rafmacalaba/datause-encoder-data huggingface dataset:
2 developing an efficient and competitive financial system that would support and facilitate the functioning of a liberal economy. To that end, reforms eliminated interest rate controls, eased the entry of new financial institutions, both bank and non-bank, and allowed new types of instruments. There were also policy measures to develop equity and bond markets. Although there were occasional setbacks and policy reversals in terms of interest rate controls, and a banking crisis in 1982, reforms have led to major changes in the sector. Relaxation of regulatory barriers has attracted a significant number of banks into the system, both Turkish and foreign. Reforms were also successful in halting the decline in financial intermediation observed prior to 1980 and contributed to financial deepening and a revitalization of the stock market. At the same time product variety increased and quality of financial services improved. Moreover, the Turkish banking system became more integrated with the external financial world and improved its financial technology and human capital.

The objective of this paper is to examine several aspects of the banking market in Turkey to assess the nature of its structure and the state of competition. What kind of market structure exists in banking after the reforms? Was the entry of new banks sufficien **t** o transform the market structure into a competitive one or did the distortions resulting from earlier financial and regulatory policies built endogenous constraints into the system thwarting competition regardless of new entry? Now that regulatory entry barriers are gone, are there mobility barriers in the system? These are some of the questions this study attempts to provide answers by drawing on market structure studies found in industrial organization literature and applying them to the Turkish banking market. The focus is on the commercial retail banking market since it is primarily through this channel that resources are mobilized and allocated. However, it must be noted at the outset that although recent developments in theory have improved our understanding of financial intermediation, there is still no
for tax inspection and informal payments. Since the question on tax compliance was discontinued after 2007, we have very few repeated country surveys. Therefore we only report the coefficients _β_ 1 of size on tax compliance for the first two models.

# **4 Results**

## **4.1 Reduced Form and IV Estimates of Size Gradients**

### **Tax Inspection**

In this section we implement the econometric specification described in section 3.2. Figure 4 plots for each of the six largest countries in our sample,<sup>21</sup> an industry’s size ranking on its average probability of tax inspection. Each dot represents an ISIC 3 industry and the size of the dot is proportional to its share of total employment within the country (based on the WBES). We plot the linear fit of size rank on tax inspection which slopes up in all six countries. We also note that on average industries with higher total employment have larger average firm sizes, however there is significant variation and some industries with a high share of total employment rank in the bottom half of average firm size.

Table 2 reports the size gradient in tax enforcement along the extensive margin (any tax inspection over the past 12 months, Panel A) and the intensive margin (number of tax inspections over the past 12 months, Panel B). Panel A shows that industry-size is associated with a higher likelihood of tax inspection. Columns 3 and 4 show that the reduced form coefficients are significant in both types of (cross-sectional) fixed effect models. In columns 5 and 6, we estimate the corresponding IV-coefficients for each model. The first stage coefficients are strong (F-statistic of 260 and 105, respectively), and the size gradients are precisely estimated. The coefficient from our preferred specification in column 5 implies that a 10 percentile increase in exogenous WBES size-rank is associated with a 2.3

> 21Bangladesh, Brazil, China, India, Indonesia and Mexico.

16
which the output gap is positive, those in which the output gap is negative but less than 4 percent in absolute terms, and those in which the output cap is >3jative and greater than 4 percent in absolute terms. The VAR is now expanded to s!x variables by replacing x in the "symmetric" model by three measures of movements in output: x+ = _Dx, x-s_ = (1-D)Zx, and _x-L_ = (1-D)(1-Z)x, where Z _iS_ a durmmy variable that takes the value 1 (respectively 0) if the (crop in tLe output gao is smaller (respectively greater) than 4 percent. The idea h-ere is teat negative shocks that entail a drop in output that is smaller than 4 percentage Points represent "normal' recessions, whereas drops in the output gaip that cAed _4_ percentage points are associated with a "crisis" of some sort. Altiough 'I .e choice ' f a 4 percent threshold may appear to be somewhat arbitrary there ev1ie,dIce to suippor **t** his choice. Lustig (2000), for instance, in a study focusing *o n _23_ Gountries in Latin Arnerica and the Caribbean during 1980 and 1998, i.+-Cndl that there were over 40 episodes where GDP per capita fell by 4 -. rcentaqe points or more. In another study, dwelling on a sample of 36 banking c.rses in 35 Orc-wuntries, Dem,irguc-Kunt et al. (2000) found that a banking crisis is typically ac-compan,ed by a decline in output growth of the order of 4 percentage 'zoints. Nevertheless, some sensitivity analysis would generally be wairranted (see the discussion below).

