rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> TM%<br>o% postr tren nnn<br>S% ‘<br>4<br>4% 44<br>% Wy =<br>2% 4 @ Household income<br>———— Household consumption expenditures<br>1% +<br>1% 2012 2014 216 218 220 222 wee 228<br><!-- End of picture text --> <!-- Start of picture text --> 8.0% Revenues<br>7.0% —Government consumption expenditures<br>4.0%3.0%<br>2.0%<br>10%<br>0.0%<br>-1,0% 2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text --> <!-- Start of picture text --> 4.0%<br>3.0% Labor wage rates, —Caphewage ra es l<br>2.0%<br>1.0%<br>0.0%<br>-1.0%<br>-2.0%<br>2012) 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 8% - —agriculture<br>7% construction—pvt tradable<br>6% — —pyt nontradable<br>5% —govt cons.<br>4% total ex. mini ed inputs<br>2%om<br>1%<br>“1% -<br>2012 2014 2016 2018 2020 2022 2024 2026<br><!-- End of picture text --> <!-- Start of picture text --> 6% —agriculture - construction<br>4% —pvtSMINCTA tradableoreo —pvtGO nontradableconsumption<br>0% |<<br>2%<br>-a% _—<br>6%<br>2012) 2015 82018 2021 2024<br><!-- End of picture text -->

<!-- Start of picture text --> agriculture +—construction —pvt trad<br>1.2% pyt nontrad —govt cons<br>0.8%<br>0.4%<br>0.0%<br>0.4%<br>0.8%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
# **Table A1 – T-tests, Joiners Vs. Match**

||**2001-**|**2002**|**2002-**|**2003**|**2003-**|**2004**|
|---|---|---|---|---|---|---|
||**t**|**p>t**|**t**|**p>t**|**t**|**p>t**|
||**20**|**01**|**20**|**02**|**20**|**03**|
|hh head sex|1.186|0.236|1.055|0.292|-0.719|0.472|
|hh head age|0.280|0.780|-0.363|0.717|-0.860|0.391|
|hh head education|1.804|0.072|-0.534|0.594|-0.334|0.738|
|hh n. of children|0.472|0.637|1.753|0.081|1.148|0.252|
|hh share of earners|-0.832|0.406|-0.947|0.344|-1.148|0.252|
|hh properties|0.380|0.704|-0.579|0.563|0.579|0.563|
|hh rural|1.398|0.163|-0.251|0.802|-1.094|0.275|
|dummy for Q2 and Q3|0.446|0.656|-0.122|0.903|0.356|0.722|
||**20**|**02**|**20**|**03**|**20**|**04**|
|hh head sex|0.639|0.523|0.935|0.350|0.000|1.000|
|hh head age|0.198|0.843|-0.363|0.717|-0.123|0.902|
|hh head education|2.093|0.037|0.411|0.682|-0.348|0.728|
|hh n. of children|0.343|0.732|0.323|0.747|-0.057|0.955|
|hh share of earners|-0.461|0.645|0.603|0.547|-0.116|0.908|
|hh properties|-1.417|0.157|0.000|1.000|0.000|1.000|
|hh rural|1.398|0.163|-0.251|0.802|-1.094|0.275|
|dummy for Q2 and Q3|0.446|0.656|-0.122|0.903|0.356|0.722|

28
From the developing world significant benefits are found. Berlinski _et al_ . (2006) exploit variation introduced by the expansion of a preschool program in Argentina to identify the impacts of the program on child outcomes. One year of pre-primary increases third grade test scores by 23 percent of the standard deviation of test score distribution. Armecin _et al_ . (2006) evaluate an important early childhood initiative in the Philippines using longitudinal data collected over three years on a cohort of 6,693 children 0-4 years at baseline in two treatment regions and a control region. The authors find significant improvement in cognitive, social, motor and language development, and in short-term nutritional status.

_Early interventions summary_ . Impact assessments of early interventions show significant short term benefits for the child, including enhanced achievement, improved health, increased non-cognitive skills, and social competence. In the medium term society benefits from greater school system efficiency; reduction in special education; reduction of grade repetition; higher learning; reduction in abuse and neglect; and lower reliance on public health care. In the long term, children benefit from higher likelihood of graduation and college enrollment; higher wages and employment; lower teen pregnancy; less delinquency. Society benefits from sound education; increased tax revenues; lower welfare dependence; and reduction in delinquency and crime.

