rafmacalaba/datause-encoder-data huggingface dataset:
answered by all respondents that can be used to impute the data, poor results were obtained when using the multiple imputation models.

However, two simple imputations proved useful in further understanding our results. For the first we substituted missing observations pertaining to firms’ perception regarding barriers to the adoption of energy efficiency measures with 0, while for the second we substituted it with the mean value. Overall, the results supported our main findings although the size of the parameters estimated did change (see Appendix C).

# **6. Concluding remarks**

This study examines energy efficiency barriers to the industrial and commercial (including public) sectors in Ukraine by conducting a survey of 500 firms throughout the country. The results from the survey are then used in empirical (i.e., Logit and Probit) models to understand the importance of various barriers to the adoption of energy efficiency.

The study finds that financial barriers, such as higher upfront investment costs of energy efficiency technologies, lack of capital and long pay-back period are the strongest barriers to the deployment of energy efficiency technologies in the both industrial and commercial sectors in Ukraine. Lack of effective government policies and existing regulation such as government permits required for the adoption of energy efficiency technologies are other key barriers. Predicted probabilities estimated by our study suggest that industrial firms are more likely to have larger investments than commercial firms despite the fact that the latter have, on average, slightly higher share of energy costs in the total production costs. Our study also suggests that energy price rises would yield more adoption of energy efficiency measures, as would the introduction of credit enhancement instruments.

Although the study suggests policy that reduces upfront costs and risk will result in more adoption of energy efficiency measures in Ukraine, the analysis also suggests heterogeneity among firms and sectors. That is, our analysis finds differences in levels of investment in energy efficiency measures among sectors, with industrial firms investing more. This raises the question whether sectoral heterogeneity be accounted for while designing energy efficiency policy instruments. We plan to further investigate this in future research.

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**Table 18. Cobb-Douglas Production Functions**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|**_Factors_**<br>Labor (log of days worked)|0.546***|0.519***|0.542***|0.514***|
||(0.116)<br>|(0.116)<br>|(0.116)<br>|(0.116)<br>|
|Capital (log)|0.125**|0.123**|0.123**|0.121**|
||(0.055)|(0.054)|(0.055)|(0.053)|
|Material Inputs (log)|0.295***<br>|0.293***<br>|0.296***<br>|0.295***<br>|
||(0.056)<br>|(0.052)<br>|(0.056)<br>|(0.051)<br>|
|Share of Paid Labor|-0.317|-0.380|-0.276|-0.347|
||(0.314)|(0.338)|(0.321)|(0.347)|
|**_Activity_**<sup>**(1)**</sup>|||||
|-Manufacturing<sup>(2)</sup>|-0.706**|-0.666**|-0.700**|-0.652**|
||(0.320)|(0.317)|(0.316)|(0.313)|
|-Food and beverages,|-0.463*|-0.508*|-0.463*|-0.501*|
||(0.271)|(0.281)|(0.270)|(0.279)|
|-Grain milling|-1.325|-1.117|-1.317|-1.071|
||(1.120)|(1.202)|(1.115)|(1.194)|
|-Hotels and restaurants|-0.215|-0.321|-0.216|-0.317|
||(0.360)|(0.342)|(0.358)|(0.340)|
|-Retail trade via stalls and markets|0.483|0.350|0.477|0.339|
||(0.401)|(0.405)|(0.399)|(0.401)|
|-Services|-0.333|-0.342|-0.334|-0.338|
||(0.425)|(0.437)|(0.424)|(0.435)|
|-Whole sale trade|0.375|0.355|0.368|0.356|
||(0.379)|(0.366)|(0.379)|(0.367)|
|-Transport services|0.164|0.303|0.164|<br>0.333|
||(0.689)|(0.859)|(0.694)|(0.855)|
|**_Characteristics of the manager_**|||||
|Manager’s age|-0.049*|-0.044*|-0.051**|-0.046*|
||(0.025)|(0.025)|(0.025)|(0.024)|
|Manager’s age squared/1000|0.418|0.356|0.432|0.372|
||(0.267)|(0.264)|(0.264)|(0.259)|
|Manager’s sex (1=male)|0.489**|0.560***|0.482**|0.551***|
||(0.197)|(0.202)|(0.197)|(0.202)|
|Manager’s schooling|-0.170**<br>|-0.176**<br>|-0.170**<br>|-0.176**<br>|
||(0.085)|(0.085)|(0.085)|(0.085)|
|Manager’s schooling squared/1000|19.266**|17.550*|19.073**|<br>17.113*|
||(9.204)|(9.013)|(9.243)|(9.027)|
|**_Local Demand_**|||||
|Mean WRSI 2006|0.106***|0.101***|0.106***|0.101***|
||(0.032)|(0.033)|(0.032)|(0.033)|
|**_Geography_**|||||
|<br>Remote||0.009||-0.044|
|||(0.392)||(0.393)|
|Location in rural town||0.565*||0.566*|
|||(0.290)||(0.296)|
|Distance to financial institution||-0.113||-0.123|
|||(0.126)||(0.127)|
|Distance to all weather road log)||0.183*||0.183*|
|||(0.100)||(0.101)|
|Distance to market (log)||0.037||0.062|
|||(0.212)||(0.214)|
|Financial institution in the||0.119||<br>0.116|
|||(0.216)||(0.216)|
|**_Investment Climate Constraints_**|||||
|Proportion of firms in community||-0.306||-0.279|
|electricity because it is unavailable||(0.307)||(0.303)|

