rafmacalaba/datause-encoder-data huggingface dataset:
Hence, this provision allows the EU to charge tariffs at levels necessary to defend its already set threshold prices under CAP reform, as long as the duty charged does not exceed the binding. In practice, this can work similarly to a variable levy or a minimum price. The actual import tariff levied on c.i.f. prices would be the difference between the domestic mark-up and the world price (c.i.f). Hence, in periods of particularly low world prices, the EU will probably charge a high tariff so as to maintain the agreed "Community preference" but at a level not exceeding the tariff binding. If world prices are high, the EU border regime may not in practice be much different from what it would have been under CAP reform. That is, when world prices are high, the EU will charge a lower tariff so as not to exceed the commitment on "maximum duty-paid import price" to maintain the level of thresiold prices already set under CAP reform policies. The determination of the actual tariff on grains would however be shipment specific, in contrast to the pre-UR regime where the levy, calculated as the difference between the threshold price and a world price determined by the Commission, is charged on all imports irrespective of' the actual price of the shipment. Under the GATT commitment, the actual tariff will be calculated based on the price of the specific grain shipment to ensure that **the** duty-paid price lor each shipment will not exceed 155 percent of the intervention price.

Based on World Bank world price projections, the ad-valorem equivalents of' the hound specitfic tariffs lor major agricultural commodities are shown in Table 3. Relative to average protection levels in recent years and in the base period (1986-88), the post-UR base and l'inal tarii' equivalents are signif-icantly higiler in most cominodities. The dil'ference is even larger when comiipared with the average during 1979-93. It' the EU applied the maximum specilic tariffs comimiitted in the UR. the estimated post-UR ad-valorem tarif'l' equivalents in 1995 and 20)()() indicate signillicant potential increases in protection in major comiimodities relative to recent levels and relative to the average protection over the last lil'teen years.

_30_
where TEi is a binary variable indicating whether individual _i_ was exposed to any traumatic events before resettlement, and _t_ is the year indicator. As in Equation (1), we include individual and survey year fixed effects. We simultaneously estimate Equations (1) and (2) using the fixed effects IV logistic regression (IV-Logit-FE) for employment status and the fixed effects IV linear regression (IV-FE) for labor income.<sup>8</sup> Both models allow us to address reverse causality and unobserved individual heterogeneity concerns.

We now discuss the validity of the IV. A good instrumental variable should meet three conditions in our context: (i) exogeneity to the dependent variables (exogeneity condition); (ii) strong correlation with refugee’s mental health (relevance condition); and (iii) affecting labor market outcomes only through changes in mental health status over time after resettlement (exclusion restriction condition).

The exogeneity condition is satisfied since the traumatic experiences experienced by refugees are typically caused by unexpected events such as war, conflict, or natural disasters. In fact, a number of economic studies have employed such exogenous events as IVs to identify causal impacts on health and education (e.g., Alderman et al., 2006; Di Maio and Nisticò, 2019). While a theoretically possible concern is that local market conditions in the host country may be among the determinants of conflict in the home country of the refugee, this concern is unlikely to be valid in practice. The roots of violent conflicts in the origin countries often come from endemic social/economic inequalities between groups in these countries themselves (Esteban et al., 2012; McGuirk and Burke, 2020).

Regarding the relevance condition, our IV is constructed based on evidence from the epidemiological literature showing that refugees are mostly exposed to stressful and potentially life-threatening situations before resettlement, and that pre-migration traumatic experiences are

> 8 We estimate the fixed effects IV logistic regressions using the general simultaneous equation models (gsem) in Stata (Stata, 2019).

10
related to the non-interest income share in a non-linear way, with most banks beyond the point where idiosyncratic risk is minimized.

In summary, we contribute to the literature in this area by (i) documenting the trends of both activity and funding shares for an international sample of 1334 banks in 101 countries leading up to the 2007 financial crisis; (ii) investigating bank and country level determinants of these shares; and (iii) analyzing the relationship between activity and funding mix on bank risk and return. Our results have important policy implications for the debate on desirability of universal versus specialized banking models.

The remainder of this paper is organized as follows. Section 2 describes the data and documents the frequency distributions and time variation of the fee income and non-deposit funding shares. In addition, we take a first cursory look at the relationships between these two variables and bank risk and return through graphical analysis. Section 3 presents evidence on the determinants of the fee income and non-deposit funding shares. Section 4, in turn, presents evidence on the impact of the fee income and non-deposit funding shares on bank risk and return. Section 5 concludes by discussing the implication of our results for the viability of different models of the banking firm. Overall, we conclude that while universal banking can be beneficial in terms of diversifying risks and increasing returns, banking strategies that rely predominantly on generating non-interest income or attracting non-deposit funding are very risky.

