rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Price pass-through rates

|LHS = Ln(Median Price), Africa|O|LS||2SLS, IV =|Gold Price||
|---|---|---|---|---|---|---|
||(1)<br>All Years|(2)<br>All Years|(3)<br>All Years|(4)<br>Pre-1993|(5)<br>Post-1993|(6)<br>Post-2002|
|Ln(Median Price), China|0.284|0.349|1.000|1.992|0.955|0.900|
||(0.121)|(0.120)|(0.168)|(0.589)|(0.158)|(0.183)|
|Ln(Total Carcass Found)||||||0.206<br>(0.063)|
|Constant|0.002|-0.823|-1.321|-0.941|-1.287|2.195|
||(0.071)|(1.396)|(1.171)|(0.915)|(1.177)|(1.239)|
|||||First|Stage||
|Ln(Gold Price)|||0.740|0.266|0.955|1.107|
||||(0.186)|(0.143)|(0.264)|(0.353)|
|N|432|432|432|166|266|188|
|First Stage F statistic|||174.349|14.956|154.138|90.728|
|Fixed Efects|None|Country|Country|Country|Country|Site|

Note: Standard errors clustered at the year level with Moulton correction in parentheses. This table reports the results of estimating equation 10 with OLS and 2SLS. The dependent variable is the log of median price in Africa at the country level. Variable definitions and sources are described in detail in the text.

to-unit elasticity and a large partial F-statistic. Whether we look at the whole period since 1970 for which we have data on prices (column 3), the time period after the trend-break year 1993 (column 5), or look at post-2002 data that have common support with the poaching data (column 6), the 2SLS estimates indicate a pass-through rate quite close to unity.<sup>9</sup> This finding is consistent with the “iceberg” trade cost assumption in the large majority of trade models. It is consistent with marginal cost pricing or constant markups, and would obtain more generally whenever there is no arbitrage in shipping ivory internationally.<sup>10</sup> We now turn to the estimation of the poaching elasticity with respect to ivory prices in China. For symmetry, we use the same set of specifications as in Table 1. Table 3 presents the 2SLS results. Not surprisingly in light of the nearly complete pass-through, the patterns are quite similar to the estimates using local prices. The OLS specifications (columns 1-4) show similar patterns as in the domestic price analysis with an elasticity of 0.13. Similarly accounting for the potential endogeneity of ivory prices, our preferred specifications (columns 5-6) estimate an elasticity of roughly 0.40 and significant at the 1 percent level. Here again, in all specifications, we can reject unit elasticity at the 1 percent level. As expected, the poaching elasticity with respect to global prices is quite close to the product of the elasticity with respect to local prices and the pass-through rate between global and local prices. Multiplying our

> 9In the pre-1993 sample the pass-through coefficient is about 2.

> 10We unfortunately do not have enough poacher-level observations (169 in total) to compute a pass-through rate from retailers to poachers.

20
Busch, Susan and Noelia Duchovny, “Family Coverage Expansions: Impact on Insurance

- Coverage and Health Care Utilization of Parents,” _Journal of Health Economics_ 24 no. 5 (2005), 876–890.

- Chemin, Matthieu. “Using Informal Risk-Sharing Groups to Extend Formal Health Insurance: Evidence from a Field Experiment”. Unpublished. (2014)

Currie, Janet, and Jonathan Gruber. “Health Insurance Eligibility, Utilization of Medical Care, and Child Health,” _Quarterly Journal of Economics_ 111 (1996): 431–466.

Dubay, Lisa, and Genevieve Kenney. “Expanding Public Health Insurance to Parents: Effects on Children's Coverage under Medicaid,” _Health services research_ , _38_ no. 5 (2003):1283-1302.

Ejrnæs, Mette, and Claus C. Pörtner. “Birth Order and the Intrahousehold Allocation of Time and Education.” _Review of Economics and Statistics_ 86.4 (2004): 1008-1019.

