rafmacalaba/datause-encoder-data huggingface dataset:
an NGO-academic partnership. Fifth, we demonstrate how analysis of clustered randomized trials can respond to Deaton’s (2012) recent concern that outliers may inappropriately determine experimental conclusions.

This paper also makes three contributions to policy debates, especially in India. The first regards the allocation of public funds in India: few prior studies have shown effects of public policies on health status. That is, although some studies have shown impacts of interventions by NGOs or medical researchers, none have been implemented by the Indian government (Das and Hammer, forthcoming). Learning about the effects of “scaled up” programs implemented by the Indian government requires studying government implementation. The second notes that because sanitation has important externalities, it has a strong theoretical claim to public resources, empirically validated by this experiment, in a way that purely curative care may not. Finally, this paper studies the height of children, which is widely agreed to measure the long-term net nutritional status of children. Indian children are exceptionally short internationally, a major policy concern which has attracted the recent attention of many economists ( _e.g._ Deaton, 2007; Tarozzi, 2008; Jayachandran and Pande, 2012; Panagariya, 2012). Stunting is often referred to as “malnutrition,” which suggests to many food as a policy response. Yet, net nutrition is a matter of food intake, of food absorption and use by the body, and of losses of nutrition due to disease. Diarrheal and other chronic intestinal disease can limit children’s ability to absorb and use improved nutrition, and may be responsible for an important part of stunting among Indian children (Spears, 2013).

# **1.1 Open defecation is widespread in India**

According to joint UNICEF and WHO (2012) estimates for 2010, 15 percent of people in the world, and 19 percent of people in developing countries, openly defecate without using any toilet or latrine. Of these 1.1 billion people, nearly 60 percent live in India, which means they make up more than half of the population of India. These large numbers correspond with the estimates in the Indian government’s 2011 census, which found that 53.1 percent 3
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greater accuracy and reliability. Integration reflects a technology’s ability to connect and interact seamlessly with other systems by exchanging data and coordinating processes. Automation enables the technology to execute processes, make decisions, and generate outcomes independently, without human intervention.

For these functions, we also document the year of the launch and the cost of each of the technologies in the grid. Though these variables do not define the sophistication of a technology per se, more sophisticated technologies tend to have been developed more recently and be more expensive. This is particularly the case for sector specific technologies used in the production of goods (e.g., agriculture and manufacturing).

We relied on multiple source collect information on the functionality, integration, and automation features, as well as the launch and the cost of the technologies in the grid. In addition to broad desk research, we consult official websites with description of specific leading brands supplying these technologies (e.g., Microsoft website provides detailed information on the features of Microsoft Excel, including prices, which is used as a _proxy_ for standard spreadsheet software, one of the technologies in the grid for business administration).

In addition, we validate the rankings by using AI-powered large language models. First, we asked ChatGPT to rank the technologies in the grid in terms of technology sophistication. Second, we ask ChatGPT to rank technology sophistication based on specific definitions of functionality, integration, and automation and compare those with the industry experts rankings resulted in the technology grid, based on the methodology we described in section 2. Third, we ask ChatGPT to identify a specific task for each of the 14 business functions and estimate the time required to perform the task with each technology.

Tables A.1 - A.13 summarize these exercises for the following business functions: sourcing, marketing, sales, payment, and quality control, among GBFs; Weeding and harvesting for agriculture; Design and sewing for apparel; Anti-bacterial and packaging for food processing; Pricing and inventory for retail services; in addition to the example for businesses administration, provided in section 2. We also added for each business function a summary of two of the exercises with ChatGPT: i) The overall ranking of technology sophistication; ii) The estimated time to perform a typical task in the business function. Next sub-section provides more details on these and additional exercises conducted with ChatGPT. Subsection A.2.1 describes the specific ChatGPT prompts used for these exercises and displays scatter plots showing the positive association between ChatGPT and industry expert rankings of technology sophistication.

Overall, the validation exercise across 14 businesses functions strongly supports the experts original ranking of sophistication. To start, within each business function, the specific features of technologies are associated with functionality, integration, and automation.

