rafmacalaba/datause-encoder-data huggingface dataset:
# Appendix Table 2a: Sizes of industries, 2000

|||Or|ganized sec|tor|Un|organized se|ctor|
|---|---|---|---|---|---|---|---|
|NIC|IndustryDescription|Plants|Empl|Output|Plants|Empl|Output|
|15|Food products and beverages<br>|21.4|1,261.4|1,234,459|1,743.6|4,622.3|392,886|
|16|Tobacco products<br>|2.2|475.2|103,597|169.7|485.8|16,820|
|17|Textiles<br>|12.3|1,245.9|773,018|724.5|2,034.3|149,788|
|18|Wearing apparel; dressing and dyeing<br>of fur<br>|2.8|329.2|141,507|2,018.0|3,257.1|93,687|
|19|Leather; luggage, handbags, saddlery,<br>harness and footwear<br>|2.2|135.7|88,793|130.5|287.3|22,159|
|20|Wood and wood products, except<br>furniture; straw and plating<br>|2.7|45.6|18,731|1,226.7|2,646.0|76,124|
|21|Paper and paper products<br>|3.2|176.1|178,617|59.6|189.9|17,998|
|22|Publishing, printing and reproduction of<br>recorded media<br>|3.0|116.7|53,516|109.4|382.3|36,042|
|23|Coke, refined petroleum and nuclear<br>fuel<br>|0.8|66.7|771,868|5.6|19.0|2,807|
|24|Chemicals and chemical products<br>|9.9|779.8|1,439,134|38.8|198.9|40,877|
|25|Rubber and plastic products<br>|6.4|251.1|258,618|50.5|216.3|42,320|
|26|Other non-metallic mineral products<br>|10.5|428.3|288,191|623.9|2,562.2|116,946|
|27|Basic metals<br>|6.5|551.0|820,940|15.5|73.1|28,293|
|28|Fabricated metal products, except<br>machinery and equipments<br>|7.9|292.6|181,534|282.1|797.0|80,883|
|29|Machinery and equipment, n.e.c.<br>|8.9|422.9|373,526|52.5|224.1|43,967|
|30|Office, accounting and computing<br>machinery<br>|0.2|17.1|36,757|0.2|0.9|309|
|31|Electrical machinery and apparatus,<br>n.e.c.<br>|3.7|229.0|237,760|35.8|183.6|283,943|
|32|Radio, television, and communication<br>equipment and apparatus<br>|1.0|109.1|166,577|4.0|25.7|2,995|
|33|Medical, precision and optical<br>instruments, watches and clocks<br>|0.9|58.8|42,137|4.7|19.3|3,511|
|34|Motor vehicles, trailers and semi-<br>trailers<br>|2.5|257.3|389,512|9.3|45.9|9,931|
|35|Other transport equipment<br>|1.8|182.7|200,111|8.3|38.2|7,273|
|36|Furniture, manufacturing n.e.c.<br>|2.1|119.3|105,705|435.0|1,078.1|90,704|
||Traditional|70.3|4,626.0|3,167,666|7,523.1|18,342.3|1,094,037|
||Modern|42.8|2,925.5|4,736,939|225.1|1,045.0|466,225|

Notes: See Table 3a. Plants and employments are expressed in thousands. Output is expressed in millions of rupees. "n.e.c." stands for Not Elsewhere Classified.
# **Table 2: Drivers of local growth in the literature and results in India’s case**

|**Bucket**|**Indicator**|**Results**<br> <br>||
|---|---|---|---|
|||**Significantly negative**<br>**Small or inconclusive**|**Significantly positive**|
||Temperature|Sridhar (2010)<br>Tripathi (2013)||
|**Geography**|Precipitation|Ghate and Wright (2013)||
||Elevation|Considered in the literature but not in India|’s case|
||Urbanization rate|Tripathi (2013)|Ghate and Wright (2013)<br>Das, Ghate and Robertson (2015)|
||Population size or growth|Ghate and Wright (2013)|Tripathi (2013)<br>Abhishek, Jenamani and Mahanty<br>(2017)|
|**Urbanization**|Population density|Tripathi (2013)||
||State capital|Tripathi (2013)|Abhishek, Jenamani and Mahanty<br>(2017)|
||Governance quality|Cidd i th litt bt t i Idi|’|
||Revenue, expenditure or debt|onsere n e eraure u no n n|s case|
||Nearby economic activity|Das, Ghate and Robertson (2015)||
|**Market access**|Distance to large cities|Sridhar (2010)<br>Das, Ghate and Robertson (2015)<br>Abhishek, Jenamani and Mahanty<br>(2017)<br>Tripathi (2013)||
||Landlocked|Ghate and Wright (2013)||
||Riverbank or seaport city|Tripathi (2013)||

