rafmacalaba/data-use-mentions-extended huggingface dataset:
The model incorporates technological progress as exogenous improvements in energy factor productivity, as well as the cost reduction trend in renewable electricity generation.

# **7. Parameters and Calibration Methods**

In this section, we provide a brief overview of the calibration process for our model. For a more comprehensive understanding of the data sources, data processing methods, and calibration techniques, we recommend referring to Goulder _et al_ . (2023).

For any CES function of the form in Equation (A1), the Lagrangian equation for obtaining the composite _V_ at minimum cost is given by: where _pi_ is the price of input<sup>_v_</sup> _i_<sup>. From this minimization problem, the optimal demand of input</sup><sup>_v_</sup> _i_<sup>per unit of the composite</sup><sup>_V_is derived as:</sup> Therefore, the share parameters of CES functions that have the functional form of Equation (A1), α _i_ can be calibrated by inverting the optimal input intensity function:

> where α _i_ is the share parameter of the CES production function, _V_ the output quantity,<sup>_v_</sup> _i_<sup>the</sup> quantity of input _i_ ,<sup>_p_</sup> _i_<sup>the benchmark price of input</sup><sup>_i_and</sup><sup>_p_the benchmark price of output.</sup>

Data for calibration includes the 2017 China’s input-output table and a firm-level dataset<sup>_v_</sup> _<u>i</u>_ from the Ministry of Ecology and Environment of 2017, which can be used to derive the “ _V_<sup>”</sup> component in Equation (A4). The elasticities of substitution ( σ ) at different levels of the nested CES structure are obtained from calibrations and various sources. Table B2
business sophistication: state of cluster development<br>(WEF GCI 11.03)<br>•<br>Enterprise ownership (government, private foreign, private<br>domestic) (ADI)|
|**Gov**|**ernment procurement**|•<br>Cost of business start-up procedure / procedures to|
|•|Adjustment of the laws pertaining to public procurement, including<br>transparency, selection criteria, national preference, etc.|register a business (WDI)<br>•<br>Time spent in meetings with tax officials / expected gifts /<br>informal payments to public officials (WDI)|
|**Corr**|**uption**|•<br>Firms using banks to finance investment (WDI)|
|•|Reforms aimed at fighting corruption in the public (e.g. customs) and<br>private sectors|•<br>Strength of legal rights index (WDI)<br>•<br>Time required to enforce a contract (WDI)|
|•|Promotion and adoption of relevant international instruments|•<br>Time required to obtain an operating license / register<br>property / start a business (WDI)|
|**Adm**|**inistrative burden**|•<br>Value of seized counterfeited goods (national statistics)|
|•|Administrative reforms towards simplification and reduction of<br>administrative procedures (e.g. guillotine reform), increase in transparency,<br>predictability, timeliness, and security of administrative decisions (e.g.<br>suppression of authorizations)|•<br>Number of registered trademarks, patents, etc. (WIPO,<br>WDI)<br>•<br>Number of competition investigations and sanctions<br>(national statistics)<br>|
|||•<br>Public procurement
# **3.1.1** **_Urban versus Rural Nonfarm Entrepreneurs_**

The differences of business constraints perceived by urban and rural entrepreneurs are quite dramatic according to a number of empirical analyses (World Bank 2006a).Figure 3.1 shows Yemen’s rural and urban business constraints perceived by entrepreneurs. As expected, in the informal settings characteristic of the country’s rural areas, government regulations are considered as less major constraints on rural nonfarm entrepreneurs, whereas those regulations, such as land access, courts, and tax rate, do matter for urban entrepreneurs. Corruption is quite a bit higher for urban entrepreneurs (65%) though it is also relatively high for rural entrepreneurs (about 40%).

**Figure 3.1 Comparisons of Urban Small and Rural Entrepreneurs**

<!-- Start of picture text --> urban small<br>rural<br>Customs<br>Electricity Business permit<br>79<br>Financial access Land access<br>65<br>Informal sector<br>57 Courts<br>competition<br>49<br>Transport 40 Tax rates<br>65<br>Corruption Crime<br>Telecommunication Labor edu<br><!-- End of picture text --> _Source:_ Yemen Investment Climate Survey 2009; Yemen RIC Survey 2010 both weighted.

_Notes:_ Informal sector competition in urban is compared with market related obstacles in rural due to mostly informal in rural: Rural enterprises are all household enterprises and small/micro enterprises, whereas urban small enterprises have 5 to 19 employees.

