rafmacalaba/datause-encoder-data huggingface dataset:
23 Of course, the averages over the period 1 P74-85 Include<sup>several policy</sup> regknes as well as significant rise and fall In overall per<sup>capita consumption, which</sup> Increased by 20.B8 from 1974 to 1981 and then decreased<sup>by 15.3X from 1981 to</sup> 1985, wlth consumption In 1985 ending at virtually the same level<sup>as In 1974. Thus,</sup> It Is Instructive to examino tine series results for key varlables<sup>over the period.</sup> Charts 1 to 6 prosent such tikn series comparlng a base case<sup>(historically given prico</sup> guarantees) with the counterfactual trade liberalizatlon for maize<sup>prices, production,</sup> food demand, per capita diroct human consumption, total demand<sup>and crop supply</sup> adjustment. Clearly the border price for malze has fluctuated<sup>more than the maize</sup> price guarantee. This difference Induces similar variatlon In production, consumption and crop supply adJustment. Under trade llberalization, maize<sup>imports reach a peak</sup> level of 13.4 million metric tons In 1983, owing to low productin<sup>the previous year</sup> and high demand In 1983. Thus, under trade liberalization, maize<sup>Imports could be</sup> expected to show significantly greater varlance Charts 7 to 10 present tine serles comparisons of the<sup>two cases for sorghum</sup> prices, production, feed demand and crop supply adJustment. Once again, significant variatlon In the border price for sorghum Induces corresponding<sup>variatlons In sorghum</sup> production; but foed demand shows less variation owing to high<sup>correlation between</sup> the border prices for maize and sorghum. Sorghum crop supply<sup>adjustment Is highly</sup> variable, but due to greater domestic productlon at border prices,<sup>the reduced</sup> sorghum Imports do not reach peak levels as large as under price<sup>guarantees. Charts</sup> 11 to 14 present tine serles data for soybeans analogous to<sup>the tine series for</sup> sorghum. While soybeans prices are more variable (but<sup>iower on average), soybeans</sup> productlon, feed demand and crop supply adjustment under<sup>border prices show quite</sup> similar patterns and levels as under price guarantees.
<!-- Start of picture text --> Colombia<br>Coca base production and prices<br>800 1000<br>900<br>700<br>800<br>600<br>700<br>500<br>600<br>400 500<br>400<br>300<br>300<br>200<br>200<br>100<br>100<br>0 0<br>2000 2001 2002 2003 2004 2005<br> Source: UNODC<br>Figure 6<br>Potential Cocaine Production Potential Cocaine Production<br> (metric tons) (metric tons)<br>1200 1200<br>1000 1000<br>800 800<br>600 600<br>400 400<br>200 200<br>0<br>0<br>Bolivia Colombia Peru Bolivia Colombia Peru<br>U$ / kg.<br>potential production (MT)<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005*<br><!-- End of picture text --> Source: UNODC Source: UNODC <!-- Start of picture text --> Source: ONDCP<br><!-- End of picture text -->

* ONDCP ́s  figures on potential cocaine production in 2005 for Colombia include new surveyed fields and, as a result, potential cocaine production is not comparable with years prior to 2005.

54
Policy Research Working Paper 9783

# **Abstract**

Amid noticeable improvements and achievements in the reproductive, maternal, neonatal, child health, and nutrition landscape in Bangladesh, existing evidence suggests that further accelerated progress hinges on strategic investment decision making. Addressing the top service utilization determinants that are both context- and time-specific is one cost-effective way of improving the unmet reproductive, maternal, neonatal, child health, and nutrition outcomes in a short timeframe. Against this backdrop, using machine learning analysis, the overall aim of this study was to help Bangladesh identify priority investment areas that could accelerate reproductive, maternal, neonatal, child health, and nutrition utilization, quality, and outcomes over the short run, by comparing the relative importance of demand- and-supply-side determinants of key reproductive, maternal, neonatal, child health, and nutrition indicators over the past decade (across two time points). Two rounds of the Bangladesh Health Facility Survey and the Demographic and Health Survey (2014 and 2017) were analyzed. The findings indicate that the relative importance of the demand-side determinants (except wealth and education status) have recently declined. Conversely, investments in key supply-side determinants (for example, availability of skilled staff, readiness for care, and quality of care) could provide a thrust toward further increases in utilization. Immediate attention is needed to address the regressive role of wealth status on utilization through, for example, demand-side financing that goes beyond user fee exemptions. Further, developing strategies to improve the engagement of community health workers in reproductive, maternal, neonatal, child health, and nutrition utilization and tapping into the potential of mobile health technology to support community health workers’ performance and women’s awareness could help to boost utilization patterns.

