas F. (2002), “Lecture Notes on Constant Elasticity Functions.” Avaiable at: http://www.gamsworld.org/mpsge/debreu/ces.pdf

- Rutherford, Thomas F. and David G. Tarr (2002), “Trade Liberalization, product variety and growth in a small open economy: a quantitative assessment,” _Journal of International Economics_ , Vol. 56 (2), March, 247-272.

- Rutherford, Thomas F. and David G. Tarr (2008), “Poverty Effects of Russia’s WTO Accession: modeling ‘real households’ with endogenous productivity effects,” _Journal of International Economics_ , Vol 75 (1), pp 131-150.

- Samuelson, Paul A. (1939), “The Gains from International Trade,” _Canadian Journal of Economics_ , Vol. 5(2), 195–205.

- Sato, Kazuo (1976), “The Ideal Log-Change Index Number,” _The Review of Economics and Statistics_ , Vol. 58(2),  223-228.

- Vartia, Y.O. (1976), “Ideal log-change index numbers,” _Scandinavian Journal of Statistics,_ Vol. 3, 121–6

35
1996 and Lin, 1997) tended to focus more on qualitative case studies with little attention paid to quantitatively evaluating the impact of existing public programs. In this paper, we aim to fill this gap. Using the 1996 household survey, we attempt to evaluate the economic and social impact of a specific gender -focused public program.

# _2.2_ **_The Community Program_**

The _Population Control and Community Development Project_ is a gender-focused community program. The key objectives of the program are: (i) to help women identify a range of potential economic and social opportunities, and (ii) to facilitate the development of public and community institutions to enable women to exploit these opportunities. In a sense, the program is an example of what Uphoff (1992, p273) refers to as a "top-down" intervention aimed at introducing, sustaining and institutionalizing "bottom-up" development.

This project was designed primarily to evaluate the impact on women's fertility decisions of public interventions aimed at enhancing women's economic and social opportunities. It is being recognised more widely in China, both among policy practitioners and within the research community focusing on assessing population policies, that family planning polices can be more effectively implemented and enforced through providing women with more social and economic opportunities than by simply deploying coercive measures aimed at controlling population growth. Leading sociologists and demographers in China have also become more aware of the potential impact of community development programs on social norms, e.g. regarding gender preference and desired family size (e.g. Peng and Dai, 1996). As a result, a team of population experts (including both policy practitioners from the Central Population Bureau and scholars from the Population Research Institute at Fudan University) initiated, designed and implemented this community program. The program was implemented in 9
Figure 37. Debit Card Ownership across Age Groups <!-- Start of picture text --> 2011 2014<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>Turkey BRIC Rest of High Rest of ECA Turkey BRIC Rest of High Income Rest of ECA<br>Developing Income Developing<br>World World<br>15‐24 25‐64 65+<br><!-- End of picture text --> Figure 38. Credit Card Ownership across Age Groups <!-- Start of picture text --> 2011 2014<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Turkey BRIC Rest of High Rest of Turkey BRIC Rest of High Income Rest of ECA<br>Developing Income ECA Developing<br>World World<br>15‐24 25‐64 65+<br><!-- End of picture text --> 42
<!-- Start of picture text --> Price Formation. Nominal Anchors. and Stabilization<br>Edicies in Hunatuy.<br>An Empirical Analysis.*<br><!-- End of picture text --> <!-- Start of picture text --> By<br>Andris Solimano<br>,PRDTM)<br>and<br>David E. Yuravlivi..r<br>(EC2CO)<br><!-- End of picture text -->

* Helpful comments by Alan Gelb, Michel Noel, Mark<sup>Stone, Artur Szentgyorgyvari and</sup> and Imre Tarafas to a first draft of this paper<sup>are appreciated.</sup>

* We acknowledge the efficient assistance from<sup>Bruce Courtney and Raimundo Soto in the</sup> preparation of this paper.
# 7 Final thoughts