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Table 4. Simulating the Characteristics of Household Heads

|||Bangla|desh|||Pe|ru|||Thai|land||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||20|00|2|010|2|004|2|010|2|000|2|009|
||Actual<br>(1)|<br>Simulated<br>(2)|Actual<br>(1)|<br>Simulated<br>(2)|Actual<br>(1)|<br>Simulated<br>(2)|Actual<br>(1)|<br>Simulated<br>(2)|Actual<br>(1)|<br>Simulated<br>(2)|Actual<br>(1)|<br>Simulated<br>(2)|
|**Education Structure**|||||||||||||
|Illiterate & Incomplete primary|63.2|61.9|57.0|57.8|55.3|55.4|49.7|49.9|59.7|64.8|46.9|40.8|
|Primary & Low Secondary|29.0|29.6|33.1|33.2|28.2|28.1|32.4|32.3|23.2|19.3|29.3|34.2|
|High Secondary & Terciary|7.7|8.5|9.9|9.0|16.5|16.5|18.0|17.8|17.1|15.9|23.8|25.0|
|_P-value of Pearson chi-square_||_94%_||_96%_||_100%_||_100%_||_56%_||_44%_|
|**Occupation**|||||||||||||
|Non-employed|9.6|10.1|8.0|7.7|16.8|17.3|13.1|12.8|20.4|21.5|20.7|19.4|
|Self-employed - Non Agriculture|24.7|25.1|24.1|23.9|48.2|48.1|50.2|50.0|22.3|22.1|25.4|24.8|
|Salaried|20.2|24.2|24.5|18.6|35.0|34.5|36.7|37.2|57.3|56.4|53.9|55.7|
|Daily workers|45.5|40.7|43.4|49.9|...|...|...|...|...|...|...|...|
|_P-value of Pearson chi-square_||_72%_||_49%_||_99%_||_99%_||_96%_||_93%_|
|**Economic Sectors for salaried wor**<br>|**kers**||||||||||||
|-Manufacturing|27.7|26.8|33.4|34.3|||||||||
|-Agriculture|8.6|7.3|5.4|6.9|31.8|29.9|25.9|27.6|20.6|16.5|11.6|15.1|
|-Industry|3.7|3.8|3.9|4.0|12.1|12.1|12.0|12.1|30.7|27.1|35.3|37.3|
|-Services|60.0|62.2|57.3|54.9|35.2|36.8|40.7|39.7|23.6|22.8|29.3|30.5|
|-Public Sector|||||20.9|21.2|21.5|20.5|25.1|33.5|23.8|17.2|
|_P-value of Pearson chi-square_<br>Source: Own estimates based on Peru's E|NAHO 200|_99%_<br>4 -2010, Thail|and's SES|_95%_<br>2000 -2009, an|d Banglad|_98%_<br>esh's HIES 20|00 -2010.|_98%_||_26%_||_38%_|

31
largely based on prior economic research. For those intangibles for which there is little indication in the literature of what share of the spending has long-term effects, the choice is admittedly more arbitrary.

# **3. RESULTS**

This section begins with a comparison of intangible expenditures in Brazil relative to other available countries. Brazil‘s expenditure pattern in intangibles is then explored in greater detail, by examining the evolution of aggregate spending components over time, the composition of spending by industry group, the importance of tangible relative to intangible investment, and estimates of the intangible capital stock.

For reasons of comparability with the estimates presented in CHS (2005, 2009), most of the estimates presented in this section are of _expenditures_ on intangibles, rather than of investment in intangibles. For most components of intangible assets, investment equals expenditures. The exceptions are brand equity and own-account organizational structure for which investment equals 60 and 80 percent of expenditures, respectively. Hence, total investment in intangibles is less than total expenditures on them.

## **3.1 A comparison of intangible expenditures across countries**

We estimate that in 2006, annual private business expenditures on intangibles in Brazil averaged 4.72 percent of GDP. While this is significantly lower than comparable rates for the US, Japan and the UK, which are in the order of 11 percent, and China at roughly 8 percent, it is not too far below estimates for other developed countries such as Italy and Spain at around 5-6 percent.

Moreover, our estimate for Brazil does not include firm specific human capital (employee training) expenditures in services firms due to data limitations. We have avoided adjusting for it by extrapolating blindly from manufacturing to services. But for the sake of illustration, suppose we do just that, assuming that as a share of gross value added (GVA), service sector firms spend the same as manufacturing firms on employee training. Since GVA in the services sector is about thrice that in manufacturing, this would quadruple our estimate of firm specific human capital expenditure to about 1.2 percent of GDP, and increase our estimate of total intangible expenditures in Brazil to 5.6 percent of GDP, placing it squarely in the neighborhood of Italy and Spain.

16
given to compensatory mechanisms in rural areas, even though the consumption of imported goods which are most susceptible to price increases is indeed higher in urban areas.

