rafmacalaba/datause-encoder-data huggingface dataset:
they cannot return it or sell it to others. These features also make it hard for individuals to borrow to finance, since a lender cannot use the training as collateral or seize it if the borrower does not repay.

Our solution to help reduce these problems, and to ameliorate the extent to which a lack of information and uncertainty makes firm owners reluctant to pay for training, was to hold demonstration sessions. After taking part in the phone survey, entrepreneurs were invited to attend a demonstration session in the same facility where training would be provided. The demonstration session started by explaining that this was a development project being undertaken to help train entrepreneurs, and that it would offer them the opportunity to get a unique training course at a potentially subsidized price. The trainers from JBDC then explained the experience of their organization in helping provide training to Jamaican firms, and outlined the course contents, giving examples of what it would cover. Participants were then given evidence on the potential effectiveness of the course in both quantitative and qualitative terms. They were told that a similar program had been implemented in several other countries, and that in a large-scale evaluation, business owners who took the course experienced a growth of 30 percent in profits over the next two years compared to similar firms not taking part (based on Campos et al. (2017)). Moreover, they were informed that preliminary results from JBDC implementing a similar program in Kingston had also increased sales and profits in the short-run (based on Ubfal et al. (2019)). In addition, they were provided with testimonial quotes from Jamaican participants who had taken the course in Kingston. These steps were also intended to build trust in the organization and its trainers.

# **3.2 Demand Elicitation Using the BDM Method**

The BDM method was implemented on our first sample of entrepreneurs, from August to November 2017. After a 30-minute description of the course by the JBDC trainers, entrepreneurs are told that the cost of providing the course is J$20,000, but that there is an opportunity to get the course at a discounted price if they purchase it at the end of the session. The BDM elicitation method is described in detail and an example of the mechanism is conducted for a token object (e.g., a pen or a notebook) with one of the participants in front of all the others. Entrepreneurs are told that it is optimal for them to reveal their true willingness to pay for the course; since over-reporting the price would imply they might have to pay a higher amount than that they are really willing to pay, and under-reporting would reduce their chances of getting the course at a price they would be willing to pay.

After this explanation, they conduct individual interviews with an enumerator. We use a 10
to abductions. When restricting the sample to women who have not moved since 1996, the coefficients for both conflict variables become larger, the effect of the casualties variable becomes significant, but is smaller than in Column (2), which suggests that some of the effect captured by casualties in Column (2) is due to the correlation of casualties with abductions. The results in Column (4) indicate that moving from the 5<sup>th</sup> to the 75<sup>th</sup> percentile of the casualties (abductions) distribution leads to an increase of 7.1 (2.7) percentage points in the probability of being married by age 15. Taking these results back to the findings on education, it is interesting to remark that, although overall conflict intensity appears to have had a small positive effect on female schooling attainment, it has tended to increase the probability of early marriage. This may explain why female enrollment has not increased despite the increase in attainment.

# Table 9 goes about here

Table 9 presents findings on the impact of conflict during marriageable age. All columns except Column (3) refer to regressions of an indicator variable equal to one if the woman is married by the age of 21 and zero otherwise, using data from the 2006 DHS. Column (3) exploits the richer migration data available in the NLSS 2003/2004, and focuses on the same treated and control cohorts as in the 2006 DHS analysis. In order to account for the fact that the youngest cohort is observed three years before the age at which they are surveyed in the 2006 DHS, in this specification I consider the effect of conflict on the probability of being married by the age of 18. Women whose entry in the marriage market coincided with the start of the conflict are no more or less likely to be married by the age of 21, when more than 80 percent of Nepalese women are married (Figure 1). No significant difference is noticeable already by age 18, even when exposure to conflict is calculated year by year, according to district of residence in each year (Column 3). Maoist abductions do not appear to have affected the probability of marriage by 21 years old for women who were entering the marriage market at the beginning of the conflict.

One potential concern with the estimates in Table 9 is that, for women who entered the marriage market at the beginning of conflict, the height of the conflict may have occurred too late in their life to affect their marriage prospects one way or the other. However, a similar conclusion is reached when estimating an alternative specification in which all women aged 13 to 27 at the start of the conflict are included and _(cohortt*casualtiesj)_ is replaced with the number of casualties while the index woman is aged 13 to 21.<sup>8</sup>

> 8 Results not reported here for conciseness, but available upon request.

26
the model using simulated method of moments and discuss the results. Following that, I perform a variance decomposition analysis of establishments’ sizes and perform the counterfactual experiments. I finish the paper with some concluding remarks.

