rafmacalaba/datause-encoder-data huggingface dataset:
firm-destination size by definition, and thus obscures the firm-size effect on exports in a gravity equation.

Several of the stylized facts uncovered using the Database are broadly consistent with recent trade models with heterogeneous firms such as those reviewed in Bernard, Jensen, Redding, and Schott (2011) and Redding (2011), while other facts point to the need to extend and modify the existing models to accommodate them. In particular, with respect to dynamics, the high and correlated entry and exit rates point to tremendous uncertainty when firms enter export markets.

The rest of the paper is structured as follows. Section II describes in detail the construction of the Database based on customs exporter-level data in each country while Section III lists, defines and presents summary statistics on the measures included in the Database. Section IV presents six stylized facts based on the Database. Section V discusses possible avenues for future research and policy analysis using the Database.

# **II. Constructing the Export Dynamics Database Using Customs Data at the Exporter-Level**

The measures included in the Database are computed using customs data from 45 countries at the exporter-product-destination-year level.<sup>8</sup> Pooling across the datasets for all countries, we obtain 15 million unique observations at the country-firm-product-destination-year level<sup>9</sup> This is the raw dataset that we use to construct the Database.

> 8 The providers of the raw datasets for each country were mostly governmental agencies, mainly customs offices. Appendix 1 provides a complete list of the countries included in the Database, the periods for which data is available, and the data sources. For Brazil, Egypt, Estonia, Laos, New Zealand, Norway, Portugal, Spain, Sweden, and Turkey we calculated the measures without having permanent access to the raw customs data at the exporter-product-destination-year level. A few of the data providers authorize the access by researchers outside the World Bank to the raw customs data at the exporter-product-destinationyear level. The list of countries for which such data access is authorized is available (and subject to updates) at http://econ.worldbank.org/exporter-dynamics-database.

> 9 This number of observations covers the countries for which we have access to the raw exporter-level customs data (i.e., it does not account for the observations for Brazil, Egypt, Estonia, Laos, New Zealand, Norway, Portugal, Spain, Sweden, and Turkey).

6
**Appendix Table 4:** 2SLS estimates of determinants of female enrollment in secondary <u>schools in 2003 [Dependent variable: Total female enrollment in grades 6-10]</u>

||Model 1||Model 2||Model3||Model 4||
|---|---|---|---|---|---|---|---|---|
||**2SLS**|**1 stage**|**2SLS**|**1 stage**|**2SLS**|**1 stage**|**2SLS**|**1 stage**|
|# of BRAC schools in the union (in 2003)|0.007||0.013||0.007||0.014||
||(0.6)||(1.14)||(0.62)||(1.28)||
|School is co-ed (base cat: female school)|||0.084|-0.149|||0.086|-0.165|
||||(8.74)**|-1|||(9.10)**|-1.15|
|Age of school|||-0.003|-0.003|||-0.003|-0.003|
||||(16.30)**|-1.17|||(16.40)**|-1.02|
|Union has a govt. school|0.008|0.832|0.021|0.261|0.002|0.437|0.026|0.095|
||-0.36|(2.61)**|-1.02|-0.79|-0.13|-1.54|-1.32|-0.32|
|Urban|0|0|0|0|0.064|-2.671|0.085|-2.565|
||(.)|(.)|(.)|(.)|-1.57|(7.52)**|(2.17)*|(7.21)**|
|# of boys madrasas in the union in 1999|||0.028|-0.988|||0.023|-0.644|
||||-1.26|(3.34)**|||-1.19|(2.30)*|
|# of girls madrasas in the union in 1999|||-0.021|0.546|||-0.02|0.54|
||||(2.30)*|(5.15)**|||(2.22)*|(5.20)**|
|# of co-ed madrasas in the union in 1999|||-0.011|0.09|||-0.012|0.11|
||||(2.64)**|-1.36|||(2.72)**|(1.69)+|
|# of boys schools in the union in 1999|||-0.009|-0.316|||-0.018|-0.297|
||||-0.63|-1.52|||-1.43|-1.58|
|# of girls schools in the union in 1999|||-0.014|-0.251|||-0.012|-0.248|
||||(2.65)**|(3.67)**|||(2.27)*|(3.75)**|
|# of co-ed schools in the union in 1999|||-0.01|0.223|||-0.011|0.22|
||||(2.73)**|(4.83)**|||(3.10)**|(5.23)**|
|# of new madrasas in the union during 1999-2003|||-0.006|-0.32|||-0.006|-0.302|
||||-0.35|-1.3|||-0.4|-1.25|
|# of new schools in the union during 1999-2003|||-0.017|0.453|||-0.017|0.475|
||||-1.6|(3.06)**|||(1.66)+|(3.27)**|
|Average age of madrasas in the union|||0|0.008|||0|0.007|
||||-0.12|(3.95)**|||-0.08|(3.82)**|
|Average age of schools in the union|||0|0.008|||0|0.007|
||||(2.56)*|(5.85)**|||(2.41)*|(5.37)**|
|# of BRAC branches in the union (in 2005)||0.205||0.229||0.206||0.233|
|||(5.01)**||(5.64)**||(5.15)**||(5.89)**|
|# of BRAC female members in the union (in 2005)||-5.637||-1.349||-5.547||-1.535|
|||(2.48)*||-0.6||(2.53)*||-0.71|
|Constant|0.062|10.812|0.087|4.452|0.061|10.654|0.084|4.661|
||-0.89|(4.93)**|(1.89)+|(2.03)*|-0.88|(5.03)**|(1.86)+|(2.20)*|
|N|12744|12744|12744|12744|13196|13196|13196|13196|
|R-squared||0.00||0.03||0.01||0.04|

