rafmacalaba/datause-encoder-data huggingface dataset:
|DV: logged total||||Intensive|margin (Se|rvitized|
|---|---|---|---|---|---|---|
|manufacturing sales<br>|Extensiv<br>|e Margin (<br>|All firms)<br>||firms)<br>||
|i|a<br>(1)|a<br>(2)|a<br>(3)|a<br>(4)|a<br>(5)|(6)|
|Exposure to import competition|-O.115*** <br>(0.025)|-0.115***<br>(0.025)|-0.097***<br>(0.024)|-0.302***<br>(0.044)|—-0.303*** <br>(0.044)|-0.209***<br>(0.036)|
|Servitization||-0.018<br>(0.018)|0.114**<br>(0.052)||0.014<br><br>(0.018)|= 0.263***<br>(0.057)|
|Exposure*Servitization|||-0.037***|||-0.060***|
||||(0.014)|||(0.013)|
|Total observations|62720|62720|62720|22315|23695|22556|
|Numberoffirms|8874|8874|8874|4756|4954|3815|

Notes: The table shows estimation results of a regression of manufacturing revenue on firm traits, including exposure to imports and servitization. Columns 1-3 use a dummy to characterize servitization, while columns 4-6 use services revenue as a share of total revenue to measure servitization. All estimations include firm and year fixed effects and report standard errors clustered at the firm-level. Significance levels are indicated with *** (at 1 % level), ** (at 5% level) and * (at 10% level).

||Serviti|zation at th|e extensive|margin|Inten|sive margin|(Servitized|firms)|
|---|---|---|---|---|---|---|---|---|
|DV: logged total<br>manufacturingsales<br>|Consu<br>bund<br>|mers'<br>ling<br>|Produ<br>Economies<br>|cers'<br>ofScope<br>|Consu<br>bund<br>|mers'<br>ling<br>|Produ<br>Economies<br>|cers'<br>ofScope<br>|
|<br>———n>smm||||<br>||D||<br>N|
|Exposure to importcompetition|-0.115*** <br>(0.025)|-0.106*** <br>(0.025)|= -0.115***<br>(0.025)|-0.111***<br>(0.025)|-0.302***<br>(0.044)|-0.227***<br>(0.045)|-0.301***<br>(0.044)|—-0.306***<br>(0.042)|
|Servitization|-0.021|0.037|-0.143***|-0.131***|0.000|0.019**|-0.006*|-0.012|
||(0.018)|(0.034)|(0.046)|(0.046)|(0.003)|(0.008)|(0.003)|(0.013)|
|Exposure*Servitization||-0.018*||-0.007||-0.005***||0.002|
|||(0.009)||(0.005)||(0.002)||(0.003)|
|Total observations|62720|62720|62720|62720|25947|25947|25947|25947|
|Numberoffirms|8874|8874|8874|8874|5198|5198|5198|5198|

Notes: See Table 4a. Columns 1-4 measure the effect of servitization at the extensive margin: with columns 1-2 measursing the correlation with respect to consumer based services and 3-4 for producer based services. 5-8 repeat the same with servitization at the intensive margin. All estimations include firm and year fixed effects and report standard errors clustered at the firm-level. Significance levels are indicated with *** (at 1 % level), ** (at 5% level) and * (at 10% level).
**Table 2: Regression results for determinants of emerging market sovereign bond spreads**