While the results from our analysis should be treated with caution (among others because the SAM multiplier analysis may overstate the impact of price shocks due to its inability to take into account behavioral reactions to price increases), they do provide some pointers and stylized facts that are worth considering when implementing policies to aim to offset part of the negative impact of higher oil and food prices for the population.

16
**Figure 3. Agricultural Calendar (Rice) and HIES Timeline**

<!-- Start of picture text --> Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec<br>15 15 15 15 15 15 15 15 15 15 15 15<br>Land preparation Main<br>& planting harvest<br>Lean<br>Season<br>Rainy season Rainy season<br>South/central South/central<br>Rainy season<br>Northeast<br>HIES 2016 – Agriculture Survey Module Recall Period<br><!-- End of picture text --> _Source_ : The seasonal calendar was adapted from FAO (2016).

Rice production in Liberia uses a labor-intensive, rain-fed system involving teamwork at certain crucial production stages, namely planting, maintenance and harvest. The _kuu_ system of labor recruitment assembles such work teams, offering members food or labor on their own farms in exchange for work (FAO, 2016). Fear of infection would be expected to reduce farmer willingness to join _kuu_ teams and can explain the impact on rice production when and where Ebola raged.

Two channels of impact of the Ebola epidemic on Liberian agriculture are thus as follows: First, there is a direct effect of the Ebola outbreak on the rural labor force through sickness or death, temporarily or permanently removing agricultural workers and thereby disrupting planting and harvesting in agriculture or, more generally, raising farm wages to the extent that a casual labor market exists.<sup>10</sup> However, given the magnitude of Ebola morbidity and mortality relative to the size of the rural population (around two fatalities

> 10 This is the mechanism that economists have explored in the context of HIV in Africa (Young 2005) and the Black Death in Europe (Voigtländer and Voth 2012).

9
to compare the restrictiveness of MRL standards across countries. More generally, the use of measures of the _relative_ restrictiveness of pesticide standards is important because it informs on the dissimilarity in the stringency of regulatory requirements across the importing country and the exporting country. As such, it allows us to consider whether the presence of relatively stricter pesticide standards in the importing country imposes additional costs on firms and thus limit their market access.<sup>20</sup> That is, our index does capture the presence of a trade barrier given that it accounts for the domestic regulation.

It is important to highlight that MRL standards are updated frequently and our estimating strategy takes advantage of this variability over time. Product registrations are withdrawn, new registrations and MRLs are established, and existing MRLs change on a regular basis. Codex MRLs for example, are updated annually every July. New Zealand typically publishes two MRL amendments per year and local officials report that they seek to update MRLs every four months. However, changes in MRLs are not always towards more restrictive standards, but rather very frequently MRLs are increased thus becoming less restrictive. Table 2 shows that on average only 45 percent of MRLs in the dataset are there for the entire panel available for each country. Of those MRLs available for all years, 9 percent became stricter whereas 10 percent became more lenient.<sup>21</sup> Analyzing the sources of variation in the index of relative restrictiveness of standards suggests that the main source is variation over time is the intensity of standards. This will be an important element to justify the estimating strategy chosen for the analysis.

# **3.2 Data on Exporters**

To measure exporter behavior we use transaction-level customs data for the period 20052012 for 42 developing countries across different regions of the world, obtained from customs agencies (or in a few cases from statistical institutes) collected by the Trade and Integration Unit of the World Bank Research Department, as part of their efforts to expand the Exporter Dynamics Database described in Cebeci et al. (2012). Each country’s raw data-set covers the universe of exporting firms in the agricultural, mining excluding HS Chapter 27 (hydrocarbons

> 20 Winchester et al. (2012) examine how the dissimilarity in sanitary, phyto-sanitary and conformity requirements (including product requirements such as maximum residue limits for pesticides) across the EU and several of its trading partners affect their bilateral trade but do not but consider the stringency of those requirements, as we do in our study. Disdier et al. (2015) study the impact of standards harmonization promoted in North-South trade agreements and show a negative impact on South-South trade as well as on North-South trade when the harmonization is on regional (rather than international) standards but only harmonization is examined, not the stringency of the standards.

> 21 The analysis of the sources of variation in the index is described in Appendix A2.

11
to find the right worker 3 months after learning of their eligibility as indicating that search frictions are important for a minority of the owners, but are not a main cause of not hiring.

Finally, the excess hiring that was created by the subsidy largely disappears within a year of withdrawing the subsidy. In most of the surveys conducted after the intervention, we asked the employers whether the employee hired under the program was still working for them, and if not, why they were not. The most common response was that the worker quit, given just over half the time. In those cases, the owner reports taking over the worker’s tasks himself in 59 percent of the cases, hiring another employee 29 percent of the time, and having a family member do the work in 12 percent of the cases.