**Note:** (a) All regressions control for district Fes. (b) All-boys madrasas are excluded from the sample. (c) Models 1 and 2 exclude urban schools from the sample.

19
**Table 6. Expected utility and risk aversion at retirement age**

||γ=1,5|γ=2|γ=2,5|γ=3|γ=3,5|γ=4|γ=4,5|
|---|---|---|---|---|---|---|---|
|Balanced International|‐2.94|‐2.23|‐2.32|‐2.81|‐3.73|‐5.31|‐7.95|
|Base Scenario|‐2.62|‐1.83|‐1.81|‐2.14|‐2.84|‐4.10|‐6.37|
|Balanced Local|‐3.12|‐2.52|‐2.83|‐3.70|‐5.35|‐8.37|‐13.96|
|Stylized Chilean Fund|‐2.83|‐2.09|‐2.13|‐2.55|‐3.37|‐4.78|‐7.19|
|Default Chile|‐2.90|‐2.18|‐2.26|‐2.72|‐3.59|‐5.07|‐7.57|
|Stylized Peruvian Fund|‐2.68|‐1.90|‐1.92|‐2.30|‐3.09|‐4.55|‐7.19|
|Stylized Estonian Fund|‐2.95|‐2.27|‐2.44|‐3.05|‐4.24|‐6.32|‐10.00|
|Annuity Immunization|‐2.62|‐1.90|‐1.99|‐2.52|‐3.62|‐5.71|‐9.66|
|Conservative|‐3.76|‐3.56|‐4.55|‐6.59|‐10.29|‐16.90|‐28.81|
|Aggressive|‐2.53|‐1.73|‐1.70|‐1.99|‐2.62|‐3.76|‐5.77|

Source: Authors’ calculations It is also interesting to note the low level of utility that is achieved by investing in the conservative strategy throughout the different levels of risk aversion. Despite the low levels of volatility, it also implies low levels of asset accumulation at retirement age. As shown in Table 5 the difference between the conservative strategy and the rest of the investment strategies become even wider at higher levels of risk aversion.

Consequently, finding the optimal portfolio is not as simple as selecting the investment strategy that maximizes the expected utility of the individuals, as other factors such as annuitization risk and asset volatility in the accumulation phase play may play an important role in selecting the decisions for the optimal portfolio. While volatility is partially captured in the utility functions, the annuitization risk is not.

# **_Fifth step. Approximating the optimal portfolio_**

Since individuals may have some other sources of retirement income, the objective of the individual may be to achieve a certain replacement rate, say 70 percent of the last wage. This objective is different than maximizing the replacement rate. By setting up the replacement rate, individuals are revealing their degree of risk aversion. For the purpose of this exercise, it is assumed that the objective is to achieve a replacement rate of 70 percent.

We made a few adjustments in the asset allocation of the investment strategies that provided replacement rates in the range of 70 percent: the aggressive fund, the annuity immunization fund, the Stylized Chile fund and the Stylized Peruvian fund. Figure 8 shows the original (A) and modified versions (B) of the different pension strategies.

