rafmacalaba/datause-encoder-data huggingface dataset:
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Goldberg, P. and N. Pavcnik (2003); “The response of the informal sector to trade liberalization,” _Journal of Development Economics_ 72:463– 496.

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Jovanovic, B. (1982);.”Selection and The Evolution of Industry,” _Econometrica_ , 50(3): 649-670.

28
commune to look at the cross‐sectional association between past military herbicide exposure and current agricultural herbicide/pesticide, coal, firewood, cigarette and alcohol expenditures. Unfortunately, the overlap between VHLSS and VNHS communes is not significant enough to combine these two datasets, and explicitly control for these variables in the regressions.

To control for environmental confounders, this dataset is further combined with data on daily sunshine information for the period 1975‐1999. Sunshine data were provided by the Meteorological Data Center (MDC), an institution under the Ministry of Natural Resources and Environment of Vietnam. The data were collected on a daily basis for the period 1975‐2006 from 108 weather stations spread across the country. Each commune in VNHS was assigned the closest weather station and hours of daily sunshine were averaged over the period 1975‐ 1999.

# **3.2. Empirical methodology**

To look at the determinants of reporting cancer, we estimate the following multivariate logistic model: where ���� is the probability that individual � in household � living in commune � reports suffering from some form of cancer in 2001‐2002, ����� is the measure of wartime herbicide exposure for commune � , and ���� is a vector of individual demographic and socio‐economic characteristics such as age (and age squared) and schooling, household‐level socio‐economic status (per capita consumption), and community‐level location (urban or rural area), sunlight exposure (commune‐level 1975‐1999 average daily hours of sunshine) and wartime bombing intensity (bombing load); ���� is the error term that is assumed to be potentially characterized by heteroskedasticity and spatial auto‐correlation. Robust standard errors are computed accordingly, clustered at the provincial level in order to account for potential spatial correlation.<sup>5</sup> Estimations are moreover weighted using population weights provided in the dataset to account for study design.

Effects of exposure for cohorts born respectively before and after the end of herbicide application campaigns are estimated jointly by adding interaction terms (exposure index

> 5 As of 1999, Vietnam was divided into 8926 communes, grouped into 61 provinces.

9
# **<mark>Table A7 – Impact of FD Law on Bank Lending to Firms (by Their Dependence to Government Spending) Given State</mark>**

# **Public Debt**

This table reports OLS estimates of the impact of the FD Law on bank lending to private firms from sectors with different dependence on government spending and across states of varying ex ante public indebtedness. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _GovernmentExposurei_ measures the dependence of firms from industry _i_ to government spending (following Belo, Gala, and Li, 2013), and is calculated as the share of revenues in industry _i_ derived from sales to the government (or to its providers). _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.

||Volum|ef,b,m|Investm|entf,b,m|Working|Capitalf,b,m|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Postm*DebtStates,16Q1|0.028***||0.092***||0.028***||
||(0.006)||(0.016)||(0.007)||
|Postm*GovernmentExposurei|0.049***|-0.025***|0.081*|0.106***|0.018|-0.047***|
||(0.013)|(0.006)|(0.042)|(0.023)|(0.014)|(0.007)|
|Postm*DebtStates,16Q1*GovernmentExposurei|-0.020***|-0.018**|-0.145***|-0.135***|-0.013*|-0.009|
||(0.007)|(0.008)|(0.024)|(0.024)|(0.007)|(0.007)|
|Observations|824,687|824,687|130,532|130,574|747,875|747,875|
|R-squared|0.80|0.80|0.870|0.871|0.800|0.800|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|State-Month FE|No|Yes|No|Yes|No|Yes|

72
# **Too Small to Be Beautiful?**

**The Farm Size and Productivity Relationship in Bangladesh**<sup>1</sup>

## **Madhur Gautam and Mansur Ahmed**

_Keywords_ : Bangladesh, agriculture, productivity, stochastic frontier, technical change. _JEL codes:_ D24, H21, Q15, Q16, Q18.

1 The authors are grateful to Mahabub Hossain for providing access to the panel survey data used in this study. The authors also want to acknowledge the constructive comments and suggestions provided on an earlier draft of this paper by Keith Fuglie, Nicholas Rada, and two anonymous reviewers. The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work.

