rafmacalaba/datause-encoder-data huggingface dataset:
# Source: Cadastre data from the city of Bogotá

Our empirical analysis highlights that households and businesses value the dis-amentiy of natural hazard risk in deciding where to locate as reflected in a negative capitalization of hazard risk in property values. We find that earthquake risk has a negative association with land values. We also examined if people living in areas at risk from natural hazards (earthquakes, landslides, and floods) invested in improving building quality when they were close to centers of economic activity. The underlying intuition is that the net benefits from investing in mitigation have a higher pay-off when the value of assets at risk is higher. To examine this hypothesis, we examined the spatial patterns of investments in structural upgrades and compliance with building standards among low income residents. Regression analysis shows that compliance with building codes is higher and quality of construction better in dwellings close to the city’s economic center. This is indicative that individual investment in mitigation increases with economic density as people have more to lose with disruptions due to natural hazard events.

**_Residents of informal settlements trade-off housing services and environmental safety to be physically integrated in the labor market_** . Evidence from Pune, India tells us that poor households prefer to live close to their work place – thus, living in centrally located slums allows slum dwellers to access jobs. Many of these slums are located on riverbanks that are prone to flooding or on hillsides. These residents are also willing to pay a premium to live in areas that are composed of households sharing their language, religion, level of educational attainment and average length of tenure in the neighborhood – community structure and social capital are very important. An assessment of the welfare impact of relocating slum dwellers from their current locations to less desirable peripheral locations shows that relative to no intervention (allowing slum dwellers to be in their current location, with the same service levels and housing conditions), upgrading services in situ is the only policy intervention examined which increases welfare of these slum dwellers. Moving to alternate locations is welfare reducing – showing that slum dwellers are willing to put up with the threat of poor housing and environmental conditions to be physically integrated in the labor market (Lall et al. 2008).

21
- Shafiq, M. 2007. _Household schooling and child labor decisions in rural Bangladesh_ . Journal of Asian Economics.

- UNICEF. 2005. _Child Labour, Education and the Principle of Non-Discrimination._ New York: UNICEF.

- UNICEF. 2007. _UNICEF zum Welttag gegen Kinderarbeit am 12.6.07_ . Bonn: UNICEF Deutschland.

- U.S. Department of Labor. 2000. _By the Sweat and Toil of Children (Vol. VI): An Economic Consideration of Child Labor._ Washington: U.S. Department of Labor.

- Van Evera, Stephen. 1997. _Guide to methods for students of political science_ . Ithaca: Cornell University Press.

- World Bank. 2010. _Tackling Poverty from Northern Ghana_ . Washington: World Bank.

- World Bank. 2010a. _Improving the targeting of social programs in Ghana._ Washington: World Bank, Draft for Internal Discussion, June.

- World Bank. 2011. _Education in Ghana: Improving Equity, Efficiency and Accountability of Education Service Delivery_ . Washington: World Bank, principal authors: Peter Darvas and Alexander Krauss.

- World Bank and UNICEF. 2009. _Abolishing School Fees in Africa: Lessons from Ethiopia, Ghana, Kenya, Malawi, and Mozambique_ . Washington: World Bank and UNICEF.

- World Development Indicators (WDI) Database. 2011. _World Development Indicators Database._ Washington: World Bank, http://data.worldbank.org/data-catalog

27
<!-- Start of picture text --> See Af<br>7 3<br>Ai) at<br>ihre<br>ig is <Pnaxe! a\<br>ce i Te! Be ~<br>us : ; a ae pa<br>oo g? Satoh Pood ‘e AN °<br>3%) of : Sve sae! i : x : — c pa<br>5S aOe Be! veLitYee | a suitneyan et Py ateneat éAle<br>VA 1 con ON a pas / ve a<br>$ y nes HE ‘ 2<br>‘ oe Z 100 0 100<br><!-- End of picture text -->

<!-- Start of picture text --> 3 |<br>telarah Jee\y<br>L*rs sae i? Va<br>Bes<br>Cease<br>ene pl<br>QUAray 100=O 100<br><!-- End of picture text -->
- 29 -

in collective bargaining. As income policies are generally rarer in LDC's<sup>15</sup> our discussion concentrates mostly upon the evidence regarding MWs.