# **2. The data**

# **2.1 The fee income share and the non-deposit funding share**

Bank-level data in this study are taken from Bankscope. Our international sample of banks is restricted to banks with a stock exchange listing to ensure a relatively high quality of data and 10
Table B13: Heterogeneous treatment effects on net ownership (Part B)

|Dependent variable|Num|ber of bed nets own|ed|
|---|---|---|---|
||(1)|(2)|(3)|
||OLS|OLS|OLS|
|Treatment|0.233**|0.255***|0.021|
||(0.095)|(0.085)|(0.123)|
|Treatment x male household head|0.032|||
||(0.119)|||
||[0.012]|||
|Male household head|0.161*|||
||(0.091)|||
|Treatment x 2nd household size tercile||-0.091||
|||(0.126)||
|||[0.192]||
|Treatment x 3rd household size tercile||0.100<br>(0.166)||
|||[0.030]||
|2nd household size tercile||-0.104||
|||(0.113)||
|3rd household size tercile||-0.312||
|||(0.194)||
|Treatment x 2nd wealth tercile|||0.235|
||||(0.144)|
||||[0.002]|
|Treatment x 3rd wealth tercile|||0.108|
||||(0.173)|
||||[0.118]|
|2nd wealth tercile|||-0.010|
||||(0.120)|
|3rd wealth tercile|||0.370**|
||||(0.159)|
|Observations|1496|1496|1495|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. One observation per household. The dependent variable is the number of bed nets owned by the household. Columns (1)-(4) report the difference between treatment and control groups using OLS regression (model 2) and the coefficients on interactions between the treatment status and the gender of the household head (Column 1), household size terciles (Column 2) and wealth tercile (Column 3). Standard errors clustered at village level are reported in parentheses. All specifications include controls for gender, age, education and marriage status of the respondent, household size, tribe and religion, information about access to water, dwelling characteristics, sub-zone dummies and village characteristics (size and share of women living in the village). The p-value for the F-test of _treatment_ + _tinteraction_ = 0 is reported in square brackets.

29
actively involved in adapting existing computer systems and databases to accommodate the Euro. Between 2000 and 2002, it is estimated that India earned approximately $3 billion in revenues from these Euro-related IT projects<sup>24</sup> . Clearly a contributory factor was the low relative level of programming costs in India that conferred a Ricardian comparative advantage in some sub-sectors of software. As late as 1995, after substantial wage increases because of a rising demand for Indian software, the annual wages of Indian software professionals were only 14% to 59% that of their counterparts in Switzerland, USA, Canada and the UK. Given the skills of Indian programmers, these cost savings led firms in some of the industrial countries to outsource their software development requirements to India.

While the precise sequence of events leading to a focus on say India rather than the Philippines which exhibited even lower wages is not easily reconstructed given the familiarity with English of educated Filipinos but the Indian expatriate community in the U.S. was certainly a part of the process. In addition, Indian programming firms had engaged in capital stretching efforts due to tariffs on computers that permitted the inefficient domestic hardware industry to charge high prices. Even after restrictions on hardware imports were relaxed in the late 1980s and early 1990s, the tariffs imposed on the imports made it too expensive to acquire state-of-the art computer systems or to upgrade frequently. With less than frontier computers, Indian software developers “worked hard and successfully to get every available bit and byte out of what computing power they had.”<sup>25</sup> This equipped Indian software programmers with the ability to write the optimized programs that set them apart from their potential low cost competitors, an unintended indirect benefit of India’s ISI policy.

The circumstances just described suggest the relevance of Brian Arthur’s view that idiosyncratic events, tariffs on imported hardware, the Y2K problem, and the shift to the Euro, exert positive feedback and generated a succession of mutually reinforcing benefits. In terms of industrial policy, of whatever form, it seems unlikely that any government could have foreseen and acted upon the demand generated by Y2K and the Euro or anticipated the impact on programming skills of expensive computer hardware.

> 24 IDSA (2000). 25 McDowell (1995)

37
will apply profit maximizing levels of inputs so that productivity will be independent of their endowments. If households are credit constrained, inputs will be used at less than the productivitymaximizing level and higher levels of liquidity would be associated with higher productivity of resource use. This implies that, at the margin, and to the extent that they do not affect credit constrained status, increases in certain factors may reduce rather than increase productivity in terms of output per hectare. For example, a marginal increase in operated land size could, for constrained households, reduce productivity due to its negative impact on the intensity of variable input use in the face of binding credit constraints. Similarly, the impact of changes in family labor endowments could have differential impacts depending on whether households are credit constrained or not and whether non-agricultural labor markets are accessible and function well.

