rafmacalaba/datause-encoder-data huggingface dataset:
of Iacovone and Javorcik (2010) who showed the importance of churning at the firm level in response to exogenous opportunities provided by increased globalization. Alvarez and L ́opez (2008) used Tobit regressions to study the determinants of industry-level rates of entry and exit into exporting using a 10-year panel of 5’000 Chilean plants. They found that within-industry heterogeneity, measured (inter alia) by the dispersion of firm-level productivity levels, played an important role in explaining firm turnover in and out of exporting. By contrast, trade costs, factor intensities, and exchangerate fluctuations were found to have only marginal impacts. Carballo and Volpe (2008) used a 6-year panel of firm-level Peruvian exports at the HS10 level to explore how diversification strategies (in terms of products and markets) affected the survival of firm-level exporting activity. They found that both geographical and product-wise diversification raised survival, but geographical diversification more so—presumably because it proxies for product quality.

# **3 Data**

## **3.1 Qualitative evidence from a World Bank survey**

Preliminary indications on how African exporters venture and survive (or not) on foreign markets can be gleaned from a 2009 survey focusing on export survival conducted by the World Bank in four African countries.<sup>3</sup> The survey, which had three sections (basic information on the firm, constraints on survival, and opportunities and plans for future expansion), asked exporters specific questions on their initial entry into and survival on export markets. On the basis of the information provided, respondents were classified into three categories: (i) current exporters, (ii) past exporters (who failed), and (iii) intermittent exporters.

As shown by Table 7 in the appendix, roughly two thirds of the respondents (a bit more among regular exporters) identified their first client through relatives, friends, intermediaries and suppliers. More formal or technologyrelated channels (e.g. trade fairs or online research) came only second, and

> 3The countries are Malawi, Mali, Senegal and Tanzania. See Appendix 1 for more background information on the survey.

6
PROGRESA treatment and control groups to identify the causal effect of parental education on skills formation in early childhood. There are likely to be other cases in which exogenous sources of variation, either from naturally-occurring “experiments” or from deliberate program design, can be put to good use.

There are very high returns to careful implementation of a variety of interventions, and rigorous evaluation of their impact. It may be that seemingly unusual combinations of programs have the potential to have the largest impact. For example, the research by Gertler and Fernald (2004) suggests that, on their own, conditional cash transfer programs like _Oportunidades_ may not lead to significant improvements in child cognitive development. This might argue for an intervention that focuses on parenting skills. The descriptive evidence in Paxson and Schady (2005) for Ecuador and the small-scale experimental evidence in the work by GranthamMcGregor and her co-authors in Jamaica both suggest that there could be very high returns to interventions that effectively improve parenting and the home environment. However, the high rate of attrition in many programs that seek to improve parenting in the United States, in particular home visiting programs, argues for innovative combinations of parenting programs with interventions that households are keen on participating in—perhaps, conditional cash transfer or feeding programs.

Careful consideration needs to be given to the evaluation of interventions. The literature on skill formation at early ages in the United States shows that there can be large differences between estimates of program effects based on experimental and non-experimental methods, and that it is hard to sign the direction of the bias ex-ante (Currie 2001; Gomby et al. 1999). In Latin America and the Caribbean, the strongest evidence on the impact of conditional cash transfer programs is based on carefully designed, experimental evaluations. A similar emphasis on innovative program design, careful implementation, and rigorous evaluation would build up the knowledge base on early childhood development in Latin America and the Caribbean. This would help identify programs and policies that ensure that children in the region can go on to have healthy and productive lives.

# **References**

Aughinbaugh, A., and M. Gittleman. 2003. “Does Money Matter? A Comparison of the Effect of Income on Child Development in the United States and Great Britain.” _Journal of Human Resources_ 38(2): 416-40.

Bassuk, E. L., L.F. Weinreb, R. Dawson, J.N. Perloff, J.C. Buckner. 1997. “Determinants of Behavior in Homeless and Low-Income Housed Preschool Children.” _Pediatrics_ 100(1):92-100.

