rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A 2 Balance Table for the Village Selection Model: Maharashtra**

||**Me**<br>|**an**<br>|||
|---|---|---|---|---|
|**Variables**|**Treated**|**Control**|**Percent**<br>**Bias**|**P-**<br>**value**|
|Interaction of SHG Status and Proportion of either SC or ST Population – Census 2011|0.36|0.29|22.40|0.07|
|Logof District HeadQuarter Distance – Census 2011|4.23|3.91|40.60|0.00|
|DensityPrimarySchool Numbers – Census 2011|1.18|0.96|33.40|0.01|
|Proportion of Forest Area – Census 2011|0.35|0.22|44.90|0.00|
|Self - HelpGroup (SHG)Status – Census 2011|0.56|0.73|-38.50|0.00|
|Proportion of either SC or ST Population – Census 2011|0.72|0.43|100.90|0.00|
|Agricultural MarketingSocietyStatus – Census 2011|0.88|0.71|41.50|0.00|
|Percentage of households with main source of light as none – Census 2011|0.03|0.01|17.90|0.14|
|Interaction of Proportion of Literate and Proportion of Female Population – Census 2011|0.29|0.30|-13.30|0.21|
|Proportion OwningLandline OnlyHouseholds – SECC 2013|0.00|0.00|-0.40|0.86|
|Total Geographical Area – Census 2011|572.14|612.41|-7.90|0.45|
|Black Topped(pucca)Road Status – Census 2011|0.67|0.74|-16.50|0.18|
|Gravel(kuccha)Roads Status – Census 2011|0.66|0.69|-8.50|0.48|
|Hand PumpStatus – Census 2011|0.91|0.89|7.50|0.46|
|Households with PTG Members – SECC 2013|0.12|0.08|18.00|0.17|
|Public/Private Bus Status – Census 2011|0.66|0.71|-10.30|0.40|
|Proportion of Government SectorJobs Households – SECC 2013|0.02|0.02|-1.70|0.81|
|Proportion of Public SectorJobs Households – SECC 2013|0.00|0.00|-5.00|0.22|
|Proportion of Private SectorJobs Households – SECC 2013|0.00|0.00|-2.80|0.31|
|Proportion OwningIrrigated Land Households – SECC 2013|0.03|0.04|-13.50|0.23|
|Proportion OwningUnirrigated Land Households – SECC 2013|0.20|0.21|-8.20|0.48|
|Proportion OwningOther Irrigated Land Households – SECC 2013|0.04|0.05|-9.50|0.39|
|Percentage of households with latrine facilities within thepremises – Census 2011|0.17|0.21|-13.80|0.15|
|Proportion OwningIrrigation Equipment Households – SECC 2013|0.05|0.07|-13.80|0.20|
|Proportion HavingKisan Credit Card Households – SECC 2013|0.02|0.02|0.90|0.94|
|DensitySecondarySchool Numbers – Census 2011|0.08|0.06|11.50|0.30|
|DensityMiddle School Numbers – Census 2011|0.26|0.29|-6.40|0.60|
|Proportion of Total Population Female – Census 2011|0.50|0.49|16.80|0.13|
|Proportion of Literates Population Person – Census 2011|0.58|0.60|-17.40|0.12|
|Proportion of Non-WorkingPopulation Person – Census 2011<br>|0.46|0.44|10.60|0.30|
|Observations||636|||

28
# **5. Performance, Costs, and Financial Efficiency**

## **a. Container-handling Systems**

**Compared with other Caribbean ports and Latin American ports in general, the OECS ports have very low throughputs** . The average annual OECS throughput, at 18,000 TEU per year, significantly trails the 300,000–350,000 TEU/year found in the other regions of Latin America. Clearly, overall throughput levels depend on the size and capacity of the port, and OECS berth lengths and port areas are also smaller. Notably, there are no ship-to-shore (STS) gantry cranes at the OECS ports, a fact that has significant implications for the productivity of these ports, since STS gantry cranes are much more productive than mobile cranes. This contrasts with the Caribbean region as a whole, where the average country has two STS gantry cranes (table 5.1).