# **3. Data and descriptive statistics**

Explicit elicitation of different types of constraints in the fourth round of a longitudinal rural household survey in Ethiopia’s Amhara region suggests that a large number of producers are credit constrained and that, in addition to supply-side considerations, high levels of risk are a key factor. While constrained and unconstrained farmers differ in predictable ways (e.g., unconstrained ones are more affluent), agricultural productivity is higher for unconstrained farmers only in one of the two regions. Being credit constrained is thus not always synonymous with failing to achieve full productive potential.

# **3.1 Sample and nature of data used**

Our analysis is based on the fourth round of a longitudinal rural household survey in the Amhara region of Ethiopia’s highlands conducted by the Ethiopian Development Research Institute (EDRI), Gothenburg University, and the World Bank in 2007. The survey includes information on demographics, resource endowments, agricultural production, and input, output, and credit markets participation for 1700 randomly selected households in 14 villages ( _kebeles_ ) in six districts ( _woredas_ ) in South Wollo and East Gojam zones of Amhara (Deininger _et al._ 2008). Information on agricultural production was collected for the main season ( _meher_ , i.e., September-February) of the 2007 agricultural year. We use data from 1587 households who engaged in crop production and have complete information on the variables of interest.

Observed loan applications and lenders’ decisions and responses to qualitative questions on borrower’s needs and perceptions are used to infer a households’ semi-formal credit regime,<sup>3</sup> in line with previous studies on credit markets (Barham _et al._ 1996; Feder _et al._ 1990; Guirkinger and Boucher 2008; Jappelli 1990). Loan applicants were classified into three groups, namely those who had (i) applications fully or partly rejected (quantity rationed), (ii) received the requested amount but wanted to borrow more under

> 3 Note that this method does not allow to measure the intensity of credit constraints (Diagne 1999).

7
**Table 1: Randomization results**

|Household Characteristics|# Obs.|Control<br>(C)|Treatment<br>(T)|productive<br>investment<br>package(T3)|P-value<br>(T-C)|P-value<br>(T3-C)|
|---|---|---|---|---|---|---|
|Age household head|1596|43.65|43.59|44.28|(0.94)|(0.50)|
|Male household head|1597|0.850|0.859|0.875|(0.71)|(0.37)|
|Literate household head|1597|0.618|0.652|0.654|(0.28)|(0.36)|
|# years education household head|1496|2.734|2.721|2.785|(0.94)|(0.80)|
|<br>Household size|1597|6.882|6.918|7.041|(0.83)|(0.41)|
|Number of hh. members under 5|1597|0.804|0.766|0.778|(0.60)|(0.74)|
|Number of hh. members 5-14|1597|2.824|2.919|2.934|(0.16)|(0.19)|
|Number of hh. members 15-24|1597|1.060|1.050|1.081|(0.89)|(0.82)|
|Number of hh. members 25-64|1597|2.039|2.050|2.067|(0.81)|(0.63)|
|Number of hh. members 65 or older|1597|0.143|0.119|0.148|(0.37)|(0.87)|
|Number of rooms in the house|1597|1.652|1.601|1.626|(0.61)|(0.81)|
|Own land|1597|0.681|0.656|0.667|(0.56)|(0.76)|
|Distance to school (min)|1597|0.327|0.261|0.262|(0.10)|(0.11)|
|Distance to health center(min)|1597|1.216|1.156|1.146|(0.70)|(0.66)|
|Distance to municipal headquarters|1597|1.693|1.578|1.623|(0.53)|(0.72)|
|At least one household member active in:|||||||
|Self-employment in agriculture|1597|0.911|0.880|0.901|(0.26)|(0.73)|
|Self-employment in livestock|1597|0.536|0.474|0.488|(0.36)|(0.52)|
|Wage labor in agriculture|1597|0.592|0.651|0.697|(0.19)|(0.04)|
|Elaboration food products|1597|0.060|0.075|0.079|(0.50)|(0.45)|
|Manufacturing (self-employment)|1597|0.036|0.029|0.036|(0.67)|(0.97)|
|Commercial activities|1597|0.082|0.080|0.066|(0.91)|(0.38)|
|Services (self-employment)|1597|0.053|0.049|0.056|(0.80)|(0.89)|
|Wage employment|1597|0.174|0.191|0.191|(0.58)|(0.59)|
|Skilled wage employment|1597|0.036|0.047|0.056|(0.43)|(0.20)|
|Total consumption per capita (cordoba)|1584|4039|4248|4255|(0.42)|(0.47)|
|Total income per capita (cordoba)|1584|3278|3602|3611|(0.13)|(0.23)|
|<br>Income from agricultural wage labor|1589|2150|1959|2024|(0.49)|(0.67)|
|income for elaboration food products|1590|115.9|120.5|124.4|(0.92)|(0.88)|
|<br>income from commercial activities|1589|175.4|205.7|154.6|(0.62)|(0.76)|
|income from manufacturing (self-employment)|1589|26.97|21.5|30.0|(0.73)|(0.86)|
|<br>Income from services (self-employment)|1590|155.7|125.0|105.0|(0.63)|(0.43)|
|<br>Income from nonagricultural wage labor|1578|681.3|975.6|904.2|(0.12)|(0.34)|
|<br>Income from temporary migration|1592|2663|2838|2432|(0.69)|(0.62)|
|Monetary income from agricultural self-employment|1591|728.5|829.5|865.4|(0.50)|(0.41)|
|Monetary income from livestock|1591|737.1|651.2|558.1|(0.49)|(0.20)|