# **3.1.2** **_Constraints Perceived by Farm versus Nonfarm Entrepreneurs_**

Farm and nonfarm entrepreneursare confronted with similar business constraints with slightly different variations (figure 3.2). The top constraint, which is perceived by rural entrepreneurs, is electricity for nonfarm enterprises and water for farm enterprises. Water for farm enterprises can be understood by
( _≈_ 1 _−_ (2 _._ 4 _/_ 2 _._ 9)<sup>2</sup> ) reduction in variance. Without further information, however, it is hard to determine how much of this cost-precision trade-off can be attributed to the fact that their double sampling estimator substituted predicted data for real data.<sup>15</sup> It is unlikely that the gains are due to a favorably low level of _τ_ as _τ_ is estimated to be around 0 _._ 6 in their case, which is in the mid range. If the gains can indeed be attributed to the use of predicted data, then it is more likely that this is due to favorable values of _wh_ and _wc_ .

# **5 Discussion**

The primary motivation for the use of prediction in economics, health sciences, and other disciplines has been to deal with various forms of missing data problems. One could also make a case for adopting prediction estimators to obtain more cost-efficient estimates of the population mean when it is expensive to observe the outcome of interest in comparison with its covariates. For example, consider the estimation of poverty and malnutrition rates. The conventional sample direct estimators in this case require household- and individual-level data on expenditures and health outcomes. Collecting this data is generally costly. It is not uncommon that in developing countries, where poverty and poor health outcomes are most pressing, statistical agencies do not have the budget that is needed to collect these data frequently. As a result, official estimates of poverty and malnutrition are often outdated. This then makes it difficult to monitor progress (or lack thereof) in times when circumstances might be subject to considerable change, such as shocks to international staple prices, domestic climate shocks, etc. Using predicted data as a substitute for real data may then offer a valuable alternative.

In recent years, a number of studies have explored the option of predicting household expenditure data into existing secondary surveys in an
# Table A8: Outcomes List and Description

|Outcome|Source|Outcome construction|
|---|---|---|
|Survey<br>completed<br>(completion<br>rate)|Monitoring data|Dummy indicator that takes the value of 1 if the<br>KI survey was completed (either by phone or<br>SMS), and 0 if it was incomplete after 3 days.|
|Consistency on tolerance towards<br>child punishment as in baseline<br>(follow up) survey.<br>Outcome de-<br>fned as ”physical punishment is ef-<br>fective”|Survey instruments<br>(KI,<br>baseline,<br>and<br>follow up surveys)|Dummy indicator that takes the value of 1 if the<br>response in the KI survey was the same as in the<br>baseline (follow up) survey, 0 otherwise.|
|Consistency on high stress level as<br>in baseline (follow up).<br>Outcome<br>defned as ”High stress”|Survey instruments<br>(KI,<br>baseline,<br>and<br>follow up surveys)|Dummy indicator that takes the value of 1 if the<br>individual reported having high stress level in<br>the KI survey and in the baseline (follow up) sur-<br>vey, 0 otherwise.|

_Notes:_ This table describes the outcomes used in the main analysis of the survey-mode experiment. It also presents the source of the data and a description of how the outcome was constructed. “KI” stands for Knowledge Incorporation Survey.

45
following export bans.<sup>5</sup> The NBR depends on the contribution of maize to household income (production ratio, PR) and its share in total household expenditure (consumption ratio, CR) as well as the magnitude of change in maize price. Based on IHS4, Table 6 summarizes PR, CR, and NBR as well as the share of households that are net-buyers, net-sellers, and self-sufficient. Maize consumption represents 23 percent of total consumption, while maize production accounts for about 10 percent of the income. About 65 percent of the households are net buyers of maize, that is, their maize consumption is higher than production (in value terms), while about 12 percent are net-sellers. About 24 percent of the households are self-sufficient, that is, they produce maize only for their own consumption without any maize sales or purchases in the market.<sup>6</sup> There is some heterogeneity by residence (urban/rural), region, poverty status, and consumption quintile. For example, rural residents and the poor have high PR and CR, and a larger share of them are self-sufficient, compared to urban residents and the non-poor households.

