rafmacalaba/datause-encoder-data huggingface dataset:
# _Regression analysis_

To evaluate the robustness of these results, both for the episodes and more generally, we turn to regression analysis, which allows us to control for country and time effects that might be driving the results. We use both the surge-country sample and a larger sample of all countries with available data from 1980 to 2005. We create a panel of five 5-year periods in order to eliminate noise that may affect annual data and because exchange rate effects may take more than one year to appear. Specifically, we consider the following relationship: where _γi_ is a country-fixed effect and _γt_ is a period-fixed effect. X are the variables that may be affected by misalignment and depreciation, including ln manufacturing exports, ln entry and the failure rate. We estimate all equations in first differences.

The coefficient β0 reflects the effect of misalignment on the set of dependent variables that is not due to country-specific effects or global growth effects. We report results for the set of countries with surges, and also using all countries for which data on misalignment are available (130 countries). We also control for country size, trade liberalization, and ultimately country-fixed effects. In this growth specification, countryfixed effects will control for country-specific growth rates or, put differently, countryspecific trends in levels.

Simple correlations between the variables of interest are reported in Table 4. The simple correlations confirm a positive correlation between the change in misalignment and export growth. In addition, increased currency competitiveness is associated with 19
WORLD, GEOGRAPHIC REGIONS, AND INCOME GROUPS 21 Sumury Projection for 25-Year Periods

# NORTHERN AMERICA

|AGE GROUP|1985|2000|2025|2050|207h|2100|2125|2150|
|---|---|---|---|---|---|---|---|---|
|TOTAL**M+F**|267936|305843|351975|362420|366135|369267|371524|372717|
|MALES|||||||||
|0-4|10320|10878|11212|11126|11088|11082|11084|11086<br>|
|5-9|9685|11396|11198|11071|11061|11072|11078|11079|
|10-14|9868|11002|11017|11010|11056|11076|11079|11078|
|15-19|10670|10630|10795|<br>11007|11072|11079|11074|11071|
|20-24|12058|10029|10770|11072|11082|11063|11053|11052|
|25-29|12277|10340|11067|11125|11052|11021|11020|11024|
|30-34|11329|11243|11565|11081|10971|10971|10988|10996|
|35-39|9882|12561|11194|10861|10876|10935|10963|10971|
|40-44|7785|12572|10845|10591|10831|10913|10932|10933|
|45-49|6410|11367|10173|10505|10842|10876|10872|10870|
|50-54|5977|9640|10232|10683|10797|10760|10751|10759|
|55-59|6035|7309|10730|10962|10590|10538|10569|10600|
|60-64|5707|5693|11441|10319|10139|10230|10332|10381|
|65-69|4712|4892|10732|9577|9530|<br>9857|9999|10049|
|70-74|3581|4353|8782|8378|8907|9351|9480|9525|
|75+|4508|6286|11723|18926|20515|21257|21920|22326|
|TOTAL|130803|150190|173475|178293|180409|182082|183196|183799|
|FEMALES|||||||||
|0-4|9839|10341|10646|10563|10526|10520|10522|10523|
|5-9|9236|10850|10639|10513|10504|10513|10518|10519|
|10-14|9384|10489|10473|10455|10500|10518|10520|10519|
|15-19|10242|10189|10277|10459|10520|10526|10521|10517|
|20-24|11829|9657|10288|10538|10546|10525|10515|10513|
|25-29|12170|9950|10619|10619|10542|10509|10505|10508|
|30-34|11372|10873|11136|10606|10489|10485|10498|10504|
|35-39|10072|12342|10818|10426|10421|10473|10496|10501|
|40-44|8045|12461|10547|10211|10410|10482|10495|10492|
|45-49|6703|11450|9931|10195|10469|10492|10481|10475|
|50-54|6361|9968|10032|10477|10516|10463|10443|10446|
|55-59|6644|7817|10719|10914|10452|10371|10387|10409|
|60-64|6541|6364|11886|10500|10204|10248|10329|10365|
|65-69|5712|5839|11682|10091|9891|10155|10269|10302|
|70-74|4805|5757|10305|9277|9705|10076|10165|10186|
|75+|8177|11304|18501|28282|30032|30829|31664<br>|32137<br>|
|TOTAL|137133|155653|178500|184127|185726|187185|188327|188918|
|BIRTH RATE||15.0|13.0|12.0|11.9|11.8|11.7|11.6|
|DEATH RATE||8.3|8.2|10.9|11.5|11.5|11.5|11.5|
|NET MIGRATI|ON<br>RATE|2.2|.8|.0|.0|.0|.0|.0|
|GROWTH<br>RATE||.88|.56|.12|.04|.03|.02|.01|
|TOTAL FERTI|LITY|1.958|2.031|2.066|2.064|2.063<br>|2.062<br>|2.061<br>|
|**e(O)**<br>**-** BOTH|SEXES|76.78|80.23|83.10|84.56|85.36|85.94|86.18|
|**IMR-** BOTH S|EXES|8.9|5.2|2.8|2.4|2.1|2.0|1.9|
Bryant, John (1980) A Model of Reserves, Bank Runs and Deposit Insurance _, Journal of Banking and Finance,_ Vol. 4(4) 335-344.