This paper is a product of the Health, Nutrition and Population Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sajisaraswathyg@gmail.com, rmohammed@worldbank.org, and hzmatarazarro@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 4: OLS Estimation of Treatment Effects with Relative Premium Interactions

|||**Panel A: **|**Average T**|**reatment E**|**fects**||
|---|---|---|---|---|---|---|
||He|alth Insuranc|e||Pension||
||Model 1|Mode 2|Model 3|Model 1|Mode 2|Model 3|
|Treated|0.073**|0.087***|0.091***|-0.009|0.001|-0.006|
||(0.032)|(0.031)|(0.032)|(0.047)|(0.046)|(0.046)|
|Treated*Price|-0.939**|-1.105***|-1.176***|0.067|0.060|0.110|
||(0.436)|(0.425)|(0.426)|(0.276)|(0.263)|(0.263)|
|Lagged Dependent Variable|0.655***|0.544***|0.532***|0.641***|0.496***|0.480***|
||(0.016)|(0.025)|(0.026)|(0.018)|(0.031)|(0.032)|
|R-squared|0.489|0.520|0.524|0.489|0.530|0.536|
|No. of Observations|4,587|4,587|4,587|2,925|2,925|2,925|
||He|**Panel B: He**<br>alth Insuranc|**terogeneou**<br>e|**s Treatme**|**nt Efects**<br>Pension||
||Model 1|Mode 2|Model 3|Model 1|Mode 2|Model 3|
|Treated|0.026|0.030|0.034|0.027|0.037|0.030|
||(0.042)|(0.041)|(0.041)|(0.066)|(0.065)|(0.064)|
|Treated*No-Contract|0.118*|0.112*|0.113*|-0.043|-0.048|-0.047|
||(0.065)|(0.064)|(0.064)|(0.094)|(0.091)|(0.091)|
|Treated*Price|-0.878|-0.858|-0.985*|-0.385|-0.404|-0.374|
||(0.589)|(0.584)|(0.582)|(0.400)|(0.391)|(0.385)|
|No-contract dummy|-0.139***|-0.121**|-0.088*|-0.070|-0.029|-0.004|
||(0.047)|(0.047)|(0.047)|(0.063)|(0.066)|(0.066)|
|No-contract*Price|-0.799|-0.702|-0.862|-0.739**|-0.750**|-0.707**|
||(0.636)|(0.619)|(0.612)|(0.353)|(0.344)|(0.349)|
|No-contract*Price*Treated|-0.679|-0.689|-0.616|0.693|0.683|0.709|
||(0.856)|(0.848)|(0.849)|(0.551)|(0.528)|(0.525)|
|Lagged Dependent Variable|0.558***|0.542***|0.529***|0.546***|0.500***|0.484***|
||(0.021)|(0.025)|(0.026)|(0.026)|(0.032)|(0.032)|
|R-squared|0.507|0.521|0.526|0.508|0.532|0.538|
|No. of Observations|4,587|4,587|4,587|2,925|2,925|2,925|
|No contract treated price efect|-1.557**|-1.547**|-1.601***|0.308|0.279|0.335|
||[0.013]|[0.012]|[0.010]|[0.402]|[0.421]|[0.337]|
|No contract treatment efect|||||||
|At median premium|0.029*|0.027*|0.028*|0.031|0.033|0.034|
||[0.077]|[0.084]|[0.072]|[0.173]|[0.143]|[0.123]|
|At 25th percentile premium|0.067**|0.066**|0.068**|0.022|0.025|0.025|
||[0.002]|[0.002]|[0.002]|[0.409]|[0.350]|[0.349]|
|At 10th percentile premium|0.112***|0.110***|0.114***|0.014|0.017|0.015|
||[0.002]|[0.002]|[0.002]|[0.684]|[0.607]|[0.642]|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: The additional variables included in Model 1 are a vector of city fixed effects; in Model 2 we also add individual level controls (age, gender, education, marital status, number of children, dummies for selfemployment, dummy for working and an interaction between self-employment and lagged (2015) dependent variable and dummy for “willingness to remain in the city permanently if policy permits.”), while Model 3 adds firm level controls (pre-treatment firm size and ownership indicator variables ) as well. Robust standard errors clustered at workplace of household head are in parentheses. P-values are in brackets. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. 32
# **Table A.26:** Effect of Turnovers in Villages Impacted by Natural Disasters