Getting children into school early and compensating for disadvantage is important. However, due to poverty and other factors, many children continue to work, even combining school and work (Basu 1999; Psacharopoulos 1997; Psacharopoulos and Patrinos 1997; Rosati and Rossi 2003; Cigno _et al_ . 2002). How to prevent child work is an ongoing challenge. Conditional cash transfer (CCT) programs aim to reduce current income poverty. They might affect child work through the cash transfers contingent on the child attending school regularly by reducing the opportunity cost of schooling. The CCT programs are not tied to the cessation of child work (PETI in Brazil is the exception). _Oportunidades_ shows an overall reduction in work for male youth with no impacts on work for female youth. PETI reduces all child work by 8 percent. In Bangladesh, Ravallion and Wodon (2000) shows evidence of significant negative effect on the labor force participation of children through subsidies in school and nutrition.

7
# **Table 7. Summary of the robustness tests results: Marginal effects of the likelihood of doing secondary activities and regression estimates of the relationship between the availability of childcare and time allocation on secondary childcare**

||(1)|(2)|(3)|
|---|---|---|---|
||Marginal<br>probit<br>estimates –|Equal weight<br>secondary<br>childcare<sup>d</sup>|Deflated<br>secondary<br>childcare<sup>d</sup>|
||secondary<br>childcare|||
|**1.**<br>**Limit the age of women to prime age: 25–49 years**<sup>a</sup>||||
|_Ref: A. No child or youngest child is 6 years and older_||||
|B. Youngest child is 0–5 years and childcare available|0.573***|2.319***|1.159***|
||(0.058)|(0.404)|(0.202)|
|C. Youngest child is 0–5 years and no childcare available|0.600***|2.342***|1.171***|
||(0.051)|(0.348)|(0.174)|
|Observations|829|829|829|
|Individual controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|Regional controls|Yes|Yes|Yes|
|**2.**<br>**Limit the age of child to 0–12 years**<sup>b</sup>||||
|_Ref: A. Youngest child is 6–12 years_||||
|B. Youngest child is 0–5 years and childcare available|0.423***|1.907***|0.954***|
||(0.056)|(0.349)|(0.175)|
|C. Youngest child is 0-5 years and no childcare available|0.446***|2.169***|1.085***|
||(0.054)|(0.326)|(0.163)|
|Observations|758|758|758|
|Individual controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|Regional controls|Yes|Yes|Yes|
|**3.**<br>**Limit the age of child to 0–8 years**<sup>**c**</sup>||||
|_Ref: A. Youngest child is 6–8 years_||||
|B. Youngest child is 0–5 years and childcare available|0.249***|1.294***|0.647***|
||(0.063)|(0.412)|(0.206)|
|C. Youngest child is 0–5 years and no childcare available|0.272***|1.584***|0.792***|
||(0.064)|(0.411)|(0.205)|
|Observations|599|599|599|
|Individual controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|Regional controls|Yes|Yes|Yes|

Note:<sup>a</sup> Full table provided in Appendix C, Table C8.<sup>b</sup> Full table is in Appendix C, Table C9.<sup>b</sup> Full table is in Appendix C, Table C10.<sup>d.</sup> For the methods of calculating time spend on secondary activities using equal and deflated weights check footnote in Figure 4.<sup>e.</sup> *** 1%, ** 5% and * 10% level of significance.<sup>f.</sup> Standard errors are in parentheses.<sup>g.</sup> Survey weight applied.

28
# Table 15: Macro Performance for Alternative **FTA** Membership (Including Trade-Productivity Dynamic Linkages)

