rafmacalaba/datause-encoder-data huggingface dataset:
36

- Lokshin, M., Bontch-Osmolovski, M., and Glinskaya, E.  (2007) _Work-related Migration and Poverty Reduction in Nepal_ .  World Bank Policy Research Working Paper 4231.  World Bank, Washington, DC.

- Maddala, G.  (1983) _Limited Dependent and Qualitative Variables in Econometrics._ Cambridge: Cambridge University Press.

- Massey, D.  (1987) _Return to Azatlan:  The Social Process of International Migration from Western Mexico._ Berkeley, CA:  University of California.

- Massey, D., Goldring, L, and Durand, J.  (1994)  Continuities in Transnational Migration:  An Analysis of Nineteen Mexican Communities, _American Journal of Sociology_ 99, pp. 1492-1533.

- Mora, J., Taylor, J. E.  (2005) _Determinants of International Migration:  Disentangling Individual, Household and Community Effects_ , Unpublished draft manuscript, Department of Agricultural Economics, University of California, Davis, USA.

Munshi, K. (2003) Networks in the Modern Economy: Mexican Migrants in the US Labor Market, _Quarterly Journal of Economics_ 118, pp. 549-599.

Ray, D. (1998) _Development Economics._ Princeton University Press. Princeton, NJ.

- Stock, J.H., Yogo, M. (2005) Testing for Weak Instruments in Linear IV Regression. In: Stock, J.H., Andrews, D.W.K., (Eds.), _Identification and Inference for Econometric Models: A Festchrift in Honor of Thomas Rothenberg._ Cambridge University Press, Cambridge, pp. 80-108. (Ch. 5).

- Schmertmann, C.  (1994)  Selectivity Bias Correction Method in Polychotomous Sample Selection Models, _Journal of Econometrics_ 60, pp.101-132.

- Schultz, T. P.  (1982)  Notes on the Estimation of Migration Functions, in:  R. Sabot (ed) _Migration and the Labour Force_ .  Westview Press, Boulder, CO, USA.

- Taylor, J. E.  (1987)  Undocumented Mexico-US Migration and the Returns to Households in Rural Mexico, _American Journal of Agricultural Economics_ 60, pp. 626-638.

- Taylor, J. E., Mora, J., Adams, R.  (2005) _Remittances, Inequality and Poverty:  Evidence from Rural Mexico_ , Unpublished draft manuscript, Department of Agricultural Economics, University of California, Davis, USA.

- Todaro, M.  (1976) _Internal Migration in Developing Countries._ International Labour Office, Geneva, Switzerland.

Woodruff, C. and Zenteno, R. (2007) Migration Networks and Micro-Enterprises in Mexico, _Journal of Development Economics_ 82, pp. 509-528.

- Wooldridge, J.  (2002) _Econometric Analysis of Cross Section and Panel Data._ Cambridge: MIT Press.
- 6.1 Water availability

- 6.2 Traffic congestion

- 6.3 Electricity

- 6.4 Telephone 6.5 Mail

- 6.6 Public transport 6.7 Airport

7. Recreation (10 points)

- 7.1 Variety of restaurants

- 7.2 Cinemas

- 7.3 Theatrical and musical performances 7.4 Sport and leisure activities

8. Consumer goods (10 points)

- 8.1 Food (Fruit and Vegetables)

- 8.2 Food (Meat and Fish)

- 8.3 Daily consumption items

- 8.4 Alcoholic beverages 8.5 Automobiles

9. Housing (10 points)

- 9.1 Household appliances and furniture 9.2 Household maintenance and repair 9.3 Housing

10. Natural environment (10 points)

- 10.1 Climate

- 10.2 Record of natural disasters

**Table 8-Worldwide Quality of Living Survey 2006** _Base City: New York – USA ( =100)_

|**Rank**<br>**2006**|**Rank**<br>**2005**|**City**|**Country**|**Index**<br>**2006**|**Index**<br>**2005**|
|---|---|---|---|---|---|
|1|_1_|ZURICH|Switzerland|108.2|_108.0_|
|2|_2_|GENEVA|Switzerland|108.1|_107.9_|
|3|_3_|VANCOUVER|Canada|107.7|_107.4_|
|4|_3_|VIENNA|Austria|107.5|_107.4_|
|5|_8_|AUCKLAND|New Zealand|107.3|_106.5_|
|6|_5_|DUSSELDORF|Germany|107.2|_107.0_|
|7|_6_|FRANKFURT|Germany|107.0|_106.8_|
|8|_7_|MUNICH|Germany|106.8|_106.7_|