Fang, Hanming, Michael Keane, and Dan Silverman. “Sources of Advantageous Selection: Evidence from the Medigap Insurance Market.” _Journal of Political Economy._ 116 no. 2 (2008): 303-350 Finkelstein, Amy, Sarah Taubman, Bill Wright, Mira Bernstein, Jonathan Gruber, J.P. Newhouse, Heidi Allen, Katherine Baicker, and the Oregon Health Study Group “The Oregon Health Insurance Experiment: Evidence from the First Year.” _Quarterly Journal of Economics._ 127 no. 3 (2012):1057-1106 Gertler, Paul, and Jacques Van der Gaag. “The Willingness to Pay for Medical Care: Evidence from Two Developing Countries.” Johns Hopkins Press, Baltimore. (1990). Griliches, Zvi, “Sibling Models and Data in Economics: Beginnings of a Survey _,” Journal of Political Economy_ , 87 no. 5 (1979): S37–S64.

20
Table A.4: Data collection instruments

|Instruments|Sections|
|---|---|
|Principal questionnaire|A. School and principal identifcation|
||B. Demographics|
||C. Education and professional training|
||D. Work experience and training needs<br>E. Pedagogical practices and languages|
||F. School basic characteristics<br>G. School environment|
||H. Interaction with colleagues (subordinate, supervisors, etc.)<br>I. Support and monitoring of teachers<br>J. Motivation|
|Teacher questionnaire|A. Class and teacher identifcation<br>B. Demographics|
||C. Class characteristics|
||D. Education and professional training|
||E. Work experience and training needs|
||F. Pedagogical practices and languages|
||G. Interaction with colleagues<br>H. Motivation|
||I. Absenteeism and event disturbing teaching|
||J. Remuneration<br>K. Perception of key factors infuencing student learning|
||L. Performance recognition or punishment|
|Student tests|A. Identifcation of school, class, and teachers|
||B. School-related student characteristics|
||C. Student environmental and familial backgrounds|
||D. French test questions|
||E. Math test questions|
|Inspection bulletin|A. Class and inspector identifcation|
||B. Teacher identifcation<br>C. Summary of scores|
||D. General material and spatial classroom arrangement|
||1. Lesson 1 – Identifcation of the lesson|
||2. Lesson 1 – Teaching and learning material preparation|
||3. Lesson 1 – Lesson planning (Competency-based approach)|
||4. Lesson 1 – Delivery of the lesson|
||5. Lesson 1 – Analysis of own performance|
||1. Lesson 2 – Identifcation of the lesson|
||2. Lesson 2 – Teaching and learning material preparation|
||3. Lesson 2 – Lesson planning (Competency-based approach)|
||4. Lesson 2 – Delivery of the lesson|
||5. Lesson 2 – Analysis of own performance|

6
# Comparison across different amounts of disaster management investment (RCP 8.5 scenario)

<!-- Start of picture text --> GDP compared to baseline Potential GDP compared to baseline<br>—_——O0% = 20% —— 40% —_—0% —— 20% ——— 40%<br>0.5 —_— 60% —— 80% —— 100% 0.5 — 60% —— 80% —— 100%<br>-0.50.0 ———————————SS1 -0.50.0, emery 1 — Vena—— on a —_____<br>-1.0 Eo 1 -1.0 Vo <A<br>-2.0 aoev -2.0 -—V7<br>“2.5<br>-2.5 fi<br>-3.0 -3.0<br>2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Consumption compared to baseline Debt/GDP<br>— 0% —— 20% —— 40% — 0% —— 20% —— 40%<br>0.5 —_— 60% —— 80% —— 100% 240 —_— 60% —— 80% —— 100%<br>0.0<br>eS 200<br>-0.5 —— SF<br>-1.0 eng“TES ee <A 760<br>a a — 120<br>an a<br>-2.5 7]<br>-3.0 40<br>2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text -->
on probability of working in the formal is not statistically different from zero. We interpret these changes in the magnitudes of the estimates as evidence that OLS estimates tend to exacerbate the impact of plant closing on labor outcomes.

