rafmacalaba/datause-encoder-data huggingface dataset:
second derivative of the loss function, or: where _i_ refers to iterations, _α_ is the step size, **H** is the Hessian and **g** is the gradient.

# 3.2 Soft-linking energy planning models to MFMod

Rather than designing an endogenous energy system with the MS, this section explores the option of soft-linking a complete energy model to the structure of the MS. While this approach offers the possibility of deriving results from a highly granular energy model (with detailed capital stocks and clear engineering constraints) that improves realism, the main shortcoming comes from the fact that some endogenous relationships between the two systems (e.g., the link between GDP and energy demand) and policy applications will be underrepresented.

Several electricity planning models are used during the CCDR process, such as the World Bank’s energy planning model (EPM) and IIASA’s MESSAGEix.<sup>9</sup> Taking the macro environment as exogenous, these models are least cost optimization systems such that the supply of energy by source is a function of the cheapest inputs in terms of variable and fixed costs. Their main input is the electricity demand, often derived from GDP and population projections among others. Assuming that supply always equals demand, they produce several indicators that then feed back into MFMod. Figure 1 demonstrates the main inputs from EPM/MESSAGE into MFMod:

1. Energy mix: Shares of electricity generated by fossil fuels vs. renewables.

2. CAPEX: The investment needs required to achieve the transition.

3. OPEX: The operating costs (e.g., fuels).

4. Emissions: The emissions related to energy.

The policy options are largely user driven. As an example, the domestic vs. imported provision of CAPEX and OPEX depends on public vs. private sector financing.<sup>10</sup> These inputs are either derived from historical shares or are based on a narrative approach where

> 9Among other techno-economic models used in CCDRs.

> 10Imported CAPEX is modeled as an import of goods and non-factor services. Imported CAPEX may also affect the financial account of the BOP but this channel is ambiguous and therefore not explicitly modeled.

14
**Table 13: The Effect of Factors within Transmission Channels on the Probability of Starting to Export, Probit**

|Dependent variable:_e_|_xp_startisc_<br>||||||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_auditisc_|0.8551**|||||0.9166*|
||(0.049)|||||(0.071)|
|_imprisc_||0.3366||||-0.1203|
|||(0.468)||||(0.827)|
|_assistisc_|||1.3256***|||1.4075***|
||||(0.008)|||(0.008)|
|_devisc_||||1.2506***||0.8537|
|||||(0.006)||(0.138)|
|_licenseisc_|||||1.2387**|0.8975|
||||||(0.014)|(0.105)|
|_constantisc_|-6.9418***|-6.4233***|-6.0867***|-7.3373***|-6.0867***|-7.7367***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Country-sector FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Adj. R2<sup>1)</sup>|-0.219|-0.267|-0.161|-0.172|-0.197|-0.121|
|Observations|55|55|55|55|55|55|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01 (p-values in parentheses).<sup>1)</sup> McFadden’s adjusted pseudo R2. Note: All regressions control for country-sector fixed effects. Standard errors are robust to heteroscedasticity.

# **_Results by Types of Requirements_**

The non-existence of demand effect, i.e. spillovers from a customer’s requirements to improve ( _impr_ ), raises the question whether only specific types of requirements to improve may be relevant to FDI spillovers. Appendix 6 shows the summary statistics and definitions of the different subindicators of _impr_ available in our dataset. A sub-indicator takes the value of 1 if the foreign customer required the supplier to make improvements before or after signing the contract, and 0 otherwise. Using the specification of equation (4), we substitute each of these sub-indicators for _impr_ . Appendix 7 shows the regression results. Of the 13 sub-indicators of _impr_ , none shows a significant impact. In sum, the regression results give evidence of strong assistance effects in global value chains, but no evidence of demand effects.