_NOTE_ : P-value account for clustering at community level. All values refer to intent-to-treat 22
# **Table 4. Base Specification, City-Industry Mean Specification**

||<br>|**Lgrow**||<br>|**LPgrow**||
|---|---|---|---|---|---|---|
||**coef**||**se**|**coef**||**se**|
|Corruption Obstaclec|-0.012||0.006|0.001||0.017|
|Security Costc|-0.109**||0.038|-0.329||0.214|
|Outagec|-0.004||0.006|0.000||0.012|
|Webc|0.036**||0.007|0.054*||0.022|
|Skill Obstaclec|0.013||0.007|0.015||0.017|
|Overdraft Facilityc|0.033**||0.009|0.040*||0.016|
|Trade Creditc|-0.001||0.007|0.075**||0.019|
|L Regu Obstaclec|0.002||0.007|-0.003||0.018|
|Tax Rate Obstaclec|-0.002||0.006|-0.025||0.019|
|Land Access Obstaclec|-0.005||0.009|0.018||0.018|
|In Big City|0.002||0.005|-0.002||0.010|
|Capacity Agglomerationc|0.066**||0.022|0.015||0.030|
|Inf Competitionc|-0.016**||0.006|-0.009||0.011|
|Other averages of firm controls||Yes|||Yes||
|country, industry, year FEs||Yes|||Yes||
|N||4,219|||3,695||
|R<sup>2</sup>Within: all BE variables||15.6%|||37.9%||
|R<sup>2</sup>Within: excluding AE||14.8%|||37.8%||
|R<sup>2</sup>Within: excluding Refined BE||15.6%|||37.8%||
|R<sup>2</sup>Within: excluding Basic BE||13.0%|||36.6%||
|R<sup>2</sup>Within: excluding Basic Protection||15.3%|||37.7%||
|R<sup>2</sup>Within: excluding Human Capital||15.5%|||37.8%||
|R<sup>2</sup>Within: excluding Infrastructure||14.5%|||37.5%||
|R<sup>2</sup>Within: excludingFinance||14.3%|||37.1%||
|R<sup>2</sup>Between: all BE variables||3.6%|||10.8%||
|R<sup>2</sup>Between: excluding AE||5.0%|||11.1%||
|R<sup>2</sup>Between: excluding Refined BE||4.0%|||10.6%||
|R<sup>2</sup>Between: excluding Basic BE||3.1%|||5.8%||
|R<sup>2</sup>Between: excluding Basic Protection||4.2%|||11.4%||
|R<sup>2</sup>Between: excluding Human Capital||3.8%|||10.7%||
|R<sup>2</sup>Between: excluding Infrastructure<br>||3.7%|||8.0%||
|R<sup>2</sup>Between: excludingFinance||2.5%|||7.7%||

*, ** represent statistical significance at the 5 and 1 percent levels.  Heteroskedasticity-corrected standard errors reported.

The unit of observation is an country-city-industry cell.