The DID estimation confirms the existence of heterogeneous effects of plant closing on labor outcomes. Highly educated workers have a reduction in wages of 9.3 percent, three times the decline for low educated workers (-2.7 percent). The increase in hours of work of high educated workers (0.57 hours) is slightly smaller than the increase of low educated workers (0.72 hours), and there is a marked difference of the effect of plant closing on the probability of working in the formal sector, the high education group have a reduction of 4.2 pp., while the low education groupan increase of 11.8 pp, in line with the results of figure 13. The DID estimation rejects the existence of gender-differentiated effects on wages, as both men and women have similar wage reductions, of 4.8 and 4.6 percent, respectively. However, we observe a slightly larger effect on hours of work for women (0.61 hours) than for men (0.48 hours), and a negligible impact on formality status. As with the OLS estimates, it is noteworthy that plant closings are unrelated to the subsequent formality status of workers, perhaps suggesting that remaining omitted variable biases are also negligible.

|Table 8. Difference in|Difference estimates||
|---|---|---|
|(1)<br>|(1)<br>|(1)<br>|
|Log of hourly wage|Hours of work|Formality|
|All sample<br>-0.0471***|0.5311***|0.005|
|(0.006)|(0.129)|(0.004)|
|Male<br>-0.0485***|0.3155**|-0.0073**|
|(0.006)|(0.148)|(0.003)|
|Female<br>-0.0461***|0.6128***|0.0114|
|(0.010)|(0.219)|(0.008)|
|High Education<br>-0.0900***|0.3417|-0.0390***|
|(0.010)|(0.203)|(0.006)|
|Low Education<br>-0.0323***|0.5276***|0.0101***|
|<br>(0.007)<br>Note: Robust standard errors in parenthese<br>regressions control marital status, age, age<br>infustry fixed effects. Standard errors are c<br>category is non-displaced workers.|<br>(0.139)<br>s. ***p<0.01, **p<0.<br>square, survey perio<br>lustered at the state l|(0.003)<br>05, *p<0.10. All<br>d, state and<br>evel. The ommited|

26
TABLE 5. Percentage Distribution of Migrants by Allocation Method

|**Census**<br>**round**|**Pure**<br>**raw**|**Raw**<br>**scaled**|**Pure**<br>**remainder**|**Remainder**<br>**scaled**|**R&R**<br>**not**<br>**scaled**|**R&R**<br>**scaled**|**Pure**<br>**interpolation**|**Interpolation**<br>**and scaled**|**Missing**|**Total**<br>**(millions)**|
|---|---|---|---|---|---|---|---|---|---|---|
|**1960**|28.61|8.73|7.10|0.51|39.23|0.20|4.72|10.51|0.40|92.3|
|**1970**|42.94|0.00|4.01|1.97|34.25|0.25|12.00|4.11|0.46|102.4|
|**1980**|30.12|0.23|4.42|0.07|48.77|0.14|11.31|4.64|0.30|118.6|
|**1990**|36.61|0.55|4.79|0.35|43.07|0.15|10.05|3.84|0.58|139.4|
|**2000**|35.62|1.18|7.56|0.33|40.22|1.07|7.08|6.23|0.72|165.3|

_Source:_ Authors‘ calculations based on data described in text.

R&R: Raw and Remainder combined.

40
Appendix Table 7 continued: RDD estimates of gaps in teacher behaviors and teacher absence associated with teachers hired pre- and - <u>post FPE (bandwidths of 5, 7, and 10 years)</u>

||Bandwidth||<br>Good<br>teacher<br>demeanor|<br>Good<br>pedagogical<br>practices|<br>Classroom<br>environment|<br>Good<br>teaching<br>practices|Share of<br>time<br>teaching|Absent from<br>school|Absent from<br>classroom|
|---|---|---|---|---|---|---|---|---|---|
|Uganda|5|Coef|0.030|0.014|0.039|0.028|-0.070|0.104|0.413|
|||SE|(0.068)|(0.038)|(0.061)|(0.041)|(0.049)|(0.162)|(0.198)|
|||Pval|0.660|0.712|0.524|0.505|0.150|0.525|0.039|
||7|Coef|0.016|0.007|0.052|0.025|-0.027|0.052|0.255|
|||SE|(0.059)|(0.039)|(0.053)|(0.037)|(0.049)|(0.140)|(0.171)|
|||Pval|0.788|0.852|0.331|0.505|0.573|0.712|0.140|
||10|Coef|0.005|-0.026|-0.015|-0.012|-0.025|0.008|0.147|
|||SE|(0.049)|(0.035)|(0.045)|(0.032)|(0.041)|(0.117)|(0.146)|
|||Pval|0.916|0.460|0.747|0.714|0.540|0.943|0.315|

Notes: Table reports coefficient estimates (Coef), Standard Errors (SE), and p-values derived from estimating Equation (1). Each estimate and associated standard error and p-value are the result of a separate regression model. Pooled model includes country fixed effects.