This paper has estimated tax multipliers for a large group of countries following the narrative approach. Speci...cally, based on a novel dataset on VAT rates in 51 countries (20 industrial and 31 developing) and contemporaneous economic records and sources, we have identi...ed 96 tax rate changes and classi...ed them into endogenous and exogenous to current (or prospective) economic conditions. The analysis has made clear the critical importance of relying on a narrative approach as opposed to the much more common (due to its considerable ease of implementation) Blanchard-Perotti approach. In terms of the bias, the Blanchard-Perrotti approach tends to overestimate the size of the tax multiplier in our global sample due to the numerous tax changes that have responded procyclically to the business cycle. In contrast, the true e¤ect of tax changes on output would be underestimated in advanced economies like the U.S. which typically follow countercyclical tax policies by increasing (cutting) taxes in good (bad) times.

When properly identi...ed, we show, as in RR, that the tax multiplier is negative, indicating that tax hikes reduce economic activity and tax cuts boost output. Why? Because increases (decreases) in the VAT rate reduce (increase) the incentives to consume, invest, and work. However, we found –both for our global sample and for RR’s U.S. dataset –that such “average”multiplier hides important nonlinearities. In line with theoretical models, which are based on distortionary and disincentive-based type of arguments, we ...nd that the tax multiplier is essentially zero under relatively low/moderate initial tax rate levels but markedly negative when initial tax levels are high. By the same token, for a given initial tax rate, larger changes in the tax rate have larger tax multipliers. These ...ndings have important policy implications given that the initial level of taxes varies greatly across countries and thus so will the potential output e¤ects of changing tax rates.

# References

Avellan, L., Vuletin, G. 2015. Fiscal procyclicality and output forecast errors. Journal of International Money and Finance 55, 193-204.

Alesina, A., Campante, F., Tabellini, G. 2008. Why is ...scal policy often procyclical? Journal of the European Economic Association 6, 1006-1036.

Alesina, A., Favero, C., Giavazzi, F. 2015. The output e¤ect of ...scal consolidation plans. Journal of International Economics 96, S19-S42.

Auerbach, A., Gorodnichenko, Y., 2012a. Fiscal multipliers in recession and expansion. In Alberto Alesina and Francesco Giavazzi, eds., Fiscal Policy after the Financial Crisis (University of Chicago Press), 63-98.

Auerbach, A., Gorodnichenko, Y., 2012b. Measuring the output responses to ...scal policy. American Economic Journal: Economic Policy 4, 1-27.

40
These results together with the original estimation of Equation (3) are reported in Panel A of Table 9. Comparing results across the panel, it is clear that the difference in sampling proportion between Shanghai and other cities does not affect the estimated treatment effect.

The fourth sensitivity test examines potential spillover effects. Although the information experiment is based on random assignment, it is possible that some individuals are working in large workplaces where members of both the treatment and control groups are present, and information may be spread from treated individuals to those who are in the control group. Similarly, migrants who move from the same sending county to the same destination city are often part of the same network, and within such a network, information may flow between members of the treatment and control groups. To test for these types of spillover effects we generate two dummy variables: one indicating individuals from a workplace where both treated and control respondents coexist (mixed firm); the other indicating that a home-county network has members of both control and treatment groups (mixed home-county). These two dummy variables are then interacted with the treatment assignment dummy variable. Three different specifications are estimated for equation (3): 1. including the mixed firm dummy and its interaction term with the assignment to treatment; 2. including the mixed home-county dummy and its interaction term; 3. including both mixed-firm and mixed-home county dummies together with their interaction terms with assignment to the treatment. The results are presented in Panel B of Table 9. As can be seen from these results, spillover effects are not affecting our main results.