Lower maize prices typically lead to welfare losses for farm households that have a surplus harvest and rely heavily on maize sales for their incomes. Whereas, those farmers who do not have enough maize harvest (and non-farm urban residents) and purchase maize for consumption would benefit from lower maize prices. As shown in Table 6, the majority of households, both in urban and rural areas, are net-buyers. Therefore, in the short run, a lower maize price due to export bans is likely to benefit these households. The ban is likely to hurt farmers that are net-sellers, but these farmers make up only 12 percent of Malawi’s households. The magnitude of the short-term gains, however, is very low. The gains make up a relatively higher share of rural households’ consumption (0.86 percent)
.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

Finally, Tables 9a to 9c shows the difference-in-difference estimates of the impact of PROSPERA on ENLACE achievement tests for seventh grade, ninth grade and twelfth grade. For seventh and ninth grade, the coefficients report the increase in points achieved on the ENLACE exams due to the program. Because of the test score standardization, the estimates represent the impacts in standard deviations if one divides these coefficients by 100.

24
quarters of (the poorer) world population are needed to get to the first 1/5<sup>th</sup> of total income, but only 1.7% of those at the top suffice to get to the last one-fifth.

Global inequality is much greater than inequality within any individual country. In Figure 3, global Gini of 70 is shown together with the Ginis for several countries. Global inequality is substantially greater than inequality in Brazil, a country that is often held, despite the recent improvements under the Lula presidency, as an exemplar of excessive inequality. And it is almost twice as great as inequality in the United States.<sup>5</sup> Figure 3. Global Gini coefficient compared to the Ginis of selected countries <!-- Start of picture text --> World<br>Brazil<br>USA<br>Sweden<br>1970 1980 1990 2000 2010<br>year<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br><!-- End of picture text --> How confident are we that these numbers truly reflect what is happening to inequality among world citizens? The global inequality numbers come from calculations done across representative national surveys which monitor incomes or consumption of households. About 120 such surveys stand behind each of the six dots shown in Figures 2 and 3. These 120 surveys include actual incomes or consumption levels for about 10 million people in the world. This is about 1.5% of the current world population, not a negligible number, and in principle, sufficiently

> 5 The vertical axis in Figure 3 shows Gini coefficient in its “natural” values, i.e., not in percentages. Thus a Gini of 0.7 displayed there is the same as a Gini of 70. For simplicity, we use the second approach throughout the paper.

9
the dataset to ensure comprehensiveness. Second, after initial survey responses were received, they were cross-checked by an external consulting company (PKF UK and its partner Auxilium), World Bank staff, and a senior supervisory expert. In cases of obvious errors and inconsistencies in the responses, the regulators were contacted and requested to correct their responses. Finally, after cross-checking the responses obtained, the dataset was sent back to the regulators for their review and correction (if necessary).<sup>5</sup> Responses to some of the quantitative questions in Section 13 and some other sections of the questionnaire were excluded from the published version of the survey due to issues of comparability. Those questions cover topics on which consistent cross-country data are already available, such as total banking system assets. These questions are indicated with an asterisk in the guide to the survey.

The detailed country-by-country survey results and the guide to the questionnaire are available online, at <u>http://www.worldbank.org/financialdevelopment. The rest of the paper</u> highlights the main findings from the survey. More detailed tables and discussion of survey results can be found in Appendix I.

# **III.   DIFFERENCES BETWEEN CRISIS AND NON-CRISIS COUNTRIES**

Much has already been written about the causes of the recent global crisis (Caprio, Demirgüç-Kunt, and Kane, 2010; Demirgüç-Kunt and Serven, 2010; Rajan, 2010, among others), and discussion on the explanations of the crisis is still ongoing. In this section, we do not attempt to sort out among the many possible contributing factors that brought about the crisis, but rather focus on the potential role of gaps in regulation and supervision. To this effect, we set

> 5 74 countries responded with minor changes.

6
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE generation capacity over the next decade—a near doubling of current installed capacity. About 300 megawatts of coal and geothermal capacity are already in the pipeline. The country will also need to develop or reinforce cross-border transmission links with Ethiopia, Tanzania, and Uganda to provide access to relatively inexpensive hydropower and improve overall system security.

**Figure 6. Kenya’s power sector has much lower hidden costs than most other African countries**

Hidden costs of power utilities in selected countries <!-- Start of picture text --> Congo, Dem. Rep. of<br>Ethiopia<br>Tanzania<br>Uganda<br>Kenya<br>South Africa<br>0 100 200 300 400 500 600 700<br>% of revenues<br>unaccounted losses collection inefficiencies underpricing<br><!-- End of picture text --> Source: Briceño-Garmendia and others 2009.

**Figure 7. KPLC pricing and management reforms have saved Kenya more than 1 percent of its GDP**

Reduction of hidden costs in power sector, 2001–08 <!-- Start of picture text --> 100 2.0<br>75 1.5<br>50 1.0<br>25 0.5<br>0 0.0<br>2001 2002 2003 2004 2006 2008<br>underpricing undercollections<br>distribution losses total as % of GDP<br>% of GDP<br>% of revenues<br><!-- End of picture text --> Source: Derived from Briceño-Garmendia and others 2009 and more recent data provided by World Bank country staff.