38
**Table 15. Empowerment outcomes (ITT), disaggregated by** **<u>gender, 2010, 2011 and 2012 cohorts</u>**

|||Pooled 2010<br>IPSW|‐2012 Cohorts<br>model||Difference<br>between men|
|---|---|---|---|---|---|
||Baseline mean for men|Baseline mean for women|Men|Women|and women|
||[Std Dev]|[Std Dev]|(1)|(2)|(3)|
|Any money of your own (1=Yes)|0.720|0.658|0.076**|0.039|0.037|
||[0.449]|[0.475]|(0.036)|(0.031)|(0.047)|
|Control over Earnings (1=Yes), if|0.729|0.608|‐0.020|0.112**|‐0.132*|
|respondent has earnings|[0.445]|[0.488]|(0.042)|(0.052)|(0.068)|
|Control Over Savings (1=Yes), if|0.557|0.576|0.087|0.016|0.071|
|respondent has savings|[0.497]|[0.494]|(0.064)|(0.038)|(0.075)|
|Control over household spending (#|3.535|3.264|0.519***|0.103|0.415*|
|categories)|[3.048]|[3.441]|(0.167)|(0.152)|(0.232)|
|Mobility (# places visited in last|5.315|4.363|‐0.175|0.119|‐0.294*|
|month out of 9)|[1.512]|[1.665]|(0.131)|(0.103)|(0.165)|
|Any Mentor Related to Work|0.730|0.527|0.005|0.102***|‐0.097**|
|(1=Yes)|[0.444]|[0.499]|(0.037)|(0.031)|(0.047)|
|Entrepreneurship Score (0‐32)|15.644|14.529|0.551|0.518|0.033|
||[5.472]|[6.294]|(0.400)|(0.372)|(0.559)|
|Financial Literacy (1=Yes)|0.670|0.584|‐0.003|0.034|‐0.037|
||[0.470]|[0.493]|(0.043)|(0.035)|(0.055)|
|Self‐Regulation Score (0‐16)|10.638|10.267|0.380**|0.373***|0.008|
||[2.306]|[2.372]|(0.182)|(0.144)|(0.229)|
|Self‐Confidence Score (0‐27)|19.165|18.927|0.025|0.434**|‐0.410|
||[3.232]|[3.356]|(0.264)|(0.201)|(0.339)|
|Clustered Standard Errors|||Yes|Yes|Yes|

Notes: Standard errors (reported in brackets) clustered at the event level where possible. *,**, and *** denote significance at the 10% level, 5% level, and 1% level.

38
# **Do We Need Big Banks? Evidence on Performance, Strategy and Market Discipline**

## Asli Demirgüç-Kunt<sup>1</sup> World Bank

## Harry Huizinga<sup>1</sup> Tilburg University and CEPR

**Key words** : Bank size, systemic risk, market discipline **JEL classifications** : G21,G28

> 1 Demirguc-Kunt: World Bank (Ademirguckunt@worldbank.org); Huizinga: Tilburg University and CEPR (Huizinga@uvt.nl). We thank Ata Can Bertay for research assistance. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.

1
only for firms to adopt advanced technology but also to make the complementary investments needed to absorb and use efficiently those advanced technologies. Our findings provide evidence of an important micro channel through which finance may affect growth in ECA: i.e., by increasing the likelihood of adoption of advanced technology by firms.

Better infrastructure (indicated by a higher value of the index) is significantly positively associated with web-use. Since our measure of infrastructure captures the quality of the communications network in the firm’s location, this finding simply reflects the fact that such quality is likely to be fundamental for the internet technology to be efficiently used by the firm. However better infrastructure is negatively associated with ISO certification, significantly so for the BEEPS 2002 sample. This result is at odds with the findings for Brazil in World Bank (2005b). One possible rationale for this counter-intuitive sign is that the effect of infrastructure on ISO certification operates through other variables also included in our regressions – e.g., foreign ownership – and as a result, the coefficient on the infrastructure index captures only partially its effect on ISO certification. Another possible rationale is that our infrastructure index does not capture the quality of the road infrastructure. Accounting for the costs of remoteness and the risk of losses in transit would be important to determine the likelihood of ISO certification by a firm. Nevertheless, we should note that our infrastructure index does exhibit sensible values, which are substantially higher for EU-8 and South Eastern European countries plus Turkey (with the exception of Albania) which are characterized by better infrastructure than for CIS countries which are characterized by worse infrastructure.