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Caprio, Gerald, Jr. and Daniela Klingebiel, (1996). “Bank Insolvency: Bad Luck, Bad Policy, or Bad Banking?” in B. Pleskovic and J. Stiglitz (eds.), Annual World Bank Conference on Development Economics, (Washington, DC: The World Bank), 79–104.

Chang, Roberto, and Andres Velasco (2000) Liquidity Crises in Emerging Markets, in B. Bernanke and J. Rotemberg (eds) _, NBER Macroeconomics Annual 1999_ Vol 14, MIT Press, 1178 Connant, Charles A. (1915). _A History of Modern Banks of Issue_ , (New York and London: GP Putnam and Sons).

Cruces, Juan, and Christoph Trebesch (2013). “Sovereign defaults: The price of haircuts,” _American Economic Journal: Macroeconomics_ , Vol, 5(3):85–117.

Das, U., Papaioannou, M., & Trebesch, C. (2012). Sovereign Debt Restructurings 1950-2010 (Vol. 2012, No. 203, pp. 1-127). International Monetary Fund.

Diamond, Douglas and Philip Dybvig (1983) _The Journal of Political Economy_ , Vol. 91(3). (June). 401-419.

34
# **Mobility, Scarring and Job Quality**

# **In Indonesia’s Labor Market**

Darian Naidoo<sup>1</sup> , Truman Packard<sup>2</sup> and Ilmiawan Auwalin<sup>3</sup> JEL Classification: J2, J3, J6

> 1 National Catholic Education Commission, corresponding author: darian.naidoo1@gmail.com

> 2 World Bank Group, Social Protection and Labour Global Practice

> 3 School of Economics, Faculty of Arts & Social Sciences, the University of Sydney
# **Innovative Firms or Innovative Owners? Determinants of Innovation in Micro, Small, and Medium Enterprises**<sup>**#**</sup>

Suresh de Mel, _University of Peradeniya_ David McKenzie, _World Bank, BREAD and IZA_ Christopher Woodruff, _UCSD_ _Keywords:_ Innovation, Microenterprises, SMEs, Development. _JEL Codes:_ O31; 033; L26.

> # We gratefully acknowledge funding from the World Bank’s Knowledge for Change trust fund, which funded this survey, and research assistance from Cristina Tealdi. The surveys on which the empirical analysis is based were carried out by AC Nielsen, Lanka.
## III. REGULATORY REGIMES

# A. _Description of regulatory regimes_

Many regulatory and institutional factors may influence the functioning of stock markets. For example, reliable information about firms and financial intermediaries may enhance investor participation in equity markets. Regulations and institutions that instill investor confidence in brokers and other capital market intermediaries should encourage investment through and trading in the stock market. Similarly, restrictive or costly regulations may impede the efficient functioning of stock markets.

To assess the relationship between stock market development and several regulatory and institutional features of emerging stock markets, we use indicators constructed by the International Finance Corporation (IFC). These indicators are available on an annual basis from 1986-1993, for twenty developing countries. Table 7 gives the average of these indicators over this period, for each country. The first column shows whether the country's firms provide comprehensive, internationally published information such as the P/E ratios and yields. The IFC gives a value of 0 if information is published and a value of 1 when the information is comprehensive and published internationally. Column 2 gives information on accounting standards. The IFC assigns values of 0, 1, or 2 for countries with poor, adequate, or internationally accepted accounting standards, where "internationally accepted" incorporates the standards used in major industrialized countries. Column 3 gives information on investor protection laws. Again, 0 indicates poor, 1 signifies adequate, and 2 means internationally accepted investor protection laws as judged by the IFC. Finally, the last three columns give IFC evaluations of the types of policies investigated earlier in this paper; they classify restrictions on dividend and capital repatriation, and entry into the stock market into "restricted" with a value of 0, "some restrictions" with a value of 1 or "free" with a value of 2.6

> 6 Furthermore, the Appendix examines what happens to stock market development following the creation of closed-end country funds on the New York Stock Exchange or the London Stock Exchange. Although there is little impact on integration, market size and liquidity rise after the inception of country closed-end mutual funds.