_:_ assess whether any of the two "control" variables (that is, either the Unemployment rate or the real wage) or any additional variable (stuch as govecrnment transfers to the poor) should be included in the VAR, a block exogeneity Kor block causality) test can be performed by estimating bcth the 'unrestf-icted' and "restricted" VARs (that is, with and without the cyclical clm rnponernt of the variable of interest) to obtain the variance-covariance matrix of the residutas associated with the unrestricted and restricted models, Qu and Oc. Deficie the likelihood ratio statistic, A, as 15
additional expenditures) separately. As Table 4 shows, the consumer surplus loss outweighs the financial loss incurred as a result of higher electricity tariffs. The burden of higher electricity tariffs on household expenditures is lower when households reduce their consumption of electricity. Previous studies have estimated price elasticity of demand for heating to fall somewhere between -0.2 and -0.4, with poorer households being in the more inelastic range of these estimates.<sup>12</sup> Therefore our calculations of welfare losses for zero and -0.5 elasticity assumptions fall within the range of reasonable elasticity estimates.

**_<u>Table 4.  Welfare losses of poorest households in different socioeconomic groups</u>_**<sup>**_1_**</sup>

|||**Share of**<br>**electricity in**|**Elasticity 0**|**Elasticit**|**y-0.5**|
|---|---|---|---|---|---|
|||**household**<br>**budget**|**Welfare loss**|**Consumer**<br>**surplus loss**|**Financial**<br>**loss**|
|**Gender of**|**Male**|11.2|5.0|3.0|1.4|
|**Household Head**|**Female**|11.4|5.1|3.1|1.4|
||**No school or primary**|||||
|**Education of**|**completed**<br>**Some secondar**|11.0|4.9|3.0|1.4|
|**Household Head**|**y**<br>**school**|11.2|5.0|3.0|1.4|
||**Secondary +**|11.7|5.2|3.2|1.4|
|**Hhld hd**|**Unemployed**|11.9|5.3|3.2|1.5|
|**ouseo ea**<br>**emloment**|**Employed**|10.1|4.5|2.7|1.2|
|**py**|**Retired**|11.4|5.1|3.1|1.4|
|**Kids in the**|**Kids up to 5**|9.3|4.1|2.5|1.1|
|**family**|**No kids or older**|11.6|5.1|3.1|1.4|
|**Disabled in**|**No disabled**|10.8|4.8|2.9|1.3|
|**family**|**Disabled in family**|13|5.8|3.5|1.6|
|**Social Assistance**|**FMS**|12.9|5.7|3.5|1.6|
||**No FMS**|9.8|4.3|2.7|1.2|
|**Dwellin**|**House**|9.9|4.4|2.7|1.4|
|**g**|**Apartment**|11.8|5.2|3.2|1.4|

Notes:<sup>1</sup> Welfare losses expressed as a percent of household expenditures and calculated assuming electricity price increase to 7 c€/kWh using the 2004 ISSP Montenegro Household Survey.

# **4. Comparison of social assistance options in the course of reforms**

Residential electricity consumption is not currently subsidized in Montenegro, since the average residential tariff of 4.85 c€/kWh, inclusive of the taxes, is approximately equal to the cost-recovery price. However, this situation will change in the future, and therefore the Government of Montenegro is seeking to design an effective social safety net program to mitigate the effect of a tariff increase on the poor. This section explores the advantages and disadvantages of alternative social assistance options in the course of reforms in the electricity sector in Montenegro.

Provision of utility subsidies is a common way of mitigating the effect of electricity tariff increases on the poor, particularly wide-spread in developing and transition

> 12 Lampietti, J., A. Meyer (2002), “Coping with the Cold: Heating Strategies for Eastern Europe and Central Asia’s Urban Poor.” Washington, D.C.: World Bank.

