rafmacalaba/datause-encoder-data huggingface dataset:
increase his efforts in addressing the same issues.

We test the prediction. The information we collected on ward members allows us to construct ward-level panel data over four elections. To identify the impact of collective actions undertaken by SHGs, we use an instrumental variables approach that exploits the variation in the timing of the creation of SHGs. To identify a causal effect, the creation of SHGs should be uncorrelated with determinants of future public good provision. After including ward fixed effects, the assumption underlying our approach is that the creation of SHGs is not correlated with pre-existing differences across wards. We assess the plausibility of this assumption using the 1991 Census data and our information on the period before the first SHGs were created. We show that the first villages in which PRADAN created SHGs have socioeconomic characteristics comparable to those in which SHGs were created later.

Our empirical results confirm the prediction of our model. We find that ward members deal with, on average, 1.5 extra public goods after SHGs start undertaking actions. In particular, they are 29% more likely to deal with alcohol issues, 35% more with forestry issues and 31% more with school problems, all of which are issues in which SHGs are particularly interested.

Our work is related to several strands of the literature. A number of studies look at the social implications of microfinance programs. Feigenberg et al. (2013) provide evidence that the frequency of meetings is a determinant of long-run increases in social interaction. Chowdhury et al. (2004) discuss why, in evaluating the impact of microfinance programs, non-client beneficiaries ought to be considered. In India, SHG membership makes socially disadvantaged women more likely to engage in community affairs (Desai and Joshi, 2014), and has a positive impact on female empowerment (Deininger and Liu, 2009; Datta, 2015; Khanna et al., 2015). Four recent papers assess the impact on female decision power of microfinance programs that provide loans through group lending. The results are more diverse: Banerjee et al. (2015), Cr ́epon et al. (2015), and Tarozzi et al. (2015) do not find significant effects on women’s empowerment in India, Morocco and Ethiopia, respectively.<sup>3</sup> But Angelucci et al. (2015) document a strengthening of women’s

> 3In Morocco and Ethiopia most borrowers are men. For this reason - as noted by the authors in both papers - the limited effect on women empowerment is not surprising.

3
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Study of the _Handbook_ has convinced me that there are major gaps in our understanding of development. Building on Sen's formulation of how development should be assessed, **I** discussed two areas for study. One is collective action, a far more pervasive component of human action than is realized. The other is the selection of roles by individuals and the costly self-investment that this entails. This concept may shed light on Schumpeter's well known but little studied entrepreneur.

# **15) References**

Arrow, Kenneth J. 1973. "Higher Education as a Filter." _Journal of Public Economics 2._ Bairoch, Paul. 1975. _The Economic Development of the Third Wordd Since 1900._ Berkeley: University of Califomia Press.

Balassa, Bela. 1971. _The Structure of Protection in Developing Countries._ Baltimore: Johns Hopkins Press.

Barro, Robert J., and Herschel I. Grossman. 1971. "A General Disequilibrium Model of Income and Employment." _Amenican Economic Review_ 61:82-93.

Basu, K. 1989. "Rural Credit Markets, the Structure of Interest Rates, Exploitation, and Efficiency." In P. Bardhan, ed., _The Economic Theory of Agrarian Institutions._ Oxford: Oxford University Press.

Behrman, Jere, and T.N. Srinivasan, eds. 1995. _Handbook of Development Economics,_ vols. 3A-B. Amsterdam: Elsevier.

Boserup, Ester. 1981. _Population and Technological Change._ Oxford: Basil Blackwell.

Boskin, M., E. Dulberger, Z. Griliches, R. Gordon, and D. Jorgenson. 1996. "Toward a More Accurate Measure of the Cost of Living." Final Report to the Senate Finance Committee (December).

Braudel, Fernand. 1983. _Civilization and Capitalism Vol. 2, The Wheels of Commerce._ London: William Collins.

Cairmcross, Alex K. 1961. _Intemational Trade and Economic Development_ 13/4:545-58.

Cave, R. E. 1983. _Multinational Enterprises and Economic Analysis._ Cambridge, Mass.: Harvard University Press.

Chenery, Hollis. 1974. _Redistribution With Growth._ London: Oxford University Press.

Chenery, Hollis. 1975. "The Structuralist Approach to Development Policy." _American Economic Review._ Proceedings issue.

Chenery, Hollis B., and Michael Bruno. 1962. "Development Alternatives in an Open Economy: the Case of Israel." _Economic Journal_ 72:79-103.

45
on nominal yields on 30-year sovereign bonds issued by that country is 13.3 percent; the risk premium (in foreign-currency terms) can thus be calculated as [(1+0.113)/(1+0.017)] - 1 = 0.094.This also implies that the household discount factor is equal to 1/(1+0.113) = 0.898, as noted earlier.<sup>18</sup> Finally, the elasticity of the risk premium with respect to the debt-output ratio, pr1, is set at 0.8 initially, and sensitivity analysis is reported later on.