|**Table 7.1 Agg**|**regate B**|**enchmark**|**of OECS Ports**<br>|**in the Lat**<br>|**in American**|**Context, 2010**<br>**Mobile**|<br>**STS**|
|---|---|---|---|---|---|---|---|
|||||||||
|**Region**|**Ports**|**Statistic**|**nnua**<br>**Throughput**<br>**(TEU)**|**er**<br>**Length**<br>**(m)**|**Area**<br>**(m^2)**|**Cranes with**<br>**Capacity>14t**<br>**(units)**|**Gantry**<br>**Cranes**<br>**(units)**|
|||Average|519,856|844|199,400|1.5|2.9|
|Central America|21|Minimum|2,229|67|15,000|0.0|0.0|
|||Maximum|2,758,506|2,205|564,000|12.0|16.0|
|||Average|246,816|604|134,495|1.6|1.6|
|Caribbean|16|Minimum|2,852|133|15,740|0.0|0.0|
|||Maximum|1,525,532|1,688|477,428|7.0|10.0|
|||Average|15,018|333|64,995|0.5|0.0|
|OECS|8|Minimum|2,852|133|40,469|0.0|0.0|
|||Maximum|27,526|495|117,360|1.0|0.0|
|||Average|560,986|1,270|336,332|2.7|2.2|
|South America|32|Minimum|36,080|238|15,000|0.0|0.0|
|||Maximum|2,722,225|4,728|1,270,500|10.0|17.0|
|||Average|475,617|986|247,855|2.1|2.3|
|Total|69|Minimum|2,229|67|15,000|0.0|0.0|
|||Maximum|2,758,506|4,728|1,270,500|12.0|17.0|

_Source:_ Adapted from Sarriera and others (2013), based on authors’ primary data collection. _Note:_ LAC = Latin America and the Caribbean; m = meters; m<sup>2</sup> = square meters; OECS = Organization of Eastern Caribbean States; STS = ship to shore; t = tonnes.

**Caribbean ports—and the OECS ports are no exception—post consistently poor growth and significant variations in cargo traffic.** The throughput of Caribbean and OECS ports increased between 1999 and 2010, while remaining significantly less than ports in Latin America as a whole. But this figure is deceptive for the OECS, because throughput data are available for only two of the eight ports included in the sample in 1999 (table 5.2).<sup>18</sup> During the 2008–10 period, when data for all eight OECS ports included in the sample are available and reflect the global economic slowdown, the OECS ports did slightly better (that is, they contracted less) than the non-OECS Caribbean ports. Still, their performance was below the Latin American average.

> 18 For the Organization of Eastern Caribbean States (OECS) ports, data are available for Castries and Vieux Fort from 1999; for Roseau from 2000; and for Campden Park/Kingstown, Long Point, St. George’s, and St. John’s from 2007.

31
Graph 4 Composition of Exports by Country <!-- Start of picture text --> 14,000,000<br>12,000,000 -___ _<br>10,000,000 -N<br>8,000,000<br>6,000,000<br>4,000,000<br>2,000,000 -<br>2,000000 ________ e___________________<br>CD C D C D C D C D C Cs CD C D CD C D C<br>to cD tD co co co co CD co cO CD CD cD (D D co co co<br>o -. - rŽ? '~? eg g J'CD-_ 4g CDCDCD~<br>O _CD O O- X 0 0 N M . w0 -4 C0 0 ) 0G CO 0 . -) 0> - 0 c X 0 CO - 0 M<br>-Total FOB Exports<br>-<br>FOB Exports to Canada<br>FOB Exports to US<br>-FOB Exports to Rest of the World<br><!-- End of picture text --> Graph **5: MEXICO** in the **Global** Market <!-- Start of picture text --> 1986 1992 1993 1994 1995 1998 2000<br>GATT | Chile APEC OCDE Costa Nicaraguar Israel<br>I Rica<br>NAFTA I |European|<br>Bolivia § | Union<br>Colombia<br>Venezuela<br><!-- End of picture text -->