29
9 This paper uses the central bank reaction function as framework for the interest rate-exchange rate relationship in emerging market economies. The literature reviewed above suggests that the exchange rate has a role in determining the policy variable, although in varying degrees across emerging markets.<sup>1</sup> The literature also indicates a wide range of topics underlying the interest rateexchange rate nexus within IT. Our empirical research approach is close to Aizenman, Hutchison, and Noy (2011) who estimate a Taylor rule reaction function for 16 emerging-market countries for the period 1989Q1 to 2006Q4 employing fixed-effects panel data methods. Our analysis differs from them in at least two important ways. First, we employ dynamic panel methods, which are better at controlling for potential endogeneity problems that emerge from having inflation and exchange rate variations on the right-hand side. With this, we allow for effects from interest rates (the policy variable) to currency, which is consistent with the UIP literature. It is also reasonable to assume that increases in domestic rates will affect the output and inflation gaps: a contractionary monetary policy that increases interest rates is expected to reduce any positive output and inflation gaps. Second, we expand the sample of countries and consider the effects of the recent financial crisis on the way that central banks respond to exchange rate developments but also to the rest of the variables in their reaction function. These considerations lead us to propose alternative empirical strategies to the fixedeffects modeling by Aizenman, Hutchison, and Noy (2011).

# **3. Empirical approach**

> 1 Exchange rates may, of course, lead to inflationary effects, which forms an empirical matter of its own. Nogueira and León-Ledesma (2009), for example, use monthly data from 1995M1 to 2007M12 for Brazil and estimate inflation as a function of its past, real output growth, changes in nominal exchange rates, and changes in the price of foreign imports. They find that, after the policy change and adoption of IT in 1999M1 long-run exchange rate pass-through declined dramatically. Under the peg, exchange rate changes are more fully transmitted to prices. Also, under a credible IT nominal shocks are expected to have only limited effects on inflation.
third party can help guide marketing efforts and product design in the future. It can lead to revealing other market or government failures where policy action is required.<sup>22</sup> Finally, take-up rates and characteristics can also be useful for gauging the potential market for taking pilot trials to scale.

However, low take-up also offers several challenges for attempts to rigorously evaluate FPD programs. The first is one of power to detect the program effect. For example, one of the ultimate goals of the work on rainfall insurance is to find out if rainfall insurance allows farmers to farm more efficiently, and protects their households against negative shocks. However, because few farmers purchased the insurance, and those who did purchase only purchased enough to cover a trivial fraction of their crops, the existing studies do not allow the researchers to determine the impacts on production and household welfare.

There are two solutions to this problem of power. The first is to employ a very large sample size, so that the resulting sample will still contain enough firms or households which take-up the program to enable the researchers to detect a program impact of a given size. However, the downside of this is that it can be very expensive. For example, consider a program such as a new loan product or business training that aims to raise the profits of microenterprises undertaking the program by 25 percent. A randomized experiment which offered the program to half the firms and used a single follow-up survey to estimate this impact would require a sample size of 670 firms if takeup was 100 percent, but need a sample size of 2,700 with 50 percent take-up, and of 67,000 with 10 percent take-up.23 An example of a randomized experiment with sample sizes of this magnitude is seen in Karlan and Zinman (2009) who randomized 58,000 direct mail offers issued by a South African lender, with 8.7 percent of those contacted applying for a loan.

> 22 For example, de Mel et al. (2009b) worked with a regional development bank to try and help microenterprises obtain loans. Despite 62 percent of firms showing up for information meetings, only 10 percent received loans. One reason was that in the absence of a credit bureau, applicants had to travel to other institutions and obtain letters from them attesting that they had no outstanding loans, thereby increasing the cost to applicants of applying for loans. This experience highlights the need for credit bureaus to cover microfinance.

> 23 These calculations were made using the sampsi command in STATA, assuming a constant treatment effect, a coefficient of variation of 1, which is in line with what one typically sees in microenterprise data after trimming outliers, that the treatment has no effect on the variance of profits, and for power of 0.90 and test significance level alpha of 0.05.

