rafmacalaba/datause-encoder-data huggingface dataset:
The paper is organized as follows. In Section 2, we obtain firm TFP measures and describe briefly the trends in TFP across industries. In Section 3, we examine the determinants of firm TFP. Section 4 concludes.

# **_2. Obtaining Firm TFP Measures_**

## **_2.1 Data_**

Our analysis uses data from a firm survey conducted by the World Bank in Bangladesh between November 2004 and September 2005 in five industries: food, leather/footwear, pharmaceuticals, ready-made garments, and textiles. The sample used in the econometric estimation includes 575 firms, where each firm has at most 5 years of production data. Appendix Table 1 describes the composition of the sample. The majority of firms in the sample belong to the ready-made garments industry. This reflects the importance of the sub-sector in manufacturing in Bangladesh, but also the sample design.<sup>6</sup> Within ready-made garments, 86% of firms belong to the woven sub-sector, 21% belong to the knitwear sub-sector, and 13% belong to the sweater sub-sector. Most firms in our sample have more than 50 workers, although the size distribution varies significantly across industries.<sup>7</sup> About a quarter of the firms in the leather/footwear industry are small or medium, i.e., have less than 50 workers. Since our survey covers mostly firms with more than 50 workers, our findings are representative only for the segment of larger manufacturing firms in Bangladesh. Our sample is constituted of relatively young firms, i.e., more than 50% of firms are less than 10 years old while a third of the firms are 10 to 20 years old. About one-half of the firms are located in Dhaka while 17% are located in Chittagong (excluding the Export Processing Zones (EPZ)).

> 6 A sample of 700 firms composed of 350 firms in the ready-made garments industry and 350 firms in the other industries was drawn. Covering 10% of registered firms in the ready-made garments industry (350 firms) was a condition required for a World Bank (2005) study which financed the survey. Effectively, data was collected for 350 firms in the ready-made garments industry and 332 firms in other industries due to survey non-response for 18 firms. The final sample of 575 firms was obtained after the elimination of firms suffering from data problems, as described in the Appendix.

> 7 We adopt the size classification used in the Bangladesh Census of Manufacturing Industries (CMI): “small” firms have less than 10 workers, “medium” firms have between 10 and 50 workers, and “large” firms have more than 50 workers. We divide the large firm category into 3 additional sub-categories: “relatively large” firms have between 50 and 150 workers, “very large” firms have between 150 and 500 workers, and “extremely large” firms have more than 500 workers.

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affected more than others with large, central markets being more at risk. If certain assets can only be exchanged in such central markets, then they lose their purpose to serve as an efficient instrument of self-insurance. Second, certain assets are also prone to lose value at home compared to others. For example, livestock requires constant upkeep which not only includes fodder requirement which gets pressurized during a conflict but other specialized care like a veterinarian visit. In contrast, some assets like jewelry are more dormant and are more resistant to losing value at home. Finally, some assets might bear particular interest to conflict groups who might target these assets specifically. For instance, cattle raiding is a strategy often employed by many groups. All these reasons suggest that conflict drives a wedge in the decision-making behavior of households living in high-conflict provinces relative to their counterparts in low-conflict regions.

Our approach is to utilize the variation in conflict intensity across the Afghan provinces to consider whether the nature of precautionary wealth is affected by conflict. We measure conflict intensity using the data on the number of terrorist attacks which we source from the GTD. The database allows us to construct the total number of such events for each province from 1973 – 2014. Figure 4a shows the variation of conflict intensity across the provinces. The conflict situation is particularly grave in the southern provinces with more than 800 terrorist attacks being reported for Helmand and Kandahar over the entire period. The variation in intensity across provinces is stark, and many central and some northeastern provinces witness much less conflict activity. For instance, a total of three attacks have been reported in the northeastern province of Panjsher and a total of eight attacks in the central province of Daykundi over the four decades.

An alternative measure of conflict intensity will be to consider a period close to the survey year. We consider many smaller periods, not only limited to periods near the survey year, and find that the cross-sectional variation of conflict intensity remains stable across all these periods. For instance, in figure 4b we show the cross-sectional variation in attacks for the year 2012 which is the year preceding the survey period. We note that the variation of intensity remains similar to the figure on the left which is also confirmed with a high correlation coefficient of 0.92. In this light, we rely on the broad horizon and aggregate the total attacks over the four decades. This concludes our outline of the data, and we detail the elements of our formal analysis of these data in the next section.