The main expected effects of an effective MW policy are:

- (i) If such policies lead to economy-wide real wage indexation, then nominal exchange rate adjustment may be unable to change the real exchange rate, while expenditure reducing policies directed at improving an existing current account deficit may only do so by increasing unemployment.

- (ii) An effective economy-wide MW will create unemployment. If, more realistically, such regulations only apply to a particular sector (i.e. the modern or covered sector), then employment in that sector is likely to fall, labor supply in total will increase, and one would expect some combination of increased informal sector employment at a lower wage, increased unemployment and quasi-voluntary unemployment. These effects are analyzed in Mincer (1976) and in Lopez & Riveros (1989). Effective minimum wages thus lead to efficiency losses both through higher unemployment and through the fact that marginal rates of substitution between inputs diverge across sectors.

- (iii) The imposition of an effective MW may be expected to raise the real exchange rate, defined here according to the price of nontradables relative to that of tradables, as formal nontradables-producing sectors will find it easier to pass the wage increase on in the form of higher prices than tradables-producing sectors.

- (iv) MWs should normally lead to a compression of skill differentials between skilled and unskilled labor. If however, wage differentials are maintained by collective bargaining, the employment levels of both skilled and unskilled workers may fall within the modern sector.
The effects of the school readiness intervention on the outcomes measured in TEHCI are presented in Table 4. The effects of the intervention on outcomes for all students are estimated using equation (1) and for the sub-populations of students using equation (2) by least squares. Full model estimates are presented in Annex Tables 1 and 2. Positive effects on overall school readiness were found only for girls of mothers with no high-school education; positive effects are found for both boys and girls of mothers with no high-school education on the average literacy domains. Annex Table 3 presents the effects for each TEHCI school readiness domain. For all children, positive effects were found only on the cultural and spiritual domain and the numeracy concepts domain. For girls of mothers without high-school education, the intervention also affected socio-emotional development, approaches to learning, and writing. Some negative effects were found for boys of mothers with high-school education.

Table 4. Effect of the school readiness intervention

||all|mothers wi<br>school ed|thout high<br>ucation|mothers<br>school e|with high<br>ducation|
|---|---|---|---|---|---|
|||girls|boys|girls|boys|
|average TEHCI score (SD)|0.04|0.2**|0.08|‐0.02|‐0.07|
||(0.06)|(0.1)|(0.08)|(0.07)|(0.05)|
|average TEHCI literacy domains (SD)|0.07|0.19**|0.17**|‐0.02|‐0.03|
||(0.05)|(0.08)|(0.07)|(0.06)|(0.05)|
|home activities index|0.03|0.01|0.05|0.01|0.02|
||(0.03)|(0.03)|(0.04)|(0.03)|(0.03)|
|attended a CPBA|0.21***|0.24***|0.2***|0.21***|0.19***|
||(0.02)|(0.03)|(0.03)|(0.02)|(0.02)|
|attended preschool|‐0.02|0.02|0.06**|‐0.07**|‐0.05**|
||(0.02)|(0.04)|(0.03)|(0.03)|(0.03)|

Standard errors included in parentheses. Statistical significance at the 1, 5 and 10 percent levels are denoted by ***, **, and *, respectively. Effects are estimated by equations (1) and (2), presented in Annex Tables 1 and 2. Effects are shown for children aged 3 and 4. Sample sizes are 3,429 children except for the home activities index model estimation which is 3,426 children.

No effects were found on the index of home activities; however, as shown in Annex Table 4, positive effects were found for individual home activities including reading to the child, singing songs, and naming or counting things. Positive effects were found for whether a child attended a 18
strategy. Results from production functions and regressions to explore how gross output and profits vary with farm size under different assumptions regarding labor market functioning are discussed in section 4. Section 5 concludes by drawing out implications for policy and research.

# **2. Conceptual background**

To frame the analysis, we discuss the conceptual basis, empirical evidence, and likely evolution of the farm-size-productivity relationship over time. While one set of explanations focused on unobserved land quality differentials, an alternative one focuses on labor market imperfections that make small producers either apply more effort than larger ones or more than the optimum amount of family labor. Credit market imperfections and constraints to mechanization imposed by plot sizes below a certain size may counter this, providing advantages to large producers that may weaken the relationship as access to mechanization becomes more important, especially if labor market functioning improves.