|||**Bureau**|**crats**|||**Alignment**|**Pu**|**blic Good**|**s**|
|---|---|---|---|---|---|---|---|---|---|
||Parent Head|Parent Served|Enthusiasm|FreqInteract|Agree|Receive Complaint|_Podes_2021|Access|Quality|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|New village head|-0.092<sup>_∗_</sup>|-0.163|0.357<sup>_∗_</sup>|0.393|0.105|0.201<sup>_∗∗_</sup>|0.704<sup>_∗∗_</sup>|0.116<sup>_∗_</sup>|0.266<sup>_∗∗_</sup>|
||(0.069)|(0.111)|(0.244)|(0.279)|(0.194)|(0.094)|(0.392)|(0.079)|(0.160)|
|Observations|510|510|508|507|510|510|167|4336|4334|
|Control mean|0.052|0.28|0.079|0.37|0.72|0.90|0.36|0.81|0.019|
|Robust p-value|0.096|0.109|0.078|0.112|0.588|0.011|0.047|0.061|0.037|
|<br>MSE-opt. bandwidth|14.4|17.9|21.8|21.1|17.0|15.8|17.3|12.3|13.7|
|Effective obs.|210|243|274|269|238|218|67|1489|1726|

_Notes_ : This table reports RD estimates of _γ_ in equation (1). The sample is restricted to villages that experienced a natural disaster (a major landslide, flood event, ocean tide, hurricane, or drought) during an incumbent’s term prior to the election. The dependent variables are: in columns 1 and 2, the same as those in Table 4; in columns 3 and 4, the same as those in Table 3; in columns 5 and 6, the same as those in Table 5; in columns 7-9, the same as those in Table 7. See Section 6 for details.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors clustered by village in parentheses in columns 1-6 and 8-9. Robust bias-corrected standard errors in parentheses in column 7.
# **What Teachers Believe: Mental Models about Accountability, Absenteeism, and Student Learning**

Shwetlena Sabarwal World Bank Malek Abu‐Jawdeh World Bank Keywords: Education; Teacher Absenteeism; Teacher Performance ; Teacher Motivation JEL Codes: I20; O15; H52 _Acknowledgements_ : This research was undertaken as background work to World Development Report 2018. We thank Deon Filmer and Halsey Rogers for their inputs and support. Survey design was supported by Deon Filmer, Eema Masood, and Ezequiel Molina. Data collection was undertaken by teams led by Marie‐Helene Cloutier, Rachel D. Cooper, Rafael E. De Hoyos Navarro, Ali Inam, María José Vargas, Min Ju Choi, Oni Lusk‐ Stover, Eema Masood, Kanupriya Misra, Ezequiel Molina, Jan Priebe, Andrew Ragatz, William Seitz, and Dewi Susanti. Financial support from Norwegian Trust Fund and Gates Foundation is gratefully acknowledged. We thank David Evans, Rafael E. De Hoyos Navarro, Tazeen Fasih, Ezequiel Molina, and Andrew Ragatz for helpful comments. We are grateful to Julie A. Schaffner, Paul Glewwe, and Uttam Sharma for sharing the data on Nepal.
# **Table 28: Piecemeal Sensitivity Analysis for Preferential Liberalization between Tanzania and the Africa Region in Professional Services**

||**Par**|**ameter val**|**ue**|**% Chang**|**e in Welfa**|**re (EV)**|
|---|---|---|---|---|---|---|
|Parameter|Lower|Central|Upper|Lower|Central|Upper|
|σ(qi, qj)– services sectors|1.5|3|4.5|0.113|0.007|0.004|
|σ(qi, qj)–goods sectors||See below||0.008|0.007|0.007|
|σ(va, bs)|0.625|1.25|1.875|0.006|0.007|0.009|
|σ(D,M)|2|4|6|0.007|0.007|0.007|
|σ(L,K)|0.5|1|1.5|0.007|0.007|0.007|
|σ(A1,...An)|NA|0|0.25|NA|0.007|0.007|
|σ(D,E)|2|4|6|0.007|0.007|0.007|
|εTZA|Central va|lues of all 4 s|ets of ε|0.007|0.007|0.007|
|εEU|parameters|are listed in|table 6B.|0.007|0.007|0.007|
|εAFR|Lower and|upper value|s are 0.5|0.004|0.007|0.010|
|εROW|and 1.5 t|imes central|values.|0.007|0.007|0.007|
|θr|NA|0|1|NA|0.007|0.002|
|θr - CRTS model|NA|0|1|NA|0.003|-0.001|
|θm|0.025|0.05|0.075|0.007|0.007|0.007|
|σ(qi, qj)–goods sectors|||||||
|Processedfood|4.8|9.6|14.4||||
|Beverages & tobacco products|1.5|2.3|3.1||||
|Textile &leatherproducts|2.8|3.6|4.4||||
|Manufacture ofbasic &industrialchemicals|1.7|2.5|3.3||||
|Manufacture of fertilizers & pesticides|1.6|2.4|3.2||||
|Petroleum refineries|2.8|3.6|4.4||||
|Glass & cement|2.3|4.6|6.9||||
|Ironsteel&metalproducts|3.7|7.3|11||||
|Mining & quarrying|2.8|3.6|4.4||||
|Grain milling<br>**Key:**|2.4|3.2|4||||