# **_Results by Types of Assistance_**

In this section, we study in more detail which types of assistance are most effective in generating positive FDI spillovers in our data sample. Table 4 shows the definitions of the different sub-indicators of _assist_ available in the dataset, while Appendix 8 shows the summary statistics. Again, assistance is measured as a dummy taking the value of 1 if a supplier obtains assistance from the multinational, and 0 otherwise. Table 14 and Table 15 report the results using the specification of equation (5) substituting various types of assistance for _assist_ and using the likelihood to start 28
**TABLE 3. Characteristics of the instrument**

||||Information||
|---|---|---|---|---|
||All firms|Small firms|Medium‐sized firms|Large firms|
||(1)|(2)|(3)|(4)|
|**Panel A**|||||
|Mean|0.30|0.24|0.31|0.39|
|**Panel B: regressions**|||||
|Mid‐size|0.057***|‐‐|‐‐|‐‐|
||(0.020)||||
|Large|0.059*<br>(0.034)|‐‐|‐‐|‐‐|
|Log sales|0.013*|0.016|0.015|0.0041|
||(0.0075)|(0.011)|(0.014)|(0.016)|
|Foreign participation|‐0.092***|0.12|‐0.0045|‐0.17***|
||(0.033)|(0.089)|(0.059)|(0.047)|
|Log wage|0.016|0.036|‐0.0010|0.0047|
||(0.018)|(0.025)|(0.030)|(0.044)|
|Skill share|0.15***|0.066|0.36***|0.086|
||(0.051)|(0.074)|(0.095)|(0.10)|
|R&D team|0.19***|0.14**|0.19***|0.23***|
||(0.028)|(0.063)|(0.047)|(0.043)|
|Computers|‐0.087|‐0.051|‐0.086|‐0.11|
||(0.076)|(0.11)|(0.13)|(0.18)|
|Observations|3,235|1,316|1,210|709|

Panel A shows the percentage of firms that report having had access to information about FONTAR program. Panel B regresses a dummy variable of information about FONTAR program on initial‐year firm characteristics.

27
In developing countries, on the other hand, there has been a gradual, generally piecemeal yet persistent decentralization of expenditure responsibilities. Sub-national expenditures in developing countries as a percent of GDP rose from 3% of GDP in 1980 to 6.1% of GDP in 1997. Sub-national expenditures as a percent of total public sector expenditures increased from 12.7% of total expenditures in 1980 to about 19.6% of total in 1998. Sub-national government’s role in education and health spending show divergent trend in transition economies and developing countries (see Figures 4 and 5).

<!-- Start of picture text --> Figure 4: Education expenditures Transition<br>as % sub-national expenditures<br>Developing<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>19801981198219831984198519861987198819891990199119921993199419951996199719981999<br><!-- End of picture text --> Source: IMF Government Finance Statistics (various years) and World Bank Fiscal Indicators. (http://www1.worldbank.org/publicsector/decentralization/fiscalindicators.htm) 13
of empire. To recap those numbers, the ratio of trade to GDP for the US rarely exceeded 15 percent during Pax Americana compared with China‘s current ratio of 57 percent (Table 3).

**Table 3:  Trade Patterns of Economically Dominant Powers in History**

||**Actual trade**<br>(percent<br>of<br>GDP)<br>**Percent**<br>**overtrading**<br>(controlling<br>for size)<sup>a</sup><br>**United Kingdom: 1870 (samp**<br> <br>|**Percent**<br>**overtrading**<br>(controlling for<br>size and income<br>level)<sup>a</sup><br>**le = 26 countries)**<br>|**Percent**<br>**overtrading**<br>(controlling<br>for<br>size, income level,<br>and<br>oil-based<br>economies)<sup>a</sup>|
|---|---|---|---|
|Exports|12.2<br>339.3|84.0|n.a.|
||**United States: 1975 (sample =**|**121 countries)**||
|Exports|6.7<br>-16.5|-49.8|-48.3|
|Imports|6.5<br>-38.7|-39.6|-47.5|
|Total trade (exports plus imports)|13.3<br>-29.7|-45.3|-47.5|
||**China: 2008 (sample = 136 cou**|**ntries)**||
|Exports|31.6<br>73.5|67.7|99.0|
|Imports|24.9<br>56.3|56.4|39.5|
|Total trade (exports plus imports)|56.5<br>70.7|68.6|74.1|