<!-- Start of picture text --> Trade Agreements Currently Being Negotiated by Mexico<br>Guatemala, Honduras and El Salvador<br>Belize<br>Ecuador<br>Panama<br>Peru<br>Trinidad and Tobago<br><!-- End of picture text --> _55_
per capita. The second indicator is the Number of Procedures necessary to incorporate a business. Third, Starting Days, measures the time required to start a business, which is defined as the number of days that incorporation lawyers indicate is necessary to complete all required procedures with minimum follow-up with government agencies and no extra payments. Fourth, we include Employment Rigidity, which is itself an average of three sub indices: Difficulty of Hiring, Rigidity of Hours, and Difficulty of Redundancy. The index and sub indices take values of 0 to 100, with higher values indicating more rigid regulation, e.g. countries where it is more difficult to fire redundant workers.

We also include measures of country-level governance (Kauffman, et al., 2009), which we predict are positively related to new firm creation. We include Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, Control of Corruption, and an aggregate Governance Composite.<sup>9</sup> All risk ratings are on a scale of -2.5 to 2.5, with higher values indicating less risk.

In addition, we include the Corporate Tax Rate for 77 countries from KPMG (KPMG, 2009). Consistent with previous literature, we predict that a higher corporate tax rate discourages formal sector participation and registration (Da Rin, Di Giacomo, and Sembenelli, 2010).<sup>10</sup> The final dataset includes 516 observations from 93 countries over the six-year period 2004 to 2009. Data is not available over the complete period for all countries. The period 2004 to 2009 is used in the analysis because it provides the most complete sample. Entry days, entry procedures and GDP per capita are logged. Since we use one-year lagged values for all control variables in the multivariate analysis, the summary statistics for all variables but Entry Density, represent values for the period 2003 to 2008 (Table 1). Univariate results show a strong

> 9 Complete data is available at: info.worldbank.org/governance/wgi/index.asp.

> 10 Complete data is available at http://www.kpmg.com.sg/publications/Tax_CorporateIndirectTaxRateSurvey.pdf.

11
# TV.

Baseline data reveal large gaps in knowledge and understanding of behaviors that are important for the development of children. Table 2 shows teacher reports of how important various behaviors are for child development, broken down by development domain. Teachers are able to select options from "very important" to "not important at all" when rating how important various behaviors are for child development. Figure 1 highlights the areas in Table 2 where there are the largest gaps in knowledge: cognitive, language, and social development. Most strikingly, only 31% of teachers agree that it is very important to encourage children to interact with others. The importance of child interaction with others is crucial for teachers to understand. Without lots of interaction, children do not develop social skills, self esteem, creativity, and imagination (Early Education Professionals (2018)). Less than half of teachers agree that practicing numbers and letters with children is very important. Also, less than half agree that it is very important that children be played with and that children should be encouraged to listen. While most teachers still select that these behaviors are important, they do not recognize that they are the most critical behaviors for development in each domain. These gaps reveal that teachers in ECED centers, who are partially responsible for the development of the children they teach, do not place enough importance on some behaviors that are critical for this development.

The largest gap in understanding in our sample comes in the realm of social development. As shown in figure 1, 31% of teachers agree that it is very important for child development for children to interact with others. Social development in children involves their ability to engage with their environment, maintain and create healthy relationships, and work with others; encouraging them to interact with others and make friends is crucial for this type of development. Still, only 50% of teachers agree that it is very important to encourage children to make friends. While it is heartening that most teachers still rate these activities as important, it is crucial to push teachers to recognize that these behaviors are critical. There is room for improvement in teacher understanding of social development in children, specifically 7
trepreneurs (investors) from establishing (investing in) businesses that would otherwise have employed people. As a result, persistent outages could reduce entry of domestic and foreign (via foreign direct investment (FDI)) firms;<sup>2</sup> (ii) In the intensive margin, shortages in electricity supply exert adverse impact on firms’ productivity and profit, given that electricity is an important factor of production. Therefore the negative impact of outages on firm performance can have negative consequences on firms’ demand for labor.