A modified version of this paper has been accepted for publication in _Food Policy_ .
# **3.3 Panel regression analysis**

Lastly, we estimate a dynamic model to understand the effects of various economic factors on the level of transport-related CO2 emissions. We assume current level of per capita emissions depends on the level of per capita emissions in the previous year, that is, transport activities respond to changes in economic factors with some lags. The model is specified as follows: C 0 1C 2G G 3G G 4 5 + 6 iitt 7B ii,tt−1 iitt (5) iitt iitt iitt 2GGCC = ββ + ββ 2GGCC + ββ CC + ββ CC2 + ββ GGDDeeDDeeTT + ββ GGTTDDT DDllee iitt iitt iitt ii tt iitt Where ββ UUffUUTTllC + + ββ is the natural logarithm of per capita CO2 emissions in country TT + γγXX + αα + λλ + εε _i_ in year _t_ . _GDPPCit_ is per capita GDP. _GDPPC2_ iitt is the square term of _GDPPC_ . _Diesel_ and _Gasoline_ are diesel and gasoline prices, 2GGCC respectively. _Urban_ is the percentage of the total population living in urban areas. _BRT_ represents the number of Bus Rapid Transit (BRT) systems a country has. is a vector of variables, including the valueadded share of agriculture, manufacturing, and service sectors. iitt is an unobserved country fixed effect, XX which includes country-specific characteristics that are fixed over time, such as culture, climate zone, and ii αα government regulation. We also include year fixed effects, , to control for common cyclical components such as a common technology shock. is an idiosyncratic error term. tt 0 7 are parameters to λλ be estimated. We opted for a relatively modest set of explanatory variables because due to limited data iitt εε ββ − ββ TTllaa γγ availability the inclusion of some further explanatory variables would have substantially cut the sample size.

# **4. Data**

Data used in this study come from several sources. The CO2 emissions and energy consumption data are from International Energy Agency (IEA) Fuel Combustion Statistics database and World Energy Statistics and Balances database, respectively. The databases report CO2 emissions and final energy consumption due to fuel combustion of transportation activities at the country level. The transportation sector is further divided into road, rail, domestic navigation, domestic aviation, and pipeline transport. The study period is from 1990 to 2018. The full sample contains 145 countries for which data are available. In 2018, the full sample of countries contributed to 84 percent of global transport carbon emissions. International aviation and marine bunkers account for the remaining 16 percent. The list of countries included in the study is presented in Table A1 in the appendix. We obtain PPP-based GDP (based on 2015 PPP rates) and population data from IEA.

9
6 development tends to grow faster. Section 5 concludes.

2. **Indicators** of Financial Policy and Financial Development rhis section reviews the quantitative measures used to capture financial policy distortions ae well as indicators of financial market development. Policies that distort or repress financial markets typically include both price and quantity reaLrictions (see World Development Report (1989) for a recent review]. Thy include real interest rate, commercial banks, reserves with the central bank, and other measures that influence the distribution of credit between the public and the private sector. The effects of negative and artificially low real interest rates on the behavior and development of financial markets have been well documented in the literature. Policies aimed at increasing the commercial banks' reserves with the central bank, whicn can act as an implicit tax on deposits when reserves earn a rate below that offered by the market, have been relatively less emphasized. Indicators of the financial market development include the structure of the financial system, the depth and size of the financial market and the extent of financial intermediatic'n. The structure of the financial system is proxied in this paper by the ratio of central bank assets to total assets of the financial system. It is expected that the ratio of the central bank's assets relative to the assets of the financial system would be smaller in more developed financial markets; this ratio would be inversely related to the importance of other financial institutions (e.g. deposit banks) in the system.4 Fifty two developing countries, for which data are available for the period 1965-89, were included in the sample.' Developed countries were not included as this might accentuate the restriction that economic structures and institutions are similar across the developed and the developing 4 See Appendix 2 on mean and standard deviation of financial variables.

s Countries included in t's sample are listed in Appendix III; they are listed in alphabetical order in the data set.
27 Sunra-national nrivate linkaaes. As Tables 11 and 12 show, among the firms sampled, over 90% of export linkages came via private channels -- both at entry and currently, and many (perhaps most) of the contacts took place abroad. Within the general dominance of private channels, identification by foreign buyers and agents ranks somewhat higher than direct efforts by firms, both initially and subsequently. (Table 12) The score for foreign buyers and agents rises as firms mature (a pattern observed also in the other cases), further confirming the importance to firms of building a reputation in the eyes of foreign buyers. The tables also reveal a striking difference between non-pribumi firms and the (small number of) pribumi firms in the importance they ascribe to direct efforts to contact buyers -- 3.6-3.8 for non-pribumi firms as compared with 1.2-1.7 for their pribumi counterparts. A plausible interpretatiaof these differences is that the availability of connections abroad provides non-pribumi entrepreneurs with a starting point for their direct efforts; but lacking such connections, pribumis perceive themselves as locked out of the international marketplace.