a. Negative value denotes undertrading. Notes: n.a. = not available. Over and under-trading are derived from a regression of the relevant trade measure on population (column 2), population and per capita GDP (PPP) (column 3), population, per capita GDP (PPP) and a dummy for oil producers (column 4). In each of these regressions, the coefficient on the dummy for the UK (1870), US (1975), and China (2008) yields the magnitude of over-trading or under-trading for the relevant country. Strictly speaking, since the variables are specified in log terms, over/under-trading is obtained as the exponential of the coefficient on the relevant dummy variable minus 1.

The fact of China‘s trade-dependence will tend to create a strong stake for China in maintaining an open trading and financial system. Historians and political scientists have argued that trade, and the intertwining of interests that it brings, is no guarantee against economic or military conflict: Germany just prior to World War I, was also a major trader. But there are two critical differences: China is trade dependent in a way that the US or even Germany never was, and moreover, given China‘s low levels of income, China‘s stake in openness might be greater because its rise to prosperity, on which is predicated the legitimacy of its government, will depend upon an open system.

Post-World War II, the United States fashioned such a system out of enlightened self-interest. In contrast, China‘s interest in this system might be much more existential and strong because of its low level of income.

Moreover, the depth of China‘s international integration may itself preclude protectionist measures. Industrial country firms have made large relationship-specific investments in China, outsourcing assembly and intermediate goods and services production; Chinese firms have formed strong links with locally-established foreign manufacturing firms, foreign banks, 12
calibration of . This means the path for GDI with either SSR in the External-Balance submodel is now similar to the BBR-HR (Figure 5, panel B), λλ= 0.15 achieving a 35ppt increase in GDI by 2030 (though through higher private rather than public investment). Moreover, the SSR-HR achieves the highest growth path—also peaks at 35ppts of baseline GDI in 2030—but outperforms the other rules in the longer-term. Finally, note that different from the default calibration, the BBR and SSR yield very different trajectories with . λλ= 1

# _C. The Effects of a Large Discovery of Oil in Angola_

In this section, we assess the effects of a large discovery of oil in Angola. Specifically, we assume a discovery of 2.7 billion barrels of oil in 2025, calibrated to match the 2002 discovery, which was the largest over the last 30 years. In all other years, as in the baseline, we assume discoveries of 400 million barrels, equal to the 25<sup>th</sup> percentile of the distribution over 1990-2017 (see Figure 6 panel A).

Note that in reality, discoveries often become useable “reserves” over a period of many years. While we assume an immediate transition here for analytical purposes—to clarify the effect of the shock—we advise that users of the LTGMNR smooth the discovery over several years.

To isolate the effect of the 2025 discovery, we assume constant oil prices at $50/barrel, as in the baseline. This last assumption means that real GDI and real GDP—which had large differences in section B—will have similar growth profiles, so in this subsection we only report results for GDP. Finally, to allow the discovery shock to generate cyclical oil revenues—and, hence, different spending profiles across fiscal rules—we set structural (long run) oil production equal to the baseline oil production.<sup>50</sup>

> 50 Note that this assumption is different from section IV.B, where structural production of oil was set equal to actual production. That assumption was designed to isolate the effect of oil price shocks on the government spending profile. However, we now want to explore the effects of higher oil output across different fiscal rules, keeping oil prices constants. This can be done assuming that structural production is equal the baseline, although similar results would be achieved by assuming that structural production follows an N-year moving average of actual production.

39
increase in income, and with an increase in agricultural investment as well as spending on fertilizer and pesticides.

Kaboski and Townsend also found that VRF borrowing was associated with an apparent reduction in net household assets. This might seem surprising, but could be due to mismeasurement (a farmer might have invested in drainage or field leveling, and this might not be picked up in survey questions), or because better access to credit reduces the need to hold assets, or because households overborrowed.

