rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix Table 3: Sample means of working hours**

||1997|2000|2004|2006|
|---|---|---|---|---|
|Total working hours (yearly)|2164.32|1854.94|1819.17|1722.57|
|___Initial age 16-20|1453.5|709.3|703.49|1583.49|
|___Initial age 21-35|2275.42|2036.58|2056.06|2003.35|
|___Initial age 36-50|2383.29|2068.61|1873.53|1748.85|
|___Initial age 51-60|2011.72|1672.67|1478.3|1275.61|
|___Initial age 61-70|1764.49|1360.02|1103.03|1005.22|
|Working hours in agriculture (yearly)|916.72|669.91|518.19|458.73|
|___Initial age 16-20|107.58|164.08|191.72|61.73|
|___Initial age 21-35|822.64|635.71|518.54|455.36|
|___Initial age 36-50|1127.1|822.72|651.66|577.52|
|___Initial age 51-60|889.38|660.43|462.47|357.5|
|___Initial age 61-70|759.29|515.51|256.58|186.82|
|Working hours in non-agriculture (yearly)|415.13|388.23|348.66|365.79|
|___Initial age 16-20|240.15|280.73|196.66|309.54|
|___Initial age 21-35|548.27|544.16|523.76|594.7|
|___Initial age 36-50|463.73|410.1|337.55|338.97|
|___Initial age 51-60|206.33|147.73|152.01|71.82|
|___Initial age 61-70|84.44|71.28|16.14|7.69|
|Working hours in family business (yearly)|174.45|172.36|174.83|189.8|
|___Initial age 16-20|69.57|108.36|32.34|114.16|
|___Initial age 21-35|188.31|193.73|243.63|276.4|
|___Initial age 36-50|206.37|199.62|164.67|194.1|
|___Initial age 51-60|142.17|118.7|147.08|66.44|
|___Initial age 61-70|84.44|71.28|13.3|7.69|
|Working hours in wage labor (yearly)|244.99|220.36|176.42|183.06|
|___Initial age 16-20|167.86|171.56|164.32|195.38|
|___Initial age 21-35|374.52|349.46|284.16|329.93|
|___Initial age 36-50|255.85|221.57|174.98|150.29|
|___Initial age 51-60|64.16|28.9|4.91|5.33|
|___Initial age 61-70|0|0|2.84|0|
|Working hours in household chores (weekly)|18.62|16.66|17.9|16.99|
|___Initial age 16-20|11.25|4.5|5.81|12.14|
|___Initial age 21-35|20.9|18.42|18.48|16.89|
|___Initial age 36-50|18.16|17.26|18.32|17.48|
|___Initial age 51-60|19.09|17.4|17.32|16.66|
|___Initial age 61-70|19.68|16.77|15.67|15.34|

Notes: Means are reported for the non-migrant women who have been in the survey for at least three consecutive rounds. The number of observations for 1997, 2000, 2004 and 2006 is 2114, 2689, 2689 and 2354 respectively. The number of observations for non-missing working hours for these four years is 1836, 2420, 2136 and 1971 respectively.

48
correlated with initial income per capita and the share of replaceable tasks in the sector.<sup>12</sup>

# **5 Partial equilibrium estimates: Robots and North-South trade**

This section reports the OLS and IV estimates of the effects of robotization in advanced economies on their exports and imports with less developed countries.

## **5.1 Baseline estimates**

Table 1 reports our baseline estimates of how robotization in Northern countries impacted their trade with non-OECD countries. All specifications control for importer-exporter-year fixed effects as well as industry-year fixed effects. Standard errors are clustered by Northern country. Our main result is that robotization in the North catalyzes trade by inducing a significant increase in imports from and an even bigger surge in exports to non-OECD countries. As a consequence, net imports from these countries decline significantly. According to our OLS estimates a 10 percentage points increase in the number of robots per million hours in developed countries is associated with both a 1.5 percentage points increase in imports from non-OECD countries and a 4.1 percentage points increase in exports to non-OECD countries, as shown in columns (1) and (2) respectively. Net imports from the South would thus reduce by 2.6 percentage points, as is documented in column (3).