Baum, C. L. 2003. “Does Early Maternal Employment Harm Child Development? An Analysis of the Potential Benefits of Leave Taking.” _Journal of Labor Economics_ 21(2): 409-48.

23
- 27 -

Table 7: Parameter estimates from six HLM contextual effects models, Philippines secondary, 1983<sup>(t-statistice</sup> in parentheses)

|Independent<br>variable|Science<br>test<br>score|Mathematics<br>test<br>score|Positive<br>attitude<br>toward<br>science|Negative<br>attitude<br>toward<br>science|Positive<br>attitude<br>toward<br>school|<br>Negative<br> <br>attitude<br>toward<br>school|
|---|---|---|---|---|---|---|
|Individual level fixed effects<br>||<br>|||***|***|
|Male|0.79***|<br>0.34***<br>|-0.10***<br>|0.23***<br>|-0.18<br>|0.24<br>|
||(10.92)<br>|(5.87)<br>|(5.15)<br>|(11.93)<br>|(8.50)<br>|(11.68)<br>|
|Age|-0.01***<br>|-0.01***<br>|-0.00<br>|0.00**<br>|-0.00<br>|0.00<br>|
||(4.31)<br>|(5.28)<br> <br>|(0.19)<br>|(3.41)<br>|(0.08)<br>|(1.54)<br>|
|SES|0.54***<br>|<br>0.24***<br>|_0.10***_<br>|-0.04**<br>|0.06***<br>|0.01<br>|
|School level effects|(11.79)|(6.66)|(8.04)|(3.01)|(4.48)<br>|(0.41)<br>|
|Mean|13.09***|<br>11.87***|0.90<br>|-0.54***<br>|0.10<br>|-0.27*<br>|
||(26.31)|(30.18)|(0.82)<br>|(4.96)<br>|(0.88)<br>|(2.35)<br>|
|Local public|-0.13|-0.43|-0.09|0.25***<br>|-0.12<br>|0.13*<br>|
||(0.23)<br>|(0.98)<br>|(1.24)<br>|(3,70)<br>|(1.80)<br>|(1.97)<br>|
|Private|-0.28|0.61|0.00|0.04<br>|0.03<br>|-0.13*<br>|
||(0.60)<br>|(1.63)<br> <br>|(0.05)<br>|(0.66)<br>|0.55)<br>|(2.37)<br>|
|Average SES|1.81***|<br>1.90***|0.32***|-0.24***<br>|0.07<br>|0.01<br>|
||(4.23)|(5.62)|(5.33)|(4.42)|(1.35)|(0.22)|
|Percent between-school<br>variance explained|18.5|30.7|30.0|31.3|10.0|9.0|

***D < .001; **p <.01; *p <.05

52. Peer effects are powerful predictors of achievement in the Philippines. Average SES scores are significantly related to both mathematics and science test scores, and the observed differences in achievement among local,<sup>government and</sup> private schools are nearly entirely accountable to differences in the average<sup>SES</sup> of students in these schools. That is, the size and statistical<sup>significance of</sup> coefficients for "Local public school" and "Private School"<sup>disappear with the</sup> inclusion of "Average SES" in the between-school model.

53. Average SES scores are also significantly related to<sup>attitudes towards</sup> science, but they have no relationship to attitudes toward<sup>school in general.</sup> The difference in average SES between schools, however, does not<sup>account for the</sup> more negative attitudes towards science and schools held<sup>by students in local</sup>
# 7. References

<mark>Arcidiacono, Peter. "Ability Sorting and the Returns to College Major."</mark> _<mark>Journal of Econometrics</mark>_ <mark>121,</mark> no. 1‐2 (2004): 343‐75.

<mark>Babcock, Linda, Maria P. Recalde, Lise Vesterlund, and Laurie Weingart. "Gender Differences in Accepting and Receiving Requests for Tasks with Low Promotability."</mark> _<mark>American Economic Review</mark>_ 107, no. 3 (2017): 714‐47.

<mark>Barbulescu, Roxana, and Matthew Bidwell. "Do Women Choose Different Jobs from Men? Mechanisms of Application Segregation in the Market for Managerial Workers."</mark> _<mark>Organization</mark> Science_ 24, no. 3 (2013): 737‐56.