# **7. Panel Data**

An effort was made, in the socioeconomic survey of 2004, to re-survey half of the rural households that had been interviewed in 2002. This produced a panel of 5,755 rural households for which information is available for both years. The panel data allow us, in principle, to get a less biased measure of the impact of VRF borrowing, because one can eliminate unobserved variable bias, provided that the bias is linear and does not vary over time. It also helps that the introduction of the VRF was a “surprise”, in the sense that it was proposed and implemented swiftly, and households in 2002 could not easily adjust their behavior in anticipation of future lending.

# Double Differencing

The simplest way to use the panel data is by double differencing. If, before the borrowing, income _Yi_ depends on covariates X _i_ , then and afterwards with ε _it_ =η _i_ + μ _it_ . Differencing gives Considering those households that did not borrow from the VRF in 2002, a regression of the differenced outcome variable on the treatment variable (which equals 1 for those who borrowed from the VRF in 2004) and the change in the covariates should estimate the impact, while “sweeping away” the effects of unobservable or mismeasured (but time-invariant) covariates. The results of this exercise are shown in the middle panel of Table 12, which shows little to no impact of the VRF on expenditure (impact of 2.0% but t- statistic of 1.04), income, or even farm or non-farm income.

To check the robustness of these results, before computing the double differences we first estimated the propensity scores using the 2002 data and the same variables as in Table 3, and then confined the double _Village Fund, draft of June 29, 2009_ _Page 18 of 34_
# Fans charts of probabilistic flood shocks. Scenario: RCP 2.6

<!-- Start of picture text --> GDP compared to baseline, in percent Capital stock compared to baseline, in percent<br>LOny {OE onneeannennnnnneeennnnennesnnennensnntentensnntenenennsnnuetuneutestunsntenunennanenmeanteg<br>001 -.005<br>002 | -.010 |<br>-.003 -01s|<br>004 -.020 |<br>“005 -.025 |<br>-.006 -.030 |<br>007 | -035<br>ARHSRRRRARRRRRRRRRRRAR RRRRRRRRRRRRARER RAKES SR<br>Consumption compared to baseline, in percent Debt/GDP, compared to baseline, in percentage points<br>002 008<br>000 - |<br>-.002 .005 |<br>004 004 |<br>| 003|<br>| .001 |<br>| “000<br>7010 MRAP AMh ana 2295 gs “pg 7001 OQPRAKRAEEGREB SeERe<br>RARRERARARRRRRRRSRRSER RAR RRRASRRRRR SAREE<br><!-- End of picture text -->
Table 8: Impacts on other outcomes