(Continued) 6
bor demand. The estimated elasticity of substitution, 3.7, is much lower than is typically assumed in papers using US data, see Ottaviano and Peri (2012). All that is required for immigration to result in a negative demand shock is that the elasticity of labor demand be smaller. For workers with lower and upper secondary education the scale effect outweighs the substitution effect, while the magnitude of these parameters is near identical for those with education beyond secondary school.<sup>38</sup> The implied wage elasticities (at the country-level) are such that a 10 percent increase in immigrants - equivalent to an around 1 percent increase in the labor force - will decrease wages for natives with at most primary education by 0.71 percent, increase the wage of those with lower secondary education by 0.38 percent, and those with completed secondary school education by 0.26 percent. The impact of immigration on wages of highly educated workers is negligible. Whether immigration affects inequality in the U.S. is a topic of continued debate. For Malaysia we find strong evidence that it does. It is worth emphasizing that it is not the natives with the highest education levels who benefit most from low-skilled immigration, but rather those just a little more educated than the immigrants. This is consistent with the idea that immigrants are engaged in tasks complementary to the skills of natives with a little more education, but that are less related to those performed by highly educated natives (see Ottaviano, Peri and Wright, 2012; and Peri and Sparber, 2009).

> 38The parameter estimates vary less across education groups than we might have expected. In part this is a result of assuming immigrants are homogeneous, thereby constraining our estimates of the elasticity of substitution. It also reflects the fact that even small variations in these parameters results in significant variation in the implied wage and employment effects.

43
Table A3: Summary statistics for the variable used (2009‐2016), continued

|**Variable**|**Observations**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|**Myanmar**||||||
|Index * ln(D/L)|1,022|‐1.26|0.35|‐2.16|0.22|
|h1|1,041|0.17|0.38|0.00|1.00|
|h5|1,040|0.26|0.44|0.00|1.00|
|e6|522|0.07|0.25|0.00|1.00|
|h8|1,019|0.02|0.15|0.00|1.00|
|**The Philippines**||||||
|Index * ln(D/L)|2,329|‐0.66|0.30|‐1.34|0.31|
|h1|1,157|0.22|0.41|0.00|1.00|
|h5|1,124|0.44|0.50|0.00|1.00|
|e6|1,624|0.18|0.39|0.00|1.00|
|h8|1,143|0.22|0.41|0.00|1.00|
|**Thailand**||||||
|Index * ln(D/L)|835|‐0.93|0.33|‐1.60|0.18|
|h1|773|0.08|0.26|0.00|1.00|
|h5|754|0.18|0.39|0.00|1.00|
|e6|498|0.12|0.33|0.00|1.00|
|h8|797|0.04|0.18|0.00|1.00|
|**Vietnam**||||||
|Index * ln(D/L)|1,741|‐1.34|0.70|‐3.10|0.27|
|h1|860|0.24|0.43|0.00|1.00|
|h5|831|0.44|0.50|0.00|1.00|
|e6|1,160|0.11|0.31|0.00|1.00|
|h8|862|0.22|0.41|0.00|1.00|

33
responses are around 1 percentage point and the peak responses are somewhere around 2 to 3 percentage points for all identification methods using annual data (panels A and C). When we use the quarterly data (panel B), the response to the inflation shock identified by the residual from an estimated Philips curve is smaller (the initial response is around 0.5 percentage points and the peak response is around 1 percentage point)<sup>12</sup> . Compared with the results of the simulation approach, these responses are generally larger and more persistent. This may imply that some secondary effects through real GDP or primary balance are captured in the estimation approach but not in the simulation approach. Another possibility is that the shocks identified in the estimation approach have somewhat persistent effects while the shock in the simulation approach is purely temporary.

Regarding the responses to the high inflation and deflation shocks identified by certain thresholds (the third identification method), we can see clear asymmetry between the impacts of inflation and deflation shocks and the latter is much larger than the former (panel D). This is probably because the downward adjustment of nominal interest rates is more sluggish (downward rigidity) in response to a deflation shock.

# **C.   Robustness checks**

Following the literature on the local projection, we examine two types of robustness checks:(1) controlling the lead variables and (2) dealing with “Nickel bias” for short time-series dimension.