# **2.1 Explanations and evolution of the farm size-productivity relationship**

A negative relationship between farm size and output per hectare, first noted in Russia (Chayanov 1926) and in Indian farm management studies (Bardhan 1973, Sen 1975, Srinivasan 1972), has been confirmed empirically so frequently as to almost be perceived as a stylized fact in the literature (Eastwood _et al._ 2010, Lipton 2009). Analytically, many studies find agricultural production to be characterized by constant economies of scale, implying that a wide range of farm sizes can coexist. As residual claimants to profit, owner-operators will be more likely to exert effort than wage workers who require supervision which, in light of the spatial dispersion of agricultural production processes, is costly (Frisvold 1994). Owner operators’ knowledge of local soil and climatic conditions, often accumulated over generations, also gives them an edge over wage workers (Rosenzweig and Wolpin 1985).

Under constant returns to scale and with well-functioning factor markets or imperfections in one market only, output and intensity of input use will be identical across farm sizes. Imperfections in more than one factor market will lead to a systematic relationship between the size of cultivated area, inputs, and yields (Feder 1985). Small farmers’ advantages in labor supervision, knowledge, and organizational advantages can be offset by their difficulty in accessing capital and insurance which arises from the high transaction cost of providing formal credit in rural markets, possibly exacerbated by the difficulty of using small farmers’ assets as collateral. Frictions in labor market participation and land markets, e.g., due to transaction costs, could motivate small farmers who are unable to rent additional land to rationally apply family labor to cultivate their fixed land endowment more intensively than they would with perfect markets. An inverse relationship can also emerge if labor and credit markets imperfections are combined with a fixed cost element for production (Eswaran and Kotwal 1986) or if there is heterogeneity in 4
in that period<sup>7</sup> . Once the national EPU index is constructed for the 18 countries, a Global EPU index is computed using the PPP-adjusted GDP-weighted average of the 18 national EPU index values. Of note, in the period between July 2011 and August 2016, the average value of the GEPU index is 60% higher than in the previous fourteen and one half years. The mean GEPU index value since July 2011 is also 22% greater than the index value during the Great Recession of 2008 (Davis, 2016). This indicates policy-related concerns have become a great source of uncertainty in the Recovery period. Hence, we interact the GEPU index with the county-level exogenous job gains or losses to check if the magnitude of the job gain multiplier changes with the time-varying level of uncertainty<sup>8</sup> .

Table 6.4 Job gain multiplier with Global Economic Policy Uncertainty Index

|||Change<br>|in number<br>|of non-tradab<br>|lejobs<br>||
|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|
|△tradable jobs|1.682**|6.762***|2.833**|9.944***|3.011***|8.617***|
||[0.846]|[2.054]|[1.199]|[3.222]|[0.618]|[1.772]|
|△tradable jobs*<br>Global uncertainty||-0.035***<br>[0.011]||-0.044***<br>[0.014]||-0.039***<br>[0.010]|
|Constant|Y|Y|Y|Y|Y|Y|
|Time fixed effects|Y|Y|Y|Y|Y|Y|
|County fixed effects|Y|Y|Y|Y|Y|Y|
|Observations|9,018|9,018|12,013|12,013|15,029|15,029|
|Number of county|3,106|3,106|3,111|3,111|3,123|3,123|
|Year|2011|-2013|2011|-2014|2011|-2015|

Robust standard errors in brackets, *** p<0.01, ** p<0.05, * p<0.1

> 7 For a detailed discussion on the construction of cross-country EPU indices see Baker, Bloom and Davis (2016).

> 8 Since our data is in annual frequency, we use the December GEPU index from 2011-2015 as the proxy for yearly GEPU uncertainty index.