A-16
13 households. As Table 1 indicates, interhousehold transfers"<sup>3</sup> play a major role in the economy, unlike in comparable models of developing countries, resulting in a strong rural-urban income linkage in this model.

# _Treatment of the Labor Market_

The model includes a treatment of the labor market which is more detailed than in other general equilibrium models of developing countries. To capture C6te d'Ivoire labor market segmentation, labor is disaggregated into three types: agricultural, skilled and unskilled labor. Skilled and unskilled labor move freely across sectors, while agricultural labor is sector specific. In the agricultural sector, there are two levels of nesting. The top nest is a CES function of skilled and composite labor. At the lower level of nesting, composite labor is a function of unskilled labor and agricultural labor. In the non-agricultural formal sectors, labor is a nested CES function only of skilled labor and unskilled labor. The informal sector uses only unskilled labor. Treating unskilled and skilled labor as mobile across sectors allows us to capture movements of labor between formal and informal sectors, as well as between agricultural and non-agricultural sectors (i.e. rural-urban migration).'<sup>4</sup>

**13** These are estimated using household data on financial savings to which regression estimates on acquisition of financial assets are applied. The difference is taken to be net transfers, which are constrained to sum to zero across all household groups. See Chia et al. (1991) for more detail on the data and to evaluate interhousehold transfers. Pyatt and Round (1984) adopt a similar treatment in estimating interhousehold transfers for Malaysia.

14 For a more detailed description of the resulting treatment of labour income in the model data set, see Chia et al. (1991).
Figure 2 Percent

## - Percent of Patients Receiving Any Norm Specified Drugs and Percent of Drugs Costs Expended by Hospital

<!-- Start of picture text --> 70<br>40.<br>0 Hernia Acute Appendicitis Cataract Hyper- Diarrhea Average<br>Tonsillitis Surgery tension<br>Diagnosis<br><!-- End of picture text -->

# % Patients Get Drugs

# % Norm Costs Expend

**Souirce: The Urban Institute**
There Goes Gravity: How eBay Reduces Trade Costs<sup>_∗_</sup> Andreas Lendle<sup>_†_</sup> Marcelo Olarreaga<sup>_‡_</sup> Simon Schropp<sup>_§_</sup> Pierre-Louis V ́ezina<sup>_¶_</sup> October 2012 JEL CODES: F13, F16 Key Words: Trade costs, gravity, online trade, eBay.

> _∗_ We are grateful to Richard Baldwin, Christine Barthelemy, Mathieu Crozet, Anne-C ́elia Disdier, Jonathan Eaton, Peter Egger, Phil Evans, Simon Evenett, Lionel Fontagn ́e, Gordon Hanson, Torfinn Harding, Beata Javorcik, Bertin Martens, Thierry Mayer, Hanne Melin, Marc Melitz, Peter Neary, Emanuel Ornelas, Cristian Ugarte, Tony Venables, and seminar participants at Oxford, the Villars PEGGED workshop, the WTO’s Trade and Development Workshop, the University of Neuchatel, the Paris Trade Seminar, the RIEF meeting at Bocconi University, ERWIT, the XIII Conference on International Economics, and the GTAP conference in Geneva for their constructive comments and suggestions. We also thank Daniel Bocian, Steve Bunnel, and Sarka Pribylova at eBay for their time and efforts with our data requests, and eBay for funding. The paper is a contribution to the DFID-supported Global Trade and Financial Architecture project. All opinions and errors are the authors and should neither be attributed to the institutions they are affiliated with, nor (as the case may be) with the clients these institutions represent.

