rafmacalaba/datause-encoder-data huggingface dataset:
- 38 -

The Japanese ESCO projects are costly investment to reduce CO2 emissions. The unit cost is about JPY60,000 or $750 per tCO2 for a 10 year period (Figure 14). In general, the existing literature shows that the cost of the normal energy efficiency investment is on average $76 per ton of oil equivalent or $11 per barrel of oil over a 10-year investment period (Taylor and others 2008). However, the Japanese ESCO investment corresponds to $320 per barrel of oil.<sup>34</sup> This is largely because Japanese public buildings are already efficient in energy use, as discussed above.

**Figure 9. Policy Objectives of Energy Efficiency Projects by Japanese Municipalities**

|90||||
|---|---|---|---|
|0<br>10<br>20<br>30<br>40<br>50<br>60<br>70<br>80<br>Reduce<br>energy costs<br>Reduce total<br>O&M costs<br>f filiti<br>Meet energy<br>efficiency<br>tdd<br>Contribute t<br>emissions<br>dti<br>Number of projects|o<br>Support<br>industrial<br>li|||
|o aces<br>sanars<br>reucon|pocy|||
|Note: Multiple answers.<br>_Source_: Author’s survey data.<br>**Table 9. Investment, Outcomes (target) and Subsidy in**<br> <br>|**Japanese E**<br> <br>|**SCO Pr**<br>|**ojects**<br>|
|Obs<br>Mean|Std. Dev.|Min|Max|
|Investment (JPY million)<br>86<br>244.9|<br>248.7|<br>17.8|<br>1,738.5|
|Energy cost savings target (JPY million)<br>92<br>31.4|<br>34.4|<br>0.9|<br>171.0|
|Payment to ESCO (JPY million)<br>84<br>30.3|<br>32.9|<br>0.7|<br>144.9|
|CO2 reduction target (tCO2)<br>83<br>793.0|<br>1,061.8|<br>24.0|<br>4,900.0|
|CO2 reduction rate target(%)<br>5<br>18.1|<br>9.2|<br>10.5|<br>30.7|

_Source_ : Author’s survey data.

34 It is assumed that carbon dioxide emissions per barrel of crude oil are 0.43 tCO2 per barrel.
assets to generate profits. This widely used productivity measure helps creditors and investors make lending or investment decisions, as it is assessed as proxy for repayment and compared with the opportunity cost of the capital.

Different studies use different definitions of capital assets. For example, the LSMS survey usually asks for information about both fixed assets (land, building, equipment and machinery, furniture, small or large tools, vehicles, and other durable goods) as well as raw material and finished-goods inventory, but does not generally collect information on financial working capital such as cash (Grosh and Glewwe, 2000). In their study of the return to capital in Sri Lanka, de Mel, McKenzie, and Woodruff (2009) include both fixed and working capital. Samphantharak and Townsend (2011) have done a comprehensive analysis of rate of return using the Townsend Thai Monthly Survey. They presented ten different measures of household enterprise income or enterprise assets, and their associated rates of ROA. This strategy allows them to study the sensitivity of ROA when only one component of either profit or assets is changed.

Following their work, we first calculate the profit (accounting for the cost of family labor). We then take a measure of capital assets by combining the working capital and the imputed value of the enterprise. Finally, we divide the profit by this imputed value of assets to generate rate of return. We also define a second measure of productivity, which is the profit margin, and defined by profit as a percentage of revenue. The profit margin is an indicator of an enterprise’s pricing strategies and how well it controls costs; that is, how cost-effectiveness its performance is across sectors. A higher profit margin indicates a high margin of safety. The two measures of productivity for different enterprise activities are reported in Table 10.

As Table 10 indicates, the transport sector has the highest profit margin across all three survey years, indicating that it is performing well. It also earns the highest rate of return on assets, on average, compared to enterprises in other sectors. This is because probably road infrastructure was 15
<!-- Start of picture text --> 0.2<br>\ rn w<br>7<br>O’.\*<br>I<br>‘¥<br>t<br>-0.2<br>-0.4<br>1995:1 1998:1 2001:1 2004:1 2007:1 2010:1 2013:1 2016:1<br>——— All countries —-—--- Advanced countries<br><!-- End of picture text -->
Panel B: Adding noncognitive skills

||Total<br>Difference|Difference in<br>endowments|Difference in<br>coefficients|
|---|---|---|---|
|Bolivia|0.652|0.116|0.536|
||_0.207_|_0.063_|_0.193_|
|Colombia|0.298|0.034|0.264|
||_0.132_|_0.042_|_0.121_|
|Georgia|1.053|0.065|0.989|
||0.404|0.048|0.397|
|Ukraine|0.107|0.057|0.050|
||_0.122_|_0.033_|_0.115_|
|Vietnam|0.067|0.093|-0.026|
||_0.093_|_0.030_|_0.097_|
|Kenya|0.097|0.139|-0.042|
||_0.300_|_0.046_|_0.292_|