Causal estimation of the impact of infrastructure services such as the quality of electricity is often beset with the challenge of endogeneity, as the incidence and intensity of electricity outages are non-random across space and time.<sup>3</sup> Local economic, social and political factors may confound the relationship between outages and the outcome variables of interest. To overcome this challenge of identification, the paper uses several estimation strategies namely: instrumental variable (IV), difference-in-difference (DID), synthetic control, and panel fixed effects.

The empirical strategy of the paper is summarized as follows: In the main analysis, I use an IV strategy that exploits plausibly exogenous variations in the incidence of outinduced variations in strikes across and time. strikes ages by lightning space Lightning are known to be a major cause of surges in electrical systems leading to over-voltage and destruction of power infrastructure thereby causing outages (Andersen et al., 2011, 2012; Andersen and Dalgaard, 2013). Leveraging this relationship between lightning activities and electricity outages, I combine granular data on measures of lightning intensity with two rounds of individual surveys from the Afrobarometer dataset in 25 countries to estimate an IV regression of the effect of outages on employment. The identifying assumption advanced here is that lightning strikes influence labor market outcomes only through the effect on the quality of electricity supply, i.e, the so-called “exclusion restriction assump-

> 2The high cost of business associated with outages could also facilitate the exit of firms either through relocation to other countries (cities) with much reliable electricity supply, or firm shut down.

> 3Like most essential services, random assignment of outages across locations is not feasible from a policy perspective and more importantly, unethical.

2
Glaeser, Edward, and Cass Sunstein. 2013. “Why Does Balanced News Produce Unbalanced Views?” NBER Working Paper 18975, National Bureau of Economic Research, Cambridge, MA.

Golden, M. and Min, B., 2013. Distributive politics around the world. _Annual Review of Political Science_ , _16_ , pp.73-99.

Gottlieb, Jessica. 2016. "Greater Expectations? A Field Experiment to Improve Accountability in Mali." American Journal of Political Science 60(1): 143-157 Greif, Avner. 1994. “Cultural Beliefs and the Organization of Society: A Historical and Theoretical Reflection on Collectivist and Individualist Societies.” _Journal of Political Economy_ 102 (5): 912–50.

Grindle, Merilee S. (2012). _Jobs for the Boys: Patronage and the State in Comparative Perspective_ . Cambridge: Harvard University Press Grossman, G.M. and Helpman, E., 2001. _Special interest politics_ . MIT press.

Guiso, L., P. Sapienza, and L. Zingales. 2006. “Does Culture Affect Economic Outcomes?” _Journal of Economic Perspectives_ 20 (2): 23–48.

Habyarimana, James, Macartan Humphreys, Daniel Posner, and Jeremy M. Weinstein. 2009. _Coethnicity: Diversity and the Dilemmas of Collective Action_ . New York: Russell Sage Foundation.

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Kahan, Dan M. 2012. “Ideology, Motivated Reasoning, and Cognitive Reflection: An Experimental Study.” _Judgment and Decision Making_ 8 (4): 407–24.

Kahan, Dan, Ellen Peters, Erica Cantrell Dawson, and Paul Slovic. 2013. “Motivated Numeracy and Enlightened Self-Government.” Yale Law School, Public Law Working Paper No. 307.

Keefer, Philip, and Stuti Khemani. 2014. “Radio's Impact on Preferences for Patronage Benefits.” Policy Research Working Paper 6932, World Bank, Washington, DC.

Keefer, P. and Vlaicu, R., 2007. Democracy, credibility, and clientelism. _The Journal of Law, Economics, & Organization_ , _24_ (2), pp.371-406.

Khemani, Stuti (2015). “Buying votes versus supplying public services: Political incentives to underinvest in pro-poor policies.” _Journal of Development Economics_ 177: 84-93.

Kitschelt, Herbert and Steven I. Wilkinson, eds. (2007). _Patrons, Clients, and Policies: Patterns of Democratic Accountability and Political Competition_ . Cambridge: Cambridge University Press.

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level of GDP. Once we compute these turning points, we proceed to calculate different business cycle features and output cost measures across emerging economies, and compare them with analogous figures for selected industrial economies.