# 1.1 Related Literature

Industry evolution has drawn a lot of attention by researchers since late 1950s. At early stages, Simon and his coauthors (Simon and Bonnini (1958), Ijiri and Simon (1964, 1977)) succeeded in generating stochastic growth models providing a good approximation to the size distribution of large U.S. manufacturing firms. However, these models lacked structural foundations. The availability of detailed longitudinal panels since the 1980s accelerated the development of theoretical models based on optimizing agents and the analysis of the regularities in establishment and industry evolution. Sutton (1997) presents a detailed summary of the findings of this early literature.

Many recent studies seek to explain these regularities in a structural way. One of these models is introduced by Klette and Kortum (2004). Their model is based on the quality-ladder model of Grossman and Helpman (1991). Producers engage in innovation activity which results in Poisson arrivals of quality improvements over the existing products. The new quality leader of a product drives the incumbent producer out of the market and becomes the monopoly supplier. Lentz and Mortensen (2008) introduce heterogeneity into the Klette and Kortum (2004) model through different quality step choices of firms. This extension enables them to match several moments of the data and perform structural aggregate productivity decomposition. Another model that is related to Klette and Kortum (2004) is Luttmer (2008). In his model, innovations come as new varieties and the number of varieties in the economy grows at the rate of the population growth. He characterizes a balanced growth path for an economy where firms grow by developing new blueprints from their goods. High skilled entrepreneurs can also develop new blueprints from scratch and set up new firms.

This paper follows all three studies mentioned above. In the dynamics of establishment evolution, it follows Klette and Kortum (2004). In the way the innovations arrive, it follows Luttmer (2008). It is similar to Lentz and Mortensen (2008) in introducing heterogeneity into the Klette and Kortum (2004) setup.

4
Policy Research Working Paper 5419

# **Abstract**

Environmental performance rating and disclosure has emerged as a substitute or complement for traditional pollution regulation, especially in developing countries. Using data from China’s Green Watch program, this study extends previous research on performance rating and disclosure by considering firms’ perceptions of public and market responses to their ratings. The results suggest that the Green Watch has significantly increased market and stakeholder pressures on managers to improve their firms’ environmental performance. More specifically, controlling for the characteristics of locations, firms, and individual managers, the analysis finds that firms with better ratings perceive positive impacts on market competitiveness, overall market value, and relationships with different stakeholders, while the firms with bad ratings are more likely to perceive deterioration. Among these factors, managers perceive a more active role for markets than for stakeholder relations.

This paper—a product of the Environment and Energy Team, Development Research Group—is part of a larger effort in the department to understand and improve environmental governance in developing countries.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at hwang1@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
<!-- Start of picture text --> 71%<br>Time on Task - 82%<br>81%<br>Supportive Learning<br>Environment _ aan— atl20%, 9,°<br>Positive Behavioral 39%<br>Expectations ~ 46% 1 58%<br>cig gs 41%<br>g 26%<br>Checks for<br>Understanding _ 44956%51%<br>eedback - = H129%<br>13%<br>Critical Thinking - 10%<br>10%<br>Autonomy - 34%34%<br>30%<br>Z aa: 29%<br>erserverance - ta,<br>Social &_ : 8%<br>Collaborative Skills 4%<br>0% 25% 50% 75% 100%<br>Proportion of Segments<br>Area _ Classroom Culture i Socioemotional Skills [| Urdu<br> | Guided Learning i Time on Task Language Used C] Saraiki<br>|| Other<br><!-- End of picture text -->
by the fact that many types of shipments are only rarely observed, which can mean that developing country inspection agencies that lack appropriate IT may not be able to recall the outcome of a previous inspection, or even to know whether or not an unusual shipment type has been observed/inspected in the past.<sup>2</sup> We argue that a key contribution of modern IT in this setting is to reduce ambiguity; the information storage and analytical tools that modern systems provide allow the inspecting agency to better use information from the import declaration to predict the conditional probability that a given shipment is non-compliant. Improved capacity to predict non-compliance leads to a better allocation of inspection resources, allowing key enforcement goals to be reached, even as the overall inspection burden is reduced. These are the primary goals of most risk management reforms.

Our focus on ambiguity is motivated by discussions with operational experts in trade logistics, but we also motivate this focus with stylized facts about conditional inspection rates in data from the Serbian customs agency. The data do not allow us to measure ambiguity directly, but we are able to show that shipment types that were more frequently observed in the prior year are less likely to be inspected in the current year. The negative relationship between lagged shipment frequency and the inspection rate remains, even after we control for heterogeneous risks. Although greater ambiguity about the compliance of infrequent shipments is not the only explanation for this empirical result, we maintain that - in the context of international trade - ambiguity is likely to be a quantitatively important explanation for the fact pattern we observe. While our theory is applicable to both developed and developing countries, the quantitative importance of ambiguity is likely to be much larger in developing country inspection agencies that lack sophisticated IT.