# **5 Conclusions**

In both China and the international policy communities there is broad recognition that social insurance has an important role to play in the process of economic development and urbanization. Indeed, the 2019 World Development Report (World Bank, 2019) emphasizes the importance of extending social insurance coverage to informal sector workers, which in China are primarily rural-urban migrants lacking formal contracts. There is a tendency for those providing policy advice, however, to simply assume that “mandating participation” in a social insurance scheme will be sufficient to cover informal sector workers. The China experience, however, should offer caution to the belief that mandates alone will suffice to raise participation. Informal sector workers, including new migrants from the countryside are often not participating in urban employee social insurance schemes for a number of reasons. Finding a way to bring such informal sector workers into social insurance systems, even with mandates and a government with relatively high administrative capacity, may pose a challenge for policy makers in many settings. Results from this paper suggest that lack of information may contribute to reducing participation among those workers who lack 24
# **II Empirical Evidence**

This section provides empirical evidence consistent with the testable predictions of the general equilibrium model in the next section. We first provide descriptive evidence before using a multilevel mixed model to examine the impact of distortionary infrastructures and policies on firms’ tax evasion. The multilevel mixed model allows dealing with the hierarchic structure of the data and takes into account the potential dependence between firms of a given country. Moreover, this methodology allows including both microeconomic and macroeconomic variables while accounting for country fixed effects.

## **II.1 Data and descriptive statistics**

We use the World Bank Enterprise Surveys (WBES) data which are a collection of a firmlevel surveys of a representative sample (random stratified sampling) of firms mainly in developing countries. Questionnaires cover a wide range of business environment topics like infrastructure, performance measures, crime, corruption, competition, access to finance. The surveys are conducted within a framework of common guidelines in the design and implementation. A module of identical questions included in all questionnaires is used for assembling the data set, which allows cross-country comparisons. The analysis focuses on African and Latin American countries having at least 200 establishments as well as tax evasion and distortions data.<sup>3</sup> The distributions of firms used in the structural model below are representative at the country level. The sample comprises 19,490 firms in 30 African and Latin American countries during the period 2002-2006.<sup>4</sup> The appendix provides a list of

> 3The sample also excludes countries that were involved in armed conflicts over the period of the survey. We do so to ensure that the measure of institutional quality, as well as the costs stemming from the business environment, are not tainted by armed conflicts. All conclusions of the paper remain the same without these restrictions and using countries with at least 100 establishments.

> 4The dataset employed in this paper does not include informal firms due to data issues. However, we acknowledge that informality is pervasive in developing countries and might be connected with low domestic resource mobilization. As discussed by Besley and Persson (2014), the informal sector is inherently hard to tax because transactions are not recorded, and incomes from informal firms are difficult to measure. Moreover, informality is a source of misallocation (D’Erasmo and Boedo, 2012) affecting both productivity and tax collection (Ordóñez, 2014). Consequently, we expect in this paper that the estimated effects of distortionary infrastructures and policies provide only lower bounds for tax evasion in African and Latin

6
when considering the whole sample of listed firms. But the fact that this ratio (even when coming from balance sheet data) is similar for debt issuers across countries is consistent with our novel results using transaction-level data. Furthermore, the smaller ratio of long-term debt to total liabilities held by other debt issuers (relative to debt issuers in bond or syndicated loan markets) indicates that the debt instruments not analyzed in this paper are relatively shorter term.

These patterns emerging from the balance sheet data also indicate that the greater proportion of firms from developing countries using other shorter-term instruments is an important source of short-termism in debt for these countries. Specifically, the difference in the ratio of long-term debt to total liabilities between developing and developing countries is 1 percentage point for debt issuers (bond and syndicated loan issuers) and 2 percentage points for other debt issuers. But the difference for all firms is larger, 4 percentage points. Hence, the differences in debt maturity across firms in developing and developed countries do not seem to come from large differences in maturity within shorter- and longer-term markets, rather from differences in their relative use of longer-term bond and syndicated loan markets versus other debt markets.

To the extent that the proportion of publicly listed firms is smaller in developing countries than in developed ones, these results could be extended to the universe of firms. That is, one would find a lower ratio of long-term debt to total liabilities and a shorter-term liability structure in developing countries when analyzing all firms. This notion is reinforced by the existing evidence showing that, within developing countries, smaller firms make up a larger percentage of total firms, whereas the size of those using bond and syndicated loan markets is similar across both sets of countries (as shown in the previous section).