9
Table 9. Cotton in Gujarat: indemnity levels

|**District**|**Weighted average loss**<br>**cost at 70% indemnity**<br>**level**|**Weighted average loss**<br>**cost at 90% indemnity**<br>**level**|**Indemnity level**|
|---|---|---|---|
|[1]|[2]|[3]|[4]|
|GJ1|0.6%|5.9%|80%|
|GJ2|8.6%|14.8%|70%|
|GJ3|4.8%|11.4%|70%|
|GJ4|2.3%|6.1%|80%|
|GJ5|0.4%|3.7%|80%|
|GJ6|10.6%|17.8%|70%|
|GJ7|0.4%|3.1%|80%|
|GJ8|16.3%|22.6%|70%|
|GJ9|8.2%|15.1%|70%|
|GJ10|4.9%|10.5%|70%|
|GJ11|0.6%|5.1%|80%|
|GJ12|22.1%|27.2%|70%|
|GJ13|6.7%|14.9%|70%|
|GJ14|3.3%|9.2%|70%|

# <u>Actuarial justification</u>

For a particular crop the average yield may vary across a district, but the shape of the yield distribution may be fairly constant across the district. In so far as this is the case it is appropriate to set the indemnity level to be fixed across a district.

The specific rule suggested for the calculation of indemnity level corresponds to the existing rule for NAIS, based on the Normal Theory Method.

The large variation in weighted average loss costs at both 70% and 90% indemnity levels (column [2] of Table 9) indicates that cotton yield risk varies considerably between districts in Gujarat, with low risk in districts like GJ7 and GJ5 and very high risk in districts like GJ12 and GJ8. The actuarial commercial premium rates of mNAIS would allow different rates to be charged in different insurance units within the same state. Moreover, the suggested rule for calculating indemnity levels would allow farmers in high risk districts to purchase a lower level of cover at a lower premium.

# _Threshold Yields_

# <u>Description of step</u>

The threshold yield for each insurance unit may then be calculated as:

# <u>Actuarial justification</u>

This approach is appropriate when the shape of the yield distribution is fairly constant across the district. However, if the shape of the yield distribution varies across the district, with differences in risk in addition to differences in average yields, this will result in a variation in premium rates across a district. In such circumstances a flexible experience based approach to setting threshold yields, with fixed premium rates would be more appropriate.

17
example, the level of net total assets is quite stable over time (in contrast to the gross figure for total assets which grows at about 5 percent a year, on average).

# **3. International Comparison of Key Financial Ratios**

Table 1 reports the main corporate financial ratios for Egypt and for listed firms in a sample of developing and developed countries (obtained from the Worldscope database). Since year 1999 is the most represented in both datasets, it is chosen as a benchmark for comparison.

Profitability of Egyptian firms in 1999 was broadly in line with international experience. The profitability ratios (both operating income and net income) are the same or slightly lower than the average for other countries shown.

Leverage in Egypt is lower than in most other countries in the sample. The ratio of total liabilities to total assets has a median of 0.51 for Egypt while the world median is 0.57. The lower leverage ratio in Egypt suggests that Egyptian firms may face some difficulties in raising debt finance. Furthermore, the interest coverage ratio (the ratio of earnings before interest and taxes, EBIT, over the interest payments) is 2.79, which is much higher than the world median of 1.77. This suggests that the firms in Egypt could service more debt than they have.

The most striking difference is in the comparison of the ratio of current liabilities over total liabilities. The ratio of 0.99 suggests that the median firm in Egypt in 1999 has almost no long-term debt, while the median ratio over all other countries is 0.74, representing a much smaller portion of the total liabilities. This suggests that firms in Egypt are constrained in their access to long-term debt (i.e., with maturity of over one year). The limited availability of long-term financing is one of the frequently mentioned constraint in informal surveys of the private sector.

The ratio of retained earnings to total assets is much lower in Egypt than in other countries. This result is in contrast with comparison of profitability ratios, which are similar in Egypt and other countries. This phenomenon is somewhat puzzling. Available data does not allow verification of the presumption that it results from companies in Egypt paying out a 4
An Empirical Investigation of Trade Diversion and Global Value Chains François de Soyres<sup>1</sup> , Julien Maire<sup>2</sup> and Guillaume Sublet<sup>3∗</sup> Keywords: Trade agreements, trade diversion, trade policy, global value chains. JEL classification: F1, F14, F15, F42, F60.