|||Percenta|ge change from|base||
|---|---|---|---|---|---|
||||Real||Export/|
||Real GDP|Real<br>Absorption|exchange<br>rate|Terms of<br>trade|output<br>share|
|Full Asian**FTA**||||||
|United States|0.19|0.21|2.22|0.11|0.18|
|Japan|1.44|1.31|7.14|-1.37|0.47|
|China|2.16|2.04|2.17|-0.30|0.63|
|ASEAN 4|4.72|4.21|2.34|-1.13|1.62|
|Asian NIEs|3.21|3.05|8.45|-0.30|1.04|
|European Union|0.0|0.06|0.0|0.49|-0.0|
|FTA Excluding China||||||
|United States|0.17|0.18|2.00|0.02|0.17|
|Japan|1.20|1.07|6.25|-1.32|0.42|
|ASEAN 4|4.22|3.76|2.06|-1.07|1.44|
|Asian NIEs|1.78|1.67|5.67|-0.23|0.87|
|China|0.17|0.40|0.01|0.78|0.0|
|European Union|0.0|0.05|0.0|0.39|-0.0|
|FTA Excluding ASEAN 4||||||
|United States|0.18|0.19|2.12|-0.04|0.18|
|Japan|1.32|1.19|6.64|-1.33|0.43|
|China|2.00|1.87|1.91|-0.36|0.57|
|Asian NIEs|2.87|2.67|7.61|-0.45|0.92|
|ASEAN 4|0.18|0.44|-0.06|0.72|-0.0|
|European Union|0.0|0.05|0.0|0.38|-0.0|
|**FTA**Excluding United States||||||
|<br>Japan|0.52|0.44|3.16|-0.84|0.23|
|China|1.77|1.60|1.80|-0.51|0.54|
|ASEAN 4|3.82|3.33|1.73|-1.14|1.33|
|Asian NIEs|1.57|1.46|4.79|-0.25|0.65|
|United States|0.0|0.05|-0.01|0.47|-0.|
|European Union|0.0|0.03|-0.0|0.26|-0.0|

Notes: _Real GDP_ (C +l+G+E-M) provides a production-based measure of economic activity. _Real Absorption_ (C+l+G) provides a welfare measure based on economywide real final demand by households, government, and investment. Changes in this measure equal the _equivalent variation_ for the economy, with changes in government consumption and investment valued according to private consumer's preferences. _Export/output share_ indicates the change in the aggregate exportVoutput ratio for the economy.

47 _Lewis, Robinson, and Wang_
with different employment density and over time, and it represents the evolution of a spatial economy. Furthermore, the presence of agglomeration forces that have not yet been balanced by congestion forces indicates the benefits of concentrating production in locations with higher employment density. Throughout the rest of this paper, we use the lens provided by this empirical relationship, interpreted using the theory in Desmet and Rossi-Hansberg (2009), to shed light on the spatial development of India.

Figure 1 shows annual manufacturing employment growth as a function of initial manufacturing employment density (in logs). In this benchmark exercises the employment data at the district level have been constructed from firm-level surveys (NSS and ASI). The picture suggests that manufacturing is dispersing through space. Low-density manufacturing districts are growing faster than high-density manufacturing districts. Note, however, that the 95% confidence intervals are extremely large in the upper tail, suggesting a rather weak relation between scale and growth for highdensity locations. Indeed, as can be seen from the bottom panel of Figure 1, some of the large cities, such as Kolkata and Mumbai, are experiencing higher growth than that predicted by the kernel regression.

Services show a distinctly different pattern. As can be seen from Figure 2, although low and mediumdensity service locations exhibit spatial dispersion, for the high-density service locations we observe increasing concentration. That is, the high-density service clusters are gaining relative to those locations with slightly lower employment density. In contrast to findings for the U.S. and Europe, high-density service clusters do not seem to be running into decreasing returns. The bottom panel of Figure 2 shows that many of the well-known IT clusters are in the upward-sloping part of the estimated relation, suggesting that they are benefitting from agglomeration economies. For example, service employment in Hyderabad and Chennai are growing at an annual rate of, respectively, 11% and 4%. If we were to run a simple regression, the predicted growth rate of these two cities would be, respectively, -7.1% and -8.2%. This underscores the importance of taking into account non-linearities in the scale-dependence of growth. Note that the upward-sloping part is also driven by some of the country’s largest cities, such as Mumbai. However, not all large cities exhibit high growth in services.

As a robustness check, we re-run these same kernel regressions, using sectoral employment at the district level constructed from the LFS. The results are shown in Figures 3 and 4. In the case of services, we confirm our previous findings: there is clear evidence of increasing concentration in the upper tail. However, for manufacturing the results look quite different. While in Figure 1 we observed spatial dispersion throughout the distribution (though insignificant in the upper-tail), we now find, as in services, evidence of spatial concentration for high-density manufacturing clusters.