Our findings on complementary inputs are not driven by any specific sub-group of countries within the ECA region. In fact, we show in Table 3 the results from estimating Equation (2) separately across three country groups: EU-8 countries, CIS countries, and South Eastern European countries plus Turkey.<sup>13</sup> The importance of human capital, R&D intensity, and access to finance for ISO certification and for web-use is confirmed in the three country groups. We also find a positive effect of better infrastructure on web-use and a negative – although weak – effect of better infrastructure on ISO certification in the three country groups.

Finally, we should note that our results on complementary inputs do not suffer from multicollinearity problems since the coefficients for the BEEPS 2002 and the BEEPS 2005 samples are qualitatively - and even quantitatively - almost unchanged when we estimate a variant of Equation

> 13 The group of South Eastern European countries includes Albania, Bosnia and Herzegovina, Bulgaria, Croatia, FYR Macedonia, Romania, and Serbia and Montenegro.

- 11 -
# TABLE 8 ASSESSING THE **EFFECUS** OF TAX **POUCES** ON KOREAN GROWTH **USIG** THE 1982 **BASE MODEL**

||Contribution of<br>indirect tax<br>component of<br>outward oriented<br>Korean growth<br>stlategy|Contribution of<br>direct tax<br>component of<br>outward oriented<br>Korean growth<br>strategy|Contribution of<br>combined tax<br>component of<br>outward oriented<br>Korean growth<br>strategy|Contribution of<br>both tax and non-tx<br>com'rponents<br>of<br>outward oriented<br>Korean growth<br>strategy|Acual average<br>amnual growth<br>rates|
|---|---|---|---|---|---|
|Annual Aveage<br>Growth Rate (%)||||||
|GDP|0.51|0.03|0.54|1.40|8.65|
|Exrs<br>of<br>**Manufictures**|1.01|0.07|1.07|2.64|35.37|
|Imports of<br>Agriculure|1.04|0.07|1.10|2.66|11 <sup>9a</sup>|
||1982 base<br>year model<br>without<br>1982 base<br>1982<br>year model<br>indirect|1982 base<br>year model<br>without 1982|1982 base<br>year model|1982 base<br>year model|Actual|
||with 1982<br>tax|direct tax|with tax policy|with export policy|Distnbutionb|
|Distribution<br>of Employment (%)|policies<br>policies|policies|neutral mix|neutral mix|1962c 1982|
|Agriculture|67.35<br>70.63|67.32|70.63|73.27|63.1<br>50.6|
|Manufacturing|32.67<br>29.37|32.68|29.37|26.73|36.9<br>49.4|

Notes: 8F*umus are based on imports of food and live animals blbe distribution is between agriculture and nonagriculture. cBased on the 1963 distribution.
downturns not having been severe enough to change U.S. government policies and firm fundamentals, as happened in middle-income and developing borrower countries.

Surplus countries, by contrast, in general show only temporary reversals. Unlike deficit countries where there may be pressure to change policies and investment behavior following a costly recession and capital outflow, surplus countries do not experience such an impetus for change.

# **V.  Which Products Are Most Affected?**

To examine product level data, we use trade value data reported by Comtrade and convert data to constant dollars using the US CPI index (base year 2000). Figure 13 shows trade growth across the episodes. We see on average a 5 percent decline real merchandise trade as compared with the 1 percent decline in aggregate trade volume.<sup>9</sup> This largely reflects the effect of exchange rates and commodity prices, both of which are better accounted for in the real trade in goods and services, as measured above. In addition, the shape is different, with a more gradual improvement, in part reflected the protracted decline in commodity prices.

Figure 14 shows export growth by product. While all industries exhibit a slowdown, it is least severe in food and beverages. In addition, trade in some durable goods, such as washing machines, driers and refrigerators, drop less sharply though take much longer to recover. In part, this could reflect a slower decline and recovery, as orders need to be placed in advance. Trade in automobiles shows a slow dip and a quick

> 9 The aggregate real trade figures above are converted from real local currency series using one exchange rate into dollars and then summed—this means that these series will be different. The real local series take into account price fluctuations in imports.   In addition, the aggregate figures include services trade.

11
The short- and long-term outcomes of refugees and IDPs will be driven by both characteristics of refugees and IDPs and the country or community hosting them and how these interact. These factors are discussed next.

# **3 Refugees and IDPs are no ordinary migrants**

Refugees and IDPs differ from those migrating primarily for economic reasons in at least two important dimensions: they leave their countries of origin in different circumstances and they might have experienced shocks and trauma that could affect their subsequent socioeconomic outcomes in their locations of destination.

