rafmacalaba/data-use-mentions-extended huggingface dataset:
loss of non-use values (i.e. values people have for natural resources even when they do not use them).

## _Methodology for Valuation of Environmental Damage_

The quantification and monetary valuation of environmental damage involves many scientific disciplines including environmental, physical, biological and health sciences, epidemiology, and environmental economics. Environmental economics relies heavily on other fields within economics, such as econometrics, welfare economics, public economics, and project economics. New techniques and methodologies have been developed in recent decades to better understand and quantify preferences and values of individuals and communities in the context of environmental quality, conservation of natural resources, and environmental health risks. The results from these techniques and methodologies can then be, and often are, utilized by policy makers and stakeholders in the process of setting environmental objectives and priorities. And, because preferences and values are expressed in monetary terms, the results provide some guidance for the allocation of public and private resources across diverse sectors in the course of socio-economic development.

Environmental damage means physical damages that have an origin in the physical environment. Thus, damages to health from air or water pollution are included as well as damages from

> 3 Source:Indiastat

2
# **MEASURING MICROFINANCE ACCESS: BUILDING ON EXISTING CROSS-COUNTRY DATA**

# **Introduction**

The workshop for which this paper was prepared was called to consider the need for a new effort to measure direct access to financial services with a focus on those at lowincome. The paper provides a selective review of the diverse sources of data that exist and considers how best to build upon them.

The financial sector is often considered to be the best-documented in terms of statistics. In some markets price and transaction data is available on a minute-by-minute basis or even more frequently. Yet one can look in vain for a table showing, on a comparable cross-country basis, the percentage of households with a savings or deposit account, let alone a clear picture of the characteristics of these households (income levels, regions, etc). Comparable information on borrowing is also lacking: we have information about the total value of lending by financial intermediaries, but we do not have systematic information for example about how many households or micro-enterprises are borrowing, or how much they are paying in interest. Information about insurance and money transmission services has been equally deficient.

Up to now, there has been no wide consensus on what data is needed in this area, or indeed on whether it should be seen as a priority area. For example, the Fund’s General Data Dissemination Standard (GDDS) does not include this area at all,<sup>2</sup> nor does the UNDP’s Human Development Report. The Bank’s World Development Indicators also lack any specific information on access to finance. On the other hand, quite a lot of relevant data of varying quality and covering widely different aspects has been collected by a variety of agencies over the years.

It is increasingly clear that a wide range of **_private and public sector data users_** do have a growing interest in identifying **_who has access to what financial services_** . This kind of information is crucial, for example
Federation Indonesia South Africa Colombia Peru Argentina Costa Rica Peru Venezuela, RB Dominican Republic Colombia Guatemala Brazil Albania Armenia Tunisia El Salvador Honduras Bolivia Nicaragua Ghana Cambodia Sri Lanka Tanzania Uganda Ethiopia Burkina Faso<br><!-- End of picture text --> Source: CEQ and World Bank databases and World Bank calculations (see World Bank, 2022b). Note: Pensions as deferred income treatment. Relevant international poverty line used for income classes: US$1.90 for LIC, US$3.20 for LMIC, US$5.50 for UMIC and HIC.

Fiscal policy has larger repercussions on rural than urban poverty in Cambodia. Areas with higher levels of pre-fiscal poverty are disproportionately impacted. In other words, the net benefits of the fiscal system are less concentrated in rural and other urban areas where more individuals are impoverished. This is largely due to the impact of indirect taxes. The modelling indicates that the current (pre-COVID) fiscal system increases short-term poverty in rural areas by 2.5 points (Figure 12), higher than the 1.8 points in non-Phnom Penh urban areas, primarily driven by indirect taxation. The larger tax result reflects the greater rate of near-poor in rural areas before fiscal policy, meaning they are more likely to fall under the poverty line when indirect taxes are paid. Notably, three features of rural life are accounted for the results: (i) home production of food and other staples is not subject to indirect taxation; (ii) current preferential indirect tax rates and exemptions are modelled; and (iii) indirect taxes on food expenditures are modeled 19
Policy Research Working Paper 9527