**Table 6. Benchmarking power indicators**

Unit Low-income **Kenya** Middle-income 15
surveyed. Importantly, earth observation data suggest that 2016, the year the MAPS data was collected, was a drought year (FAO, no date). The data may thus reflect a similar situation as in 2015 in Ethiopia where self-reported losses where conspicuously high compared to average yields and suggest that in drought years we observe an “overreaction” in farmers’ reports.

# <u>What do we conclude and still need to know?</u>

While farmer reports and expert assessments show some overlap, they do not match very well and display some striking discrepancies that are not explainable by small differences that would be expected in the appraisal process. This may serve as further suggestive evidence that while eliciting losses on a percent scale gives a decent ordinal proxy in most cases, forming an exact point estimate is cognitively challenging, may lead to satisficing behavior, and is thus prone to error. This seems to be exacerbated in drought years where farmers’ reports are particularly high. At the same time, we cannot ascertain the accuracy of enumerator reports with confidence either as enumerators may err just as well when it comes to losses at the extensive and intensive margins. This makes it hard to proclaim enumerator assessments a credible ‘gold standard’. In order to better understand the accuracy in farmers’ self-reported losses, finding a dependable, “objective” measure of losses is thus still an open question for future methodological research.

# <u>Question 3: Can data from past harvests or unaffected plots serve as a suitable benchmark for attainable harvest?</u>

The FAO methodology heavily relies on the ability to obtain an accurate estimate of the attainable yield. For this, it suggests drawing on past harvest data to calculate the multi-year trend (or average) of realized yields as an indication of in-season attainable yields (Conforti, Markova and Tochkov, 2020). Alternatively, using same-season realized yields from plots unaffected by any disaster, for example average yields from an agriculturally comparable district, would be a possibility (
Policy Research Working Paper 6169

# **Abstract**

All-weather rural roads usually improve not only villagers’ terms of trade, but also their educational attainments and health. Obtaining empirical estimates of the benefits generated by the first is straightforward, not so those generated by the others. The object of this paper is to estimate the relative sizes of their respective contributions to total benefits in connection with the all-India rural roads program _Pradhan Mantri Gram Sadak Yojana,_ using an overlapping generations model featuring the production and consumption of goods and the formation of human capital in the presence of both morbidity and mortality. Based on survey evidence from upland Orissa in India and Bangladesh, as well as elements of more usual forms of calibration, the model yields a ratio of commercial to non-commercial benefits of about two-to-one in the first generation, falling to three-tofour in the second. This is broadly consistent with the valuations expressed by respondents in the Orissa survey, who ranked the latter benefits at least on a par with the former.

This paper is a product of the Trasnport Unit, South Asia 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 clive.bell@urz. uni-heidelberg.de.

_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
The sharp result that comes out of this simple example follows from three assumptions. First, the income shock is saved. Second, investing in foreign capital is not an option for the country. Third, the marginal unit of wealth is allocated among assets as the average one is. We maintain the first two assumptions throughout the paper without (excessive) apologies. The first assumption is a basic tenet of consumption-smoothing models of savings. Despite some empirical failures of the simplest of these models, we feel the jury is still out regarding the relative importance of consumption-smoothing as a savings motive at the business cycle frequency that we focus on here.' The second assumption can be easily removed.<sup>2</sup> If we keep the other assumptions, a favourable income shock still leads to a current account deficit if and only if the share of domestic capital in the country's wealth exceeds one or, equivalently, if and only if foreign debt exceeds the stock of outward foreign investment. Otherwise a favourable income shock leads to a current account surplus.

The bulk of the theoretical effort of this paper is devoted to assessing the merit of the third assumption underlying our simple example, namely, that the marginal unit of wealth (savings) is invested in the same proportions as the stock of wealth. To do so, we construct a simple world equilibrium model in which productivity varies across countries and intemational borrowing and lending takes place to exploit good investment opportunities. In the model, we distinguish between production uncertainty and random changes in technology. In each date, some countries have "good" production functions that exhibit high average productivity, while other countries have "bad" production functions that exhibit low average 1 The importance of consumption-smoothing depends on the frequency of the data one is analyzing. It is obviously important for the analysis of quarterly data (most people spend more than they eam over Christmas and other holidays, and somewhat less than they earn in other times), and almost as surely is a bad