<mark>Becker, Gary S. "Investment in Human Capital: A Theoretical Analysis"</mark> _<mark>Journal of Political Economy</mark>_ 70, no. 5 (1962): 9‐49.

Becker, Gary S: _Human Capital Revisited_ . Chicago: University of Chicago, 1989.

<mark>Bharadwaj, Prashant, Giacomo de Giorgi, David Hansen, and Christopher Neilson, “The Gender Gap in Mathematics: Evidence from Low‐ and Middle Income Countries,” October 2012. NBER Working</mark> Paper 18464 <mark>Booth, Alison L., and Patrick Nolen. "Gender Differences in Risk Behaviour: Does Nurture</mark> Matter?." _The Economic Journal_ 122, no. 558 (2012).

<mark>Buser, Thomas, Muriel Niederle, and Hessel Oosterbeek. "Gender, Competitiveness and Career</mark> Choices." _The Quarterly Journal of Economics_ 129, no. 3 (2014): 1409‐447.

<mark>Cárdenas, Juan‐Camilo, Anna Dreber, Emma Von Essen, and Eva Ranehill. "Gender Differences in Competitiveness and Risk Taking: Comparing Children in Colombia and Sweden."</mark> _<mark>Journal of</mark> Economic Behavior and Organization_ 83, no. 1 (2010): 11‐23.

<mark>Daymont, Thomas N., and Paul J. Andrisani. "Job Preferences, College Major, and the Gender Gap in</mark> Earnings." _The Journal of Human Resources_ 19, no. 3 (1984): 408‐28.

<mark>Dercon, Stefan, and Abhijeet Singh. "From Nutrition to Aspirations and Self‐Efficacy: Gender Bias</mark> over Time among Children in Four Countries." _World Development_ 45 (2013): 31‐50.

<mark>Dreber, Anna, Emma Von Essen, and Eva Ranehill. "Outrunning the Gender Gap ‐ Boys and Girls</mark> Compete Equally." _Experiential Economics_ 14, no. 4 (2011): 567‐82.

<mark>Fafchamps, Marcel, Måns Söderbom, and Najy Benhassine. "Wage Gaps and Job Sorting in African</mark> Manufacturing." _Journal of African Economies_ 18, no. 5 (2009): 824‐68.

<mark>Fryer, Roland, and Steven Levitt. "An Empirical Analysis of the Gender Gap in</mark> Mathematics." _American Economic Journal: Applied Economic_ 2, no. 2 (2009): 210‐40.

<mark>Ganguli, Ina, Ricardo Hausmann, and Martina Viarengo.</mark> _<mark>Closing the Gender Gap in Education: Does It</mark> Foretell the Closing of the Employment, Marriage, and Motherhood Gaps?_ Working paper. 2011.

31
|Variable|Definition|Source|
|---|---|---|
|USREER|6 month log difference of U.S. Real Effective Exchange rate<br>from BIS. Base year is 2010 (Weighted basket of foreign<br>currencies vs USD)|Bloomberg|
|XRATE|6 month log difference of real exchange rates of each EMDE<br>country in the sample (USD to country local currency)|Bloomberg|
|Domestic Pull Factors|||
|GDPPC|Real GDP per capita in U.S. Dollars|IMF World<br>Economic<br>Outlook|
|GROWTH|Year-on-year percentage changes in real GDP|IMF World<br>Economic<br>Outlook|
|EXT|Total debt owed to nonresidents repayable in currency, goods,<br>or services as a percent of GDP|IMF World<br>Economic<br>Outlook|
|CA|Current account balance as a percent of GDP|IMF World<br>Economic<br>Outlook|
|PCRED|Total domestic private credit to the real sector by deposit<br>money banks as a percent of GDP|IMF<br>International<br>Financial<br>Statistics|
|Country-Industry Issuance|||
|ABOVE_AVG_ISSUANCE|Indicators variable which assumes value 1 for a given month<br>in which a country-industry’s total external bond issuance is<br>above its monthly2000-07 average and 0 otherwise|Author’s<br>calculations|

28
reported profit are much more infrequent than data on sales, and because we are concerned about firms’ profit underreporting problem.