# **Abstract**

Theory suggests several ways in which exporting may benefit women’s employment. However, the empirical evidence is mixed and limited, especially for developing countries. This paper uses firm-level survey data for 91 developing countries to estimate the relationship between exporting and the share of women workers at the firm. The analysis pays close attention to endogeneity concerns. First, it proxies a given firms’ exports by the average exports of all other firms in the same country-year-industry cell. Second, it exploits the repeated cross-section nature of the data and analyzes how changes over time in exporting activity are associated with changes in the share of women workers. The strategy is more immune to endogeneity problems than pure cross-section regressions. Third, it tests several mechanism or mediating factors as predicted by the theory through which exporting impacts women’s employment prospects. The predictions are confirmed in the data, an unlikely scenario if exports were a mere proxy for other correlated drivers of women’s employment. The results show a large, positive impact of higher exports on the share of women workers. A conservative estimate is that for each percentage point increase in the ratio of exports to total sales, the share of women workers increases by 0.16 percentage point. Consistent with the theoretical predictions, this positive relationship is much larger (more positive) in industries that rely more on women workers, in country-industry pairs where competitive pressure is largely from international markets in comparison to less competitive domestic markets, when social attitudes and labor laws are more favorable toward women’s work, and when the law and order situation is more business friendly.

This paper is a product of the Global Indicators Group, Development Economics. 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
results show that the proposed tariff structure will increase consumption for all households in Rajasthan by reducing the retail cost of energy. However, consumption gains will be regressive: the average gains for households in the top income quintiles will be significantly more than the poorest ones. Moreover, the proposed tariff structure will reduce revenues for the utility by 32 percent relative to current levels.

Moving away from ad-hoc determination of tariffs, we then examine if normative principles of electricity pricing can be used to set revenue-sufficient prices that are progressive and efficient. These principles recommend a two-part tariff comprising of variable and fixed costs. The variable cost of energy should be set to the social marginal cost of electricity (the cost of producing an extra unit of energy) for all households. The fixed cost should however vary across households and should be based on their willingness to pay for power.

The social marginal cost of electricity in Rajasthan is calculated using publicly available data on energy generated by power stations at 15-minute block intervals. The marginal cost of generating power is estimated using bidding data from a segment of the wholesale electricity market. The marginal cost of transmission and distribution losses and the external cost of carbon dioxide emissions as a result of thermal power generation is also calculated to estimate the overall social marginal cost of power in Rajasthan. In calculating these costs, the paper shows that including the marginal cost of carbon emissions in the retail price of energy can increase electricity prices by approximately 50%. This result indicates that given India’s current electricity generation mix, passing the full cost of carbon externalities to the retail price of electricity may lead to an impractical rise in the price of power.

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West Forest, which more than offset the decline in poverty among export crop farmers in the Savannah (table 17).20 The large increase in poverty among export crop farmers in West Forest is puzzling, and is considered below in more detail. The reduction in poverty was greater among export crop farmers whose major source of export crop revenue is coffee than among those who depend most heavily on cocoa, as expected.

**Table 16: Headcount Index of Poverty by Socioeconomic Group**

_(Poverty Line= 75, 000 CFAF per year in 1995 CFAF)_

||_Headc_|_ount Index_|_Weig_<br>_popu_|_hted_<br>_lation_|
|---|---|---|---|---|
|_Socioeconomic Group_|_1993_|_1995_|_1993_|_1995_|
|Export crop farm|0.394|0.432|16576|1582|
|Food crop farmer|0.492|0.584|13507|1149|
|Agriculture worker|0.531|0.364|843|237|
|Public sector employee|0.076|0.140|6062|446|
|Private formal sector employee|0.071|0.075|4137|431|
|Private informal employee|0.254|0.310|2464|976|
|Self-employed formal sector|0.078||125||
|Self-employed informal sector|0.248|0.293|8229|267|
|Unemployed|0.202|0.528|661|60|
|Inactive|0.271|0.191|4096|312|

**Table 17 Household Expendit re Per Capita and Headcount Index of Poverty Among Farm Households**

||_East_|_Forest_|_West_|_Forest_|_Sava_|_nnah_|
|---|---|---|---|---|---|---
importance of these other factors suggests that tax policy is a poor instrument to compensate for various negative factors in the investment climate of a country. Many countries from South America to Sub-Saharan Africa have offered investment incentives for businesses to locate in underdeveloped, more costly, and otherwise unattractive regions with little success in generating (sustainable) investment flows to those areas. This experience strongly suggests that the fiscal investment incentives popular in developing countries have not been effective in making up for fundamental weaknesses in the investment climate.