# **5.  Macroeconomic Effects and Optimal Allocation of Resource Windfalls**

We now turn to estimate the macroeconomic effects of resource windfalls, together with an analysis of optimal allocation rules. First, the model is log-linearized in the vicinity of the initial steady state to obtain its solution.<sup>19</sup> We then consider an unanticipated and temporary positive shock to the real price of natural resources by 10 percent. The resource windfall corresponds therefore to the log-difference between actual resource revenues and their steady-state value (as defined in the log-linearized version of the model), weighted by their initial steady-state value.

# **5.1 Macroeconomic Effects**

The properties of the model is illustrated using two extreme cases: a _full spending_ case, where the windfall is spent entirely by the government (in proportions given by the

> 18This expression is derived from the steady-state relationship between the world interest rate and the discount factor, r<sup>W</sup> = <sup>-1</sup> - 1.

> 19The model is solved using DYNARE. An appendix summarizing the log-linearized equations is available upon request.

25
13 the availability of bank loans over those reporting an improvement rose to 20 percent from 14 percent in the previous survey, which covered the first half of 2011 (figure 12b). While the deterioration was linked largely to the general economic outlook, companies also complained about the increased unwillingness of banks to provide loans, as well as increased borrowing costs and collateral requirements (figure 12c).

**Figure 11. European Bank Lending Conditions and Drivers**

<!-- Start of picture text --> a. Net Percentage of Banks Reporting Tight b. Factors Affecting Credit Standards<br>Conditions<br>80 200<br>Weighted Net Percentage, Jan 2003 - Apr 2012 Weighted Net Percentage, Jan 2003 - Apr 2012<br>60<br>150<br>40<br>100<br>20<br>50<br>0<br>0<br>-20<br>-50<br>-40<br>Firms Household Mortgages Economic Outlook Liquidity position<br>Other Household Credit Access to Market Financing Capital Positions<br>Jan 03 Jun 03 Nov 03 Apr 04 Sep 04 Feb 05 Jul 05 Dec 05 May 06 Oct 06 Mar 07 Aug 07 Jan 08 Jun 08 Nov 08 Apr 09 Sep 09 Feb 10 Jul 10 Dec 10 May 11 Oct 11 Mar 12 Jan 03 Jun 03 Nov 03 Apr 04 Sep 04 Feb 05 Jul 05 Dec 05 May 06 Oct 06 Mar 07 Aug 07 Jan 08 Jun 08 Nov 08 Apr 09 Sep 09 Feb 10 Jul 10 Dec 10 May 11 Oct 11 Mar 12<br><!-- End of picture text --> Source: ECB Lending Survey.

# **18. Available information suggests that certain areas of banking have been hit harder than others.**

- Less profitable, capital-intensive projects are disproportionately affected, including infrastructure finance and loans to SMEs. As discussed, the most recent ECB SME lending survey suggests that SMEs are experiencing difficulties in accessing bank credit across Europe. High lending rates are also discouraging UK SMEs from borrowing from banks, which reportedly failed to meet their SME lending targets in late 2011 (under ―Project Merlin‖). Access to credit is particularly difficult for SMEs in Central and Eastern Europe (CEE) and Central Asia.

- Similarly, global trade financing volumes were down 6 percent in the first nine months of 2011 compared to 2010. This decline was in part due to limited access to U.S. dollar funding by European banks, which account for about one-third of the trade financing market.<sup>8</sup> Anecdotal information suggests that trade financing experienced a significant

> 8 Large French banks, in particular, provide a significant share of trade financing in emerging Europe, Asia, Latin America, and West Africa (figure 13).
The second dimension of financial knowledge we introduce is what we refer to as financial _awareness._ In comparison to financial numeracy which deals mathematics-related questions, financial awareness emphasizes knowledge about fundamental financial planning tools as well as the details of basic financial products and services. We capture this type of knowledge through the following four questions in our endline survey.

- (1) Shantiben is preparing a budget for her household. Which of the following needs to be included in the budget? (a) Income only; (b) Expenses only; (c) Income and expenses.

- (2) Do you think you can open a savings account in a bank with amount as low as Rs 50? (a) Can open an account; (b) Cannot open an account.

- (3) If I have a savings account in a bank and the bank closes down for some reason, will I get my money back? (a) Will get my money back; (b) Will not get my money back.

- (4) Manojbhai recently borrowed some money from a local moneylender. He wanted to buy some clothes for his children for Diwali (festival). What do you think about Manojbhai’s loan? (a) It is a productive loan; (b) It is an unproductive loan.