# **3 Econometric Specification**

In the previous section, we discussed some details of the data that we use in our analysis. We also highlighted the heterogeneity in wealth accumulation, incomes, income uncertainty and 9
**IMPACT ESTIMATION OF DISASTERS: A GLOBAL AGGREGATE FOR 1960**

# YASUHIDE OKUYAMA2

_Graduate School of International Relations, International University of Japan, Niigata, Japan_

# SEBNEM SAHIN

_GFDRR - The World Bank, Washington, D.C._

> 1 This paper was prepared as a background paper to the joint World Bank - UN Assessment of the Economics of Disaster Risk Reduction. Funding from the Global Facility for Disaster Reduction and Recovery is gratefully acknowledged. The authors would like to express gratitude to Apurva Sanghi for his guidance, encouragement, and patience.  We also thank Reinhard Mechler of the IIASA for providing us the precious disaster data.

> 2 Contact author: Yasuhide Okuyama, International University of Japan, 777 Kokusai-cho, Minami-Uonuma, Niigata – 9497277, Japan, E-mail: okuyama@iuj.ac.jp, Tel: + 81 257 79 1424
i i 1 ∗log (I I i i (1) ii ii ii SSha aa = αα + BB IIaa ) + UU We include nation/year fixed effects in each regression and the regressions are population weighted. The data source for population is the World Development Indicators. In estimating equation (1), it is important to note that the within nation/year variation in durables ownership and the income at the specific quintiles allows us to estimate 1. The nation/year fixed effects control for nation specific durables prices. BB In Tables 2 and 3 we report our estimates and the lowest income category is the omitted category. Consider column (1) where we report the automobile ownership regression results. A doubling of personal real income increases the probability of owning a vehicle by .139*log(2) or 9.6 percentage points. This functional form embodies diminishing returns to scale and highlights that the growth in durables ownership is most affected by low-income nations growing richer. We find similar magnitude income effects for air conditioners and refrigerators, washing machines, cell phones, computers, and televisions.

# **The Environmental Implications of the Growth of Middle Class Consumption**

Rising electricity consumption does not result in more greenhouse gas emissions if a nation’s electricity grid is quite green. To measure each nation’s grid we use data from 2014 from the World Development Indicators database. We take U.S. emissions factors for coal fired power plants, natural gas and oil fired power plants and weight these by nation specific shares to yield the national emissions factor. In Table 4, the electricity grid emissions factor is measured in 1,000 pounds of carbon dioxide per MWh. The formula for this variable is based on 2.21*share of power from coal + 0.91*share of power from natural gas + 2.13 * share of power from oil. While these emissions factors are based on U.S. data, there is little reason to believe that they sharply vary across the world. We use the carbon emissions factors for coal, natural gas and oil to create a single index of a given nation’s grid carbon intensity. Table 4 shows that the largest nations in the world often feature a dirty grid and this means that the rising durables 9
- Shorrocks, A. F. (2013): “Decomposition Procedures for Distributional Analysis: A Unified Framework Based on the Shapley Value.” _Journal of Economic Inequality_ , Vol. 11, Issue 1, pp. 99-126.

- Subramaniam, Remesh, (1996): Gender-Bias in India: The Importance of Household FixedEffects. _Oxford Economic Papers_ , New Series, Vol. 48, No. 2, pp.280-299.

- Sudarshan, Ratna M. and Shrayana Bhattacharya (2009): _Through the Magnifying Glass: Women's Work and Labor Force Participation in Urban Delhi_ . Economic and Political Weekly, Vol. XLIV, No. 48, November 28.

- Thomas, Jayan Jose (2012): _India's Labor Market during the 2000s: Surveying the Changes_ . Economic and Political Weekly, Vol. XLVII, No. 51, December 22.

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become an Important instrument for audlt. If the taxpayer identification number is tied to other means of identification, such as a social security card, a driver's license or a passport, it also can be a potentially powerful means of tax enforcement. As noted above, the master file number is a potentially powerful aid to tax administration, but Is must be combined with political will and human organization to be effective.

b. Application to customs Cooputer technology can aid in customs administration to identify importers, clear goods, manage tariff files, control inventories and complle external trade statistics. **These** functions are usually divided into two categories: central processing and customs office functlons. At the central processing site, maintenance and compilation of the "Integrated tariff," and data centralization and !nformation services are the main functions. Automated ciearance requires the creation of a set of files (the integrated tariff) that would contain, for each customs tariff heading, all the data needed to verify that goods meet requirements (quotas, licenses, health regulations, etc.) and to calculate duties and taxes payable. The data centralization and informotion services of computer system are designed to produce external trade statistics, analyses of Import and export prices, and insurance and freight studles. Useful information, such as general
Thirty-nine percent of the women cite the reservation of the presidential seat as their primary motivation for running for office, 33 percent cite past work with the community as their motivation, and 28 percent cite ‘other’ reasons. There are no women presidents in unreserved GPs.