6
the growth incidence curve of the Oil supply shock scenario. Unlike the situtation in the 1970s, more and more African countries, including populous ones like Nigeria, are now exporting oil and the benefit of higher oil prices offset their cost effects on production and transportation. By 2015 and relative to the reference case, higher oil prices and production in the oil supply shock scenario are likely to benefit the large majority of the population in SSA. Despite modeling them as shocks that do not deviate significantly from historical magnitudes, the Conflict and Drought scenarios will damage households’ welfare the most, confirming that internal shocks like wars and broad food price shocks still pose the greatest risks to Africa’s future, as in the past (Arbache et. al. 2008 and Raddatz 2007). Their corresponding incidence curves shift downward significantly across the entire income spectrum.

# **Figure 14 Growth Incidence Curves for Six Scenarios in SSA, Growth 2015 on 2010**

<!-- Start of picture text --> 0 2 0 4 0 6 0 8 0 1 0 0<br>P e rcen tiles o f P e r C ap ita H o use ho ld Inc om e/C o ns um p tion , 20 10<br>B u sin e ss a s U s u al P ro lo ng ed R ec e s s io n C o lla p se o f K F lo w s<br>O il S u pp ly S h oc k D ro u g h t C o n flic t<br>2 5<br>2 0<br>1 5<br>P e r C a p ita In co m e , % c h a n g e 1 0<br>5<br><!-- End of picture text --> Source: Authors’ simulations and calculations.

The good news is that welfare will generally recover by 2025. Figure 15 shows that the growth incidence curves of the alternative scenarios shift back and become more or less congruent to the reference case in 2025. The key assumption of course is that all the shocks are temporary and there is a rebound shortly after the shocks. This means that there are no more external shocks arising from the global economy from 2016 onward. More importantly, the internal shocks such as civil conflicts and drought are all of short duration and their magnitudes are not significantly higher than historical norms for limited shocks.

Viewed another way, Figure 1 6 normalizes the Baseline scenario as the benchmark case by setting it to the zero horizontal line. The figure shows the percentage-point differences between the growth incidence curves of our five alternative scenarios relative to the Baseline. The left-panel is derived from the 2015-2010 growth incidence curves, while the right-panel from the 2025-2010 curves. The graph confirms the Oil scenario’s largely positive impacts in 2025 (4.25 percentage points above Baseline), and that households in the first two deciles who benefited the most will gain relatively less as things return to normal by 2025 (1.25 percentage points above Baseline). There are modest differences between the reference case and the cases pertaining to a prolonged recession in high-income countries. The -2.5 percentage point differences relative to the Baseline and that will affect the poorest households 20
# 6 Conclusions

There has been a resurgence of interest in agricultural marketing issues in many developing countries in recent years. The uneven progress in agricultural commercialization following structural adjustments, market deregulation and trade liberalization in many African countries brought the need for the development of market infrastructure and institutions at the forefront of policy discussions (Fafchamps, 2004; Kherallah et al, 2000). The rising incomes, increasing urbanization and a growing middle class in a number of Asian countries- already past their green revolution in cereal crops- have exposed the weaknesses of the traditional marketing arrangements with overly congested market places and their rudimentary facilities (World Bank, 2007). Investments in market infrastructure and facilities and in rural road networks now form a core of rural development strategies in many countries. While the weaknesses of the existing marketing systems are discussed at length (Acharya, 2004), there is a lack of empirical evidence on how these investments, particularly those in market facilities, will bene fi t farmers. This is surprising in the context of the debate on the possible impact of commercialization of agriculture on the poor (von Braun and Kennedy, 1994). In this paper, we examine the impact of market facilities and infrastructure on the farmer’s decision to sell at the market using Indian agricultural marketing survey data.

In order to pinpoint the channels through which facilities and infrastructure available at the market can in fl uence a farmer’s decision to sell at the market, we develop a simple model of farmers’ choice of location of sales, extending on the work of Fafchamps and Hill (2005). The theoretical model formalizes the idea that market facilities interacting with distance to market in fl uences transaction costs and hence a farmer’s decision to sell at the market. The model predicts a positive relationship between propensity to sell at the market and the market access index, de fi ned as a ratio of number of facilities available at market to the square of 27
individuals subjectively rank themselves compared to others in their country on an economic ladder. Subjective economic ranking is defined as the response to the following question: “Please imagine a 10-step ladder where on the bottom, the first step, stand the poorest 10% people in our country, and on the highest step, the tenth, stand the richest 10% people in our country. On which step of the 10 is your household today?”. It differs from questions on well-being, life satisfaction or objective income rankings. The position on the economic ladder thus reflects a _relative_ measure of welfare, because it does not ask respondents about their absolute level of welfare but, rather, how does their welfare compare to that of the richest and the poorest in society.