Sunbcontracting. The field-surveys uncovered clear evidence that pribumi firms, lacking the international connections that were important for the -export marketing efforts of non-pribumi firms, used subcontracting as a substitute channel of export marketing. Sale of the six pribumi in our sample got started as subcontractors, and subcontracting received the highest of all average scores (3.7) assigned by pribumi firms to the various channels for entry into export markets (Table 12). By contrast, only 2 of 28 non-pribumi firms got their export start as subcontractors, and the average score of 2.3 These differences remain when controlling for the size of firm.
# **References in Supplementary Appendix**

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Bussolo, Maurizio, Olivier Godard, Jann Lay, and Rainer Thiele. 2006. _The Impact of Commodity Price Changes on Rural Households: The Case of Coffee in Uganda_ . Policy Research Working Paper 4088. Washington: World Bank.

Buys, Piet. 2006. _Road Network Upgrading and Overland Trade Expansion in Sub-Saharan Africa_ . Policy Research Working Paper 4097. Washington: World Bank.

de Jager, A., D. Onduru, and C. Walaga. 2003. “Facilitated Learning in Soil Fertility Management: Assessing Potentials of Low-External-Input Technologies in East African Farming Systems.” _Agricultural Systems_ . 79, 2: 205-223.

Eberhardt, M. 2006. “Technical Progress and Structural Change as Sources of Productivity Growth: Some Macroeconomic Evidence.” MPhil thesis, Department of Economics, University of Oxford.

Henao, Julio, and Carlos Baanante. 2006. _Agricultural Production and Soil Nutrient Mining in Africa: Implications for Resource Conservation and Policy Development_ . Muscle Shoals, Ala.: International Center for Soil Fertility and Agricultural Development.

Kaizzi, Crammer K. 2002. “The Potential Benefit of Green Manures and Inorganic Fertilizers in Cereal Production on Contrasting Soils in Eastern Uganda.” PhD, thesis, University of Bonn and German Center for Development Research (ZEF).

Kappel, Robert, Jann Lay, and Susan Steiner. 2005. “Uganda: No More Pro-Poor Growth?” _Development Policy Review_ 23, 1: 27–53.

Matsumoto, Tomoyama, and Takashi Yamano. 2009. _Soil Fertility, Fertilizer, and the Maize Green Revolution in East Africa_ . Policy Research Working Paper 5158. Washington, DC: World Bank.

Milner, Chris, Oliver Morrissey, and Nichodemus Rudaheranwa. 2000. “Policy and Non-Policy Barriers to Trade and Implicit Taxation of Exports in Uganda.” _The Journal of Development Studies_ 37, 2: 67-90.

Musinguzi, Polycarp, and Peter Smith. 2000. _Saving and Borrowing in Rural Uganda_ . Department of Economics, Discussion Papers in Economics and Econometrics. Southampton, U.K.: University of Southampton.

Nandwa, S. M. and Mateete A. Bekunda. 1998. “Research on Nutrient Flows and Balances in East and Southern Africa: State-of-the-Art.” _Agriculture, Ecosystems and Environment_ 71: 5–18.

Nkonya, Ephraim, Crammer Kaizzi, and John Pender. 2005. “Determinants of Nutrient Balances in a Maize Farming System in Eastern Uganda.” _Agricultural Systems_ 85: 155–82.

Okidi, John A., Sarah Ssewanyana, Lawrence Bategeka, and Fred Muhumuza. 2005. _Operationalizing Pro-Poor Growth_ . Kampala: Economic Policy Research Centre.

63
teachers show for the official teaching schedule. The influential Public Report on Basic Education in India (PROBE Team, 1999, p. 63), for example, included the following typical vignette: _When the investigators reached the primary school in Jotri Peepal (Bharatpur, Rajasthan) shortly after noon, no teacher was in sight. One teacher, who had apparently left for lunch, soon appeared. He said that the school actually had three teachers, but that the headmaster and another teacher had gone elsewhere on official duty._ _The villagers contradicted this story. They said that the two absconding teachers did not turn up at all. The only one who did was the one the investigators had met . . . . He too was highly irregular and opened the school at will._ There is no indication in this story that the villagers viewed themselves as having made an implicit bargain with the absent teachers. Instead, they clearly felt cheated: here were three teachers with job security and relatively good wages who did not “turn up” at school as they were supposed to, and so children were left untaught.