22
|Argentina, Bolivia,|**_le_**<br>**_ion_**|Russia, Lithuania,<br>Turkey, Azerbaijan|
|---|---|---|
|Brazil, Chile,|**_esa_**<br>**_tit_**||
|Colombia, Peru,|**_ol_**<br>**_pe_**||
|Nicaragua, Russia|**_wh_**<br>**_com_**||

Source: Ljung (2007) and authors' compilation

# **3. Assessing the Reform Impacts**

Several approaches have been used in the literature to assess impacts of energy sector, particularly, power sector reforms. These include social cost-benefit analysis, econometric analysis, efficiency and productivity analysis, macroeconomic analysis and specific case studies.

A social cost-benefit analysis (SCBA) in principle considers energy reforms and restructuring as an investment and compares the costs of investment with the benefit which is the change in actual and projected performance relative to a defined counterfactual of what would have happened in the absence of reforms and restructuring (Jones et al., 1990). Hence, a SCBA is expected to estimate the overall welfare impact of energy sector reforms and the distribution of welfare. However, governments do not necessarily perform social-cost benefit analysis prior to reform and tend to rely on less formal types of assessment (Jamasb, Newbery and Pollitt, 2005). Moreover, energy sector reforms are multi-dimensional activities with many interacting factors, which cannot be captured by a SCBA.

_Econometric analysis_ is used to test hypothesis through statistical analysis of reform determinants and performance thereby quantifying the effect of various reforms on the energy sector performance indicators. Performance metric regressions based on cross-section, panel data econometrics and time-series econometrics are applied for this purpose. Statistical tests to assess the significant differences in the performance metrics before and after reforms are often carried out by conducting a t-test. However, a t-test for significant performance differences cannot control for the effects of other variables as in a multivariable regression analysis.

_Efficiency and productivity analyses_ are desirable for assessing the effectiveness to transform inputs into outputs, relative to best practice. Both parametric and non-parametric methods are used in measuring productivity and efficiency. Parametric methods use specified production or cost functional forms and apply econometric techniques. Typical parametric methods include regression analysis and stochastic frontier analysis (SFA). In contrary, non-parametric methods 12
# **_Car Imports_**

Because trade classification does not distinguish between new and secondhand cars, our study focused on informal traders and secondhand cars. The analysis then proceeded in several differentiation phases to assess the importance of informal trade.

During the first phase, we excluded buses from tariff line 8703 because interviewees rejected the hypothesis that buses were being imported by informal traders.

During the second phase, we attempted to identify cars sold on the informal market. In 2012 approximately 32,000 vehicles were imported into Douala port for consumption<sup>6</sup> purposes. The breakdown of car imports by value appears in table 2.

**Table 2. Breakdown of Imported Cars by Value, Douala port, Cameroon**

|Category of vehicles by declared value|Percentage of total number<br>of secondhand vehicles|
|---|---|
|≤1 million CFAF(€1,500)|49.0|
|>1 million and≤2 million CFAF(€3,000)|28.0|
|>2 million and≤3 million CFAF(€4,500)|10.0|
|>3 million and≤4 million CFAF(€6,000)|4.0|
|>4 million and≤5 million CFAF(€7,500)|2.0|
|>5 million and≤10 million CFAF(€3,000)|3.0|
|>10 million and≤15 million CFAF(€3,000)|0.8|
|>15 million and≤20 million CFAF(€3,000)|1.6|
|>20 million CFAF|1.6|

_Source:_ ASYCUDA system.

What can be observed? The market is clearly segmented for cars under € 4,500 (87 percent), which are presumed to be secondhand cars. Because all cars are imported from Europe and car taxation is approximately 53 percent, new car values should not be under € 4,500. This gray area of values between 4,500 and € 7,500 is the range at which cars are most likely to be secondhand, but not necessarily bought by informal traders. These cars could also be European imports for personal use by individuals who are not resellers.

> 6 The cars were identified in ASYCUDA as “supplementary units.”

10