Overall, we found that after a firm innovates, its sales per worker increase by 18%, although the significance is only at the 10% level. Obviously, without the appropriate instrument to capture the exogenous component of product innovation, the results suffer from biases. We will go back to discuss the sources of biases and how we try to mitigate them in more detail in Section 3. We will argue that if the biases are not systematically correlated with countries’ institutions, the cross country comparison of the institutions’ impacts -our ultimate interest- is valid.

We found that the return to innovation is higher in countries with better institutions _._ Overall, if a country is ranked 1 percentile higher in the world’s rule of law and regulatory quality rankings, the sale return to innovation is about 1.7-1.9% higher and the sale per worker return is about 0.85-0.95% higher. This implies that in countries with better rule of law and regulatory quality, the incentive to innovate for firms is higher. We also zoom into two important components to the return to innovation: property right protection and patent right protection. We found that in countries with good property and patent right protection, the return to innovation is also higher, with about the same magnitude. We will go back to these points in greater details.

# **2. Data and Variables**

The data are the World Bank Enterprise Survey- a rich firm-level survey database that provides information about firms’ characteristics such as ownership, size, sector, region in which it is located, annual sales, capacity utilization, employment, competition etc. In order to analyze the change within a firm, we specifically select firms that appear in at least 2 surveys (i.e. we have panel data). In our sample, 6,191 firms appear in two surveys and 256 firms appear in three. There are 44 countries with 6,447 unique firms. The detailed list of countries and firms is in Table A1 in the Appendix. Note that the innovation module in the survey only exists in LAC and ECA. At the end, only LAC and ECA countries remain. The data span from 2002 to 2010.

The innovation module in Latin America is quite different to that in Eastern Europe and Central Asia. In Latin America (LAC) we use the following question to get data for innovation: _During the last three years, did this establishment introduce onto the market any new or significantly improved products? (Yes/No/ Don’t know)_ We define that a firm innovates when it answers Yes to this question.

4
the income, consumption and asset management behavior (Khandker 2012; Khandker and Mahmud 2012; Pitt and Khandker 2002).

# **3.   Seasonality in household borrowing and income: Descriptive evidence**

Using Household Income and Expenditure Surveys (HIES) of 2000 and 2005, Khandker (2012) demonstrates that seasonality is pronounced in household consumption and income, and that income seasonality affects significantly the seasonality of consumption and poverty beyond and above seasonal variations in consumption and poverty. This phenomenon is most acute in the Northwest region of Bangladesh (Khandker and Mahmud 2012). Findings also indicate that seasonal variations severely affect poor people who mainly live on agricultural income, and microcredit has so far played a good role in mitigating seasonal hardship. Seasonality still matters, however. Using the 2010 HIES data, Khandker and Samad (2016) show that although the severity of seasonal hunger has declined, consumption and poverty are still influenced by the seasonality of income caused by the agriculture cycle.

In order to explore the seasonality of household borrowing and income, we use panel data collected by the World Bank with help of Bangladesh Institute of Development Studies (BIDS) in 1998/99 and Institute of Microfinance (InM) in 2010/11 (see Khandker and Samad 2014 for a detailed description of the data set).<sup>2</sup> Using these data we can divide the borrowing sources into three categories (microcredit, formal or commercial, and informal) and the whole year into four seasons ( _Boro_ , _Aus_ , _Monga_ and _Aman_ ). Figure 2 shows the trend in the amount of loans from each source and aggregate loans from all sources combined across four seasons during 1998/99. As the figure shows, the aggregate loans from all sources is the lowest during the _Monga_ season. As for the individual sources, the pattern is more pronounced for microcredit and informal sources than for formal sources.<sup>3</sup> As shown in Figure 3, the

> 2 While the panel surveys also include data from 1991/92, we could not use them because they are not suitable for calculating season-specific income that would be consistent with incomes calculated from 1998/99 and 2010/11 data. So, the analysis of this paper is restricted to 1998/99 and 2010/11 survey data only. Nevertheless, using the 1991/92 along with the 1998/99 and 2010/11 panel surveys, our results on the seasonality of income and borrowing still hold.