Panel C: Adding cognitive skills (reading proficiency scores)

||Total<br>Difference|Difference in<br>endowments|Difference in<br>coefficients|
|---|---|---|---|
|Bolivia|0.712|0.113|0.599|
||_0.208_|_0.063_|_0.193_|
|Colombia|0.292|0.032|0.259|
||_0.146_|_0.042_|_0.136_|
|Georgia|1.131|0.061|1.070|
||_0.357_|_0.049_|_0.350_|
|Ukraine|0.098|0.053|0.045|
||_0.117_|_0.033_|_0.109_|
|Vietnam|0.064|0.094|-0.029|
||_0.096_|_0.030_|_0.100_|
|Kenya|0.091|0.140|-0.048|
||_0.271_|_0.047_|_0.264_|

Panel D: Adding cognitive skills (complexity of computer use at work)

||Total<br>Difference|Difference in<br>endowments|Difference in<br>coefficients|
|---|---|---|---|
|Bolivia|0.709|0.136|0.573|
||_0.213_|_0.063_|_0.200_|
|Colombia|0.298|0.020|0.278|
||_0.110_|_0.044_|_0.097_|
|Georgia|0.906|0.049|0.857|
||_0.330_|_0.051_|_0.323_|
|Ukraine|0.104|0.048|0.056|

25
Table 2A. Changes in exchange rates: stylised facts (in percent)

||Mean|Volatility|Maximum|Minimum|
|---|---|---|---|---|
|**Real effective excha**|**nge rate**||||
|1971:1-2016:3|0.22|6.11|47.80|-22.66|
|1971:1-1984:4|0.62*|6.50***|47.80|-15.39|
|1985:1-2016:3|0.10|5.99|39.02|-22.66|
|**Nominal effective e**|**xchange rate**||||
|1971:1-2016:3|0.30|6.42|43.50|-23.70|
|1971:1-1984:4|-0.32***|6.91***|31.92|-20.53|
|1985:1-2016:3|0.58|6.18|43.50|-23.70|
|**Output**|||||
|1971:1-2016:4|2.30|2.61|28.08|-9.26|
|1971:1-1984:4|2.60***|2.62|10.76|-4.10|
|1985:1-2016:4|2.24|2.61|28.08|-9.26|

Note: The mean indicates the average year-over-year growth rate. Volatility is the standard deviation of the growth rate. Maximum (minimum) is the maximum (minimum) growth rate. The sample consists of 18 advanced economies. *** and * indicate that the results for the period Q1 1971 to Q4 1984 are statistically different from those for the period Q1 1985 to Q4 2016 (or Q3 2016) period at the 1% and 10% levels, respectively.

Table 2B. Correlations between exchange rates and output (correlation coefficient)

|||**Lags**||||**Leads**||
|---|---|---|---|---|---|---|---|
||-3|-2|-1|0|1|2|3|
|**Real effective exch**|**ange rate**|||||||
|1971:1-2016:3|-0.11|-0.10|-0.08|-0.04|0.01|0.05|0.08|
|1971:1-1984:4|-0.18|-0.21|-0.15|-0.09|-0.05|-0.03|-0.04|
|1985:1-2016:3|-0.10|-0.08|-0.06|-0.03|0.02|0.06|0.09|
|**Nominal effective e**|**xchange r**|**ate**||||||
|1971:1-2016:3|-0.07|-0.06|-0.04|-0.01|0.02|0.04|0.06|
|1971:1-1984:4|-0.06|-0.05|-0.01|0.03|0.06|0.06|0.02|
|1985:1-2016:3|-0.07|-0.05|-0.03|0.00|0.03|0.06|0.07|

Note: The average within-country correlation between the year-over-year growth rates of exchange rates and output is presented. The sample consists of 18 advanced economies. Lags (leads) indicate that output is shifted one or more quarters forward (backward) relative to exchange rates.