> ∗This is a background paper for the World Bank’s World Development Report 2020 "Global Value Chains: Trading for Development". We thank the WDR 2020 team, Raphaël Godefroy and participants of the SCSE annual meeting in Québec for their comments. The views expressed in this paper are solely those of the authors and do not necessarily reflect those of the World Bank.
## **CHAPTER 1**

# INTRODUCTION

Sectors that export primary commodities have been discriminated against by the governments of many developing countries. Even though economic theory teaches us that such trade intervention is generally not the best solution even in the presence of domestic market distortion, the restriction of agricultural commodity exports by quota tends to be preferred by the policymakers. In many cases, this is because of domestic political economy considerations. The cotton production sector in India is a prime example. The prime motivation of this case study is to investigate the crucial political factors behind the second-best trade intervention in the context of the Indian cotton economy.

Cotton export has been restricted by the Indian government for many years. Despite the substantial degree of economnic liberalization since 1991, the government has not considered changing the policy, and as a result, India is far from being a major exporter in the world cotton market (Table 1). Considering one fourth of the world's cotton-cultivated land acreage is in India and one eighth of the world's cotton is produced in the country, the market share is extremely small.

**Table 1:** **_World Cotton Export Market_** (million tons)

|Country|Exp|orts|
|---|---|---|
|United States|1,589|(26.8)|
|Uzbekistan|_1,150_|(19.4)|
|Tadzhikistan|360|(6.1)|
|Turkey|280|(4.7)|
|Greece|175|(3.0)|
|China|150|(2.5)|
|Egypt|94|(1.6)|
|Pakistan|20|(0.3)|
|**India**|**18**|**_(0.3)_**|
|World Total|5,927|(100.0)|

( ): percentage shares in the world market Sources: Office of the Textile Commissioner (1995), ICAC (1994) I
# Appendix 1: Two other reviews of the literature.

Table A.1: Confirmation ratios of the significance of various inputs sorted by direct impact on educator welfare from 96 studies in developing countries

|Input|Number of<br>studies|Positive<br>(significant)|Negative<br>(significant)|Insignificant|Confirnation<br>Percentage|
|---|---|---|---|---|---|
|Teacher's salary|13|<br>4|<br>2|<br>7|<br>30.8|
|School teacher-pupil ratio|30|<br>8|<br>8|<br>14|<br>26.7|
|Teacher's education|63|<br>35|<br>2|<br>26|<br>55.6|
|Teacher's experience|46|<br>16|<br>2|<br>28|<br>34.8|
|School facilities|34|<br>22|<br>3|<br>9|<br>64.7|

Source: Harbison and Hanushek, 1992.

Table A.2: Confirmation ratios of the significance of various educational inputs sorted by their **direct utility to educators, from studies in Latin America.**

|Characteristic|Number of<br>studies|Positive<br>relation|Negative<br>relation|No relation|Confirmation<br>Percentage|
|---|---|---|---|---|---|
|Teacher's satisfaction|43|<br>4|<br>2|37|<br>9.3|
|School student/teache**r**atio|21|<br>2|<br>9|<br>10|<br>9.5|
|Teacher's years of schooling|68|<br>31|<br>4|33|<br>45.6|
|Teacher's years of experience|62|<br>25|<br>2|35|<br>40.3|
|Teacher's subject knowledge|19|<br>9|<br>1|<br>9|<br>47.4|
|School infrastructure|70|<br>23|<br>2|45|<br>32.9|
|Access to textbooks and other reading<br>materials|17|<br>13|<br>0|4|<br>76.5|
|School access to other instructional<br>materials|34|<br>14|<br>3|17|<br>41.2|

Source: Velez, Scheifilbein, and Valenzuela, 1993.

49
# **Table 8: Effects of Peer Adherence Support (PAS) and Food on Self-Reported Adherence**