> 3 This is not surprising, given that formal financial institutions lend little but mobilize more from their rural branches.

7
bank interest rate changes on output. Interestingly, we find a puzzling evidence regarding the effect of macroprudential policy on economic activity: macroprudential policy does _not_ have effect on economic activity. We then turn to our new identifying strategy. To the best of our knowledge, this is the first instance in this macroprudential literature in which such a narrative approach has been used. Section 4 discusses the narrative analysis and classifies the motivation for each RR change. Using historical documents, including IMF and central banks reports, we classify changes in RR into (i) _endogenous_ changes, which were mainly motivated by current or prospective output fluctuations (i.e., when output growth differs from normal) and (ii) _exogenous_ changes, which were triggered by reasons exogenous to the business cycle, including microprudential factors and financial liberalization. As discussed before, since it proves impractical to analyze macroprudential policy without considering the role of monetary policy, Section 5 extends the analysis to identify unanticipated IR shocks following Romer and Romer’s (2004) approach. With our new measures of exogenous RR and IR shocks in hand, Section 6 examines their effects on output and also on market interest rate spreads (to have a sense of the transmission mechanism). These effects are estimated using Stock and Watson’s (2018) methodology. Our series of exogenous changes might only capture part of the unobserved true structural shocks due to, for example, measurement errors. While measurement errors would lead to bias if the shock were treated as the true shock, they do not compromise the validity of the shocks as an instrument. Thus, as proposed by Stock and Watson’s (2018), rather than using our series of exogenous changes directly as shocks, we use them as instruments for the original series within the local projection framework of Jorda (2005). We show that refining the identification method proves to be critical to correctly assessing the output effects of changes in reserve requirements. In particular, when properly identified, the effect of reserve requirements on economic activity is _negative_ . That is to say, an increase in reserve requirements reduces output while a cut in reserve requirements increases it. We show that the countercyclical nature of endogenous changes in reserve requirements is at the heart of this bias. Section 7 offers some concluding remarks.

5
A2.3 **South America**

# **A2.3.1 Argentina**

Index: IFC emerging markets weekly index NB: These are weekly, not daily, beta estimates.

||Central Costanera|Central Puerto (elec)|Telef6nica de|
|---|---|---|---|
||(elec)||Argentina|
|1994-95|0.74**e**|0.87|**-**|
|1992-95|-|-|0.86**e**|

Index: Dow Jones Composite 65 (daily beta estimates)

||Central Costanera|Central Puerto ADS|Telefonica de|
|---|---|---|---|
||ADR||Argentina ADR|
|1994-95|1.92 e|1.21|**-**|
|1992-95|-|-|1.57**e**|

A2.3.2 **Chile** Index: IFC emerging markets weekly index NB: These are weekly, not daily, beta estimates

|[|Endesa (elec)<br>Emelsa (elec|)<br>Emelat (elec)<br>Entel (telecoms)|
|---|---|---|
||Five-year<br>beta|_0.95_**_e_**<br>0.72**e**|0.77**C**<br>1.20**e**|
||Enersis (elec)<br>Enersis ADR*|Chilgener (elec)<br>Chilgener ADR*|
||Five-year<br>beta|1.24**e**<br>1.09e(1993-95)|1.09 e<br>0.76**e**(1994-95)|

* ADR is quoted in $ and the beta calculation uses daily data and the Dow Jones Composite 65 index

_55_
the gross enrollment rate at SMK has actually declined (figure 1). As a result, the share of SMK enrollment declined from 27% to 18% (figure 2).