-40-
Table 4b: Industry traits and unorganized employment changes

||Manufacturing<br>1989-2005|Manufacturing<br>2000-2005|Services<br>2001-2006|
|---|---|---|---|
||(1)|(2)|(3)|
|Log labor intensity<br>A. Correlation with change in|0.022<br>unorganized sector|-0.075<br>share of employment|-0.026|
|Log capital intensity|-0.095|0.031||
|Log materials intensity|-0.065|0.051|0.040|
|Log average wage|0.020|0.183|-0.093|
|Log financial dependency|-0.063|0.025||
|Log import dependency<br>B. Correlation with unorgani|0.067<br>zed sector log employ|0.093<br>ment growth||
|Log labor intensity|0.113|-0.049|-0.186|
|Log capital intensity|-0.054|0.058||
|Log materials intensity|-0.064|0.020|0.211|
|Log average wage|-0.103|0.151|-0.058|
|Log financial dependency|0.046|-0.159||
|Log import dependency|0.126|-0.003||

Notes: Table documents correlations between industry traits and unorganized sector employment changes. The top panel considers the change in the unorganized sector's share of industry total employment. The bottom panel considers the log growth in unorganized sector employment across the period as measured by log (unorganized employment end / unorganized employment start). Industry traits are measured in 2000-2001. Intensity measures are measured relative to industry sales. In both panels, a negative correlation indicates that the district trait is associated with a decline in relative unorganized activity across the period. No correlation is statistically significant at the 10% level.
# **A.1.1 Net Flows vs Gross Flows**

In the literature and in the data, there is some ambiguity of terms when referring to net and gross flows. Essentially, there are three distinctions:

**Gross Flows:** Strictly speaking, gross inflows and outflows refer to one-way flows without netting out any capital flowing in the opposite direction. This definition of gross flows is generally what comes to mind when the term is used. Nevertheless, data that actually matches this definition are quite scarce.

**Net Inflows and Outflows:** What is commonly called “gross flows” in the literature is actually more accurately described as “net inflows” and “net outflows”. There are no comprehensive datasets on flows that are truly gross. Instead, researchers tend to use net inflows and net outflows, which can be obtained from the IMF’s BOP dataset. Net inflows are gross liability flows, net of repayments. Net outflows are gross asset flows, net of disinvestment. Thus, although these measures are often called “gross”, they can be positive or negative. The separation of flows into asset and liability flows allows interpreting liability flows as net inflows from foreign agents, and asset flows as net outflows by domestic agents. This is the primary working definition of capital flows, which we use across all data sources for consistency.

**Net Flows:** This relates to the net movement of capital into and out of a country. This is the equivalent of the negative of the current account, that is, the difference between Net Inflows and Net Outflows (or equivalently the difference between Gross Inflows and Gross Outflows).

**Stock/Position Data:** In general, there is no standard definition of “net” stocks, as some

47
Another result of the smaller flexibility of model 3 is observed in table 10, where the elasticity for manufacturing in the CES national output, value added and intermediates function changes from a prior of 1.5 to a posterior of 0.79 in 1990 and 0.10 in 2011. Finally, for the same Korean example, on prediction side, table 16 also shows that model 3 gets worse scores (statistics) than model 1 and 2, meaning that model 3 is less reliable in terms of data validation.