The estimated association between robot intensity and trade flows strengthens substantially when the extent of robotization is instrumented using the triple interaction between the share of jobs in a sector that are replaceable (which serves as a proxy for the technological feasibility of robotization), a country’s initial GDP per capita (which serves as a proxy for labor costs and hence the economic incentives to robotize) and the global robot stock (which serves as a proxy for the rental rate of robots). The estimates in columns (4)-(6) suggest that a 10 percentage points increase in robot adoption is now associated with a 6.1 percentage points increase in imports from and a 11.8 percentage points increase in exports to non-OECD countries. Net imports would fall by 5.7 percentage points. The fact that IV estimates are higher than OLS estimates could reflect a local average treatment effect or attenuation bias associated with measurement error in our robotization variable, and lends credence to a causal interpretation of the association between robotization and trade. Note also that our instrument performs well and strongly predicts robot

> 12Figures A3 reveals that Denmark is an outlier with regard to changes in robot density. The econometric results reported below do not depend on its inclusion in the estimation sample.

17
# Table 10. MFI status regression  (using Logit model w/o MFI status dummies & lending methodology

# dummies)

P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via logit model. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa) and log of MFI's total asset values.

## <u>Panel A: Overall Score Nominal</u>

||Bank dummy|Credit Union &<br>corp dummy|NGO dummy|NBFI dummy|Group A (Bank,<br>NBFI or Rural<br>Bank)dummy|Group B (NGO or<br>NBFI dummy)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|OVERALL SCORES (0-100)|-0.0133|-0.0434**|0.0124|0.009|0.0018|0.0194|
||[0.519]|[0.048]|[0.597]|[0.671]|[0.933]|[0.252]|
|Observations|778|778|778|778|778|778|
|Number of countries|41|41|41|41|41|41|

## <u>Panel B: Regulatory Framework and Practices</u>

||Bank dummy|Credit Union &<br>corp dummy|NGO dummy|NBFI dummy|Group A (Bank,<br>NBFI or Rural<br>Bank)dummy|Group B (NGO or<br>NBFI dummy)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Regulatory Framework and Practices (0-100)|-0.0262|-0.0309|0.0144|0.0122|-0.0016|0.0230*|
||[0.195]|[0.159]|[0.464]|[0.492]|[0.922]|[0.079]|
|Observations|778|778|778|778|778|778|
|Number of countries|41|41|41|41|41|41|

54
# **Figure 3.2: Historical coverage trends**

<!-- Start of picture text --> (a) Electricity<br>1.0<br>0.9<br>0.8<br>0.7<br>0.6 TOTAL<br>0.5 URBAN<br>0.4 RURAL<br>0.3<br>0.2<br>0.1<br>0.0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br>year<br>(b) Water<br>1.0<br>0.9<br>0.8<br>0.7<br>0.6 TOTAL<br>0.5 URBAN<br>0.4 RURAL<br>0.3<br>0.2<br>0.1<br>0.0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br>year<br>% of population<br>% of population<br><!-- End of picture text -->

# **(c) Sanitation**

<!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6 TOTAL<br>0.5 URBAN<br>0.4 RURAL<br>0.3<br>0.2<br>0.1<br>0.0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br>year<br>% of population<br><!-- End of picture text -->

_Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística - Guatemala 20
# **Foreword**

Economic policy-making remains more an art than a science, in common with many other areas of public policy. We know a few things about the technicalities of reform, for instance how to streamline customs procedures or simplify the process of business registration. But it is less clear how one gets these and other more complex policies agreed upon and implemented. Some believe that we can study the matter and develop typologies and recipes, almost a manual. I very much doubt this makes sense. The management of societies may not lend itself to engineering-type approaches, where we can hope to discover principles of how things work and then design manuals. Fundamentally, the process of reform and implementation is about humans agreeing, compromising and acting together. In the process, innovation is possible. At the same time credibility and some level of predictability matters. The equivalent of immutable physical laws remains elusive. For reform we cannot write a blueprint. Like for change management in firms, the best we may be able to do is to generate interesting case studies that help sharpen judgment and inform policy-makers about the process and impact of reforms.

What interest do policy-makers have to sharpen their judgment and learn from other experiences? Here, competition matters. Not competition between firms, but between jurisdictions. When some countries pull ahead, when they see incomes rise - that makes policy-makers in other places wake up. Statistics that allow us to compare the performance of different jurisdictions make success and failure visible. GDP statistics show us who earns more, who grows. The benchmarking of "competitiveness" gets policy-makers’ juices flowing, often under pressure from the media who highlight deficiencies revealed by statistics. Just consider the impact of the OECD PISA study on comparative performance of education systems or the World Bank/IFC reports on "Doing Business".