|||||I|TT regres|sion (N=94|7)||
|---|---|---|---|---|---|---|---|---|
||||Thera|py only|Cas|h only||Both|
|Outcome, z-score|Round|Control|ITT|Std. Err.|ITT|Std. Err.|ITT|Std. Err.|
|||mean|||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Prosocial behavior|12–13m|0.018|0.041|[.088]|-0.075|[.085]|-0.017|[.090]|
|Post-traumatic stress (5 questions)<br>_†_|2–5w|0.067|-0.031|[.096]|-0.013|[.101]|-0.127|[.101]|
||12–13m|0.136|-0.124|[.101]|-0.061|[.100]|-0.167|[.104]|
|Neuroticism (8 questions)<sup>_†_</sup>|2–5w|0.057|0.011|[.098]|0.021|[.101]|-0.077|[.104]|
||12–13m|-0.019|0.044|[.097]|0.035|[.102]|-0.153|[.096]|
|Locus of control (8 questions)<sup>_†_</sup>|2–5w|-0.001|0.000|[.098]|0.057|[.104]|-0.089|[.100]|
||12–13m|0.010|-0.032|[.101]|-0.111|[.098]|-0.022|[.106]|
|Self esteem (8 questions)<sup>_†_</sup>|2–5w|-0.099|0.112|[.099]|-0.012|[.100]|0.191|[.102]*|
||12–13m|-0.071|0.078|[.098]|0.060|[.100]|0.190|[.101]*|
|Appearance (6 questions)|2–5w|-0.118|0.085|[.081]|0.131|[.081]|0.203|[.080]**|
||12–13m|0.016|-0.102|[.078]|-0.085|[.077]|-0.109|[.082]|
|Substance abuse (0-3 index)|2–5w|1.378|-0.249|[.070]***|-0.060|[.072]|-0.197|[.071]***|
||12–13m|1.091|-0.065|[.061]|0.063|[.062]|-0.057|[.060]|
|Quality of social networks|2–5w|-0.241|0.147|[.062]**|0.109|[.061]*|0.325|[.062]***|
||12–13m|0.066|0.063|[.092]|-0.044|[.092]|0.139|[.095]|
|Peers (20 questions)<sup>_†_</sup>|2–5w|-0.160|0.207|[.091]**|0.014|[.095]|0.235|[.094]**|
||12–13m|0.040|0.011|[.088]|-0.070|[.089]|0.017|[.090]|
|Family (4 questions)Ţ|2–5w|-0.192|0.106|[.099]|0.131|[.105]|0.307|[.099]***|
||12–13m|-0.019|0.124|[.099]|0.070|[.100]|0.129|[.097]|
|Ex-commanders (4 questions)|2–5w|-0.141|-0.011|[.038]|0.013|[.038]|-0.038|[.038]|
||12–13m|0.176|0.004|[.076]|0.026|[.078]|-0.139|[.074]*|
|“Big men” (5 questions)<sup>_†_</sup>|2–5w|-0.012|0.015|[.104]|0.039|[.107]|0.172|[.107]|
||12–13m|0.120|0.001|[.155]|-0.130|[.152]|0.071|[.160]|
|Subjective well being (3 questions)|2–5w|-0.237|0.166|[.087]*|0.170|[.087]*|0.425|[.094]***|
||12–13m|-0.020|0.057|[.072]|-0.009|[.072]|0.184|[.074]**|
|Executive function|2–5w|-0.103|0.076|[.075]|0.059|[.077]|0.024|[.085]|
||12–13m|0.110|-0.094|[.077]|-0.078|[.076]|-0.109|[.078]|

_Notes:_ The table reports intent to treat estimates of outcomes that were not a priori specified as of primary interest. We calculate the impact of each treatment arm in the short and long run, controlling for baseline covariates and block fixed effects. We focus on pre-defined composite measures, typically defined by survey module. The overall summary indexes are the standardized mean of its composite outcomes, standardized. Heterosketastic robust standard errors are reported in brackets. *** p<0.01, ** p<0.05, * p<0.1 _†_ These variables were not collected during every phase/round, so their regressions have a smaller sample size.

29
# **Selected References**

Ahier, J. (ed.). 1999. _Education, Training and the Future of Work._ London: Routledge.

Carnoy, B., and others. 2000. _Aprendiendo a trabajar: Una revisi6n del Colegio Nacional de_

- _Educaci6n Profesional Tecnica y del Sistema de Universidades Tecnol6gicas de Mexico._ Processed.

Campos, M. 2000. _Estudio de Costos del CONALEP._ Processed.

Boud, D., and J. Garrick. 1999. _Understanding Learning at Work._ London: Routledge.

- CONALEP (Colegio Nacional de Educaci6n Profesional Tecnica). 1994. _Encuesta de Empleo a Egresados del CONALEP, Cohorts 1991, 1992 and 1993._ Final Report.

- _---------. 1999. Encuesta de Empleo a Egresados del CONALEP, Cohorts 1991, 1992, 1993, 1994 and 1995._ Final Report.

- Gill, I., and A. Dar. 1995. "Costs and Effectiveness of Retraining in Hungary." Internal Discussion Paper, Europe and Central Asia Region. The World Bank.

- Frantz, N. 1998. "Identification of National Trends and Issues for Workplace Preparation and their Implications for Vocational Teacher Education." _Journal of Vocational and Technical Education_ 14(1). Fall 1998. Blacksburg, Va.

- Heckman, J., and others. 1998. "Matching as an Econometric Evaluation Estimator." _Review of Economic Studies_ 65(2). April.