# **2.1 Methodological issues**

Typically, research on business cycles has focused on time series _adjusted for long‐run trends_ , with the latter being obtained by using some specific de‐trending technique —say, a deterministic trend model, the Hodrick‐Prescott filter, and the band‐pass filter, among others. In contrast, influential early studies such as Burns and Mitchell (1946) defined business cycles as sequences of expansions and contractions in the _levels_ of either total output or employment (which were evaluated without any type of preliminary de‐ trending). This is the position advocated by the (so‐called) _classical cycle_ approach (dominant in NBER studies of business cycles).<sup>2</sup> The _classical approach_ consists of finding the turning points in an aggregate series — typically, the (log) level of real GDP— in order to identify peaks and troughs. Following this principle, Harding and Pagan (2002) argue that this traditional cycle measure has the advantage that the results are independent of the trend‐cycle decomposition technique chosen by the researcher. These authors extend the Bry and Boschan (1971) algorithm to identify cyclical turning points in quarterly series –i.e. the _BBQ_ algorithm.<sup>3</sup> In fact, the _BBQ_ algorithm requires that:

> 2 The NBER dating methodology shows evidence that the world economy has suffered major slowdowns in economic activity (that is, declines in growth rates that remain positive). In fact, they argue that the post‐ 1973 slowdown in the world economy has had more adverse consequences than a mild and short recession. The analysis of these so ‐called _growth cycles_ requires the computation of the level of economic activity as fluctuations in real GDP around their trends. In this case, the estimation of trend outputs —although not necessarily needed for business cycle analysis— is key to undertake growth cycle analysis (Zarnowitz and Ozyildirim, 2001).

> 3 Harding and Pagan (2002) also show that their algorithm is preferable to date turning points than competing ones, such as the Markov Switching (MS) model (Hamilton, 1989). They argue that BBQ is superior to the MS due to the fact that the latter depends on the validity of the underlying statistical framework.

5
Table 9: Heterogeneous effects of mining by marital status.

|VARIABLES|(1)<br>Working|(2)<br>Service|(3)<br>Sales|(4)<br>Agriculture|
|---|---|---|---|---|
|active (20 km)|0.021*|0.015**|-0.012|0.001|
||(0.013)|(0.007)|(0.008)|(0.012)|
|inactive (20 km)|0.067**|-0.007|0.012|0.045*|
||(0.028)|(0.009)|(0.016)|(0.026)|
|partner|0.070***|-0.004***|0.015***|0.066***|
||(0.002)|(0.001)|(0.001)|(0.002)|
|active*partner|0.019|0.010|-0.038*|0.029|
||(0.029)|(0.009)|(0.021)|(0.034)|
|inactive*partner|0.007|0.009|0.020*|-0.015|
||(0.013)|(0.008)|(0.010)|(0.012)|
|Observations|507,088|507,088|507,088|507,088|
|R-squared|0.200|0.091|0.136|0.356|
|F test: active-inactive=0|2.237|3.760|1.877|2.283|
|p value|0.135|0.0525|0.171|0.131|
|F test: act*partner-inact*partner=0|0.140|0.00798|5.955|1.548|
|p value|0.708|0.929|0.0147|0.214|

Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and region fixed effects, regional time trends, living in an urban area, age, years of education, and religious beliefs. Please see Table 3 for more information about coefficients of interest.*** p<0.01, ** p<0.05, * p<0.1 22
mentation of the model requires more complex econometric techniques than those found in earlier literature. This is the topic of the next section.

# 3 Empirical implementation

We ...rst discuss the econometric issues surrounding the estimation of the parameters of the risk sharing model, as well as those related to the construction of the permanent income innovations required for such task. Then we brie‡y summarize our data sources.

## 3.1 Implementing the risk sharing model

The core of our empirical analysis is the estimation of (7) using a cross-country time-series dataset. This poses a challenging problem because each country’s consumption path depends in nonlinear fashion on the risk sharing parameters of all countries. To make this explicit in what follows, it is useful to rewrite (7) as where uit is potentially heteroskedastic and possibly correlated across countries and over time.