It is in that perspective that we have embarked on a review of reform episodes. Our hope is that the experiences and lessons of the cases analyzed in this paper will indeed help sharpen judgment. Additional work is being done on reform episodes monitored by our "Doing Business" project. Between our work and that of others there will be close to one hundred cases written up. The challenge is to find useful ways of sharing experiences among relevant policy-makers and to develop the best possible ways of learning - maybe in the spirit of business school case study teaching and, just maybe, in the more distant future by using emerging multi-player games to explore governance and policy problems.

Michael Klein Vice-President, Private Sector Development, World Bank-IFC Chief Economist, IFC
use of village land, which they were entitled to own after tenancy reform. Education is also, relatively speaking, more equitably distributed. The first primary school was established in Sugao in 1887 and primary education was made compulsory in the rural areas of Satara district in 1946. Mahar’s have been enrolled in village schools since 1892.

However, overall income inequality throughout the study period is markedly higher in Sugao than was observed in Palanpur (Tables 9-11). While in Palanpur inequality, as represented in the General Entropy 0 measure was below 0.2 in three of the survey years and only as high as 0.289 in 1962/3, it was above 0.4 in all three survey years in Sugao. As in Palanpur, there is a slight suggestion of inequality declining over time (from about 0.43 in 1942 and 1958 to 0.407 by 1977), but even in the final year for which Sugao income data are available, overall inequality was very high relative to Palanpur.<sup>xx</sup> Part of the reason for this is that non-farm sources of income is Sugao are higher than in Palanpur – primarily from migrant remittances, but also from village industries and shops.

# [Tables 9-11 about here]

When we examine decomposition results for Sugao we find that both the conventional as well as the ELMO decompositions indicate that group differences are not particularly salient to an understanding of overall inequality in the village. The conventional betweengroup inequality contribution is generally well below 10% (in contrast to Palanpur where in several instances it is above 25%), and even the ELMO statistic is only above 15% in one instance. As argued by Elbers et al (2008) comparing the conventional between-group contribution in Sugao against that in Palanpur is potentially misleading, because of the different number and composition of population groups in these two villages. However, the 24
# **Table 4: Testing the significance of the financial channel in the transmission of the 2007-2008 crisis**

Table shows results from regressing local market returns expressed in US dollars. Estimations include time dummies and country fixed effects. Robust standard errors, clustered at the country level, are shown in parentheses. *, **,*** denote significance at 10, 5 and 1 percent significance, respectively.

|Variables|||Return|s in US $|||
|---|---|---|---|---|---|---|
|US Stock Returns*US Holdings of Foreign Equity*Pre-Lehman|0.278***<br>(0.071)||||||
|US Stock Returns*US Holdings of Foreign Equity*Post-Lehman|0.215***<br>(0.041)||||||
|US Stock Returns*Foreign Holdings of US Equity*Pre-Lehman||-0.019<br>(0.080)|||||
|US Stock Returns*Foreign Holdings of US Equity*Post-Lehman||-0.051<br>(0.046|||||
|US Stock Returns*Chinn-Ito Index of Financial Openness*Pre-Lehman|||-0.037<br>(0.086)||||
|US Stock Returns*Chinn-Ito Index of Financial Openness*Post-Lehman|||0.089*<br>(0.053)||||
|US Stock Returns*FDI Inflows/GDP*Pre-Lehman||||-0.031<br>(0.089)|||
|US Stock Returns*FDI Inflows/GDP* Post-Lehman||||-0.007<br>(0.041)|||
|US Stock Returns*Portfolio Inflows/GDP*Pre-Lehman|||||0.257***<br>(0.062)||
|US Stock Returns*Portfolio Inflows/GDP*Post-Lehman|||||0.136***<br>(0.046)||
|US Stock Returns*Other Inflows/GDP*Pre-Lehman||||||0.069<br>(0.079)|
|US Stock Returns*Other Inflows/GDP*Post-Lehman||||||0.086*<br>(0.047)|
|F-test null Pre-Lehman interaction=Post-Lehman interaction|0.89|0.35|3.26|0.09|4.08|0.05|
|F-test(p-value)|0.349|0.556|0.075|0.763|0.047|0.823|
|Observations|1539|1499|1667|1604|1587|1566|
|R-squared|0.558|0.534|0.519|0.524|0.531|0.522|
|Number of countries|74|72|80|77|76|75|