Same conclusions can be derived when we compare model 1 and 2 with model 4 for Sub-Saharan Africa analysis.

# CONCLUSIONS AND FUTURE RESEARCH

In this paper, we define a formal Bayesian approach to estimate parameters of a CGE model under noisy and limited data environment. The cross-entropy estimation method described in the paper is potentially applicable to all calibrated CGE models and their SAM database.

Inferring information about parameters and elements of a country or regional CGE model could be problematic because of incomplete, infrequent, inaccessible, or uncertain data such as dated or poorly constructed SAMs and auxiliary information. To estimate behavioral parameters and structural change in CGE models, we implement a three-step cross-entropy estimation method. The data step adjusts the historical SAMs of Korea and Sub-Saharan Africa to a common base year taking into account that the SAMs are measured with errors and relative price indices are scant.<sup>18</sup> Next, a parameter estimation step calculates (filters) parameters and structural change simultaneously within the specifications of the country/regional CGE model postulates. The last step provides some statistics that are useful to measure noise and information gains – possibly because of structural change and/or because of model specification – in recovered parameters as well as statistics that differentiate models through data validation measures.

The approach can be easily used to estimate how economic transformation will bring about structural change and less rigidness in the production and demand behavior of a developing country. There are, of course, trade-offs in the number of parameters to be estimated simultaneously due to the degrees of freedom and data constraints. As illustrations, we estimated the Armington and CET elasticities for Korea and SubSaharan Africa that were consistent with the case where labor augmenting productivity captured compositional changes in the sector value-added and indirectly, its effects on the final demand vectors. There are other possibilities, such as allowing for trade elasticities and trade shares to evolve in order to capture the effects of globalization and increasing trade.

We have a few suggestions for future research. One is to employ peripheral estimates of historical producitivty change and trade patterns in order to improve the estimates of the evolution of value added and trade elasticities. Second, a research focus on the combination of our current ex-post analysis method with ex-ante (prediction) techniques. Lastly, the approach seems ideal for regional or global CGE modeling where

> 18 The selected base year was 2005.

20
# 4. The Estimation Results

The results for four alternative specifications of the educational capital stoek variablesprimary education, secondary education, the sum of primary and secondary education, and primary and - secondary education separately are presented in Tables 5 and 6, We first test the hypothesis that the educational effects are the same for all regions. This hypothesis can be rejected for all four specifications at the 0.01 percent level of significance. Similarly, the hypothesis of no educational effects can also be rejected. **The** hypotheses that tlie growth trends are independent of the regions and that there **is no break in the trend between pre-1980 and post-1980 can also be rejected at the 0.01 percent level of significance (See Table 7).** 16
On Promoting Fiscal Discipline: The Role of Exchange Rate Regimes, Fiscal Rules and Institutions<sup>_∗_</sup> Kady Keita<sup>_†_</sup> Camelia Turcu<sup>_‡_</sup> Keywords: Cyclicality of fiscal policy, Exchange rate regimes, Fiscal rules, Institutions, 2SLS. JEL classification: E32, E62, O23.

> _∗_ We thank participants to the Annual Doctoral Meeting on Development 2017, ICEFM 2017, AFSE 2018, INFER Annual Conference 2018 and FIBA 2018 for insightful comments. Special thanks to Carine Meyimdjui, Marin Ferry, Yannick Lucotte, Tobignar ́e Yabr ́e, Ren ́e Tapsoba, Alexandru Minea, Peter Claeys, Helena Sanz Morales and Eckhard Janeba for constructive remarks that helped us improve the paper. All remaining errors are ours.

> _†_ PhD Candidate University of Orl ́eans, LEO CNRS and Consultant, World Bank. Email: kadysynthia.keita@univ-orleans.fr

> _‡_ Professor, University of Orl ́eans, LEO CNRS and INFER. Email: camelia.turcu@univ-orleans.fr
directly, its export VAT rebate is calculated based on the final export prices.<sup>4</sup> If a production firm exports indirectly through a trading company, however, its export VAT rebate in China is calculated based on its domestic selling prices (i.e., the domestic purchasing price of the trading company), rather than the final export price by the trading firm. In this case, firms may have incentive to underreport the domestic purchasing or selling price to evade the VAT. This paper explores how firms evade such an export tax in the case of indirect exports.