50
percent).<sup>23</sup> Put another way, migrants share about 2.7 percent of their very substantial growth by insuring family members at their original location.<sup>24</sup> This is a lower-bound estimate because we cannot exclude the possibility that we are only measuring a subset of relevant shocks: if shocks are self-reported then respondents may fail to mention those that were effectively insured. Fortunately, the survey provides an alternative shock measure, which is not self-reported. We have historical rainfall data from the Tanzanian Meteorological Agency for gauges in 8 weather stations across the Kagera Region. Each baseline village can be linked to its closest rainfall station as both databases record GPS location. The mean distance to a rainfall station is 19 km and the median is 15 km. For each village we can calculate average monthly z-score deviations of rainfall during the two rainy seasons, in relation to the 30 year average (1980-2010) for that village. Rainfall shocks are then constructed by truncating the positive yearly average rainfall deviations to zero. We calculate a non-migrant household’s own shock as the most negative shock in the 1994-2009 period. The first two columns in Table 10 show that rainfall shocks are important in determining consumption growth, with every standard deviation decrease in (negative) rainfall deviation causing consumption growth to decline by 20 to 32 percentage points, depending on the specification.

# _[Table 10 here]_

Knowing that rainfall shocks drive the incomes of the stayer households, we can use them as an alternative shock indicator. We replace the network shock variable with the baseline village rainfall shock variable in Equation (6). This rainfall shock is constructed as the most negative rainfall deviation in the baseline village after the migrant left. With baseline village fixed effects, the specification exploits the variation arising from the fact that migrants leave at different times.<sup>25</sup> Column 3 repeats the results from Table 8 for comparison purposes and column 4 replaces the network shocks in non-migrant households with the baseline village rainfall shock.

> 23 We use geometric (rather than arithmetic) means to calculate the average annual growth rates.

> 24 The 95%-confidence interval for the annual growth penalty is [0.0002, 0.1515] and for the 'kin-tax' [0.01, 5.40].

> 25 Replacing the baseline village fixed effects with district fixed effects yields nearly identical results.

21
The discretized transaction dynamics of the model are as follows: where, It = UIt + DIt, and Rt = URt + DRt. The typical dynamics of the basic SIR model and our model are shown in Figure 1.

Similar to Cakmakli et al. (2020), we assume that effective vaccination (Vt ) yields immediate treatment for COVID-19 and directly reduces the number of susceptible people. The number of effectively vaccinated individuals is calculated as the assumed number of people receiving vaccinations multiplied by the estimated effectiveness of COVID-19 vaccines. In all scenarios, the effectiveness of COVID-19 vaccines is assumed to be 85 percent—broadly in line with the average of recently reported effectiveness—to accommodate the rollout of several vaccines of varying effectiveness (Fitch 2020; Moderna 2020; Pfizer 2020). Moreover, in the baseline scenario, 71.5 percent of a country’s population is assumed to be amenable to inoculation based on global survey evidence (Lazarus et al. 2020). Lastly, given the lack of precedent for the emergency rollout of vaccines on a global scale, Vt is assumed to follow a pre-set exogenous path rather than a stochastic process. Specifically, the vaccine rollout is assumed to follow a sigmoid curve: a slow initial rollout gives way to large-scale vaccination efforts and a subsequent tapering as the population amenable to vaccination rapidly declines (Figure 2.A).

The SIR model is converted into a state-space form that can be summarized as follows: where yt denotes the observable variable, which in our case, is the seven-day moving average of new daily confirmed COVID-19 cases reported by the Center for Systems Science and Engineering at Johns Hopkins University (Dong et.al, 2020).<sup>4</sup> xt summarizes the evolution of the five possible states: St, UIt, DIt, URt, and DRt . The function p(xt|xt−1, θ) denotes the system associated with moving from xt−1 to xt, which in our application is the SIR model described in equation (1). θ= {γt, βt, α, ... } summarizes all the parameters and hyper parameters, which as in Zhou and Ji (2020), includes the following critical stochastic parameters:

> 4 The moving average is used to temper the volatility in weekly reporting patterns.

5
# _Cultural center for creative content industries_

Creative content providers also concentrate in Beijing, but do not cluster in specific locations as the IT firms in Zhongguancun area. Beijing is the cultural center of the country, with the most national universities and research institutes, as well as the largest population with higher education in China, which provides both the content producers and the demanding buyers for cultural goods and services. However, there is still another reason responsible for this spatial concentration of content industry, that is, the monopoly control in this sector by the central government, especially of print publishing and public media.

The co-location of the IT industrial cluster and the content sector in Beijing provided a good environment for the rapidly expanding Internet economy in the late 1990s and the beginning of the new millennium. The potential growth of a new interactive media in the country with the largest population in the world gave both technologists and venture capitalists promising opportunities. The population of Internet users in China keeps increasing quickly, reaching 84 million in 2004, 10% of which are residents in Beijing (about 4 million) and Shanghai (about 4.3 million). And Beijing still has the largest number of Internet domains in China, 84,144, about 24.9% of the national in total, leading second-ranked Shanghai (8.5%) by nearly three times (CNNIC, 2004).