||**Model 1**|**Model2**|**Model3**|
|---|---|---|---|
|_Local Macroeconomic variables_||||
|GDP growth rate|-8.34|-7.19|-7.56|
||(0.000)***|(0.000)***|(0.000)***|
|GDP per capita|<br>-4.82|<br>-3.44|<br>-4.50|
||(0.000)***|(0.002)***|(0.000)***|
|Inflation|131.59|137.73|140.15|
||(0.010)***|(0.007)***|(0.005)***|
|Private credit/GDP|-0.61<br>|-0.49<br>|-0.51<br>|
||(0.167)|(0.266)|(0.248)|
|Fiscal balance/ GDP|-1.08|-2.39|-1.36|
||(0.493)|(0.132)|(0.382)|
|Exports/GDP|1.08|1.84|1.42|
||(0.169)|(0.019)**|(0.069)*|
|Foreign bank claims/GDP|<br>1.45|<br>1.50|<br>1.36|
||(0.066)*|(0.058)*|(0.083)*|
|Country credit risk rating index|15.55|15.87|15.28|
||(0.000)***|(0.000)***|(0.000)***|
|Country financial crisis dummy|||80.73<br>|
||||(0.000)***|
|_Global factors_||||
|US 10 year treasury bond yield (BP)|-0.20|-0.16|-0.18|
||(0.003)***|(0.018)**|(0.005)***|
|US 10 year T-bond yield — 2 year T-bond yield|17.79|15.30|17.82|
||(0.001)***|(0.007)***|(0.001)***|
|Volatility<sup>(1)</sup>|42.64||38.76|
||(0.000)***||(0.000)***|
|World Industrial production index, Y-O-Y growth||-9.487||
|||(0.000)***||
|_Bond attributes_||||
|Euro denominated bond|-6.05|-6.98|-6.96|
||(0.576)|(0.519)|(0.515)|
|Log (maturity)|-4.73|-6.39|-2.90|
||(0.463)|(0.320)|(0.650)|
|Log (value)|-6.94|-7.81|-5.60|
||(0.108)|(0.070)*|(0.190)|
|Floating rate notes|-94.10<br>|-95.66<br>|-93.13<br>|
||(0.000)***|(0.000)***|(0.000)***|
|Guarantee|<br>22.39|<br>15.41|<br>25.82|
||(0.106)|(0.266)|(0.060)*|
|Eurobond|0.63|1.92|0.24|
||(0.952)|(0.855)|(0.982)|
|Rule 144A|<br>-6.44|<br>-3.09|<br>-6.50|
||(0.523)|(0.760)|(0.515)|
|Non negative-pledge issuer|10.57|11.99|10.73|
||(0.211)|(0.158)|(0.199)|
|Bond rating at launch|<br>13.87|<br>13.54|<br>14.02|
||(0.000)***|(0.000)***|(0.000)***|
|Country effects (not reported here)||||
|Observations|1087|1087|1087|
|R-squared<br>_Note_: p values in parentheses<br>* significant at 10%; ** significant at 5%; *** significant at 1%:|0.71|0.71|0.72|

1) Our volatility indicator is derived from a detail factor analysis of several indicators, VIX, commodity prices (agriculture, energy, and metals) and the TED spreads, as described in Dailami, Masson (2009).

17
that such sanitation programs can be widely successful.<sup>11</sup> So, the effect of the planned experiment that did not occur in Nanded and Nandurbar is to change the set of villages eligible to be randomized into the treatment or control groups. Deaton (2012) observes that econometric theory appears to have no ready name for this set, and suggests it be called the “experimental panel.” Because we can still produce internally valid results for Ahmednagar, our analysis will focus on this district.

# **2.4.1 Effects on child height**

Physical height has emerged as an important variable for economists studying development, labor, or health (Steckel, 2009). Height is a persistent summary measure of early life health; early-life height predicts adult height (Schmidt et al., 1995), as well as human capital and economic productivity (Case and Paxson, 2008; Vogl, 2011). Puzzlingly, South Asian people are much shorter than their income would predict (Deaton, 2007; Ramalingaswami et al., 1996): they are, for example, shorter on average than people in Africa who are poorer, on average. Moreover, children’s height is much more steeply correlated with cognitive achievement in India than in the U.S., suggesting more depth and greater variance of early life disease (Spears, 2012b). Medical and epidemiological evidence, as well as econometric decomposition, suggests that widespread open defecation could be an important part of the explanation for Indian stunting (Humphrey, 2009; Spears, 2013), and well-identified nonexperimental evidence suggests that community sanitation can improve Indian children’s heights (Spears, 2012a).

Height of children under 5 is, therefore, the central dependent variable in our analysis.<sup>12</sup>

> 11It should be noted that such choices are usually motivated by the opposite concern: to place programs in the most favorable circumstances. It showed quite a bit of courage to tackle the hardest cases first. 12Although many epidemiological studies use survey-reported diarrhea as a dependent variable, we make no attempt to study this noisy and unreliable outcome measure (Schmidt et al., 2011). For example, Zwane et al. (2011) show that households randomly selected to be surveyed more frequently report less child diarrhea. More broadly about survey reported morbidity, in a survey experiment in India, Das et al. (2012) find that changing the recall period reverses the sign of the apparent health care - economic status gradient. Finally, Humphrey’s (2009) evidence of height shortfalls due to chronic enteropathy indicate that diarrhea is not an indicator of or necessary condition for losses in human capital.