27
# **School Component Sample of Community Treatment**

||Control mean|(1)|(2)|(3)|
|---|---|---|---|---|
|**Exposure**<sup>**~~+~~**</sup>|||||
|High exposure to the treatments<br>(HW message through three channels)|0.2|0.0113<br>(0.047)|-0.0207<br>(0.043)|-0.0275<br>(0.043)|
|Medium exposure to the treatments|0.499|0.100*|0.0659|0.065|
|(HW message through two channels)||(0.058)|(0.051)|(0.050)|
|Low exposure to the treatments|0.717|0.0851*|0.0591|0.0637|
|(HW message through one channel)||(0.046)|(0.041)|(0.039)<br>[8.9%]|
|Controls||NO|YES|YES|
|District dummies<br>Clustered standard errors in parentheses; *<br>+Channels: mass media (radio and/or printed|** p<0.01 ** p<0.05 * p<br>materials), promotiona|NO<br><0.1<br>l events and perso|NO<br>nal training/educatio|YES<br>nal sessions|

||Control mean|(1)|(2)|(3)|
|---|---|---|---|---|
|**Knowledge**|||||
|Events that require handwashing (summary index)|0.222|0.0188<br>(0.021)|0.00894<br>(0.020)|0.0159<br>(0.020)|
|Best method to wash hands is using water and soap (Percentage)|0.882|0.0513**<br>(0.023)|0.049**<br>(0.021)|0.044**<br>(0.022)|
|||[5.7%]|[5.5%]|[4.9%]|
|Main cause of diarrhea is not washing hands with water and soap|0.94|0.0131<br>(0.016)|0.0163<br>(0.016)|0.0118<br>(0.015)|
|Controls||NO|YES|YES|
|District dummies<br>Clustered standard errors in parentheses; *** p<0.01 ** p<0.05 * p<0.1||NO|NO|YES|

64
While personal factors including family relations, religion and age are also important for subjective wellbeing, they are not the purview of policy and the focus of this paper.

In an additional effort, national statistical offices could consider increasingly adding questions to their household surveys, in which respondents themselves are asked about the factors that affected their quality of life, which is a unique feature of this data set. For example, employment issues seem to be the most important factor affecting subjective wellbeing in Colombia when asking respondents themselves. When people 18-25 years of age were asked which issues or problems do you worry about frequently, the majority (55.9 percent) stated employment related issues (work, employment, salary, income, economic or labor stability). The second most likely reported cause of being worried was security, crime and gangs at 15.9 percent, followed by issues related to education (obtaining, completing or paying for education) at 8.6 percent. Unemployment was cited by about one quarter of all respondents in the country (23.6 percent) as the single most important problem facing their municipality/community. When respondents were asked to which extent do you believe that the current government combats unemployment, 45.6 percent of respondents stated poorly to not at all while 31.1 percent indicated well to very well (the remaining 23.2 percent reported neither poor nor well).

In sum, our findings on the correlates of wellbeing in Colombia provide important complements to our understanding of income-based welfare, and can help inform policies and programs that aim to improve subjective wellbeing and important objective indicators of welfare. Many of the policies that we posit would likely help reduce the prevalence of negative shocks and vulnerabilities that affect people’s subjective wellbeing. Having data on both objective and subjective indicators, and changes in those indicators, gives policymakers a broader basis of understanding from which to tackle the challenge of improving human wellbeing in a developing country as complex and challenging as the Colombian context. At the same time, given that some baseline findings for Colombia are similar to those in other countries around the world, some of our findings could be relevant for other developing countries in Latin America and beyond.

17
highly educated adults had “widespread misunderstanding” of how climate change worked, misunderstandings that caused them to systematically underestimate the need for immediate action to stabilize the climate. In particular, research subjects conceived of the global climate as a system which was analagous to a bathtub filling over, once the input stops the tub level can be lowered right away. According to research however, once in the atmosphere, climate gases will continue to rise even if emissions fall. The authors write that, “these beliefs – analogous to arguing a bathtub filled faster than it drains will never overflow – support wait-and-see policies but violate conservation of matter. Low public support for mitigation policies may arise from misconceptions of climate dynamics rather than high discount rates or uncertainty about the impact of climate change.” Furthermore, the authors link this misunderstanding to the failure of response of U.S. policymakers.