We develop a theoretical model that links the equilibrium allocation of inspection resources to the underlying probabilities of non-compliance and to ambiguity surrounding those compliance with import regulations.

> 2During field work in a middle-income country, one of the authors visited a border station in which an agency tasked with inspecting imports for sanitary risks stored its inspection records in paper form. The documents from several prior years were kept in large canvas bags piled together in the corner of a room. It is unlikely that these records were at all useful for informing subsequent inspection decisions in real time. The information that was retained may well have resided only in the minds of individual inspectors.

2
**Chart I Production of Major Crops**

<!-- Start of picture text --> 11<br>0.5 -<br>75 .5<br>7<br>6. ......l ...... .. .. . . . . . .<br>6 II P I I I I I I I I<br>75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93<br>|-- RIce -4-- Maize -o- Conton a Wheat -x- Sugarcane|<br><!-- End of picture text -->

Source: Statistical survey of Pakistan 1992-93 Notes: Production is shown in log levels to ensure that all crops are on a comparable scale.

The significant deceleration in the 1970s was due to uncertainty created by land reforms (and their selective implementationl in 1972 and 1977, and severe climatis shocks, a cotton virus that depressed production for most of the 1970s, as well as political disruptions. The pickup from the 1970s, and the acceleration since 1988 can be attributed to the introduction of new varieties of cotton and improvements in cotton management, as well as a gradual improvement in agricultural incentives. The exceptional overall perfornance since 1988 (particularly the rapid growth in 1989 and 1992) was also driven by cotton, but performance is correspondingly weakened when the cotton crop is poor.

Growth in cotton production has been a dominant factor in Pakistan since the early 1980s. Cotton production nearly trebled between the year of cotton debacle (in 1984) and 1992. Growth in cotton was driven by use of quality seed, increase in pesticide use, and attractive incentives. The steady depreciation of the rupee and lower export duties contained the decline in average cotton export prices to about 12 percent between fiscal 1990 and 1992. This compares with a 29 percent decline (in $US) in international cotton prices during this period.

Between 1989 and 1992, cotton production grew at over 10 percent per year, contributing one-half of the overall growth in the crop sector during this period, or one-third of growth in agriculture. The dependence on cotton for good growth and export performance means that Pakistan is exposed when the cotton crop suffers any setbacks. Recent developments indicate that the cotton-driven boom has now run its course and new sources of growth will have to be found in the future.

3
**Table** 5: **Savings Rate Regressions for Transition Economies**

||1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|Constant|0.258|0.229|0.263|0.274|0.223|0.246|
||(2.09)**|(1.84)*|(2.14)**|(2.24)**|(1.76)*|(2.02)**|
|Dependency Ratio|-0.129|-0.137|-0.154|-0.146|-0.082|-0.132|
||(1.07)|(1.12)|(1.28)|(1.22)|(0.66)|(1.10)|
|Urbanization Ratio|0.048|0.061|0.085|0.060|0.137|0.070|
||(0.49)|(0.62)|(0.86)|(0.62)|(0.13)|(0.72)|
|GDP Growth|-0.063|0.009|-0.094|-0.105|-0.140|-0.084|
||(0.83)|(0.86)|(1.00)|(1.13)|(1.44)|(0.89)|
|M2/GDP|0.016|0.002|0.009|0.005|0.036|0.019|
||(0.41)|(0.58)|(0.23)|(0.12)|(0.93)|(0.52)|
|CPI Inflation|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|
||(3.20)**|(3.18)**|(3.24)**|(3.19)**|(3.04)**|(3.33)**|
|Terms of Trade Change|-0.013|-0.017|-0.011|-0.013|-0.014|-0.013|
||_(0.50)_|(0.63)|(0.43)|(0.48)|(0.51)|(0.48)|
|GDP per capita|1.7E-5|1.7E-5|1.3E-5|1.4E-5|1.9E-5|1.6E-5|
||(2.47)**|(2.32)**|(1.85)*|(2.22)**|(2.70)**|(2.40)**|
|War|-0.201|-0.195|-0.202|-0.198|-0.205|-0.205|
|___________________________|(6.11)**|(5.87)**|(6.16)**|(6.07)**|(6.06)**|(6.33)**|
|Current Liberalization Index|0.020|-0.016|||||
|________________________|___(0.26)|(0.21)|||||
|Lagged Liberalization Index|-0.168|-0.158|||||
||(2.14)**|(1.74)*||_|||
|Lagged Internal Liberalization|||-0.086|-0.127|||
|**_______________________**|**_______**||(1.33)|(4.71)**|||
|Lagged External Liberalization<br>|||-0.089||-0.127||
|L<br>______________________<br>_|I|1(1.29)|||(4.67)**||
|Lagged Price Liberalization|||0.073|||-0.137|
||||( .00)|||(3.33)**|
|k2|0.616|0.638|0.627|0.621|0.589|0.620|
|Observations|131|131|131|131|131|131|
|Time dummies included?||Yes|||||