To express the empirical model in more compact form, it is convenient to introduce some notation. Let �ci = (�ci1; :::; �ciT )<sup>0</sup> and "i = ("i1; :::; "iT )<sup>0</sup> denote the (column) vectors of consumption growth and permanent income innovations of country i, and de...ne the T � N matrix Z = ("1; :::; "N ) : In addition, let � = (�1; :::; �N )<sup>0</sup> ; the vector of risk sharing parameters for all countries, and de...ne the N � N diagonal matrices H and W0; which have along their main diagonal the population of each country Hj0 and its per capita GDP share !j0, respectively, with all o¤-diagonal elements equal to zero. After some straightforward manipulations the observations of (8) corresponding to the i-th country can be compactly 11
# **<u>Table 2: Psychic Costs of Vaccination</u>**

|Dependent variables:<br>|Attended<br>clinic<br>|Received<br>vaccine<br>|Attended<br>clinic<br>|Received<br>vaccine|
|---|---|---|---|---|
|_Panel A: No covariates_|(1)|(2)|(3)|(4)|
|Vaccine condition|0.006<br>(0.016)|0.038*<br>(0.019)|-0.004<br>(0.034)|0.029<br>(0.039)|
|C300|||0.170***<br>(0.040)|0.174***<br>(0.044)|
|C800|||0.293***|0.292***|
||||(0.039)|(0.041)|
|Vaccine condition* C300|||0.046<br>(0.045)|0.042<br>(0.050)|
|Vaccine condition * C800|||-0.008<br>(0.038)|-0.007<br>(0.041)|
|Observations|2482|2482|2482|2482|
|R-squared|0.001|0.002|0.096|0.092|
|Control mean of dependent variable|0.743|0.712|0.563|0.531|
|Fixed effects by village (80 villages)|X|X|X|X|
|_p-values of F test:_|||||
|Vaccine condition + Vaccine condition|* C300 = 0||0.105|0.010|
|Vaccine condition + Vaccine condition|*C800=0||0.622|0.439|
|_Panel B:With covariates_|(1)|(2)|(3)|(4)|
|Vaccine condition|-0.007|0.025|-0.018|0.009|
||(0.018)|(0.021)|(0.035)|(0.039)|
|C300|||0.167***<br>(0.040)|0.162***<br>(0.043)|
|C800|||0.286***|0.275***|
||||(0.040)|(0.042)|
|Vaccine condition* C300|||0.053|0.059|
||||(0.042)|(0.046)|
|Vaccine condition * C800|||-0.002|0.007|
||||(0.040)|(0.043)|
|Observations|2214|2214|2214|2214|
|R-squared|0.024|0.025|0.119|0.113|
|Control mean of dependent variable|0.746|0.713|0.567|0.540|
|<br>Covariates|X|X|X|X|
|Fixed effects by village (80 villages)|X|X|X|X|
|_p-values of F test:_|||||
|Vaccine condition + Vaccine condition|* C300 = 0||0.213|0.022|
|Vaccine condition + Vaccine condition|*C800=0||0.436|0.625|

_Source:_ Authors estimate _Notes:_ These are based on the analysis sample of 2,482 women. 268 observations with missing covariates are dropped in Panel B. Robust standard errors clustered by village (80 villages) are in parentheses. Covariates are Vaccine condition & Fear, Woman's age, Age squared, Muslim, Any education, Marital status = Single, Currently pregnant, Have children, Has paid work, Monthly HH earning per capita (naira), Distance to clinic (km), Transport to clinic (naira), Opportunity costs to clinic (naira), Ever used clinic, Received tetanus vaccine before, Likely to contract tetanus, Number of people who die of tetanus, Very worried about Tetanus, Tetanus is very bad, Very important to be protected from tetanus, Vaccine efficacy, Vaccines protect from disease, Needles are scary, Vaccines have side effects, Vaccines give diseases, Vaccines give HIV, Heart rate (beat/min). Control mean of dependent variable is the mean under Clinic condition in columns (1) and (2) and under Clinic condition and C5 in columns (3) and (4). * significant at 10%; ** significant at 5%; *** significant at 1% 34