**Figure 1: GER at senior secondary level: SMA and SMK: 2000-2006**

<!-- Start of picture text --> 40.0%<br>35.0% 34.4%<br>33.6%<br>30.0%<br>27.5% 27.8%<br>25.8%<br>25.0%<br>20.0%<br>15.0%<br>10.0% 10.1% 9.3% 9.0%<br>7.5% 7.5%<br>5.0%<br>0.0%<br>2000 2001 2002 2005 2006<br>SMA enrollment rate SMK enrollment rate<br><!-- End of picture text --> _Source: Indonesia National Social Economic Survey (SUSENA)._

**Figure 2: Enrollment share of SMA vs. SMK: 2000-2006**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2000 2001 2002 2005 2006<br>total SMA enrollment Total SMK enrollment<br><!-- End of picture text --> _Source: Indonesia National Social Economic Survey (SUSENAS)._ 6
11 of either delegadon or **devulution** on the efficiency and equity of resource allocadon may **be** much less beneficial than often alleged (Box **_5)._** ~~~~~~~~. **. ..................** ....................... ... _,5._ ..... s .e .W., .ia .:..:::.::.o ,ol **vee..** {.e.a; ... ,.:ez Oni.: a: **:** .s.out th ay w y a **poor..** ,ult te .... ss., **of. :o:a** effec _.govrnmt_ **e** adwinis:::a:i: pulc e .n **:::deeop** Aoss* **i** ble ie :o:i: KW::l _thit n in_ ot.u **800 rinaoXlIra fo** ucj: onsn oacoadeul.etnt m a cousntryn : .. **..** . : v.s : : : **use b. :.** :: **f** - disc...... : **s** : :' ,ss.e: .ss; **,::** ... **:.....:** : :: s :, : **s:s:** s :. , **6efo** yan v **o pt m h nits o d** **_b4_ s os p i . w p a diefcAm o a poss ib** _ho_ **ibandtctvo** _,:_ wr - ed peopl toe a **aaeei:::n lobt** tgovernt **sen an..** goi'Knmints.Oue **_nbiwer toW_ 6hs** bviul t **nabem** lte **toe poa~ tholtnes ton** kei ss :: :' "ssk **''UIeffls gm Aia:"°** 'tZ s. n:d.:s' s:' e,.''= .. : _:l::::s:oMA:_ The .endrs :. ::: _o_ **tlsti** s~~~~~~~~~~~~~Uo' perbap **trho o:adt** o Ns .. ~O o **..** . **.** . n e: .. **ets** w t ..... **_.1o_** 1dyAla te d **_m_** of o **e..** :: **verm** _t_ **izatzve::** T thaese countries, local citizens look: to the national govrnement to fixpotholes on thair stree se~~~:C ss ;S **Ak SP.M** .1: **.ec** 1]p: **9V> adtys,:** _t_ do .o th G ofve ounties u **aS** stnwmm re: **was.~ if there** of tinppor ~ ariabdle?i ~ ~ ~ ~ ~ sae sliu **fohr.** For ezamp1e;:a number ofi4~c **reDvenues a^s well** asre8ponaIibiity frimortaint pub1i~ service functions, but without stng up an:: who hey izi,whattw **he** ypay, e th buss _t~_ etc~ **ad eAve nto redomou of ateioran** lsloa ::::alntaliimakimm anrsom insevicesi hedt o heastreh. of pb :: d Whehe ::::s:over- or::imde-nrollod, local governmet inall :too: **W le tea eomor~** ilo ***Duppst** mdWricnro isIeginngt _aY_ ee conties receive **il nfobr)** pvea ~n t~ aa jenmes in nume o way In. ome m n=s' for exampe th: a mount of natina fundin deeved depeds pon _the_ aizof:lhe local buget deficit: a sperversei: no cs btter r no Fund foprton an wnteac _thscmo_ ytmi evs eas i pay localtes not to mantin ansioa point, To **iehs** b.esaen impdiciyralssumed thav stin fclties (whc tey woud have :to do out of tei o: fud)t tihesrcueo local governmmesim n nationa :: n ordeir t:o strenten escr appaent ned for:new ones (which th eoa government will pay for).:-i::: adeliyquratle: nthatnto whocr doeswhae tatd _tnder_ what rulds, may bein proerly sdeterined bay Source World Bankc (1993.), bird and Wallch (1993) ::: ::

# Government Structure: Constraint or Vaniable?

To this point, it has been ixnpliciily assumed that the structure of local government is a policy variable: that who does what, and under what rules, may be freely determined by central policy-makers. In some instances, however, the structure of public sector decision-