27
**Figure 6: R&D expenditures for selected countries by source of funds, 2009**

<!-- Start of picture text --> 4.0<br>100% 6.6 1.4 3.5 1.3 5.7 8.0 6.9 6.5<br>90% 2.3 16.6<br>80% 17.7 25.4 23.4 28.4 27.1 12.1 6.3<br>70% 38.9<br>33.4<br>60% 32.6 66.5<br>50%<br>40%<br>75.3 72.9 71.7 67.3 67.3<br>30%<br>20% 50.7 47.6 44.5<br>26.6<br>10%<br>0%<br>Japan Korea (2008) China Germany United States France (2008) Canada United Russian<br>(2008) (2008) Kingdom Federation<br>Industry Government Other national sources Abroad<br><!-- End of picture text --> Source: OECD MSTI (2011).

The federal contribution to R&D is not homogeneous across strategic sectors, nor is private sector participation. The federal contribution to R&D is by far the largest in industry nano-systems and material systems as well as in living systems (78 percent), and is also high in ICT (58 percent) and transport, aviation, and space systems (53 percent). On the other hand, private funding of R&D (extra-budgetary funds), including foreign R&D, varies within strategic sectors, from 18 percent in living systems to 48 percent and 63 percent in rational use of natural resources and energy and energy efficiency, respectively (see Table 1).

**Table 1: Gross domestic expenditure on R&D in priority areas of science, technology, and engineering by** **<u>source of funding, 2007 (thousand US$)</u>**

||Total|ICT|Nano-<br>technology<br>and<br>materials|Life<br>sciences|Natural<br>resources|Energy<br>and<br>energy<br>efficiency|Transport,<br>aviation,<br>and space<br>systems|
|---|---|---|---|---|---|---|---|
|Gross domestic expenditure<br>on R&D in priority areas of<br>Science, Technology and<br>engineering|6,570,375|1,026,233|465,476|279,025|520,033|533,353|3,392,642|
|_Sources of funding_||||||||
|Federal budget|3,584,383|591,761|362,885|217,878|246,696|192,164|1,784,651|
|Regional and local budgets|147,631|18,984|6,596|9,785|23,946|3,286|86,617|
|Extra budgetary funds|2,838,361|415,487|95,995|51,362|249,391|337,903|1,521,374|

Source: Indicators of Science (2009) – MoES, ROSSTAT, and the Higher School of Economics.

Strategic sectors accounted for around 44 percent of all R&D expenditures. Among the strategic sectors, the majority of resources – 52 percent – were employed by the transport, aviation, and space systems sector, followed by ICT at 16 percent, in 2007 (Table 1). The heavier expenditures in transport may guarantee Russia’s place at the technological frontier in the field, especially in space and aircraft technology. Additionally, the relatively large resources devoted to ICT may partly explain the emergence and growth of software start-ups and software exports in recent years. Formal investments in R&D in ICT 11
dimension, the industry-year portion of the importer-industry-year fixed effects play a very stabilizing role. Quantitatively similar results are also found when excluding the Tobacco and Petroleum sectors (314, 353, 354). Column 5 shows that winsorizing the sample at the 2%/98% level delivers similar results, indicative that outliers are not overly influencing the measured elasticities.

It was earlier noted that computational demands require that the main estimations employ the ISIC2-level industry groups when preparing importer-industry-year fixed effects. Columns 6–8 test this choice in several ways. First, column 6 shows that the results hold when estimating the full model with ISIC2-based cells, so that the importerindustry-year fixed effects exactly match the cell construction. The weaker variation reduces the coefficient estimates by half, but the results remain statistically and economically important. Columns 7 and 8 alternatively estimate the model using the sample from Kerr (2008) that focuses on a subset of the UNIDO data in the 1985–1997 period. The Kerr (2008) sample is substantially smaller in size than the present one, and so there is greater flexibility with respect to these fixed effect choices. The choice of industry aggregation for the importer-industry-year fixed effects does not make a material difference in this sample.<sup>10</sup> Finally, column 9 shows the results with exporter-level clustering. The labor productivity and export development of industries within countries may be correlated with each other due to the presence of general-purpose technologies, learning-by-doing (e.g., Irwin and Klenow 1994), and similar factors, and Feenstra and Rose (2000) show how the export ranges of countries can change over time in systematic ways across industries. Clustering at the exporter level allows for greater covariance across industries in this regard, returning lower standard errors. Most papers in this literature use robust standard errors on cross-sectional data, which would translate most closely to bilateralroute clustering in a panel model. Unreported estimates consider bilateral-route clustering and alternatively bootstrapped standard errors, and these standard errors are smaller than those reported in column 9.