Harriet Buckeley describes the assumption that information is the limiting factor in generating public response as the “information deficit model.” While information deficit explanations are indispensable, they do not account for the behavior of the significant number of people who do know about global warming, believe it is happening, and express concern (Hellevik and Høie 1999). From this perspective people are presented as individual agents acting “rationally” in response to information made available to them. According to the information deficit model of public response to environmental issues, the public needs to be given more knowledge about environmental issues in order to take action . . . In this approach the contextual dimensions of environmental concern are ignored so that public perceptions are seen as stable, coherent, and consistent, and to exist within individuals rather than being located within the inter-subjective contexts of institutions and discourse (2000, 315-316). Yet as Read et al. (1994) point out, two simple facts are essential to understanding climate change. If significant global warming occurs, it will be primarily the result of an increase in the concentration of carbon dioxide in the earth’s atmosphere. And the single most important source of carbon dioxide is the combustion of fossil fuels, most notably coal and oil. So how can it be that such highly educated people don’t know this basic fact?

Paradoxically, as evidence for climate change pours in and scientific consensus increases, interest in the issue throughout many Western nations declined during the 1990s and into the early 2000s (e.g. Hellevik, 2002, Immerwahr, 1999, Saad 2002). For example, Gallup polls for 19
sticker treatment tests the hypothesis that lack of knowledge contributes to the high contamination of recyclables, which is a considerable problem for collectors.

Independently of the bin randomization, we also randomly assigned households with a cell phone number to one of two SMS reminder treatment groups or a control group that received no message. Prior research has found that identity and social context can dramatically change the impact of a message (Cialdini and Goldstein 2004). As such, half of the SMS messages were generic reminders, and half were personalized messages stating the name of the recipient and the name of the collector asking their client to remember to recycle. These messages were sent weekly to each phone number on the evening before the collector was scheduled to pick up the recyclables. Approximately 80 percent of the messages reached the intended recipient.<sup>14</sup> A total of 1,802 individuals who were already enrolled in the recycling program were identified in different zones of the district of Castilla. We visited the individuals at their homes to administer a short survey that captured basic demographic information and requested cell phone numbers and permission to contact respondents with information about recycling. A longer survey that captured more precise information about socioeconomic situations and recycling behavior was administered to 10 percent of the sample. All households were randomly assigned into treatment groups. During the data collection, 17 households could not be located, largely because the household moved elsewhere. We dropped these observations from our analysis, leaving a sample of 1,785 households in the participation intensity study. Of these households, approximately 50 percent (829 observations) provided a valid cell phone number. Among these, we randomly assigned households to one of three equal-sized groups: a generic SMS 21
9 across households within societies. Interpreting these differentials and estimating how policy might affect them requires a framework that accommodates the salient constraints on the family and accounts for critical, features of the personal distribut.. **o** fn family consumption and human capital investment. A more complete model nust also describe who is found in a family or, more precisely, family composition. Adults that **aro** observed to **tsside** in families **are** not a random **sample;** they **aro selacted** according to criteria that are likely to also influence what they expect to receive from the family's resources. A sample selection bias is thereforo potentially present in any analysis restricted to families. The decision rules that govern family formation and marriage should be combined with an analyst.s of the constraints doetrmining intrafamily resource allocations. Though elements of such a theor7 are at hand (McElroy, 1988), empirical applications are limited and largely confined to U.S. data. But the theoretical approach is wedl suited to the study of woaen's productivity in low income countries. 3 Models of Marriage and Family Behavior Adults presumably maintain their attachment to their existing family, or marry, or go their separate way, because there **are** net gains in doing so as compared to other foregone opportunities In marriages, so long as both partners share the net gains in excess of the opportunity costs given up from alternative arrangements, the union has at least an economic **basis** for continuation (Backer, 1974). The division of output botween partners uight be studied **as** it responds to market forces (Becker, 1981:42). But it has not **received** attention **because** it **is** hard to attribute consumption to a particular family member, and traditional models of the family do not prescribe distributional rules.