- 21 -
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
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|WPS2365 Leading Indicator Project: Lithuania|Stephen S. Everhart<br>Robert Duval-Hernandez|June 2000<br>|M. Geller<br>85155|
|WPS2366 Fiscal Constraints, Collection Costs,<br>and Trade Policies|Keiko Kubota|June 2000|L.Tabada<br>36896|
|WPS2367 Gender, Poverty, and Nonfarm<br>Employment in Ghana and Uganda|Constance Newman<br>Sudharshan Canagarajan|June 2000<br>|M. Clarke<br>31752|
|WPS2368 Seeds of Corruption: Do Market<br>Institutions Matter?|Harry G. Broadman<br>Francesca Racanatini|June 2000|S. Craig<br>33160|
|WPS2369 How the Proposed Basel Guidelines<br>On Rating-Agency Assessments<br>Would Affect Developing Countries|Giovanni Ferri<br>Li-Gang Liu<br>Giovanni Majnoni|June 2000|E. Mekhova<br>85984|
|WPS2370 A New Model for Market-Based<br>Regulation of Subnational Borrowing<br>The Mexican Approach|Marcelo Giugale<br>: Adam Korobow<br>Steven Webb|June 2000|M. Geller<br>85155|
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|WPS2372 Financial Openness, Democracy,<br>and Redistributive Policy|Mansoor Dailami|June 2000|W. Nedrow<br>31585|
|WPS2373 Reciprocity across Modes of Supply<br>in the World Trade Organization:<br>ANegotiating Formula|Aaditya Mattoo<br>Marcelo Olarreaga|June 2000|L.Tabada<br>36896|
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|WPS2375 Asset Distribution, Inequality,<br>and Growth|Klaus Deininger<br>Pedro Olinto|June 2000|M. Fernandez<br>33766|
|WPS2376 The Effect of Early Childhood<br>Development Programs on Women's<br>Labor Force Participation and<br>Older Children's Schooling in Kenya|Michael M. Lokshin<br>Elena Glinskaya<br>Marito Garcia|June 2000|P. Sader<br>33902|
|WPS2377 Reforming the Water Supply in<br>Abidjan, Cote d'lvoire: Mild Reform<br>in a Turbulent Environment|Claude M6nard<br>George Clarke|June 2000|H. Sladovich<br>37698|
for the Li Ka-shing group in Hong Kong, Sato (1993) for the Salim group in Indonesia, Okumura (1993) for the Mitsubishi group in Japan, Taniura (1993) for the Lucky Goldstar group in Korea, Koike (1993) for the Ayala group in the Philippines, Numazaki (1993) for the Tainanbang group and Taniura (1989) for the Formosa group in Taiwan, Suehiro (1993) for the Charoen Pokphand group, and Vatikiotis (1997) for the Dhanin Chearavanont group in Thailand.

These studies provide us with some insights into the evolution and peculiarities of corporate control in East Asia. In particular, most of these papers suggest that the dominance of most business groups lies in the privileges that they could solicit from the government: exclusive exporting or importing rights, protection from foreign competition for extensive periods of time, including the granting of monopoly power in the local market, procurement of large government contracts, etc. The case-study literature does not, however, allow for cross-country comparisons; neither does it provide evidence on the existence of state ownership, as the focus is on particular families and their business empires. In some cases, the literature also does not document the precise mechanisms through which the ultimate owners are able to exercise and extend their control.

The recent contributions of La Porta et al. (1998) and La Porta, Lopez-de-Silarnes, and Shleifer (1998) go some way towards filling this gap in our knowledge. The former study documents the ownership structure of the ten largest non-financial corporations for a cross-section of 49 countries, including nine East Asian countries. The results show that although ownership concentration of East Asian corporations is high, it is not significantly different from that in other countries at similar levels of economic and institutional development. The latter study investigates in great detail the control structure of the largest twenty publicly traded corporations in 27 rich countries, including four (Hong Kong, Japan, Korea, and Singapore) East Asian countries. It traces controL to the ultimate owners of each company and distinguishes among five types of owners. Ownership in the majority of Japanese and Korean corporations is found to be widely dispersed, corporations in Hong Kong are predominantly controlled by families, wlile about half of the sampled companies in Singapore are controlled by the state.

See Young (1995), Amsden and Singh (1994), and Rodrik (1997) for surveys of the literature.

_5_
heating, respectively. Much of the energy budget of the households, particularly of the poor, is spent on heating during the cold winter months. Given heating energy expenditures are a substantial component of the total budget, an increase in price of gas will have important impact on their welfare.