38
opposite trend is occurring in a number of other emerging markets. At the same time, foreign banks are increasing their presence in LAC7 and emerging markets more broadly; the LAC region and Eastern Europe have the highest penetrations, which are noticeably larger than those in Asia, China, and other developed economies (Claessens and van Horen 2013). The increase in concentration might raise concerns about banking competition in the LAC region. When fewer and larger banks (higher concentration) exist, banks might be more likely to engage in anticompetitive behavior (Berger 1995). The literature has linked bank competition with lower prices for banking products, increased access to finance, and greater bank efficiency. However, some studies have shown that, at times, concentration is not a reliable measure of competition and that the link between concentration and performance is not always negative (see, for example, Cetorelli 1999; Jackson 1992). Empirically, Anzoategui, Martínez Pería, and Rocha (2010) show that although banking systems in LAC countries exhibit a high degree of concentration, competition does not seem to have declined during the 1990s and 2000s.

**3.2. Bond Markets**

Despite their considerable expansion between 2000 and 2009, private (corporate and financial institutions) bond markets in LAC7 countries remained relatively small in comparison to those in more developed countries and to public bond markets. For example, private bond market capitalization typically represented around 40 percent of GDP in developed countries during the 2000s, whereas it stood at only 10 percent and 23 percent across LAC7 and Asian countries, respectively, over the same period (figure 7a). A positive development is that private bond markets across LAC7 countries have grown more as a percentage of GDP than government bonds, gaining space in relative terms and hinting at less crowding out by the public sector. Issuance data also suggest a significant size difference between private and public bond markets. While issuance of bonds by the private sector stood at around 1 percent of GDP per year in LAC7 countries, public sector bond issuance was around 5 percent of GDP on average for most of the 2000s.

Bond market liquidity remains a concern in LAC7 countries. While turnover between 2008 and 2009 was around 60 percent in G-7 countries and reached 146 percent on average across other developed nations, it was merely 12 percent in LAC7 countries (figure 7b). In addition, the differences in turnover levels are significant relative to other emerging markets, some of which have experienced increased liquidity over the past 10 years. Trading volumes in secondary markets have been increasing in emerging Asian countries, for example, growing 11
# **Tables**

|**TABLE I. Descrip**<br>Variables|**tive Stati**<br>(1)<br>Yea|**stics by**<br>(2)<br>r 2006/0|**Survey R**<br>(3)<br>7|**ound**<br>(4)<br>Yea|(5)<br>r 2009/1|(6)<br>0|
|---|---|---|---|---|---|---|
||Mean|S.D.|N|Mean|S.D.|N|
|_Panel A. Maternal characteristics_|||||||
|Age of the mother (years)|31.43|6.64|1095|33.71|6.64|1095|
|Indigenous ethnic group|0.16|0.37|1095|--|--|--|
|Less than primary school|0.57|0.50|1095|--|--|--|
|Literate|0.79|0.41|1095|--|--|--|
|Attended antenatal care in 2002|0.94|0.23|1095|--|--|--|
|_Panel B. Child characteristics_|||||||
|Child is a boy|0.50|0.50|1095|--|--|--|
|Weight at birth|3.21|0.51|1095|--|--|--|
|Long-term health problems|0.09|0.09|1095|--|--|--|
|Age (in months)|63.5|4.71|1095|94.9|3.58|1095|
|Child is the eldest|0.16|0.37|1095|0.23|0.42|1095|
|Height for age Z-score|-1.42|1.08|1095|-1.05|1.02|1095|
|PPVT score (raw)|29.9|17.4|1095|47.6|12.9|1095|
|PPVT Z-score|0.06|0.98|1095|0.07|0.95|1095|
|_Panel C. Household characteristics_|||||||
|Wealth index|0.49|0.22|1095|0.56|0.20|1095|
|Housing quality index|0.41|0.24|1095|0.44|0.24|1095|
|Consumption of durable goods index|0.37|0.23|1095|0.45|0.23|1095|
|Live in urban area|0.58|0.49|1095|0.76|0.43|1095|
|Household size|5.52|2.13|1095|5.44|1.94|1095|
|School aged children in the household<br>(n)|1.33|1.25|1095|1.09|1.05|1095|
|_Panel D. Community characteristics_|||||||
|Violent crime in community|0.33|0.47|1095|0.36|0.48|1095|
|<br>Social assistance(education)available<br>Table I present summary statistics (mean an<br>These variables are available in the first thre|0.95<br>d standard<br>e rounds of|0.22<br>deviation)<br>the Peruv|1095<br>of the var<br>ian Young|0.98<br>iables used<br>Lives Surv|0.13<br>in the ana<br>ey. Sampl|1095<br>lysis.<br>e is|

Table I present summary statistics (mean and standard deviation) of the variables used in the analysis. These variables are available in the first three rounds of the Peruvian Young Lives Survey. Sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009. A detailed description of the rest of variables is presented in Appendix section.

26