||MCA adherence<br>index|CASE adherence<br>index|CASE cut-off|Missed a drug<br>collection visit|Missed a routine<br>check-up|Missed a clinic or<br>hospital visit|
|---|---|---|---|---|---|---|
|**A. Intent-to-treat analysis**|||||||
|Treatment|-0.304 *|-0.783 ***|-0.072 **|0.017|0.028|0.014|
|Treatment * ITT PAS only|0.086|0.275|0.041|**0.052 ***|0.006|**0.065 ***|
|Treatment * ITT PASnFood|-0.020|-0.071|-0.006|0.013|-0.038|0.001|
|n (N)|618 (1,270)|617 (1,268)|618 (1,270)|618 (1,267)|618 (1,263)|618 (1,263)|
|F-test (p-value)|1.05 (0.384)|2.11 (0.062)|1.6 (0.157)|1.65 (0.143)|1.95 (0.084)|1,61 (0.154)|
|Treatment|-0.307 *|-0.792 ***|-0.073 **|0.016|0.027|0.012|
|Treatment * ITT AnyPAS|0.034|0.106|0.018|0.033|-0.015|0.034|
|n (N)|618 (1,270)|617 (1,268)|618 (1,270)|618 (1,267)|618 (1,263)|618 (1,263)|
|F-test (p-value)|1.19 (0.315)|2.19 (0.068)|1.32 (0.261)|1.68 (0.153)|1.73 (0.142)|1.20 (0.310)|
|**B. Instrumental variable (IV) analysis**|||||||
|Treatment|-0.150|-0.706|-0.080|-0.028|0.065|-0.019|
|Treatment * PAS only|-0.085|0.223|0.084|0.094|0.011|0.120|
|Treatment * PASnFood|-0.097|0.055|0.026|0.027|**-0.099 ***|-0.000|
|n (N)|388 (794)|388 (794)|388 (794)|388 (792)|388 (790)|388 (790)|
|F-test (p-value)|4.05 (0.670)|60,634 (<0.001)|16,730 (<0.001)|35.35 (<0.001)|31.45 (<0.001)|47.04 (<0.001)|
|Treatment|-0.150|-0.699|-0.077|-0.026|0.067|-0.017|
|Treatment * AnyPAS|-0.093|0.113|0.046|0.051|-0.059|0.043|
|n (N)|388 (794)|388 (794)|388 (794)|388 (792)|388 (790)|388 (790)|
|F-test(p-value)|4.06(0.541)|60,703(<0.001)|16,780(<0.001)|33.55(<0.001)|26.00(<0.001)|42.80(<0.001)|

_Note_ : Results are for Fixed Effect (FE) Instrumental Variables (IV) linear panel regression models. Results are adjusted for age, sex, education and treatment duration. _Source_ : Authors’ own calculations.

32
following unexpected contingencies and thus bail out efficient exporters that experience financial difficulties (Huberman and Kahn, 1988). While differentiating these two channels is notoriously hard, in the second part of the paper we try to gain intuition into the mechanisms that drive the baseline results.

In a first set of tests, we build on the prediction of the banking literature that banks’ soft budget constraints are more severe in institutional settings in which banks are more susceptible to the pressure of external parties (e.g., the government) to rollover credit to businesses, and less subject to independent monitoring of bank auditors and supervisors on their decisions to grant such rollover. Matching information from our main databases with data from the World Bank Banking Supervision Survey, we obtain that in countries where banks are less independent from the pressures of external parties and where bank managers are subject to less rigorous scrutiny of bank auditors and supervisors that prevent the release of misleading information, domestic banks reduce the dynamism of the export sector relative to stock market investors and to foreign banks (i.e., they reduce exporters’ entry, exit and turnover, in line with the soft budget constraint hypothesis).

In a second set of tests, we exploit the prediction of the banking literature that, to the extent that banks’ behavior instead reflects long-termism, this benefit should manifest itself especially following negative shocks (e.g., crises or recessions) or in settings where ex ante it is harder to write detailed contracts that can account for unexpected shocks. However, we uncover no evidence that domestic banks moderate the exit of exporters more during recessions or financial crises than in tranquil times. Moreover, the differential effect of foreign banks is no more pronounced for products characterized by higher informational opacity or contractual complexity (measured according to the methodologies in Rauch, 1999, and Nunn, 2007). Overall these findings cast doubts on the benign interpretation that domestic banks are a buffer against the realization of unforeseen shocks over the course of export projects, while they tend to support the alternative hypothesis that domestic banks indulge in a soft budget constraint behavior in the export sector. The presence of foreign banks would help mitigate this effect of bank-oriented financial systems and complement decentralized stock markets in enhancing the dynamism of the export sector.

In the last part of the paper, we investigate the implications of the baseline findings for the concentration of the export sector. Relative to capital markets, banks turn out to reduce the concentration of the export sector. As highlighted by Eaton et al. (2008), especially in developing countries, the export sector features a continuous entry and exit of producers, the majority of which however experiment with selling small amounts in foreign markets. Jointly, the results for export dynamics and for export concentration suggest that domestic banks might slow down the selection and exit of small exporters, contributing to 4
# : INVESTIGATION TIMELINE

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