The above discussion suggests a positive correlation between export tax rates (i.e., the nonrefundable part of VAT) and the probability of indirect exporting, which is measured by the share of indirect exports in our product level analysis. That is, the higher the implicit export tax, the more likely it is that exporting will be done through an intermediary. We test this hypothesis using China’s Customs export data at the transaction level for the year 2005, which is the first year after the full liberalization of trading rights under China’s WTO commitments.<sup>5</sup> This correlation is empirically robust, especially for differentiated products whose values are easier to underreport than homogenous products, and is also stronger for the exports of state-owned enterprises (SOEs), which are more politically connected with the government than private or foreign firms.

In addition, although the VAT rebate in the case of indirect exporting is not based on the final export price of a trading company, the trading company may also have an incentive to underreport its exporting prices to minimize the chance of being caught because the purchasing and selling price differential is used by the Chinese tax authorities to detect evasion behaviors. Such a view is supported by a product-country level analysis. This implies that the tax evasion through domestic trading firms may be associated with cross border evasion in the case of indirect exports. Therefore, both types of evasion should be reflected in the underreported export values and the data reporting discrepancies between China and

> 4 Ferrantino, Liu and Wang (2012) provide empirical evidence for how production firms may have incentive to underreport their export values in the case of direct exports, while the current paper investigates the evasion through indirect exporting.

> 5 We choose to use the data for year 2005 for the following reasons. First, before 2005, the export and import right was under the examination and approval system. Production firms would have to choose indirect exports if they did not have export right, which is unrelated to tax evasion. Second, two other related papers on Chinese intermediary firms, Ahn, Khandelwal, and Wei (2011) and Tang and Zhang (2012), also use the data of 2005. To facilitate the comparison with their work, we follow them by choosing the same year.

3
Table 10. Fixed effects logit model Dependent variable: Crisis indicator (1 = crisis, 0 = no crisis)

||Debt crisis|Banking crisis|Currency crisis|
|---|---|---|---|
|Change in U.S. real interest rate|-0.121<br>(0.130)|-0.021<br>(0.106)|0.257**<br>(0.104)|
|GDP growth|-0.095***|-0.013|-0.008|
||(0.034)|(0.026)|(0.022)|
|Short-term debt|0.056***|0.012|-0.015|
||(0.020)|(0.017)|(0.016)|
|Debt service|0.032**<br>(0.015)|0.026***<br>(0.010)|0.001<br>(0.011)|
|Reserves cover|-0.586***<br>(0.154)|-0.256***<br>(0.082)|-0.219***<br>(0.085)|
|Change in government debt|0.018*<br>(0.010)||0.013**<br>(0.007)|
|Change in private debt||0.055**<br>(0.027)|0.067**<br>(0.029)|
|Change in government debt<br>x Change in private debt|||0.003***<br>(0.001)|
|Concessional debt|-0.059**<br>(0.023)|||
|Funding ratio||-0.001<br>(0.003)||
|Currency overvaluation|||0.131***<br>(0.016)|
|Currency mismatch|||0.037<br>(0.049)|
|FDI|||-0.087<br>(0.059)|
|No. of observations|1,186|1,705|1,688|
|No. of countries|35|55|63|

Note: ***, **, * denote statistical significance at the 1 percent, 5 percent, 10 percent level respectively. Standard errors are in parentheses.

33
these projections we use the same set of assumptions that we used initially to calculate i*n.

In the "availabilities" case, the user must enter information not only on existing debt and the terms and conditions of new debt, but also the amount of foreign credit that we assume is forthcoming. In this case, i*t and F*t are both calculated in the DM and exported to the RMSM-X. There, they are used to determine the solution to the RMSM-X macroeconomic model.

Note the differences between the old RMSM system and the one proposed here. In the former, the demand for foreign credit is obtained in the RMSM model independently from the supply, as if there were a perfectly elastic supply for credit. The old DM also projects stocks of debt and exports them to the RMSM. Any divergence between debt stocks projected in the DM and demand for credit calculated in the RMSM is captured by a variable called GAPFIL. If GAPFIL is bigger (smaller) than zero, the user must choose whether supply is increased (decreased) or demand is decreased (increased). In the first case, new borrowing has to be projected and the source of this borrowing must be identified. In the second case, the trade balance and/or international reserves must be reduced The procedure by which the supply and demand of foreign credit are reconciled involves therefore an iterative process in which many runs of the DM and RMSM model might be necessary.