**(1) Controlling the lead variables.** According to Teulings and Zubanov (2014), the local projection without controlling for the shocks in the regressors between periods t and t+h when estimating the impulse response at horizon h could bias the impulse response. For the robustness check, we add the lead variable for the inflation shock to the baseline specification.

Figure 4.2 shows the results, which are broadly in line with the baseline results.

**(2) Nickel bias for annual data** **_._** Nickell (1981) points out that the presence of lagged dependent variables in panel estimations would lead to bias if the serial correlation of the dependent variables is high and the time-series dimension is short. While the time-series dimensions of quarterly data are relatively larger than the cross-sectional dimension (N=19), those of annual data are shorter. Hence, as a robustness check, we exclude the fixed-effects term when we use the annual data.

Figure 4.3 shows the results, which are in line with the baseline results for the initial impact although the long-run impacts are larger.

> 12 The coefficient on the inflation shock tends to be smaller in the case using quarterly data because the quarterly inflation shocks identified by the residual tend to be larger than the annual inflation shocks while the size of the debtto-GDP ratio is the same between the cases using quarterly and annual data.

10
Policy Research Working Paper 10715

# **Abstract**

Environmental degradation is the largest public health challenge of the century and is likely to be exacerbated by climate change. This study undertakes a comprehensive examination of the health implications of environmental hazards in Cambodia, simultaneously addressing extreme temperatures, precipitation patterns, and air pollution. It leverages data from the Demographic and Health Surveys and satellite-derived metrics on temperature, precipitation, and fine particulate matter. The analysis identifies a positive association between temperature and the occurrence of diarrhea and cough among children and a nonlinear relationship between precipitation and these health outcomes. Furthermore, the study demonstrates that pollution significantly impacts cough incidence. To anticipate future trends, climate simulations are employed to forecast the incidence of child diarrhea in Cambodia under different climate and development scenarios. The projections indicate that diarrhea incidence could increase to 19 percent by 2040 without significant adaptation measures that would lessen the adverse impact of weather. For instance, the acceleration in toilet ownership over the last decade reduced diarrhea incidence by at least 1.2 to 1.4 percentage points. Nevertheless, the path ahead requires proactive efforts to improve sanitation and hygiene. The forecasts suggest that, without additional strategies to counter climate change’s adverse effects, only universal toilet ownership would contain the climate-driven increase in diarrhea incidence expected by 2040.

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 sbaquie@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
respondents and a set of covariates ( _X_ , including some suitable non-linear function of income) and postulates a regression model of the form:

> where the error term,  , is uncorrelated with _X_ . If using ordinal data from a ladder question then an ordered response estimator is typically used, such as the ordered probit, in which case (9) is the underlying continuous variable generating the ordinal responses relative to thresholds that are taken to be fixed. If instead one is using the MIQ, then<sup>_y_</sup> _i_ min is the dependent variable.

This section provides an overview of some of the lessons to date in estimating such a model. The next section will discuss a number of issues in estimating such a regression.

Recall that one of the conditions for existence of a SSPL is that subjective welfare is an increasing function of income. This is invariably indicated in cross-sectional studies; indeed, I have not seen an exception in the literature though some studies find stronger income effects than others.<sup>18</sup> Stevenson and Wolfers (2008) argue that the income effect on subjective wellbeing is robust across countries, within countries, and over time.

There have been some estimates of SSPLs.<sup>19</sup> Interestingly, the estimates to date suggest that the overall poverty rate based on the SSPL is roughly similar to that implied by objective poverty lines.<sup>20</sup> It may well be that the choice of parameters in the ―objective‖ absolute lines already approximated the expected SSPL in the specific context.

However, the structure of the poverty profile has turned out to be different in some respects. While objective poverty lines often imply that larger households are poorer, this is not typically the case in cross-sectional studies using the subjective approach, which tends to suggest greater economies of scale in consumption than normally assumed, at least in developing countries. For example, in using the ELQ to test the welfare consistency of prevailing objective

> 18 Clark et al. (2008) review the literature on the income effect on subjective welfare.

> 19 I focus on application to developing countries. The applications to date include Pradhan and Ravallion (2000) using data for Jamaica and Nepal, Ferrer-i-Carbonell and Van Praag (2001), for Russia, Taddesse and Shimeles (2005) for Ethiopia, Gustafsson (2004) for urban China, Lokshin et al. (2006) for Madagascar, Bishop and Luo (2006) for urban China and Carletto and Zezza (2006) for Albania.

> 20 An exception to this finding is reported for the US by de Vos and Garner (1991), where the SSPL is well above the prevailing (absolute) line, though the US line has not been updated in real terms since the 1960s; a more current absolute line for the US would probably be closer to the SSPL.