Notably, financial awareness does not involve any calculations. Instead, it relates to whether individuals are familiar with the different parts of a household budget, bank account opening requirements or deposit insurance in their local context, and the use of loans for productive purposes. Financial awareness may therefore be regarded as a simpler and more rudimentary form of financial knowledge than financial numeracy.

The third dimension we implement is financial _attitudes,_ which involve the individual’s perspectives about the benefits of financial products. We consider financial attitudes as an important element of financial literacy because it has serious implications for financial outcomes: for instance, if an individual holds a negative attitude towards savings, then s/he will be less inclined to set money aside for the future. To assess financial attitudes, our endline survey presented households with the following three questions.

- (1) Rameshbhai does plastering on tall buildings. It is a dangerous job and he is worried that if he gets injured his family’s income will become inadequate to meet their needs. If Rameshbhai comes to you for advice what would you suggest? (a) Take up some other (different) work; (b) Purchase health/life/accident insurance; (c) Increase savings.

- (2) Vimlaben has a very bright child who is currently in secondary school but will probably do well in university. She is worried how her family will pay for the child’s education. If Vimlaben comes to you for advice what would you suggest? (a) Buy child life insurance

10
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 6

## **Agricultural suitability and forest cover**

These data (see figure 4) were taken from the global agroecological zoning (GAEZ) data assembled by IIASA and FAO, (http://www.iiasa.ac.at/Research/LUC/GAEZ/index.htm, version 1.0). That exercise used agroclimatic data to create a suitability index (SI) for rainfed cropping under mixed input levels. Here we use categories from Plate 56, distinguishing marginal and unsuitable lands (SI <25) from more favorable lands, and distinguishing forested and nonforested areas.

## **Data assembly**

We used the GPW grid cells as our reference. We defined population density as GPWreported population (UN adjusted) divided by reported non-water, non-ice area of the cell. Centroids of the GPW grid cells were used to extract data from the accessibility maps, the GAEZ data, and a dataset of country boundaries.

# **Results**

Figure 4 maps the population density data from GPW 3. Keep in mind that these represent, for the most part, municipio level averages. Table 2 tabulates population proportions<sup>5</sup> , for LAC as a whole and for selected countries, by population density threshold, breaking out those areas more than one hour travel time from cities with more than 100,000 people (here designated as ‘remote’). The proportions in all cases refer to the total national population, so for instance, we read that 46% of all Argentinians live at population densities below 150, and 44% of all Argentinians live in cells that both have densities below 150 and are more than one hour travel time from a city of 100,000. The appendix graphs this data in the the form of cumulative densities, showing for each density d, the proportion of national population (or area) with density less than or equal to d. Because we are agnostic about density thresholds, each graph is presented both on a density scale of 0 to 50 people/km2, and on one of 0 to 500 people/km2 According to the tabulations, about 13% of LAC populations live at ultra-low densities, of less than 20 per square kilometer. Essentially all these people are more than an hour distant from a large city, and more than half live more than four hours distant. This group plays a key role in environmental management, because they occupy the great bulk of the continent’s territory and forests.