Now, the DX does not calculate the value of supply at a predetermined interest rate, but the whole supply schedule. The 41
GHANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE very high rates of growth in power demand are rapidly outstripping what the Akosombo Reservoir can supply, and as a result the country must use the expensive oil-fired plant to meet demand. Furthermore, the country remains exposed to future hydrological shocks.

To reduce its reliance on expensive oil-based generation, Ghana must diversify its generation mix to include gas. This process is already underway. Significant capacity of combined cycle gas turbine plant has already been commissioned, though it is not yet fully operational. Gas supply from Nigeria via the West African Gas Pipeline is expected to come on stream soon, and further supplies of more abundant and lower cost gas can be expected in the medium-term as Ghana develops its own hydrocarbon potential. The average total cost of gas-fired generation is estimated to be $0.06-0.08 per kilowatt-hour. Once gasfired generation is available, the cost differential between hydro and thermal plant will be much lower, making the power system (and the economy) much more resilient in periods of low rainfall. For this reason, developing substantial gas-fired generation capacity is one of the most urgent challenges facing the power sector.

**Table 8. Benchmarking power indicators**

||Unit|Low-in<br>count<br>Mid 2|come<br>ries<br>000s|**Gha**<br>**Mid 2000s**|**na**<br>**Late 2000s**|Middle-income<br>countries<br>Late 2000s|
|---|---|---|---|---|---|---|
|Installed power generation capacity|MW/mil. people||24.4|**131.7**||796.2|
|Power consumption|kWH/capita||99.5|**293.4**|**370.0**|4,473|
|Power outages|Day/year||40.6|**116.4**||5.6|
|Firms’ reliance on own generator|% consumption||17.7|**29.5**||0.5|
|Firms’ value lost due to power outages|% sales||6.1|**6.0**||0.8|
|Access to electricity|% population||15.4|**44.3**|**56.0**|59.9|
|Urban access to electricity|% population||71|**77.0**|**83.8**|83.7|
|Rural access to electricity|% population||12|**20.9**|**34.4**|33.4|
|Growth access to electricity|% population/year||1.4|**1.8**|**3.2**|1.8|
|Revenue collection|% billings||88.2|**89.6**|**89.3**|99.9|
|Distribution losses|% production||22.1|**25.4**|**25.6**|15.7|
|Cost recovery|% total cost||90.0|**80.0**|**92.0**|125.7|
|Total hidden costs as % of revenue|%||67.5|**37.3**|**118.3**|3.5|
|U.S. cents|**Mid 2000s**|**Ghana**|**Late 200**|Predom<br>ge<br>**0s**|inantly hydro<br>neration<br>|Other developing<br>regions|
|Power tariff (residential at 75 kWh)||**8.4**||**13.1**|10.27|5.0 – 10.0|
|Power tariff (commercial at 900 kWh)||**13.9**|||11.73||
|Power tariff (industrial at 50,000 kWh)||**9.1**|||11.39||

Source: Eberhard and others 2009. Derived from AICD electricity database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data 22
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# **Data Description**

<mark>The data set includes information on 12 developing countries in the Indian and Western Pacific Oceans and 35 container ports (table 2). The analysis is constrained to one importing country, the United States, for which good-quality data are available. Figure 1 shows the importance of exports to the United States as a share of total exports from the countries considered in the analysis.</mark>

**Table 2 Container ports included in the analysis**

|_Region_|_Ports_|
|---|---|
|Africa|Mombasa (Kenya); Port Louis (Mauritius); Cape Town, Durban, East London, Port<br>Elizabeth (South Africa); Dar es Salaam (Tanzania)|
|East Asia and Pacific|Tanjung Perak, Tanjung Priok (Indonesia); Danang, Davao, Iloilo, Manula, Zamboanga (the<br>Philippines); Kuantan, Kuching, Port Klang, Tanjung Pelepas (Malaysia); Haiphong, Ho Chi<br>Minh (Vietnam)|
|South Asia|Chittagong (Bangladesh); Chennai, Cochin, Jawaharlal Nehru, Kandla, Kolkata, Mumbai,<br>Mundra, Pipavav, Tuticorin, Visakhapatnam (India); Karachi, Mohammad Bin Qasim<br>(Pakistan); Colombo (Sri Lanka)|

**Figure 1 Exports to the United States to selected countries as a percentage of total exports, 2007**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>percentage of total exports<br>Exports to the United States as a<br><!-- End of picture text --> _Source_ : Data from UN Comtrade.

11