11
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There are no special privileges for the trading firm, and it does not operate in a zone with special trading privileges. The shipment is considered to move via ocean transport between the country’s largest overseas trading partner and the most frequently used seaport. The TAB indicators consider all the time required at the port, in customs, for inland transport and for document processing when moving goods between the chosen port and the most populous city in the country. The figures reported by TAB summarize survey responses about the time and cost associated with trading such a shipment.<sup>14</sup> The unit measures of time reported in the TAB customs indicators are the number of days that are normally required for goods to clear customs. The use of days as units complicates our attempt to exploit them as measures of border outcomes. Figure 1 shows a histogram of days in import and export customs, respectively, for the data in our sample. The data are discrete and generally take low values, which rules out estimators that require continuous variation on the left hand side. While the data are in discrete form, our real interest is in the continuous latent variable, time. A related complication is that it is likely that there is considerable heterogeneity in average clearance times across countries for which the same number of days is reported. For example TAB reports that it takes one day to clear import customs in both Singapore and Romania, but we might expect clearance to be faster in Singapore. Our estimation technique acknowledges that time is continuous even though the data are discrete. We estimate empirical relationships between a latent variable representing continuous time and the explanatory variables including border policies, based on a probabilistic model that the time in customs will end on a given day. This allows within-day heterogeneity in the predicted values of time spent in customs.

We also explore the implications of policy for three continuous measures of border performance: the monetary costs of clearing import and export customs as reported in the TAB database, and an index value of customs performance. The TAB estimates of the monetary cost of customs clearance use an identical definition of a shipment as do the estimates of clearance time, but asks respondents to report administrative charges, customs broker charges, and other charges, as well as a total. The reported total is the measure of cost we use. The index measure of customs performance we investigate as an outcome variable is the _customs_ subcomponent of the World Bank’s Logistics Performance Index (LPI). All three of these measures are continuous.

> 14Because survey response numbers for some countries are quite small, unrepresentative, and noisy, the summary indicators reported by TAB reflect, in some cases, judgements of the TAB team.

7
———. 2007. Informalidad en Colombia: ¿Qué Hacer? Presentación de Sra. Carolina Renteria, 29 May 2007, Bogota.

———. 2008a De la asistencia a la promoción social. Hacia un sistema de promoción social, Sistema de Indicadores Sociodemográficos para Colombia, SISD 35, Bogotá, Colombia.

———. 2008b. _Una Aproximación a la vulnerabilidad_ . Sistema de Indicadores Sociodemográficos para Colombia, SISD 34, Bogotá, Colombia.

ELS. 2008. Encuesta Social Longitudinal de Fedesarrollo. Etapa XIII 2008. Bogota: Fedesarrollo.

Gaviria, A., C. Medina, and C. Mejía. 2006. “Assessing Health Reform in Colombia: From Theory to Practice.” _Economia_ 7(1): 29–63.

Holzmann, R., and O. Jorgensen. 2003. “Manejo social del riesgo: un nuevo marco para la protección social y más allá.” _Revista de la Facultad Nacional de Salud Pública_ , Universidad de Antioquia, Medellín, Colombia.

Holzmann, R., and J. Stiglitz. 2000. _New Ideas About Old Age Security_ . Washington DC: World Bank.

IADB (Inter-American Development Bank) 2010. _The Age of Productivity. Transforming Economies from the Bottom Up._ Development in the Americas Flagship Report. Washington DC: IADB.

ILO (International Labor Organization) 2008. _Panarama Laboral para América Latina y el Caribe 2008_ . Geneve: ILO.

Kugler, A., and M. Kugler. 2009. "The Labor Market Effects of Payroll Taxes in a Middle Income Country: Evidence from Colombia." Economic Development and Cultural Change 57: 335-58.

———. 2007. “Pueden los programas sociales disminuir la productividad y el crecimiento económico? Una hipótesis para Mexico. _El Trimestre Económico_ 74: 491–540.

———. 2008. _Good Intentions, Bad Outcomes, Social Policy, Informality and Economic Growth in Mexico_ . Washington DC: Brookings Institution Press.