We use the three waves of the Life in Transition Surveys for European countries covering the years 2005, 2010 and 2015. All surveys ask the question used to define our subjective economic standing. LiTS I was conducted in 2006 and surveyed almost 29,000 individuals across 29 countries. LiTS II was conducted in 2010 and surveyed almost 39,000 individuals across 29 countries. LiTS I was conducted in 2015 and surveyed almost 51,000 individuals across 34 countries. Our total sample for the study ranges from about 45,000 – to 60,000 individuals depending on the availability of relevant variables.

In order to contrast the respondent’s subjective ranking with objective information, we rely on the expenditure data collected by LiTS to estimate respondent’s consumption level. Each respondent is then placed in the objective decile of his/her country-year’s consumption distribution that emerges from the LiTS data. We also estimate an asset index based on principal component analysis of asset data also collected by LiTS. The assets that feed into this index are durable goods (like cars, washing machines, motorbikes and others) but also include some services (like heating or internet connection, for instance). In the same way as consumption, respondents are placed in the objective decile of his/her country-year’s asset index distribution.

# **2.2 A bunching in the middle**

Most individuals tend to feel they are located in the middle of the economic ladder, with responses concentrated in the fourth and fifth decile of the 10-step economic ladder. In 2006, 2010, and 2016, about 30% of the respondents felt they were in the fifth decile, and almost half felt they were either in the fourth or fifth subjective decile. When aggregating all countries, a majority of the population in LiTS countries tend to feel in the middle steps of Cantril’s ladder. Few people feel that they are among the poorest, and symmetrically few people feel they are among the richest. Looking at the distribution of responses per year, a similar bunching in the middle of the 10-step ladder appears for all three years.

5
# **Some Simple Analytics of Trade and Labor Mobility**<sup>1</sup>

Erhan Artu ̧c _World Bank_ Shubham Chaudhuri _World Bank_ John McLaren _Department of Economics University of Virginia_ JEL codes: E24, F13, F16 Keywords: Trade Policy, Sectoral Mobility, Wages

> 1The authors are grateful to seminar participants at Brown University, the University of Calgary, Dartmouth, and Syracuse, as well as participants at the Tuck-Dartmouth Summer Workshop in International Economics and the Conference on Trade and Labour Perspectives on Worker Turnover at the Leverhulme Centre for Research on Globalisation and Economic Policy (GEP), University of Nottingham. Special thanks are due to Doug Irwin, Carl Davidson, Steven Matusz, and Marta Aloi. This project was supported by NSF grant 0080731 and by the Bankard Fund for Political Economy at the University of Virginia. The findings do not necessarily represent the view of the World Bank’s Board of Executive Directors or the governments that they represent. Any errors or omissions are the authors’ responsibility.
- 25 -

<!-- Start of picture text --> Figure 11. Volume of coffee exports to EU market, 2001-05<br>(Millions of kilogram)<br>(890) (375)<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Source: Eurostat .<br>UgandaCote d'IvoireEthiopiaCameroon KenyaTanzaniaBurundiCongo, Dem. Rep.Rwanda TogoMadagascarCongo, Rep.ZambiaZimbabwe BrazilColombia Peru<br><!-- End of picture text --> Exchange rate appreciation may hamper competitiveness improvement of coffee and cocoa products, as expected. But this is somewhat inconclusive. The weather conditions, though omitted from the table, are generally significant in a statistical sense. Hotter-than-usual summer and cooler-than-usual winter temperatures are more productive. More precipitation in summer is welcome, but more precipitation in winter would raise production costs.

**Milk.** As shown in Table 8, the price coefficient tends to be significantly negative. Regarding the infrastructure impacts, only water infrastructure seems to contribute to increasing dairy productivity. Of particular note, the positive effect of water access in rural areas has been found robust in a statistical sense. By contrast, the statistical significance of the general water access coefficient is lost in the model where the current infrastructure variable is used.

Similar to the case of coffee and cocoa, economies of scale are likely to matter in the dairy industry. However, the evidence is less conclusive than the coffee case; it depends on specification. This may be intuitively acceptable because the size of dairy production is not large in Africa, except South Africa (Figure 12). Still, the table indicates that there are many opportunities for African countries, for instance Kenya, to develop neighboring markets within the region, rather than importing dairy products from European countries. Current efforts to promote intraregional trade could pay large dividends to the dairy industry.