17
<!-- Start of picture text -->
-25 -20 -15 -10 -5 0 -25 -20 -15 -10 -5 0<br>Change in GDP growth Change in GDP growth (pct points)<br>Earnings growth change Employment growth change Unemployment Participation<br>Fitted earnings change Fitted employment change Fitted unemployment Fitted participation<br>10<br>0<br>-10<br>-20<br>8<br>6<br>4<br>2<br>0<br>-2<br><!-- End of picture text -->

**Figure 7 Figure 8**

To assess the role of these other factors, this paper classifies countries along seven dimensions. The first two are region and level of per capita GDP, which are intended to capture structural differences in the economy and in labor market institutions. Countries are also classified according to the relative size of the manufacturing and export sector, two areas of the economy where crisis impacts were most severe. Exports were also affected by countries’ exchange rate regimes, as those with currencies pegged to the dollar suffering larger falls in exports, since the dollar appreciated roughly 15 percent against the Euro in the latter half of 2008.<sup>19</sup> Finally, countries are grouped according to their level of labor regulation (high/low) to investigate the expectation that stringent labor market regulations, if enforced, discouraged employers from shedding workers during a downturn. Two measures of firing costs from the 2009 Doing Business Indicators are used for this purpose: the difficulty of firing index and the firing cost.<sup>20</sup> Only a few of these characteristics are closely correlated with each other.<sup>21</sup>

> 19 We use the IMF’s _de facto_ exchange rate-regime to classify countries by their currency regime. http://www.imf.org/external/np/mfd/er/2006/eng/0706.htm

> 20 Countries are classified as high manufacturing, high export, difficult to fire, and high firing cost countries, based on whether the indicator exceeds the median across countries in the sample. The difficulty of firing index measures whether redundancy is allowed as a basis for terminating workers, and the existence of rendundancy regulations and requirements. The redundancy cost indicator measures the cost of advance notice requirements, severance payments and penalties due when terminating a redundant worker, expressed in weeks of salary Additional information on the redundancy indices is available at http://www.doingbusiness.org/Methodology/Surveys/EmployingWorkers.aspx 21 In only 4 cases do the pair-wise correlations between characteristics exceed 0.25.Latin American countries are most likely to be upper middle income and least likely to be lower middle income. Europe and Central Asian countries and middle income MICS tend to have larger manufacturing sectors, but Latin American countries are less likely to have large manufacturing sectors. Finally, countries with high redundancy costs are far more likely to be in East Asia or Latin America than Europe, and are less likely to be middle income countries. Somewhat surprisingly, the correlation between a high difficulty of redundancy and high redundancy cost is weak and negative.

9
**Table 2. Regression results: estimates of consumption equation (log consumption per capita)**

|**Household characteristics**|Coefficient|Standard error|
|---|---|---|
|Household size|-0.150***|0.004|
|Number of household members under 15|0.020***|0.004|
|Number of females 15 to 64|0.065***|0.009|
|Number of household members 65 and older|0.088*|0.047|
|Wealth quintile 1 (reference group)|ref.||
|Wealth quintile 2|0.232***|0.018|
|Wealth quintile 3|0.228***|0.010|
|Wealth quintile 4|0.130**|0.055|
|Wealth quintile 5|0.283***|0.106|
|Head has at least secondary education|0.247**|0.108|
|Head is female|-0.096***|0.013|
|Main livelihood: Salaried employment (reference group)|ref.||
|Main livelihood: Daily laborer|-0.180***|0.027|
|Main livelihood: Agriculture|-0.176*|0.091|
|Main livelihood: Manufacturing, trade, services|-0.024|0.084|
|Main livelihood: Pension, remittances, donations|0.106**|0.043|
|Ability to raise 300 birr within a week|0.056|0.085|
|Rural household|-0.229***|0.069|
|**Woreda level characteristics**|||
|Average distance to road|-0.001|0.001|
|% of households in agriculture|0.304***|0.082|
|% of household heads employed|-0.101|0.093|
|% of household heads with at least secondary school education|0.429*|0.227|
|Average dietary diversity<br>|-0.009|0.022|
|σeıȷ<br>2<br>�|0.122||
|R sq for Level 1|0.402||
|Obs. Level 1 (household)<br>|30,141||
|σu0ȷ<br>2<br>�|0.042||
|R sq for Level 2|0.329||
|Obs. Level 2(community)|543||

Note: Interaction terms between woreda and a number of the household characteristics are also included in the regression but not reported.