σ(qi, <u>qj): Elasticity of substitution between firm varieties in imperfectly competitive sectors σ(va, bs): Elasticity of substitution between value-added and business services</u> <u>σ(D, M): Elasticity of substitution between domestic and imported varieties</u> <u>σ(L, K): Elasticity of substitution between primary factors of production in value added</u> σ(A1,...An): Elasticity of substitution in intermediate production between composite Armington aggregate goods <u>σ(D, E): Elasticity of transformation (domestic output versus exports)</u> εTZA: Elasticity of national service firm supply with respect to price of output εEU: Elasticity of EU service firm supply with respect to price of output εAFR: Elasticity of AFR service firm supply with respect to price of output εROW: Elasticity of Rest of World service firm supply with respect to price of output θr: Share of rents in services sectors captured by domestic agents θm: Shares of value added in multinational firms due to specialized primary factor imports <u>Source: Authors’ estimates.</u> 96
Indonesia, India, and Brazil. A detailed discussion of these studies can be found in Duranton (2008), Overman and Venables (2005), and Henderson (2005). These three reviews lament the paucity of work estimating agglomeration effects in developing countries and call for more work on the topic. Six years after the last of these surveys was written, this call has yet to be answered. There are only a handful of recent studies not reviewed in these surveys. These studies focus on Turkey (Coulibaly, Deichmann, and Lall 2007), China (Bosker, Brackman, Gerresten, and Schramm 2012; Combes, Démurger, and Shi 2013), India (Chauvin, Glaeser, and Tobio 2013; Ghani, Kanbur, and O’Connell 2013 _a_ ), and Colombia (Duranton 2014 _a_ ).

Methodologically, the literature on agglomeration effects in developing countries is often less advanced, and identification concerns have not been taken seriously. In the rare cases where they have been considered (such as Combes et al. 2013 for China; Chauvin et al _._ 2013 for India; Duranton 2014 _a_ for Colombia), three interesting features occur. First, as in developed economies, better controls for individual effects lower the estimates of the agglomeration elasticities. This finding is unsurprising because larger cities in developing countries tend to be populated by more educated workers. Second, in China and India, instrumenting for city size tends to raise the estimates for agglomeration effects instead of lowering them if we expect workers to move to high-wage cities. This may occur because policy in these two countries has sought to limit the migration of workers to larger and more productive cities. Hence, this perverse finding may not be as perverse as it seems and may only reflect a different policy context for these two countries. Third, after the best attempts to address estimation issues, estimated agglomeration effects in China and India remain much higher than the corresponding coefficients for developed economies. For instance, Combes et al. (2013) suggest agglomeration externalities of 10 to 12 percent for China. In Colombia, the estimated 9
**Table 9. Resolution of business disputes**

||Appeal to<br>court|Suffer in<br>silence|Private<br>negotiation|Appeal to the local<br>government|Negotiation through<br>business association|
|---|---|---|---|---|---|
|**Panel A:**|**Resolution of**|**business dis**|**putes (over tim**|**e)**||
|1995|0.089|0.014|0.874|0.022|.|
|2000|0.111|0.011|0.851|0.025|.|
|2004|0.359|0.099|0.579|0.173|0.136|
|2006|0.319|0.103|0.527|0.212|0.146|
|**Panel B:**|**Percentage of**|**firms that s**|**atisfied with bu**|**siness disputes resolut**|**ion (over time)**|
|1995|0.538|0.414|0.756|0.63|.|
|2000|0.639|0.583|0.858|0.789|.|
|2004|0.775|0.324|0.892|0.875|0.937|
|2006|0.831|0.539|0.905|0.909|0.975|