López, H., and J. Nuñez. 2007. “Pobreza y desigualdad en Colombia: diagnóstico y estrategias.” Departamento Nacional de Plantación, Bogotá, Colombia.

Maloney, W. and J. Nuñez. 2004. “Measuring the Impact of Minimum Wages Evidence from Latin America” In Law and Employment: Lessons from Latin America and the Caribbean, ed. J. Heckman and C. Pages, Chicago: University Chicago Press.

Mesa-Lago, C. 2000. “Desarrollo social, reforma del Estado y de la seguridad social, al umbral del siglo XXI”, Serie Políticas Sociales No.36, CEPAL, División de Desarrollo Social, Santiago Chile.

Mondragón-Vélez, C., X. Peña, and D. Wills. 2009. Labor Market Rigidities and Informality in Colombia. Universidad de los Andes. Unpublished.

Nuñez, J. 2008. “Mercado laboral y SPS en Colombia: desincentivos al trabajo y al progreso.” 20
and the World Bank subsequently initiated the Firm Analysis and Competitiveness Surveys (FACS) as the next large data collection program. The focus of the FACS was oriented towards larger firms outside SSA, and Morocco was the only African country surveyed as part of FACS.

At the end of the 1990s and in the early 2000s new survey initiatives took place in SSA, as Investment Climate (IC) surveys and the World Business Environment Survey (WBES) were fielded across a wide range of countries. The IC surveys carried out in SSA so far have been organized as part of the RPED, and have covered Kenya, Tanzania, Zambia, Zimbabwe, Mozambique, Nigeria, Eritrea, Ethiopia, Côte d'Ivoire, Cameroon, Senegal, and Mauritius. The nature of these surveys is similar to the earlier RPED surveys in terms of firm and sector coverage, but the survey instruments are more oriented towards investment climate issues and thus far the main objective appears to have been to collect cross-section data (as distinct from panel data). The WBES was launched in 80 countries and one territory in 1999 and 2000, including 17 countries in SSA (Batra, Kaufmann and Stone, 2003). The surveys in SSA covered between 52 and 137 firms per country, and both manufacturing and non-manufacturing firms were included. The bulk of the survey instrument was concerned with firm and business environment attributes (e.g. governance, regulations, economic policy, public services etc.). The WBES data is cross-section data.

The firm survey is not the only way of generating data useful for analysis of private sector performance and the role of the investment climate. In fact, because many aspects of the investment climate are constant across firms within an economy, it will sometimes be better to measure the key dimensions of the investment climate at the country level - perhaps by means of a few case studies - and then conduct the empirical analysis at the aggregate level. One major project based on such a procedure has generated the World Bank and IFC _Doing Business Database_ (World Bank, 2005). This database contains data on various aspects of the regulatory environment facing private firms in a large number of countries and economic regions - e.g. how long it takes to start a business, how hard it is to hire and fire workers, how well contracts are enforced and so on. The construction of these data is based on casestudies of laws and regulations in each country and does not involve surveying individual firms.

5
74 _distribution_ of such services among population groups, with a concentration of health resources to large and medium-sized cities, receiving about 80 percent of the total resources (UNDP 2000, p. 3), which cannot possibly reflect the regional distribution of the need for health-care resources. Naturally, the uneven distribution of health services may also depend on differences of competence across local areas in the field of health care organization.

Another country-specific inefficiency in China is related to strict price controls on specific types of health services. This has induced hospitals and health clinics to finance much of their health-care provisions by revenues from the sale of drugs. This, in turn, has created strong incentives both to charge high prices for drugs and to over-prescribe drug medication (Hesketh and Zhu, 1997).<sup>146</sup> Clearly, efficiency considerations require a new price system for health services with a more symmetric price system for drugs relative to other types of treatment.

Indeed, reforms are underway in health care, including announced ambitions to improve the situation in rural areas,<sup>147</sup> although China has a particularly long way to go in these areas.<sup>148</sup> As in the case of pensions, the financing of future health-care insurance in urban areas is supposed to rely on a combination of paygo and funding (with individual accounts), the latter organized along lines similar to Singapore and Malaysia.<sup>149</sup> Presumably, individual accounts are particularly useful for relatively inexpensive, mainly “out-patient” treatment, rather than expensive hospital treatment. Costly treatment (including “catastrophic health care”) is supposed to be covered by the paygo (redistributive) part of the system. However, the individual accounts already seem to have run into financial difficulties (in a similar way as the pension system), thereby forcing the government to inject new money from the general budget into the paygo part of the

> (preventive health care) in spite of the fact that such treatment is particularly important in poor countries (UNDP, 2005, p. 58).