# **III. New evidence from the mid-80s: A look for Details**

The relative little importance of tax policy does not mean that it does not exert any impact on FDI. Looking at foreign direct investment figures, it is certainly not a coincidence that FDI in tax haven countries in the Caribbean and South Pacific grew more than fivefold between 1985 and 1994, to over $200 billion. Ireland's tax policy has been generally recognized as a key factor in its success to attract international investors over the past two decades. In fact, the simple position described in the preceding section was not completely accurate. It is not true that tax policy and incentives fail to attract investors; they do affect the decisions of _some_ investors _some_ of the time.

Even in the 1 970s, there were researchers who started to look into more detailed FDI data and came out with conclusions that made the result of previous studies more vulnerable to criticism. Forsyth in his 1972 study provides support for the view that inducements and incentives may often not play a key role in influencing the decision to undertake a particular foreign investment. However, once other factors have provoked the decisions to set up production facilities in a broad area, then the more precise location decision may be strongly affected by such factors.

> 6 It has to be noted that in principle the locational decision of the firm may be affected by the marginal and average rates in two opposite directions. When the average level is high in one country, it is possible
percent had done any advertising in the last six months, only 9 percent have a sales target for the next year and only 3 percent had made a budget of what costs are likely to be over the next year.

Only 18 percent of these current business owners reported ever having had any form of business training, and the majority of this was technical training. Fewer than 4 percent of owners had ever taken a training course which covered record-keeping, marketing, customer and supplier relations, or management of employees.

Among the potential business owners, only 18 percent have never worked before, but only 8 percent have previously engaged in self-employment. Half of them claim to have taken concrete steps towards opening a business in the past year. These women are about two years younger on average than the current business owners, but are otherwise similar in many respects. In particular, the potential and current business owners have similar levels of education, similar cognitive ability (as measured by digitspan recall and raven tests), similar attitudes towards risk, 6
initial survey estimators to their corresponding true values as the total sample size _n_ tends to infinity ensures that the bootstrap MSE approximates the true MSE (under the model with the true values of the parameters).

# **6 Extended Census EB estimators**

The EB estimator in MR (2010) requires linking the survey and census households, which may not be possible in real applications. In fact, the survey sample is likely not a subset of the available census. Hence, here we consider the Census EB (CEB) estimator, which avoids this step. In fact, in most cases, the number of sample households for a given area is much smaller than the number of census households and, in such cases, the CEB estimator is expected to perform very much like the original EB.

CEB small area estimators are obtained with a similar MC simulation approach as the one used by MR (2010) and described in Section 3, but simulating the vectors of welfare for all the census households

> 17These may come from the census or administrative records (ELL, 2003 p356).

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financial institutions and another 30% stated it had been “somewhat important”. Only one PCR, notably that of Mexico, was deemed “of little importance” for supervision; Argentina and Brazil both indicated that the registry was “very important” for banking supervision.<sup>17</sup> Argentina, Brazil and Mexico all view bank supervisors as the primary users of PCR data.

Regulators overwhelmingly report that they look at the financial institutions’ use of credit reports in lending as part of the supervision process (53/57) and most use PCR data to support both their on-site (53/57) and off-site (49/57) supervision responsibilities. More than 80% (47/57) use PCR-data to analyze trends in banking and credit. However, only about 30% (17/57) report that they use statistical models to analyze PCR data; Argentina and Mexico are among this group of 30 but Brazil is not. The most frequent use of public registry data is to calculate the total indebtedness of borrowers with the financial system. A simple summation by borrower across institutions is the most important application for the rich datasets found in public registries for 30 of the 57 PCRs in the survey.<sup>18</sup> Supervisors also indicated the importance of the ratings in PCRs as a supervisory tool. Ratings are often used to support more forward-looking provisioning policies. Rather than relying solely on information on a loan’s past-due status, regulators may require banks to assign a more forward-looking “rating”. The rating is typically assigned by the lender to either the borrower (the most common) or to each line of credit according to a common scale determined by the regulator. Ratings may depend on arrears and cash-flow projections and estimated probabilities of repayment to obtain a more forward-looking measure.<sup>19</sup> Consistency rules between the ratings of different lenders to the same borrower are often established e.g.; two lenders cannot input ratings that differ
Policy Research Working Paper 9273

# **Abstract**

This study examines the asset gains to households in Kerala, India, from two types of labor migration: moving overseas versus moving within India for employment. It draws on panel data from waves of a representative household survey conducted in 1998 and 2003. Migrant households as a whole experienced higher asset gains than non-migrant families over this five-year period. Contrary to theoretical expectations, asset gains were similar for households with an overseas migrant and those with a domestic migrant. Although less educated individuals tend to venture overseas, a wage premium over non-migrants enables them to earn as much in low-skill jobs abroad as more educated workers relocating within India can.