# **Innovative industries in Shanghai**

Shanghai, as well as its surrounding region in the Yangtze River Delta, has a solid industrial base and innovation capability, particularly related to manufacturing. Table 11 compared the R&D activities in manufacturing in Beijing and Shanghai, showing the advantage of the latter.

**Table 11: The R&D activities in manufacturing in Beijing and Shanghai, 2000**

||Beijing|Shanghai|National in total|
|---|---|---|---|
|R&D personnel|5794|7117|240609|
|R&D investment (billion Yuan)|1.1|1.06|20.0|
|Patents|1120|2053|25257|

_Source_ : National R&D Resources Survey 2000.

17
**Table 7.** Effect of Information on WTP for Solar Lantern with Controls

|Variables|Coef|Se|
|---|---|---|
|Information|14.391|8.962|
|Gender of household head (1=Male, 0=Female)|31.321|30.748|
|Marital status (1 if married, 0 otherwise)|-17.784|30.317|
|Education of household head in years|-0.259|1.331|
|Age of the household head in years|-0.262|0.317|
|Household size|-1.347|2.435|
|Value of total assets in Birr|0.000|0.000|
|Number of rooms in the house|2.667|5.462|
|Household connected to grid (1 if yes, 0 otherwise)|-55.462***|14.882|
|Household uses solar light (1 if yes, 0 otherwise)|-3.121|11.250|
|Household uses kerosene for lighting (1 if yes, 0 otherwise)|12.906|10.755|
|Household uses fuel wood for lighting (1 if yes, 0 otherwise)|30.371*|15.979|
|Access to credit (1 if access to 400 Birr or more, 0 otherwise)|16.523**|7.992|
|Distance to the market|1.524*|0.909|
|Distance to the nearest road|0.032|0.041|
|Distance to the nearest town|0.052|0.286|
|Village has access to grid (1 if yes, 0 otherwise)|5.231|9.287|
|Constant|94.889***|33.550|
|Observations|806||
|R-squared|0.042||

Note: *** p<0.01, ** p<0.05, * p<0.1 We noted that there are some extremely large WTP values, with some of these being much higher than the maximum price of the solar lantern. Since we informed participants about the price range (the minimum and the maximum), considering the nature of the bidding mechanism we used, one may expect that for the respondent to be able to ensure buying the solar lantern, it would just be enough if WTP is the same as the highest price in the range and not more. Some subjects’ WTP was quite high; in the most extreme case, it was 8 times the market price of the solar lantern. While such values may be a reflection of the true WTP of the subjects, some of these values may be considered too high. To examine possible effects of extremely large WTP, we run a regression after excluding these values. The main results are robust to removal of extremely large WTP (Appendix 1).

To examine the possible effects of difference in WTP of those who paid and collected the solar lantern a month later compared with those who immediately paid and collected the solar lantern, we discounted the WTP of respondents who paid about a month later at the rate 28
_IL_<sup>_progA_</sup> can be interpreted as the tax rate to be applied on all wealth gaps above a no-tax area defined by a deduction _r_ , which is the exogenous richness line. In Romania nobody is above the richness line, therefore the sum of richness gaps is zero and the index cannot be computed.

_IL_<sup>_progB_</sup> is the smallest proportion of rich individuals that alone can defeat poverty by transferring all their incomes above the endogenous affluence line. A value of the index greater than one means that the number of rich individuals should be a multiple of the actual number of individuals above the poverty line, and therefore it is not possible for the country to defeat poverty alone (and this is the case for Romania).

Finally, _IL_<sup>_progC_</sup> is the ratio between the poverty line and the endogenous affluence line: the smaller the ratio, the larger the gaps between the two lines. If the affluence line in a country is much higher than the poverty line, this means that the distribution of income above the poverty line is very unequal.

Table 2: Poverty and Income Lever indexes and ranks (1-higher to 8-lower) for a selection of European countries, 2008