*, ** and *** refer to statistical significance at 10%, 5% and 1% levels, respectively. Source: Author calculations from 2016 HCES/WMS.

15
shocks, the household fixed effects results indicate that boys and girls are negatively impacted and there is no additional statistically significant negative impact for girls, although the number of households with children born before and after the shock and of each sex is limited.

Next, we test the robustness of the crop failure results using household-level agricultural production and rainfall data.<sup>17</sup> We use the DSA agricultural survey, which was administered to a subset of the UNICEF households, and which contains household-level production information for October 1988 to September 1991. Using this production information, we can estimate the direct impact of a production shock experienced at the time of birth by a child’s household on the child’s health. Since the production data only cover three years, we exclude children born outside the three year time interval. Of the 921 children under age five in the DSA households, there were 534 born between October 1988 and September 1991. An additional 61 children could not be included in the regressions due to missing data on household production in the year of the child’s birth. The smaller sample of households in the DSA survey, the reduced number of birth cohorts to examine, and missing production data for some households reduce the sample of children we can use in the regressions to 473 children. This smaller, but still nationally representative sample of children provides insight into the mechanism driving the earlier crop failure results seen using province-level variation. Finally, we use rainfall data collected by the Rwandan Meteorological Service, which are available on a monthly basis for each province until December 1991.

In Table 7, we present these robustness tests. We define the household production shock as the difference between the value of farm output during the year the child is born and the three

> 17 To our knowledge, no household-level measures of the impact of the civil war in the north of Rwanda in the 1990 to 1991 period exist that cover the entire region and are sufficiently disaggregated (i.e. number of casualties, property damaged).

17
**Figure 31: Non-mineral Exports by Product and Federal Region, 2011, Percent**

<!-- Start of picture text --> 16 32 35 97 33 20 68 22 27 (% shares of non‐mineral exports)<br>100%<br>90%<br>80% Machinery & Transport<br>70% Equipment Exports<br>60%<br>Metals Exports<br>50%<br>40%<br>30% Wood Exports<br>20%<br>10%<br>Chemical Exports<br>0%<br>Food & Agriculture<br>Exports<br><!-- End of picture text --> _Source_ : Rosstat.

0
more state presence, access to and utilization of public services is still lower than in the control group. We present similar findings for the agricultural sector. Even though the land can still produce commercial crops that could exploit increasing returns to scale, we find that commercially exploited plots are smaller and a larger share of individuals work in subsistence agriculture in former guerrilla areas. We also find lower productivity and some evidence that the agro-based industry sector is less developed in former guerrilla areas. This pattern could also be explained by distrust of agricultural elites associated with the state. In interviews, we learned that even though commercially oriented farmers would like to invest in these areas, residents are reluctant to let them due to high distrust of out-groups. This is true even when the new entrants have no ties to elites from the 1970s. In this section, we also rule out alternative mechanisms such as an increase in violence, selective migration, lower public and private investment, and child recruitment.

# **VI.A Transformation of social norms: lower trust in and engagement with the state and associated groups**

Both FMLN documents and scholarly work suggest that the organization of the rural population was a key strategy rebels employed against the state (FMLN, 1983, 1984; Binford, 1997; Pearce, 1986). In guerrilla areas, the FMLN’s social base set up participatory government to replace the municipal administration. As noted above, they eliminated state and judicial authorities and established community-based organizations—first the PPL and later the “dual powers” plus a variety of others—to represent peasants and address key development issues. (Binford, 1997; FMLN, 1984). In particular, guerrillas implemented communal models of agricultural production with a focus on subsistence crops.

Self-governance can promote the formation of social capital but also reduce trust in and engagement with the state and related groups. First, if self-governance presents an alternative to absent state institutions, it may reduce engagement with the state and the government even when the state regains control. Moreover, such disengagement can reduce demand for state services and create informational problems that prevent the efficient provision of public goods. Second, disengagement can lead to lasting norms of distrust of the state and associated groups such as large agricultural producers, preventing investment and reinforcing the subsistence economy through the present day. We study the validity of this mechanism by examining contemporary attitudes towards the state, public goods provision, and agricultural outcomes.