> 146 As much as half of the total spending on health care is reported to consist of costs for drugs, while more normal figures in developed countries usually are 10-15 percent (Blumenthal and Hsiao, 2005). 147 See, for instance, China’s Social Security White Paper, 2004.

> 148 By the end of 2003, about 110 million individuals in urban areas, comprising 80 million employees and 30 million retirees, were reported to have been covered by health insurance, while the coverage in rural areas was still extremely low.

> 149 The individual’s entire contribution (two percent of the earnings) and a third of the contribution covered by the employer (six percent of the wage bill) are supposed to be paid into an individual’s (funded) personal account, while the remaining two thirds of the employers’ premium is allotted to the paygo part of the system (i.e., the common “health insurance pool”). See, for instance, Social Insurance Research Team (2003).
sign when it is included in these regressions, so we have excluded it from the estimates. Education and rainfall retain the correct signs, although only female primary enrollment is significant at 5%. These are the maximum likelihood estimates in any case, and we need to project drought effects, so we retain the two drought equations in Table 6.

To explore the implications of our results, we compare actual historical losses with a counterfactual case in which countries at each World Bank development level are assigned the same female primary enrollment ratio as the “best practice” country in the same World Bank class in the same year (e.g., all Low Income countries in 1985 are assigned the highest female primary enrollment ratio among Low Income countries in 1985). We perform this experiment to see how much difference feasible policy changes could have made for extreme weather risk. As Table 7 and Figure 4 show, our results strongly indicate that more progressive policies would have made an enormous difference. From 1970 to 1999, the CRED database records 153,079 deaths from floods in low income and lower middle income countries. In our “best practice” counterfactual, by contrast, flood deaths number 91,541: 61,538 fewer people lose their lives. For numbers affected, the estimated differences are very large. From 1960 to 1999, the CRED database indicates that 2.12 billion people in developing countries were affected by floods. In the counterfactual best practice case, this falls by 465 million to 1.65 billion. For droughts, the number affected falls by about 667 million – from 1.34 billion to 676 million.

We conclude that a huge number of weather-related tragedies could have been averted if more developing countries had focused on progressive but feasible female education policies. Countries that focused on female education suffered far fewer losses from extreme weather events than less-progressive countries with equivalent income and weather conditions. It seems reasonable to assert that what has been true in the past will also be true in the future. Given the significance of income and female education in determining vulnerability to extreme weather events, we would expect countries’ future resilience to increase with economic growth and improvements in education.

12
**Figure 4: Churn in the Number of Workers**

<!-- Start of picture text --> Change in Workers R3 to R4 Change in Workers R5 to R6<br>-4 -2 0 2 4 6 -5 0 5<br>Change in Number of Workers Change in Number of Workers<br>Treatment Control Treatment Control<br>.8 .8<br>.6 .6<br>.4 .4<br>Fraction Fraction<br>.2 .2<br>0 0<br><!-- End of picture text --> Notes: R3 to R4 is the change in the number of workers between the last pre-intervention survey and first survey during the wage subsidy. R5 to R6 is the change in the number of workers between the last survey during the intervention and first survey after the intervention.

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Figure 2. Difference in days between onset and IMD long-range forecast of monsoon (19782006) <!-- Start of picture text --> 1975 1985 1995 2005<br>years<br>20<br>10<br>0<br>Actual onset - IMD forecasted onset -10<br><!-- End of picture text --> Note: Data for this Figure come from Indian Meteorological Department (http://www.imdpune.gov.in/)
<!-- Start of picture text --> g<br>stS<br><<br>g<br>oO<br>£<br>a<br>god<br>°<br>April 3 April 28 June 15<br>SAP receipt date (among non-4Ps)<br><!-- End of picture text --> <!-- Start of picture text --> :<br>2<br>aTT SSL<br>SL<br>+ SS —<br>~<br>”<br>a<br>o<br>0 1 2 3 4<br>Survey Wave<br>os 4Ps Non-4Ps<br><!-- End of picture text -->