> _†_ Graduate Institute, Geneva. email : andreas.lendle@graduateinstitute.ch

> _‡_ University of Geneva and CEPR. email: marcelo.olarreaga@unige.ch

> _§_ Sidley Austin LLP. email: sschropp@sidley.com

> _¶_ University of Oxford. email: pierre-louis.vezina@economics.ox.ac.uk
The rest of the paper is organized as follows. Section 2 describes the data and main variables. Section 3 shows the main regression results, Section 4 provides robustness checks. Section 5 concludes.

# **2. Description of data**

The sample consists of 147 countries for which information on our main variables is available. The time period covered by the study is 2003-2008. In the analysis we utilize several sources of data including the World Bank’s Doing Business project and World Development Indicators, Polity IV, Freedom House, Djankov et al. (2007), and La Porta et al. (1999). A definition of all variables and their sources is provided in Table 1. Summary statistics and correlations between the main variables are provided in Tables 2 and 3, respectively.

# 2.1 _Dependent variable_

The dependent variable is based on data collected by the World Bank’s Doing Business project. As part of its annual exercise, Doing Business compiles ten sets of indicators covering various aspects of the business climate including starting a business, paying taxes, obtaining licenses, getting credit, protecting investors, employing workers, international trade, property registration, closing a business and enforcement of private contracts. Information is also available on an annual basis on important reforms on each of these indicators. This information is coded as a dummy variable which equals 1 if a country implemented a positive reform during the year on a given indicator and 0 otherwise. A positive reform, as defined in Doing Business, is one that makes it faster, 5
# **Industrial environmental performance in China: The impact of inspections**

## By

Susmita Dasgupta* Benoit Laplante* Nlandu Mamingi** and Hua Wang*

### Development Research Group The World Bank

* Development Research Group, The World Bank, 1818<sup>H Street, N.W. Washington,</sup> D.C. United States, DC20433.

** The University of the West Indies, Bridgetown, Barbados.
Douidich, M, Ezzrari, A., Van der Weide, R. and Verme, P.(2013) Estimating Quarterly Poverty Rates Using Labor Force Surveys: A Primer, World Bank Policy Research Working Papers No. 6366 High Commission for the Plan (2000-2010) National Accounts, Haut Commissariat au Plan, Maroc http://www.hcp.ma/Comptes-Nationaux-Croissance-Base-1998_a166.html High Commission for the Plan (2010) Dynamique de la Pauvreté au Maroc, 1985-2007, http://www.omdh.hcp.ma/Dynamique-de-la-pauvrete-1985-07_a184.html Jovanovic, B. and Moffitt, R. (1990), An Estimate of a Sectoral Model of Labor Mobility, Journal of Political Economy, 98, issue 4, p. 827-52.

Verme, P. (2006) Constraints to growth and job creation in low-income Commonwealth of Independent States countries, World Bank Policy Research Working Paper No. 3893 Wolnicki, M., Kwiatkowski, E. and Piasecki, R. (2006), Jobless growth: a new challenge for the transition economy of Poland, International Journal of Social Economics, 33, issue 3, p. 192-206.

22
19 reasoning could be applied to rural to rural migration using the parameter estimates from the rural regression, and to rural to urban migration as well by combining the results of both regressions<sup>4</sup> .

Providing policy simulations using the above methodology would go beyond the scope of this paper because more detailed work would have to be prepared regarding various assumptions and policies. But it is worth noting again that the regression coefficients in Tables 4 and 5 give an idea of the magnitude of the transfers which would be needed in order to nullify (costless) migration and conditional between group inequality. In order to avoid urban to urban migration from the Mymensingh to the capital city of Dhaka, per capita consumption in urban Mymensingh would have to rise by 39.75 percent. If investments in urban Mymensingh were to generate a fifteen percent increase in consumption, this might not be sufficient to stop migration to Dhaka, but it could result in migration into Mymensingh from adjacent districts such as Tangail/Jamalpur who with the policy would then be conditionally poorer than Mimensingh (the conditional difference in consumption between the two areas is 7.5 percent in 1995-96).