# **6.3 Why was there a survival impact?**

The only long-term impact of the wage subsidies appears to be on firm survival, with no impact on paid employment, sales or profitability. The evidence presented above suggests that the survival impact is not coming through relieving labor market constraints on firms. Instead, the most likely explanation for the survival effect appears to be that the subsidy provided firms with extra profits during the intervention period, and this small amount of additional capital allowed firms to survive shocks that would otherwise shut them down. De Mel et al. (2012) show one-time grants of 10,000 and 20,000 SLK helped small firms survive. If workers earn their marginal product, then the maximum subsidy for a firm was 28,000 SLK. These firms are larger on average than those in De Mel et al. (2012), so this amount is equivalent to less than two months of baseline profits. If it were to have an effect, it should therefore be for the smallest firms. Appendix 7 provides heterogeneity analysis that shows the survival effect is larger for firms with below median baseline capital and below median profitability, although results are at most significant at the 10 percent level. We therefore view this evidence as suggestive, but consistent with the survival effect coming through a credit rather than labor market channel.

# **7 Discussion and Conclusions**

Microenterprises in developing countries face many potential constraints to growth, and understanding which of these constraints are binding is crucial for designing policies to address them. Moreover, as firms with fewer than five employees are the source of livelihoods for the majority of urban workers in most low- and middle-income countries, binding constraints to their 27
<!-- Start of picture text --> e<br>i t , i t , ri d , 1, t i t , sign( ri d t , , ) vi d t , , i d t , ,<br><!-- End of picture text --> Above, _ri de_ , 1, _t_ is the return in excess of the market return and _vi d_ , , _t_ is the volume on day _d_ in month _t_ for stock _i_ . This measure is motivated by the Campbell, Grossman, and Wang (1993) model and is meant to capture temporary price fluctuations arising from order flow.

I also include in the analyses quoted and effective spread and quoted depth calculated from intra-day data. I use a 5-second delay to match trades with quotes and apply the same filters discussed in Hvidkjaer (2006). The quoted percentage spread is calculated for each trade as the ratio of the quoted bid-ask spread to the prevailing transaction price. The effective percentage half-spread is calculated for each transaction as the absolute value of the difference between the transaction price and the quote midpoint, divided by the bid-ask midpoint. The quoted depth is the average of quoted bid-ask lots multiplied by bid-ask quotes. In addition, I compute a _realized spread_ , which is the ex-post realized bid-ask spread paid by the investors for each transaction in the dataset. The calculation is the same as in Barber and Odean (2000): where _Pcrsp_ is the closing price from CRSP, and _Psell_ and _Pbuy_ are the purchase and sale prices from the dataset. This measure includes the bid-ask spread, market impact of the trade as well as the intra-day return on the day of the trade. The total spread is the sum of the realized buy and sell spreads. Previous studies (Korajczyk and Sadka 2008, and Eckbo and Norli 2002) have shown that there is high correlation among these liquidity measures and that there is a common component that accounts for most of the variation across individual liquidity measures.

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Policy Research Working Paper 6663

# **Abstract**

Under neoclassical assumptions, and the usual ceteris paribus stipulations, a profit maximizing firm is expected to increase production in response to rising prices. These situations normally produce the rather wellknown upward sloping supply curve for the firm which can usually be generalized to the industry. This paper examines whether these situations held for firms in the iron ore mining sector in Liberia between 1951 and 1985 under a system where royalties were levied on the per unit level of production and on the basis of the price of the ore. It investigates whether iron ore mining companies have an incentive to increase ore production when prices are low to attract lower total royalty payments under conditions where: (a) base-mining operations are vertically integrated and firms employ transfer pricing between mining and upstream processing entities and (b) large quantities of ore can be shipped at relatively low prices and held in inventory either as ore or addedvalue products, such as steel, to take advantage of higher prices in the future. The paper specifies and estimates two simple linear supply models of the Liberian iron ore industry. It uses data for 1951–1985, the period for which the most consistent and reliable data exist prior to the start of the 14-year conflict in 1989. The analysis finds that the price coefficient estimates from both models are robust but negative and suggest that a perverse response in the supply behavior of mining companies in Liberia over the period 1951–1985 cannot be ruled out.

This paper is a product of the Poverty Reduction and Economic Management Department, Africa Region. 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://econ.worldbank.org. The author may be contacted at egraham@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