## **3.1 Selection into migration**

The decision to migrate is typically understood to be the result of the comparison of expected welfare in the origin and destination countries, net of (expected) migration costs (Roy, 1951; Sjaastad, 1962; Borjas, 1987). Unlike economic migrants, a forced migrant’s first move is likely based on safety considerations and proximity instead of economic considerations. Anecdotal evidence also suggests that forced migrants have little time to prepare for their move. For instance, half of the Syrian refugees surveyed in Kurdistan and more than onethird of those surveyed in Jordan had no more than one day to prepare for their move (Krishnan et al., 2020).

Whether, when, and where people move is influenced not only by awareness and experience of violence but also by the opportunities they have to move (Schon, 2019). Who leaves depends on conflict dynamics and varies between contexts in countries of origin (Ib ́a ̃nez, 2014; Beber et al., 2021). If forced migrants are positively or negatively selected with regard to their human capital and welfare also differs between countries of destination. Compared to the Syrian labor force before the conflict, Syrian refugees in neighboring countries have lower levels of education and are less likely to have worked in a (high) skilled profession (Krishnan et al., 2020; World Bank Group, 2018). In contrast, the education level of Syrian refugees in Europe is high compared to their country of origin (Aksoy and Poutvaara, 2021; Br ̈ucker et al., 2020; Buber-Ennser et al., 2016). Furthermore, asylum seekers surveyed in Italy in 5
and tariffs on capital and consumer goods. As late as 1991, the Indian economy was one of the most heavily protected economies globally with ERPs averaging 125 percent; the highest was 355 percent (Krueger, 2002). A classic example of the government‘s commitment to drive industrialization was that goods such as cereals, metals, petroleum, ores and fertilizer could be imported by only the government of India (Joshi and Little, 1996, 63).

It is curious that during the 1950 – 1970s, nearly all developing countries pursued ― _dirigiste_ capitalism‖ but except for Japan,<sup>18</sup> Korea and other East Asian Tigers, they did not succeed. _Why?_ The NSE contends that the crux of Japan and East Asia‘s success was that their development followed closely their comparative advantage or was CAF and their governments played the facilitation role (Lin 2009, Lin and Monga 2010, Lin and Monga 2011). In contrast, in the 1960s and 1970s, across Latin America, Africa and South Asia well-intended governments adopted IS and protection to achieve dynamic growth in industries that were CAD according to their endowment structures. They assigned a high priority to the development of capital-intensive heavy industries when, in fact, capital was scarce. To compensate for the absence of private firms in capital-intensive sectors, governments identified them and leveraged large state owned enterprises to produce capital-intensive products.

**Table 3.2: Examples of developing countries that followed IS**

**to achieve CAD industrialization**

|Country|Industry|Time|Main producer<br>at that time|Real GDP per<br>capita<br>latecomer<br>country|Real GDP<br>per capita<br>leading<br>country|Income ratio<br>– follower<br>versus leader|
|---|---|---|---|---|---|---|
|**China**|Automobile|1950s|USA|577|10,897|5%|
|**DRC**|Automobile|1970s|USA|761|16,284|5%|
|**Egypt**|Iron, steel,<br>chemicals|1950s|USA|885|10,897|8%|
|**India**|Automobile|1950s|USA|676|10,897|6%|
|**Indonesia**|Ships|1960s|Netherlands|983|9798|10%|
|**Senegal**|Trucks|1960s|USA|1511|13,419|11%|
|**Turkey**|Automobile|1950s|USA|2093|10,897|19%|
|**Zambia**|Automobile|1970s|USA|1041|16,284|6%|

Source: J.Lin, 2011, Table 3.1.

> 18 Japan‘s system of administered protection was very opaque and its distribution system was heavily biased against imports ((H. Hughes and A.O. Krueger, 1983 – Effects of Protection in Developed Countries in Developing countries‘ Exports of Manufactures pp 17).

32
reviewing what a typical business training program entails, and the challenges of learning whether such programs work or not. I discuss what is realistic to expect from business training given the length of such programs and what we know about the returns to education and capital from other settings. A general point from this is that, even if training works as well as formal education, the gains from interventions currently in use may be difficult to detect. I then summarize in a couple of simple figures the range of estimates of the impacts of business training on business profits and sales. The results show very wide confidence intervals from many studies, which I use to reemphasize the point that not finding a significant effect of training is not the same as finding that training has no effect. However, a meta-analysis of what we learn from all these studies combined suggests an average impact of 10 percent on profits (95 percent confidence interval (C.I.) of +4, +16) and 5 percent on sales (95% C.I.: 0.2, 9.2). That is, that overall training does deliver some benefit to firms, just a benefit that is too small for most experiments to have detected.