Moreover, if absence reflected an implicit contract, the efficient way to implement it would be to have both teachers and students absent on the same days. For example, if teachers regularly stayed away from school on Fridays, parents could recognize this pattern and keep their children home on those days – in the same way that during harvest season, schools close in rural areas in some countries so that both teachers and students can help with the harvest. But in fact, we tend to see no such pattern. In countries that we have investigated, absences do not tend to be heavily concentrated on particular days of the week, and days of the week are often not even significant predictors of absence in a statistical sense.

# **Why are teachers absent?**

The short answer to this question is that we do not know for certain, in part because we lack independently verified information with which to answer this question. When we ask head teachers or the teachers themselves, we obtain answers that vary substantially from setting to setting. However, one consistent pattern was that two common explanations – illness and “other official duties” – appear to explain only a small percentage of absences, even if we take the head teachers’ statements at face value. Overall, only about 10 percent of absences were attributed to illness in the multi‐country study (Chaudhury _et al_ , 2006), although there is some evidence that illness is more important as an explanation in Zambia, where the HIV/AIDS epidemic has hit especially hard (Das _et al_ , 2007). And “other official duties” that cause teachers to miss school, such as election monitoring and public health campaigns, appear to be a minor reason for absence even in India, where they
Table 1. Population and Arable Land Resources of Eastern Europe and the USSR, 1988

||||Population|||||
|---|---|---|---|---|---|---|---|
||Total||Econ|omically activ|e|Arable l|and|
||(millions)|% of<br>world<br>total|Total<br>(millions)|In<br>agriculture<br>(millions)|Agric. as<br>% of total|('000 ha)|%of<br>world<br>total|
|Bulgaria|8.99|0.18|4.50|0.60|13.30|3,825.00|0.30|
|Czechoslov.|15.62|0.31|8.20|0.80|9.80|5,000.00|0.40|
|GDR|16.67|0.33|9.50|0.80|8.40|4,694.00|0.30|
|Hungary|10.60|0.21|5.20|0.70|13.50|5,048.00|0.40|
|Poland|37.86|0.74|19.30|4.30|22.30|14,480.00|1.10|
|Romania|23.05|0.45|11.60|2.60|22.40|10,080.00|0.70|
|EEUR/6|112.79|2.22|58.30|9.80|16.80|43,127.00|3.20|
|USSR|283.68|5.55|143.60|20.40|14.20|228,200.00|16.60|
|EEUR+USSR|396.47|7.77|201.90|30.20|15.00|271,327.00|19.80|
|World|5,112.00|100.00|2,285.00|1,083.00|47.40|1,373,200.00|100.00|

_Source: FAO Production Yearbook 1988,_ Rome, 1989; _Hungarian Statistical Pocket Book, 1989,_ Central Statistical Office (KSH), Budapest, 1990.
2

# **I Introduction**

What are the gains from a better education, more land ownership, or a different occupation in Bangladesh? Do the gains differ in urban and rural areas? Have they remained stable over time? Do household size, family structure, and gender matter for well-being? Do consumption, poverty, and inequality depend more on household characteristics, or on the characteristics of the areas in which they live? What are the micro determinants of growth in per capita consumption over time? While the answers to such questions have important policy implications, they have been missing because of a lack of access to nationally representative household data covering a period long enough to analyze trends over time. Using in a consistent way five comparable and successive national surveys, this paper analyzes at once the micro determinants (and changes thereof) of consumption, poverty, growth, and inequality in Bangladesh for the period 1983 to 1996. The analyzis of the determinants of consumption is carried in a straightforward way by regressing log per capita consumption on household and area characteristics, and by interpreting parameter estimates as the returns to these characteristics. Assuming normal errors, the parameter estimates can also be used to predict the probability of being poor. By running the same regressions for various survey years, one can easily decompose the growth in per capita consumption into the impact of changes in household characteristics, and the impact of changes in the returns to these characteristics. Finally, and this is a new idea, conditional between group inequality measures can be computed from the same regression estimates so as to assess the micro determinants of inequality. These conditional inequality measures avoid the pitfalls of standard group decompositions of the Gini index which, when applied to one a variable at a time such as education, cannot assess the impact of education on inequality without bias since they do not control for characteristics correlated with education.

The methodology is applied to five rounds of the nationally representative Household Expenditure Surveys (hereafter FES) of the Bangladesh Bureau of Statistics (hereafter BBS) spanning the years 1983 to 1996. Bangladesh is an important case study because of the extent of poverty there and the lack of research on inequality. For poverty, this paper follows up on work by Rahman and Haque