|Country|_P_ <sup>0</sup>||_P_ <sup>1</sup>||_IL_<sup>_pro_</sup>|<sup>_p_</sup>|_IL_<sup>_fla_</sup>|<sup>_t_</sup>|_IL_<sup>_prog_</sup>|<sup>_A_</sup>|_IL_<sup>_prog_</sup>|<sup>_B_</sup>|_IL_<sup>_progC_</sup>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||_index_|_r_|_index_|_r_|_index_|_r_|_index_|_r_|_index_|_r_|_index_|_r_|_index_<br>_r_|
|Czech Republic|0.05|8|0.009|8|0.007|8|24.97|8|0.060|8|0.053|8|0.177<br>8|
|Estonia|0.20|5|0.053|5|0.057|6|190.88|5|0.535|7|0.063|6|0.251<br>7|
|Hungary|0.20|6|0.050|6|0.088|4|180.40|6|1.878|2|0.063|4|0.349<br>3|
|Latvia|0.35|3|0.130|2|0.176|3|643.58|2|1.159|4|0.154|3|0.318<br>4|
|Lithuania|0.35|2|0.111|3|0.181|2|550.44|3|1.385|3|0.154|2|0.359<br>2|
|Poland|0.24|4|0.064|4|0.085|5|244.61|4|0.698|6|0.063|5|0.255<br>6|
|Romania|0.80|1|0.376|1|3.620|1|-|1|-|1|3.750|1|2.606<br>1|
|Slovakia|0.10|7|0.028|7|0.036|7|89.62|7|0.740|5|0.056|7|0.283<br>5|

_Source: our elaboration on EU-SILC 2008 cross-section data. ‘r’ indicates rank._ Table 2 also shows the ranking _r_ of the the set of countries considered, which can be used for the purpose of aid allocation. In general, a donor may want to select the index that best corresponds to widely accepted normative criteria. While proportional and progressive criteria are usually the norm in tax systems worldwide and therefore justifiable in the context of aid allocation, regressive criteria are much less popular and more difficult to defend. One can also look at the consistency across indexes. The Czech Republic, for example, comes consistently in the last place while Romania comes in first place across all 22
MEXAGMKTS: A Model of Crop and Livestock Markets in Mexico by Gerald T. O'Mra and MerLnda Ingco Table of Contents <!-- Start of picture text --> I. Introduction 1<br>I. Historical Perspective 1<br>M. The Strategic Role of Agriculture In Mexican 8<br>Economic Policy<br>IV. Role and Structure of XAGMKrS Model 10<br>V. Functional Specification of MEXAGMKTS Model 12<br>VI. Application of MEXAGMKTS to Counterfactual Trade 19<br>Liberalization Scenario<br>Footnotes 32<br>References 33<br>Appendix A Parameter Estimates of MEXAGMKTS Model 34<br>Appendix B: Graphical Presentation of Validation Test<br>of MAGMKT 44<br><!-- End of picture text -->
Data on financial inclusion mandates and reforms are introduced and analyzed in section 4. Section 5 concludes with a discussion.

# **2 Counting the unbanked**

We follow Beck _et al._ (2007) to predict the extent of access to formal deposit services offered by regulated financial institutions by households according to the following model: where _HH sharei_ is the percentage of adults with a bank account in country _i_ based on various household surveys collected on or after 2006. This data are compiled from various sources: recent Living Standard Measurement Surveys (World Bank, various years) where available, as well as regional sources: for the European Union, the European Commission’s Eurobarometer, Special Barometer 260 (2007); for Africa, FinMark Trust’s FinScope; for Latin America, Tejerina and Westley (2007), the MECOVI database, and Barr _et al_ . (2007); and Nenova _et al_ . (2007). These data are referenced and expanded upon in Claessens (2006), Honohan (2008), Gasparini _et al_ . (2005) and Beck _et al._ (2007). See Table 1 for further details. Note that although some of these surveys are at the household level and some are at the individual level, Honohan (2008) argues that they can be used interchangeably.

Beck _et al._ (2007) point out that the logarithmic specification on the right-hand-side of (1) is due to outreach indicators having fat tails. Note also that the dependent variable takes values between zero and one only, and to avoid the predicted values from falling outside this range, it is possible to estimate equation (1) using a Tobit specification. Beck _et al._ also state that the coefficients and significance levels are similar under Tobit and OLS, which is also confirmed by our estimation results. Nevertheless, for the predicted values to lie within zero and one as well, Tobit specification is preferred. Table 2 provides the results from estimating equation (1) by both OLS and Tobit using 46 observations. As expected, a larger number of accounts and a larger number of branches per adult are associated with a greater percentage of banked households. The model is then used to predict the percentage of banked households for the rest of the world for which household survey data do not exist. The correlation between the actual and predicted values is 87 percent. See Figure 1 for the fit of the estimated model.

Given the data constraints for the explanatory variables, it is possible to construct out-of-sample predictions for 55 countries. Combined with the actual data available for 46 countries, this enables us to pin down the percentage of banked households for a total of 101 countries. Kendall _et al._ (2010) develop a different methodology and predict the number of accounts for each type of regulated financial institution in the _Financial Access_ database. They then convert the number of accounts into the number of banked adults. With this methodology, they are able to predict the 4