Tamil Nadu is a leader in women’s empowerment in India, and this is reflected by the high literacy rate for women: 64 percent, compared to the all-India female literacy rate of 54 percent.<sup>21</sup> The education level of female presidents in our sample is also very comparable to that of male presidents: 47 and 41 percent of male and female GP presidents, respectively, have at least a high school education, and 82 and 74 percent have at least a middle-school education. However, on a knowledge test<sup>22</sup> of the GP president’s duties and the panchayat system in general that was given to presidents during the individual interviews, female presidents scored much lower than their male counterparts (Figure 1). For example, only 28 percent of female presidents in women-reserved GPs answered at least 11 of the 19 questions correctly, compared to 90 percent of male presidents. Gender strongly predicts presidents’ test scores even after controlling for presidents’ age, whether this is their second term, education, income, caste, religion, political party affiliation, and motivation for running for office (Table 5). The correlation between gender and test scores is not only strongly significant but of large magnitude as well. Given such poor knowledge of the GP, we might expect the performance of female presidents in women-reserved GPs to suffer. In particular, we might expect to see fewer public goods provided by female presidents in women-reserved GPs because the GP president plays a key role in procuring public goods for the GP. This knowledge disparity is much more

> 21 These statistics come from the 2001 Census. 22

> The 19 questions on the knowledge test are given in Appendix A1.

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# **Table 7: Estimated costs of selected agricultural residues (corn stover and crops straws) delivered to a bio-refinery**<sup>15</sup>

|**Source**|**Feedstock**|**Estimat**|**ed cost**<sup>**a**</sup>|
|---|---|---|---|
|||$/ton|$/lt ethanol|
|Gallagher et al. (2003)|Corn stover|17.13-18.21|0.0591-0.063|
|Perlack & Turhollow (2003)||43.10-51.60|0.149-0.178|
|Petrolia (2008)||57-69<sup>b</sup>|0.188-0.224|
|Petrolia (2006)||38-43|0.131-0.148|
|Tokgoz et al. (2007)||76.00|0.262|
|Frederick et al. (2008)||54.67|0.189|
|Gallagher et al. (2003)|Winter wheat, continuous|20.16-28.04|0.070-0.097|
|Gallagher et al. (2003)|Winter wheat, fallow|38.18|0.132|
|Gallagher et al. (2003)|Spring wheat, continuous|24.17|0.083|
|Gallagher et al. (2003)|Sorghum|21.25-23.16|0.079-0.086|
|Gallagher et al. (2003)|Barley|21.78|0.070|
|Gallagher et al. (2003)|Oats|23.18|0.089|
|Gallagher et al.(2003)|Rice|25.21|0.090|

Source: Elaborated by authors. See Table 1 for yields used to convert $/ton to $/lt.<sup>a</sup> Inflation adjusted to 2008.<sup>b</sup> These numbers are for a 50 million gallon a year plant. Costs between $55 and $93 per ton were obtained by varying the plant size and the harvesting method.

Table 7 shows that crop residue costs range from $17 to $76 per ton delivered. This wide range reflects differences in items to include in the cost calculation, and their magnitudes. Among the factors differentiating estimates are different perspectives on the sizes of yields, distances to conversion facilities, and storage needs, as well as the margin garnered by the grower as return on investment in producing feedstock versus other uses of land. As an example, the estimate by Gallagher et al. (2003) includes only harvest, transport, and increased fertilizer costs. Feedstock acquisition, storage, and opportunity costs such as their feed value are not included here.<sup>16</sup> Tokgoz et al. (2007) assume significantly higher baling and transport cost, in addition to a margin of roughly $10 per ton garnered by farmers. Perlack and Turhollow (2003) include costs of collecting, 15 Sugarcane bagasse is an exception to the harvest cost calculations. The harvest, transport, and fertilizer replacement costs for bagasse are associated with the primary sugar crop. Thus, the incremental costs of harvesting and delivering bagasse to the processing plant are essentially zero.

16 On the other hand, opportunity costs such as hunting rights on areas with standing rice residues are included. According to the authors, the competition with feed uses is relevant in situations where crop residues are in limited availability. Higher opportunity costs (feed values, at about 48$/ton for corn stover) are included only if there is a need to bid the residues away from the livestock sector.

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al'. possible poverty lines will show an unambiguous decrease in aggregate poverty between the two dater This was found to hold for both urban and rural areas.