11
# **1 Introduction**

A growing literature has focused on the misallocation of resources in order to answer why some countries are poorer and have lower productivity levels.<sup>1,2</sup> Guner _et al._ (2008) identify size-dependent policies as one of the direct sources of misallocation. The size-dependent policies favor small establishments and levy higher regulations or taxes on bigger establishments. In this paper, I study the relationship between size-dependent distortions and economic development in the presence of bribery opportunities.

I define bribery as a transfer from a private party to government officials in order to ‘get things done’. These ‘things’ can include acquiring valuable licenses and permits to operate or avoiding taxes. In this paper, I ask and quantitatively answer the following questions: What is the inferred magnitude of distortions when a model is disciplined to account for the plant size distribution and bribery data? What are the aggregate consequences of an increase in the size dependency of the distortions under the presence of bribery opportunities? Finally, how large are the possible gains from removing the distortions with and without the bribery opportunities? These questions have not been answered by the previous literature, even though bribery is prevalent among developing countries. Answers to these questions provide a better understanding of the relationship between bribery, plant size distribution, GDP per capita, and the effects of various policies, such as removing the bribery mechanism and distortions in an economy.

First, I document the facts related to bribery and the plant size distribution in a typical developing country, Türkiye. Choosing this country does not imply particularly large levels of bribery and size dependent distortions compared to other countries. I use Turkish data as an example because the bribery spending data for different size groups as well as the plant size distribution data are available for Türkiye. Using representative, plant-level data from the World Bank’s Enterprise Surveys (WBES), I show that while bigger enterprises pay higher bribes, they spend a lower fraction of their value added (VA) on bribery. More specifically, 6.8% of big plants and 8.7% of small plants<sup>3</sup> in Türkiye experienced a bribe request in 2013. However, big enterprises spent 5.2% of their VA on bribery, whereas small enterprises spent 18.3% of their VA on bribery. Moreover, according to the Annual Industry and Services Statistics of Türkiye, 99.6% of all plants are small. Although big plants constitute only 0.45% of the total plants, they account for 43.6% of all formal employment.

> 1See Restuccia & Rogerson (2008), Guner _et al._ (2008) and Hsieh & Klenow (2009).

> 2See Restuccia & Rogerson (2017) for a survey of the literature.

> 3Big plants refer to the plants that employ more than 100 workers and small plants refer to the plants with fewer than 100 employees.

1
governing individual interaction, and are also influenced by values and beliefs; for example, Harrison and Huntington (2000) discuss the link between formal institutions and culture. In the long run, culture influences the evolution of institutions (North 1990). However, political actions also can influence culture through institutions, making culture and institutions difficult to disentangle. Formal institutions are readily observable, and provide some insight into culture, while informal rules are more difficult to observe. Indeed, social norms and institutions can be in harmony, or in direct conflict with each other.

Fernandez (2008) outlines three different empirical approaches to the study of the role of culture in economic outcomes. The first of these is survey-based, and uses country-level economic indicators to examine their correlation with beliefs and values (from, e.g., the World Values Survey). The difficulty with this approach has to do with reverse causality, which is mitigated by using an instrumental variables technique (see also Guiso et al. 2003; 2005; 2006; Tabellini 2005). The second approach is what she terms the “epidemiological” approach, where outcomes of immigrants are compared to natives in a host country. The major strength of such an approach is that it holds institutional factors constant and only allows norms and values of the country of origin to vary, which then allows causal relationships between culture and behavior to be isolated and identified. The major drawback of such an approach is that the intrinsic factors are not perfectly transmitted. That is, (1) immigrants may not be a representative sample of their population, and (2) intrinsic factors may change as a result of the migration. The third approach utilizes historical case studies as „natural experiments‟ (e.g., Greif 1994; Nunn and Wantchekon 2011). The drawback of these studies is their limited generalizability.

Experiments help address the limitations of the methods used above. To reiterate the discussion above, the primary benefits of experiments are isolation of variables, identification of causal mechanisms, test-bedding alternative policy proposals, and replication. For the study of corruption, the unique benefit of experimentation is that formal institutions can be implemented in the lab, with the informal aspects of culture to left to vary independently. The major drawback is that results in the lab environment and with lab-created institutions may not fully translate into field settings.

The relationship between cultural attributes and behavior in simple experimental games has been explored in several studies. In one series of studies, adult subjects are recruited in villages in a number of small-scale societies, and typically play several games designed to gauge 6
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