> 10. This extra check also has the advantage of linking the two studies closer together since the Kerr (2008) paper focuses extensively on productivity growth due to technology transfer. Stability to the somewhat different data preparation steps in Kerr (2008) is comforting.
Appendix Table A4 uses data from the in-clinic survey to assess whether patient volumes and patient characteristics are balanced across the patient information and extended training treatment groups. Each row represents a single regression, where we regress the outcome of interest on an indicator for clinic-days where the patient information intervention was in place, and an indicator for clinics selected for extended training. All regressions include strata and survey date fixed effects.

On average, enumerators interviewed 6 eligible acutely ill patients per observation day, with no significant differences across treatment arms. The average patient suspects malaria 59 percent of the time, presents with 3.4 symptoms, is 17 years old, and is malaria positive (per RDTs administered during the home survey) 22 percent of the time. Panel B shows that most patient characteristics are balanced across treatment, though patients in the patient information arm are less likely to report fever, slightly older, and less likely to be pregnant; those in the extended training arm are more likely to present with nausea/vomiting/diarrhea – and had been sick for slightly longer. Importantly, malaria positivity both measured via RDTs and predicted based on symptoms – is balanced across treatment arms.

Panel C studies balance in terms of survey respondent and household characteristics. Those randomly selected for patient information are less likely to report Bambara as their ethnic group and more likely to be literate (and consequently less likely to have a primary school education or less); those visiting clinics selected for the extended training are less likely to be literate, have less education, and belong to slightly bigger households. Given our design, we cannot rule out the possibility that some of these differences are due to selection. To address concerns regarding balance, our main results use double lasso to select covariates as described in footnote 19. As a robustness check, Appendix B reports results without additional controls. Overall, our findings are very similar – we therefore believe it is unlikely that balance issues affect our main conclusions.

# **4 Results**

## **4.1 Empirical Approach**

We begin our analysis by documenting the impact of the extended training treatment on health providers’ beliefs about the accuracy of RDT and microscopy tests. Next, we study – – how this treatment and the patient information intervention impacted malaria testing at the clinic, the allocation of malaria treatment, and patient satisfaction.

the square of patient age and illness duration. We also include pairwise interactions of the aforementioned characteristics. Missing values for all characteristics are dummied out and recoded to the mean prior to forming interactions. Missing dummies are also included in the potential covariate set and set of pairwise interactions.

13
# **APPENDIX A EXTENDED RESULTS DISCUSSION**

This appendix provides further avenues of analysis for the CLASS results, including differences by teacher experience and school location. This analysis did not provide much insight into the usability of CLASS in adjusting teacher training programs. Additionally, given the sample size for certain levels of analysis, these results should be seen only as a starting point for further testing and observation, rather than conclusive evidence of differences in teacher groups.

## **A.1 By school type**

While all schools observed are in Guangdong’s poorer counties, the conditions within each of these counties vary depending on whether one is in the countryside or in the county seat. For this study, half of the schools observed are in county seats, while half are in the countryside. Despite this distinction between urban and rural in our data set, it is likely that _all_ schools observed would be classified as rural relative to true urban centers such as Guangzhou, Shanghai, or Beijing.

While the gap between urban and rural student achievement is clear, as noted in the introduction in relation to Programme for International Student Assessment (PISA) scores, as well as in Chinese college attendance and other indicators, the gap in teaching quality is not necessarily as simple. Often, teachers who graduate from teachers’ colleges, known in China as “normal universities,” are sent to rural classrooms for some years before being able to transfer to more urban schools. Likewise, high-performing vice principals and administrators from urban schools who wish to be promoted are often assigned to rural and under-resourced schools to share best practices. Finally, China has been implementing a program of rural school consolidation, merging several smaller schools into one larger school to leverage resources. All this is to say that there is often a large amount of support for rural schools, despite other challenges. Younger, recently graduated teachers benefit from the latest pedagogical instruction, and experienced principals can support a staff looking to improve an under-resourced school.

**Figure A.1: Differences between Urban and Rural Classrooms**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2 Rural<br>1 Urban<br>0<br><!-- End of picture text --> Source: Guangdong class observations; authors’ calculations.

24