Table 4 presents the estimates of equation (1) using data from the Latin American Public Opin28
Figure A9: Urban/rural poverty rates and GDP <!-- Start of picture text --> (A) Official definition<br>Urban Rural<br>80 80 MWI<br>60 60 NER<br>MWI AGO<br>UGA<br>40 UGA 40 TZALSO<br>TZA BFA<br>NER TCDETHGNB<br>20 TCDETHBFA LSO AGO 20 GIN SEN GHACIVMRT<br>GNB CIV GAB<br>GIN SEN GHAMRT GAB<br>7 7.5 8 8.5 9 9.5 7 7.5 8 8.5 9 9.5<br>Log of GDP per capita Log of GDP per capita<br>R2 = 0.1358 R2 = 0.3075<br>(B) DOU<br>Urban Rural<br>80 80 MWI<br>60 60 NER<br>MWI AGO<br>UGA<br>40 UGA 40 TZALSO<br>TZA BFA<br>NER TCDETHGNB<br>20 TCDETHBFA LSO AGO 20 GIN SEN GHACIVMRT<br>GNB CIV GAB<br>GIN SEN GHAMRT GAB<br>7 7.5 8 8.5 9 9.5 7 7.5 8 8.5 9 9.5<br>Log of GDP per capita Log of GDP per capita<br>R2 = 0.3286 R2 = 0.3139<br>(C) DB<br>Urban Rural<br>80 80 MWI<br>60 MWI 60<br>AGO<br>NER UGALSO<br>NER<br>40 UGATZA 40 TZA<br>TCD<br>20 TCDETHBFA LSO AGO 20 ETHGNBBFA GIN SEN GHACIV<br>GNB GIN SEN GHACIVMRT GAB MRT GAB<br>7 7.5 8 8.5 9 9.5 7 7.5 8 8.5 9 9.5<br>Log of GDP per capita Log of GDP per capita<br>R2 = 0.4117 R2 = 0.2405<br>Poverty rate in urban areas Poverty rate in rural areas<br>Poverty rate in urban areas Poverty rate in rural areas<br>Poverty rate in urban areas Poverty rate in rural areas<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: GDP per capita is measured in PPP (constant 2017 international $). For the DOU and DB methods, WorldPop 250m is used. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method, and the categories “Core” and “Suburb” for the DB method. Poverty is measured using the $2.15 poverty line.

33
particularly striking, with an urban poverty rate in China in 2002 that is barely 4% of the rural rate, while it is 86% for India. Urban poverty incidence in China is unusually low relative to rural, although problems in the available data for China are probably leading us to underestimate the urban share of the poor in that country.<sup>26</sup> The regional differences in the urbanization of poverty are clear in Figure 2, plotting the urban share of the poor by region. The share is lowest in East Asia, due in large part to China. The urban share of the poor is highest in LAC, which is the only region in which more of the “$1 a day” poor live in urban than rural areas (the switch occurred in the mid-1990s). For LAC, almost two-thirds of the $2 a day poor live in urban areas.

South Asia and SSA are clearly the regions with highest urbanization of poverty at given overall urbanization, due to their relatively high urban poverty rates relative to rural; these are also the regions with the highest overall poverty rates. In 2002, 44% of the world’s urban poor by the $1 a day line are found in South Asia, and 35% are found in SSA; these proportions fall appreciably when one focuses on the $2 a day line, for which 39% and 22% of the urban poor are found in South Asia and SSA respectively.

There are other notable regional differences. In the aggregate and in most regions, poverty incidence fell in both sectors over the period as a whole (though with greater progress against rural poverty in the aggregate). LAC and SSA are exceptions. There rising urban poverty came with falling rural poverty. The (poverty-reducing) population shift and rural components of equation (1) for LAC and SSA were offset by the (poverty-increasing) urban component.

While the urban poverty rate for the developing world as a whole was relatively stagnant over time for $1 a day, this is not what we find in all regions. Indeed, the urban poverty rate is

> 26 The main problem is that (until recently) the sample frame for China’s national urban and rural surveys has been based on the registration system rather than street addresses.  Thus recent migrants to urban areas are thus likely to be undercounted in the urban surveys since their registration will still be rural. On the (plausible) assumption that rural migrants are poorer than the average urban resident, we will underestimate urban poverty incidence.

19