I then turn to discussing two areas where I see a lot of innovation and promise in efforts to improve business training. The first is in considering alternatives to traditional classroom-based business training that aims to teach a broad range of basic business practices like accounting, marketing, human resource management, inventory management and forward planning. These alternatives use psychology, heuristics, incorporate the role of gender, use peers or mentors, aim to customize practices to local context, or use more technical lean manufacturing approaches all in efforts to help firms improve. A meta-analysis of these approaches finds average impacts of 15 percent on profits (95% C.I.: +8, +22) and 11 percent on sales (95% C.I.: +3, +20), suggesting an improvement over traditional training.

However, to date most training studies have been conducted on relatively small scales under close researcher control. The second issue I consider is then how to deliver business training at scale, so that it can reach tens of thousands or even millions of firms. I consider the roles of using the market, using online technologies, and using filtering or funnel approaches to offer more services to the firms that will benefit most from them. I then conclude by highlighting what I see as the pressing open research and policy questions given the existing state of evidence.

My focus is on business training programs for the typical micro and small firms in developing countries. Many of the lessons will also be relevant for small firms in developed countries, for which there are far fewer experimental evaluations (Fairlie et al. 2015 is one exception). I do not 4
side. Following Baldwin and Taglioni (2006), importer-year and exporter-year fixed effects are used.

Fourth, EIAs and their particular clauses, such as harmonization, may be endogenous to trade flows. One approach to address this issue consists of using instrumental-variable techniques. Critical to this approach is the selection of instruments, which should be correlated with the probability of an EIA between two countries but uncorrelated with their bilateral trade flows. In their study using disaggregated data, Chen and Mattoo (2008) use standards harmonization in adjacent industries (i.e., industries classified in the same two-digit sector) as an instrument. Because our focus is on aggregated trade flows, the definition of instruments is more complex in our case. As an alternative to IV estimation, we rely on country-pair fixed effects to control for the potential endogeneity of EIAs and their clauses, following Baier and Bergstrand (2007) or Anderson and Yotov (2011). Although this approach may not eliminate all of the potential endogeneity bias, it is intuitively plausible that standards harmonization, particularly on regional standards, correlates positively with trade flows, biasing coefficients upward. Our key result is that harmonization depresses SouthSouth trade, and harmonization on regional standards _fails_ to raise North-South trade. This result is unlikely to be driven by endogeneity bias. Furthermore, as highlighted by Helpman et al. (2008) and Arkolakis et al. (2012), firm heterogeneity may bias aggregate trade flow results and affect our empirical results. However, the decision by firms to enter markets is likely dominated by cross-sectional variation. Therefore, the inclusion of country-pair fixed effects in our estimations should largely account for this potential firm-heterogeneity bias.

Finally, another source of bias may arise from the potential dependence between some EIAs. Although the diversity in TBT harmonization clauses included in EIAs suggests that this source of bias is limited, this issue is controlled for by clustering errors. For North-South trade and because our main variables of interest vary by country-pair, errors are clustered at 14
i) Elimination of non-existing HS codes: First, we combine all the codes existing under the three different HS classifications (HS1996, HS2002 and HS2007) and end up with a unique aggregated list of 6065 HS 6-digit categories.<sup>18</sup> Next, we eliminate the observations with an HS 6-digit code that is not included in that aggregated list. This elimination accounts for about 0.5 percent of total exports in our cross-country raw dataset.<sup>19</sup> ii) <u>Creation of a time-consistent HS classification: While the HS 6-digit classification</u> allows comparisons across countries in a given year, it has undergone transformations over time. The World Customs Organization (WCO) revises the HS classification on the basis of the value of trade realized under each product during the previous period. Three major revisions took place in years 1996, 2002, and 2007.<sup>20</sup> The modifications introduced in each of these revisions have taken two forms: i) two different codes with low trade volume were converted into a single code and ii) an existing code with an increasing trade volume was split into various codes. For example, code 030269 (other fish, fresh or chilled, excluding fish fillets or other fish meat) which included swordfish and toothfish in the HS2002 classification was split into codes 030267 (swordfish), 030268 (toothfish), and 030269 (for other fish) in the HS2007 classification. These modifications create problems for the tracking of trade volumes for certain products over time. In the example above, exports of swordfish under code 030267 would appear as a new export from 2007 onwards, while in reality they might have already been exported before but were recorded under the code 030269.

> 18 The number of HS categories included in the original classifications HS 1996, HS 2002, and HS 2007 are 5209, 5224, and 5053, respectively.

> 19 Most of this elimination results from eliminating observations with a product code belonging to HS Chapter 99 as this is reserved for national use and the HS 6-digit codes under this chapter differ across countries. 20 In addition to these major transformations, there are also smaller modifications introduced at the end of every year.

10