t-statistics in brackets. * and **: significant at the 10% and 5% level.

10
# **Figure 3**

Marginal Effect of Individuals Using the Internet by Intensity of COVID <!-- Start of picture text --> 2 3 4 5 6 7 8 9 10 11 12 131415<br>COVID Growth Shock (2020)<br>.4<br>.3<br>.2<br>.1<br>Change in Digital Payments from<br>Change in Individuals Using the Internet<br><!-- End of picture text --> Notes. Figure derived from Table 5, model 3. Horizontal scale is logarithmic. The line reflects average marginal effects, the whisker endpoints mark the 90% confidence intervals.

42
the Poisson estimator has a negative coefficient, but this result is not robust as it is not present with the alternative estimators.

In brief, the conclusions drawn from previous sections apply across LAC sub regions. The results for the aggregate bilateral stocks from the OECD do not show much variation across regions within LAC, and strongly support the complementarity hypothesis. The overall effects of China and India are less important in the U.S. data, and the positive effect of China found for LAC in section 5.1 is mainly due to its positive effect in the Southern Cone countries’ FCS. Differences across sub regions come out in the manufacturing sector data, where there is surprisingly weak evidence that China and India might have had negative effects on Central America and the Southern Cone respectively, as these results are not robust across estimators.

# **6. Conclusions**

In this paper, we empirically estimate the impact of China and India on foreign investment in other economies, with special emphasis on LAC. Using bilateral outward stocks data from UNCTAD and OECD, we find that China and India had a positive effect on the FCS in LAC and the rest of the world. This result is robust to the use of Poisson and Negative Binomial estimators, and to the inclusion of time dummies and fixed effects. The evidence based on U.S. foreign investment data across industries also suggests that the effect of China has been positive on aggregate for all sectors, but there is little evidence that this is the case in the manufacturing sector. It is worth mentioning that there is some evidence of a negative effect in Central American countries, although this evidence is not confirmed by the count data estimators. In contrast, India appears to 19
# B.  Where do the unbanked get financial services?

Individuals who do not use banks still need to obtain financial services. If they have any financial savings, they need a means to safeguard their savings. Even if they receive income in the form of a check, they need a means to convert the check into cash. Finally, they many need to borrow periodically. This subsection draws on the OCC survey data to examine where and how the unbanked obtain these financial services.

Table 3 presents data on the forms in which the unbanked keep financial savings and contrasts this with the banked. As noted in Table 2 above, 86 percent of the unbanked said that they had no formal sector financial savings. The OCC survey also asked about the use of informal means of financial savings, such as holding cash, money orders, uncashed checks, jewelry that can be sold, etc. As shown in Table 3, 21 percent of the unbanked respondents said that they use such means and 16 percent of the banked did. Allowing for both formal and informal means of financial savings, 69 percent of the unbanked had no financial savings and 22 percent of the banked had none.

**Table 3 Use of informal savings methods**

||**Percentage**<br>**among the**<br>**banked**|**Percentage**<br>**among the**<br>**unbanked**|
|---|---|---|
|Kept savings in money orders, uncashed<br>checks, cash, jewelry, loans to others, by<br>participatingina savings circle, etc|16.3|20.5|
|Had neither informal financial savings<br>nor formalsector financialsavings|21.9|68.6|

Table 4 examines how people receive their incomes and convert that income into cash. Among banked individuals, 38 percent received an electronic deposit to their account, 49 percent received a check, and 6 percent were paid in cash. Among the unbanked, 51 percent were paid by check, 19 percent were paid in cash, and 17 percent receive an electronic payment through a non-bank. Almost all of the people in this latter category live in New York City where many check-cashing outlets (CCOs) participate in a network that allows them to distribute cash to people who are paid electronically but who do not have bank accounts.

16