This paper is a product of the Social Protection and Jobs 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 author may be contacted at gseshan@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
both import shares and export shares are useful in the weighting, we take one half the shares of both exports and imports as the weights. We obtained the data for the import and export shares from the COMTRADE database. The resulting elasticities are reported in table C1.

For the sensitivity analysis, we must take upper and lower bounds of the elasticities. Unfortunately, Broda et al. only report point estimates in their on-line Excel file. In the paper itself, however, Broda et al. (2006, table 4, p. 36) list the standard error of the median Dixit-Stiglitz estimate for the country. The standard errors are rather small for most countries. In the case of Madagascar, the median Dixit-Stiglitz elasticity is 3.6 with a standard error (of the median estimate) of 0.27.

Although we don’t have standard errors at the product line level, we use the overall estimate of the standard error to guide our choice of upper and lower values of the estimates. The low standard error suggests that we do not need to take very large ranges to check for robustness. For central values of 3.6 or less, we took plus or minus three times the average standard error of 0.27. CGE modelers have often doubled and halved the central estimate in the sensitivity. For central values greater than 3.6, we took plus or minus 50% of the value of the parameter, which gives a wider band than plus or minus three times the standard error. For these elasticities, this presents a tougher test of the robustness of our model.

Broda, Christian , Joshua Greenfield and David Weinstein (2006), “From Groundnuts to Globalization: A Structural Estimate of Trade and Growth,” National Bureau of Economic Research Working Paper 12512. Available at: http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/T radeElasticities.html.

111
Figure 7: State-Industry Total Factor Productivity <!-- Start of picture text --> State−Industry Total Factor Productivity<br>Year<br>Aggregate Average<br>Reallocation<br>19851986198719881989199019911992199319941995199619971998199920002001200220032004<br>.2<br>.1<br>TFP<br>0<br>−.1<br><!-- End of picture text --> Total factor productivity (TFP) decompositions for the population of firms, conducted at the stateindustry level, using the OP method. “Aggregate” indicates market-share weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. Source: Authors’ calculations based on Annual Survey of Industries data.

42
that can indirectly affect the food supply. For instance, some of the non-edible components, upon further processing, may be used as feed for livestock, which is an important source of calories for human consumption. Meanwhile, the FAOSTAT production data reports the total production quantity of the crop, which includes both edible and nonedible components of the crop. In the present study, we assume that the relative ratio of edible and non-edible components for a given crop remains similar over the years. As such, the indirect contribution of calorific content from non-edible components should not severely bias the results.

# **4. Results**

## **_4.1 Production and yield patterns_**

Figure 3 plots the global area, calorific production, and calorific yield across the 144 crops over the sample period, indexed to 100 in 1961. Global crop production nearly quadrupled over the past six decades on a calorific basis. Most of the increase reflects yield advancements, with cropland expansion experiencing much smaller growth. Figure 3 shows that the aggregate global yield has risen from approximately 4,330 _KCal/ha_ in 1961 to almost 11,000 _KCal/ha_ in 2021. Such growth, amounting to a 158% increase, closely mirrors the 152% increase in the global population over the same period.

Figure 4 further shows that much of the yield growth was driven by the Big-4 commodities (maize, wheat, rice, and soybeans), whose yields almost tripled from 4,826 _KCal/ha_ in 1961 to 14,323 _KCal/ha_ to 2021. In contrast, the yield of the “other” category (which includes the remaining 140 crops) grew by less than 100% over the sample period, with their 2021 calorific yield only half that of the Big-4.

Table 1 provides a more detailed decomposition of the area, yield, and production growth of the Big-4 versus other crops. We compute the average area, yield, and production for the first (1961-63, Panel A) and last (