- Hobart, B. 1999. "Globalization and its Impact on VET." _Review of Research._ Adelaide. NCVER.

48
Table 1. Log Ratio of Human Capital (H00=H90)

||OLS 1|OLS 2|IV 1|IV 2|
|---|---|---|---|---|
|ln(m90)|0.042**|0.032**|0.082**|0.077**|
||(0.021)|(0.006)|(0.036)|(0.037)|
|ln(H90)|-0.285***|-0.273***|-0.278***|-0.278***|
||(0.039)|(0.038)|(0.040)|(0.040)|
|ln(�90)|0.119**|0.121**|0.125**|0.116**|
||(0.058)|(0.049)|(0.060)|(0.053)|
|S15=24|-2.227***|-2.346***|-1.897**|-1.907**|
||(0.860)|(0.792)|(0.821)|(0.807)|
|S49=55|-10.44***|-10.11***|-10.57***|-10.37***|
||(3.215)|(2.972)|(3.112)|(3.086)|
|UR90|0.228*|0.263**|0.410***|0.380***|
||(0.137)|(0.117)|(0.148)|(0.147)|
|R90|-0.000*||-0.000*||
||(0.000)||(0.000)||
|I90|-0.000||-0.000||
||(0.000)||(0.000)||
|REGD|yes|yes|yes|yes|
|F-statistic|32.255|41.265|36.438|40.021|
|N. Obs|88|105|104|105|
|Hansen test (i)|||4.622|3.567|
|p-value|||0.099|0.168|
|First-stage F stat.(ii)|||15.52|16.43|
|Anderson test (iii)|||38.398|38.793|
|p-value|||0.000|0.000|
|Cragg-Donald test (iv)|||13.398|13.707|
|C-test (v)|||1.763|1.319|
|p-value|||0.184|0.251|

Between brackets, Standard errors. Reported heteroskedastic-robust.

IV excluded instruments: distance to OECD, dummies for landlocked and small island. � p<0.1, ** p<0.05, *** p<0.01

- (i) Hansen J statistics - Over-identi...cation test.

- (ii) F statistics for ...rst stage regression - Instrument relevance.

- (iii) Anderson LR statistics - Instrument relevance.

- (iv) Cragg-Donald statistics Instrument relevance.

- (v) C-test - Exogeneity of ln(m90).

19
**Table** **<u>5.</u>** Benchmark welfare <u>census model in IHS</u>

||**All districts-50%**<br>**poorest HH**<br>|
|---|---|
|**R-squared**|53.71|
|**Importance of variables**||
|Urban (1) or rural (0)|0.24|
|Child dependency ratio|0.09|
|Ownership of a cell phone|0.09|
|House with improved floor|0.09|
|Household size|0.08|
|Access to piped water|0.05|
|Ownership of a television|0.05|
|Household overcrowding|0.04|
|Fuel cooking: firewood|0.03|
|Household size (squared)|0.03|
|Ownership of a radio|0.02|
|Access to flush toilet|0.02|
|Ownership of a car|0.02|
|HH head literacy|0.01|
|Highest educated women attained primary|0.01|

The selected model is used to predict the benchmark welfare variable in the census extract. This variable is generated at a household level first, then aggregated to a village welfare measure as the average of the bottom half of households in each village.

# 3.2 Criteria used for evaluating prediction accuracy

We use three main criteria for evaluating the accuracy of the predictions against the benchmark: the Spearman Rank Correlation, the Area Under the Curve (AUC), and the R-squared , which is equal to the share of the variation explained by the prediction. While the latter is a standard measure of prediction accuracy, the first two warrant a brief explanation.

The Spearman rank-order correlation coefficient (rs) is a statistical measure of the strength and direction of a monotonic relationship between two variables measured on a continuous scale. The rank correlation between two variables, X and Y, is calculated as follows: Where ρR(X)R(Y) denotes the Pearson correlation coefficient applied to variable ranks; cov(R(X)R(Y)) is the covariance between two ranked variables, and σR(X)σR(Y) are the standard deviations of the ranked variables.

12