About a quarter of LAC population is estimated to live at densities below 50, again essentially all of them more than an hour distant from a large city, and nearly half in very remote (> 4 hours travel to a city) conditions. While this group may include some prosperous ranchers, it certainly contains a large number of very poor people. Almost half (46%) of LAC lives at population densities below 150, and more than 90% of this group is at least an hour distant from a city, and about a third of them (18% of LAC total) are more than four hours’ distant (see table 3). Even at densities of up to 500, the great majority of 5 Population totals are reported in table 6.
<!-- Start of picture text --> Business Administration Sourcing<br>5 Enterprise Resource Planning (ERP) 5 SRM integrated with production planning<br>4 Computers with specialized software 4 Supplier Relatio n Management (SRM) not integrated with production planning<br>Mobile apps or digital platforms social media/digital platformsSupplier Relation Management (SRM) not integrated with production planning<br>3 Mobile apps or digital platforms 3 socia l media / digital platforms<br>2 Compute r s with standard sof t ware (Excel) 2 PCs manua l ly updated (Excel)<br>Computers with standard software (Excel) PCs manually updated (Excel)<br>1 Handwri t ten processes Functionality 1 Manual search of suppliers Functionality<br>Integration Integration<br>Automation Automation<br>0 0<br>0 1 2 3 4 5 0 1 2 3 4 5<br>Expert rankings Expert rankings<br>Marketing Payment<br>5 Big data analytics or machine learning (AI) 5 Virtual or cryptocurrency<br>Customer Relationship Management (CRM) Online payment through platforms (PayPal)<br>4 Customer Relati onship Management (CRM) 4 Online or elec t ronic p ayment (bank wire)<br>Prepaid, debit, or credit card<br>3 Struc t ured surveys (phone, onlin e) 3 Prepaid, debit, or credit card<br>Online chat (WhatsApp) Cheque, voucher, or bank wire<br>2 Online chat (WhatsApp) 2 Cash Cheque, voucher, or bank w i re<br>Cash<br>1 Face-to- f ace chat Functionality 1 Exchange of goods Cash Functionality<br>Integration Integration<br>Automation Automation<br>0 0<br>0 1 2 3 4 5 0 1 2 3 4 5<br>Expert rankings Expert rankings<br>Sales Quality Control<br>5 Electronic orders integrated with supply chain manag 5 ement systems Automated systems for inspection (laser, sensors)<br>Online sales (e-commerce) via own website<br>4 Onl i ne sales using externa l platforms (Amazon) 4 Statistica l process co ntrol with software<br>Sales through social media platforms or apps<br>3 Sales through social media p l atforms o r apps 3<br>Human inspection supported by computers or phones<br>2 Sa l es by phone, email , or sales representatives 2 Human inspection supported by computers or phones<br>1 Sales at establishment premises Functionality 1 Manual visua l or writ t en processes Functionality<br>Integration Integration<br>Automation Automation<br>0 0<br>0 1 2 3 4 5 0 1 2 3 4 5<br>Expert rankings Expert rankings<br>ChatGPT rankings ChatGPT rankings<br>ChatGPT rankings ChatGPT rankings<br>ChatGPT rankings ChatGPT rankings<br><!-- End of picture text --> Figure A.12: Comparison between expert’s versus ChatGPT’s in General Business Functions Note: This figure compares the experts’ rankings of technology sophistication (horizontal axis) with ChatGPT’s rankings across the dimensions of functionality, integration, and automation (vertical axis).

93
I_ucas assumes a production function involving capital, effective labor (labor adjusted for skill level) and the avcragc level of skill, intended to capture external effects of human capital. Individuals maximizc a utility function with respect to consumption and lcisure. Solving thc first-order conditions and following the optimality relation betwecn capital aid skill level, the rcal wage rate for a given skill level 'the marginal product of skill) is determined.

In the first step internal effects of education are isolated using wage functions estimated using microdata, while in the second step the resulting industry wage premiums are regressed on average human capital as well as industry-specific characteristics to account for the external effects of human capital.

In filtering out internal effects to human capital a sample of workers between the ages of 18 to 65 is used to estimate an earnings function with the log of hourly wage<sup>2</sup> as the dependent variable, as follows (for a list of control variables, see footnote 1): logW = ao + a3Xj + b1<sup>Z; - e</sup> A problem that had to be overcome was the absence of more detailed industry classification data beyond nine highly aggregated industry categories and, therefore, the absence of sufficient data points for the second stage regressions. The solution chosen is to use occupation in order to subdivide the broad industry classifications into homogeneous categories such as professionals in manufactur-ng, manual workers in electricity and so on; 21 such 2 Only income from worker's principal job is considered.

8
# **1 Introduction**

Non-tariff measures have become progressively more important trade policy instruments as applied tariff rates have fallen across the world in recent years. From a development perspective, technical regulations and product standards are a particularly important type of non-tariff measure because they highlight the fact that the favorable market access accorded under duty and quota-free preferential schemes remains conditional on compliance with regulations in areas such as consumer safety. Previous research shows that product standards and technical regulations in the large, developed markets can have two contradictory sets of effects for developing country exporters. On the one hand, the costs of compliance—retooling, product re-design, testing, and certification—can be substantial enough to keep many small and medium enterprises out of international markets, thereby affecting the pattern of international specialization (e.g., Essaji, 2008). But at the same time, foreign standards can also provide the impetus for firms and sectors to upgrade production technologies and realize beneficial productivity gains (e.g., Maertens and Swinnen, 2009). The question of which types of standards tend to promote which set of effects is clearly of vital policy importance to developing country exporters. The issue of how best to direct technical assistance resources so as to support the upgrading of standards systems and development of compliance mechanisms in developing countries is also an important part of broader Aid for Trade discussions.

Most previous work on standards and technical regulations has focused on the rules themselves, rather than their design and application or enforcement through specific at-the-border mechanisms. There are a number of recent exceptions, however. Karov et al. (2009) focus on identifying the trade impacts of US phyto-sanitary regulations at the product-country level by analyzing the effects of treatment requirements and grants of new market access. Similarly, Alberini et al. (2005) examine implementation of the FDA’s seafood HACCP program using a dataset of plant inspections. Neither paper, however, deals with the food import refusals mechanism that is the focus of the present paper. Buzby et al. (2008) and 2