One could also assess the impact on conditional between group inequality of investments in household characteristics such as education. If the returns stay the same (which is not unrealistic in the near term given their stability so far), and if the policy consists in decreasing the share of the illiterate and increasing the share of those completing primary school, all that will change are the conditional ranks for each education group, so that the new conditional between group Gini can be computed.

# V. **Conclusion**

This paper analyzed the micro determinants (and changes thereof over time) of consumption, poverty, growth, and inequality in Bangladesh from 1983 to 1996 using simple regressions. Education, demographics, land ownership, occupation, and location all affect consumption and poverty. The gains in

> 4 More generally, given a budget B for geographic investments (provided by the government or by international **6** donor agencies), the budget constraint is B= **k** Ik. The k equations **k +** tk = 8 and the budget constraint form a linear system from which, given B and the **6k'S,** S can be computed or alternatively, given the objective 5 and the estimated **Ok'S,** the necessary budget B can be obtained.
**Table 2a**

**Composition of Foreign Assets and Liabilities, 2004**

||**Ch**<br>Assets|**ina**<br>Liabilities|**In**<br>Assets|**dia**<br>Liabilities|
|---|---|---|---|---|
|Portfolio Equity|0.3|2.9|0.1|9.1|
|FDI|1.9|25.7|1.3|6.4|
|Private Debt|13.3|11.9|2.6|17.0|
|Reserves|31.8||18.3||
|**Total**|**47.3**|**40.5**|**22.3**|**32.6**|

Variables are expressed as a percentage of GDP. Source: Authors’ calculations, based on dataset constructed by Lane and Milesi-Ferretti (2006).

**Table 2b**

**Asymmetries in the International Balance Sheet, 2004**

||**China**|**India**|
|---|---|---|
|Net Portfolio Equity|-2.6|-9.0|
|<br>Net FDI|-23.8|-5.0|
|**Net Equity**|**-26.5**|**-14.1**|
|<br>Net Private Debt|1.5|-14.6|
|Reserves|31.8|18.3|
|**Net Debt**|**33.3**|**3.7**|

Variables are expressed as a percentage of GDP. Net Private Debt equals non-reserve debt assets minus debt liabilities. Source: Authors’ calculations, based on dataset constructed by Lane and Milesi-Ferretti (2006).
with both weighted and unweighted measures. That is also the case with the optimal HP filter-albeit significance levels are lower with weighted terms of trade. With the nonparametric filter, the dummy variable is significant (at a 5 percent level) with unweighted terms of trade and not significant (although with the correct sign) when the terms of trade weighted by the share of exports in output are used. Thus, the overall conclusion from the regression results presented here is that favorable movements in the permanent component of the terms of trade tend to have the asymmetric effect hypothesized earlier on private savings. The use of a series of control variables, weighted and unweighted terms of trade, three different detrending techniques, and two different ways of measuring "favorable" disturbances give some degree of robustness to the results.

# **5** Concluding Remarks

The purpose of this paper has been to examine whether terms-of-trade shocks have an asymmetric effect on private savings. The first part used a simple three-period framework to argue that, in the presence of binding borrowing constraints in bad states of nature, savings rates can be sensitive to favorable movements in the permanent component of the terms of trade-in contrast to what the conventional consumption-smoothing framework would predict. Households in developing countries (particularly those that have limited creditworthiness to begin with) may indeed be unable to smooth consumption in the face of adverse shocks to world commodity prices and the terms of trade, because they are subject to credit constraints that become more binding in such situations. As a result, to maintain a smooth consumption path, domestic agents may be forced to dissave by a larger amount than they would otherwise when faced with a significant deterioration in their terms of trade. This argument also suggests that, to the extent that domestic agents internalize the possibility of facing tighter credit constraints in bad states of nature, they may also consume less and save more in good times.

The second part described the econometric methodology and the empirical specification of the model, which controls for various standard determinants of private savings. The third part presented and discussed the regression results, based on panel data for non-oil commodity exporters of sub-Saharan Africa covering the period 1980-96. Overall, they suggest that transitory movements in the terms of trade have a positive effect (albeit less 16