From Figure 1 we can also assess the sensitivity of this conclusion to possible underestimation of price increases facing the poor. The 1987 poverty line (in 1984 prices) which would be needed for the 1987 headcount index of poverty to equal that in 1a84 is Rp 12,818. Thus, an additional inflation rate of at least 16.5 percentage points (on top of the CPI based estimate of about 20 percent) would have been needed over the three years to reverse the conclusion that poverty has decreased by this measure.

Similarly, the true inflation rate would need to be about 14.1 points higher over the three years to equalize the headcount indices for the two dates at the higher poverty line. Thus the conclusion that poverty has decreased would be robust to even quite substantial measurement error in the CPI; the inflation rate would need to have been underestimated by at least 50 percent to reverse our conclusion.

A potentially important observation about the results in Figure 1 is that the poverty lines are found on a steep segment of the consumption distribution. This is illustrated more clearly by the density function of consumption given in Figure 2. (This is a non-pararatric estimate using a Gaussian kernel). The lower poverty line is very close to the mode, where the slope of the comsumption distribution function reaches its maximum. This has two implications of interest here: i) Estimates of the headcount index of poverty will be particularly sensitive to the exact location of the poverty line, as our comparison of the lower and upper poverty lines in Table 1 has suggested.

ii) Poverty levels will be very responsive to horizontal shifts in the distribution of consumption; indeed, when the poverty line is at the
their implications for spending in future years (e.g. decisions on staffing levels, associated non-salary costs, implied pension liabilities, procurement of equipment, and other sustaining costs). This implies that the security sector needs to be fully incorporated in the annual budget formulation process, subject to aggregate fiscal constraints and sector ceilings like any other sector. It also means that the security sector must be fully incorporated in mediumterm fiscal projections and planning. Finally, all government transactions in the security sector should be on-budget, including revenues earned directly by security sector entities.

Third and equally important is the need for **transparency and information** , without which decision makers cannot make informed choices with respect to the budget. Legitimate issues of confidentiality may constrain public dissemination of some information, but this should not prevent sufficient relevant information from being made available to decision makers, if necessary through devices like closed budget hearings and the like. Inclusion of the security sector in the PFM system is essential to achieve the principles of honesty and predictability, i.e. basing the budget on unbiased projections of revenue and expenditure, and stability in policies and funding.

# **3.2. Allocative Efficiency**

Allocating fiscal resources efficiently across and within sectors is arguably the most difficult of the three levels of effective public finance. Section 2 has made the case that security counts. Although spending on defense and other security components can enhance growth prospects, given potential inefficiencies in the sector and competing needs from many other sectors, there is a need for (i) a few benchmarks to guide allocations, and (ii) a solid process for making allocation decisions. We focus here on the first element, coming back to the second in Section 5.

A first guidepost is to take into account the **allocation of fiscal resources in other countries** . This is inevitably a simplistic approach given that different countries face very different security-related risks (including due to the differential influence of geography), while costs also are country-specific (given significant economies of scale in the security sector and frequent use of alliances to share costs). In addition, cross-country comparisons are all the more difficult in the security sector because the quality of available data is poor.<sup>8</sup> Finally, this approach also takes a narrow focus on a small number of security institutions, while Section 2 suggested that the perspective needs to be broadened. Nevertheless, and keeping these caveats in mind, based on available data defense spending ranges in most countries between 1-5% of GDP (Treverton, 2005; Table 3).

> 8 The Stockholm International Peace Research Institute (SIPRI) has been conducting research on this area for some time and has documented a number of issues related to data on military expenditures.  Some military expenditures can be off-budget, buried in ministries other than the Ministry of Defense, etc.

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**Figure 2: Wages over time in the study villages**

<!-- Start of picture text --> Round 1 Round 2<br>Public Works Public Works<br>01may2009 01jun2009 01jul2009 01aug2009 28apr2010 13may2010 28may2010 12jun2010 27jun2010<br>date of the survey date of the survey<br>Non Public Works Non Public Works<br>01may2009 01jun2009 01jul2009 01aug2009 28apr2010 13may2010 28may2010 12jun2010 27jun2010<br>date of the survey date of the survey<br>number interviewed<br>wage paid<br>BREGS stipulated wage rate<br>20 150 30 150<br>15 130 130<br>110 20 110<br>10<br>90 90<br>Frequency Frequency 10<br>5<br>70 rupees per day 70 rupees per day<br>0 50 0 50<br>150 150 80 150<br>130 60 130<br>100<br>110 110<br>40<br>90 90<br>Frequency 50 Frequency<br>70 rupees per day 20 70 rupees per day<br>0 50 0 50<br><!-- End of picture text --> _Note_ : The figure reports data on wages owed, based on Block 23 for casual labor done in the week preceding the survey. Wages are nominal. _Source:_ Authors’ estimates based on BREGS Survey.

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