**Panel C. The determinants of court usage**

|VARIABLES|(1)<br>Usingc|(2)<br>ourt to solve business|(3)<br>disputes|
|---|---|---|---|
|Party member||0.019|0.014|
|||(0.014)|(0.020)|
|Membership in Congress or||0.030***|0.053***|
|Consultative Conference||(0.008)|(0.015)|
|Privatized firm|0.110***||0.100***|
||(0.021)||(0.022)|
|Ln(asset)|0.042***|0.024***|0.037***|
||(0.005)|(0.003)|(0.006)|
|Firm age|0.004*|0.002*|0.003|
||(0.002)|(0.001)|(0.002)|
|Female|0.004|-0.019|0.001|
||(0.032)|(0.013)|(0.032)|
|Education|0.015***|0.007***|0.015***|
||(0.003)|(0.001)|(0.002)|
|Ln(GDP PC), Province, Sector,|Yes|Yes|Yes|
|Year||||
|Observations|3,649|10,335|3,415|
|R-squared|0.087|0.098|0.094|

_Data sources: National Surveys of Privately Owned Enterprises in China._ Standard errors are clustered at provincial level and reported in parentheses. Significance levels 0.1, 0.05 and 0.01 are noted by *, **, and ***, respectively.

45
Theoretically, the expected signs of these impacts are ambiguous. First, the effect of technology on labor demand depends on the degree to which workers are substitutes or complements for technology. In addition, it also depends on the degree to which technology allows businesses to become more efficient and thus expand output and employment. Second, increased enforcement of regulations, on the one hand, raises the cost of maintaining a formal workforce, such that areas facing stricter enforcement will have increased difficulties in adjusting labor to technology shocks, limiting employment. On the other hand, labor laws purposefully improve job quality through worker protections, such that companies in strictly-enforced areas hire more, as workers view formal employment as a more attractive option than informal employment, and firms demand more workers to meet per-worker hour restrictions.

Our research linking access to digital technology and labor market outcomes is innovative for four main reasons. First, we exploit a matched employer-employee database from the Brazilian Ministry of Labor covering the formal-sector labor force, between 1996 and 2006, and match it with subnational data on the municipal rollout of internet service over time. According to data from the Brazilian Census Bureau on the provision of internet services in each municipality, our time-period covers a significant expansion of internet services throughout the Brazilian economy. To our knowledge, we are the first to explore this unique data set with time-series and within-country variation in access to digital technologies.<sup>2</sup> Second, the administrative data allow us to analyze the impacts of digital technology adoption on the overall levels of employment at the city and industry level, across broad sectors and regions of the country. Third, we exploit a unique concordance between the Brazilian Classification of Occupations (CBO) and the U.S. Department of Labor's Occupational Information Network (O*NET) to assign a numerical index capturing the importance of distinct "activities" in each occupation. These skill indices allow us to construct measures of “routine” tasks and “non-routine” tasks used in each occupation. With these variables, we calculate each municipality-by-industry’s average task intensity across

> 2 Dutz, et al. (2017) rely on a different database from Brazil to consider internet rollout over time and across municipalities in the country.

3
**Figure 12: Correlation between expansion of the middle class and income growth**

<!-- Start of picture text --> eca<br>per<br>chl<br>venarg<br>xlc cac<br>xea<br>mexcol bra<br>mna<br>sas<br>ssa<br>1.5 2 2.5 3 3.5<br>Income growth<br>40<br>30<br>20<br>10<br>Growth of the middle class (in points)<br>0<br><!-- End of picture text --> _Source:_ World Bank staff calculations.

Note: ARG-Argentina, BRA-Brazil, CAC-Central America & Caribbean, CHL-Chile, CHN-China, COL-Colombia, ECA-Europe & Central Asia, EUR-EU27 & EFTA, MEX-Mexico, MNA-Middle East & North Africa, PER-Peru, SAS-South Asia, SSA-Sub-Saharan Africa, USA-United States, XEA-Rest of East Asia, XHY-Rest of high-income, XLC-Rest of Latin America

# **_Within-country inequality: How economic growth is distributed_**

Changes in the distribution within countries are no less important. Worsening inequality can offset – or, at least, weaken – the positive effects of growth on the expansion of the middle class and can increase the risk of social alienation of people left behind at the bottom of the income scale.

In fact, if the rich or middle class member in 2005 were the sole beneficiaries of the economic growth over the 2005-2030 period, the middle class would not expand at all. The incidence of growth is thus crucial. Whether growth is pro- or anti-poor determines whether a given amount of growth is more or less efficient to lift people out of poverty and move them up into the middle class. Comparing the base case projection to another scenario where economic growth is distribution-neutral (i.e shifting the whole distribution rightward without changing its shape) reveals a different pattern of expansion of the middle class across countries in LAC.

28