The poorer households spend a relatively larger share of their budget on electricity. Electricity expenditures are more burdensome and account for a larger share of expenditure than gas. As such, spending on electricity is somewhat regressive: the poorest quintile spends 4.0 percent of their budget on electricity while the richest quintile spends only 2.9 percent – which indicates relatively inelastic demand for basic electricity uses such as lighting. This result is similar to the finding of a survey of household electricity expenditure in other ECA countries (Lampietti, et al., 2004; Junge, et al., 004.), which indicates that the poor spend a much larger share of their budget on electricity than the rich. In contrast, household spending on gas is neither regressive nor progressive on which the middle quintiles spend a larger share of their budget (4.8 for third quintile), compared to 3.9 for the richest quintile and 3.0 percent for the lowest quintile.

On the other hand, one of the important observations from Table 6 is that expenditures on heating energy take up a larger portion of the budget of the poor than the upper income groups. Households in the poorest quintile spend nearly twice as much (6.5 percent) of their budget on heating overall than households in the highest quintile of the income distribution (3.4 percent). This may suggest that there is a negative income elasticity of heating energy demand. Since heating needs during the winter months is a necessity, it is not surprising that poor households spend a higher share of total expenditures on heating. While higher energy prices could encourage energy efficiency investments, the immediate impact on the poor and their choice of heating fuel should be of vital concern.

12
**TABLE 5. ASP EFFECTS ON EMOTIONAL REGULATION**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Arousal<br>(stress)|Valence|Positive<br>Valence<br>Diference|Negative<br>Valence<br>Diference|Locus<br>of<br>Control|CRT|Raven|
|Treatment|0.069|-0.352**|-0.437**|-0.244|-0.249**|-0.012|-0.035|
||(0.121)|(0.159)|(0.195)|(0.187)|(0.110)|(0.127)|(0.121)|
|Observations|308|308|308|308|295|295|295|
|Mean control group|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|SD - control group|1.00|1.00|1.42|1.28|1.00|1.00|1.00|
|MDE T = C|0.21|0.21|0.21|0.21|0.21|0.21|0.21|

***, **, * significant at 1%, 5%, and 10%, respectively. Bootstrapped standard errors at the course-school level are in parentheses. All outcomes have been standardized at the control-course level with a mean of 0 and standard deviation 1.0. All regressions include as controls: a second-order polynomial of student’s propensity for violence and stratification-blocks fixed effects. Differences in number of observations are due to variation in the response rate for each outcome.

45
than similar private enterprises. On average, state-owned enterprises export about 7 percentage points less of their output than similar private enterprises do.<sup>24</sup> It is generally assumed that foreign-owned enterprises will find it easier to export than similar domestically owned enterprises. Foreign-owned enterprises might have easier access to international marketing and distribution networks, especially when the foreign owner is affiliated with a multinational corporation, making it easier for them to enter international markets (Blomström and Kokko, 1998, p. 7). Similarly, foreign-owned enterprises might have better access to finance, making it easier for them to bear the fixed costs associated with entering export markets.<sup>25</sup> The effect of foreign ownership on export behavior might be especially important in developing or transition economies, since domestic enterprises in these countries might be more likely to lack the skills and resources needed to set up marketing, distribution and service networks. Some studies have found that foreign-owned enterprises are more likely to export than domestically owned enterprises in Africa. For example, Grenier _et al_ . (1999) find weak evidence that foreign owned enterprises in Tanzania are more likely to export.

The coefficient on the dummy variable indicating that the enterprise is foreign-owned is positive and statistically significant in the regressions in Table 8 and Table 9. This suggests that foreign-owned enterprises export more than similar private domestically owned enterprises. The (positive) impact of foreign ownership is more modest than the (negative) impact of state ownership. On average, foreign owned enterprises export about 3.4 percentage points more of their output than similar domestically owned enterprises in the countries in this sample.

_Age._ Older firms do not appear to export more, or less, than similar younger firms. The coefficient on the age of the establishment is small, statistically insignificant and positive when customs and trade regulations are treated as exogenous and small (see Table 8), statistically

> 24 Differences are the change in the unconditional expected value of the observed (rather than the latent) variable when the dummy variable is set to one (rather than to zero).  The change, which is a non-linear function of the independent variables, is calculated using the coefficient estimates from Column 4 of Table 8 and setting all variables (other than the variable of interest) to their average values.  Cong (2001) describes the calculations.

> 25 Foreign-owned enterprises might have better access to finance either because they are perceived to be more efficient than other enterprises or because they have access to finance in their home countries.  Cotton _et al_ . (2004) and World Bank (2004a) present evidence consistent with the idea that foreign-owned enterprises are less constrained by access to finance for Uganda and Tanzania respectively.

17