rafmacalaba/datause-encoder-data huggingface dataset:
experimental design techniques to ensure a balanced representation of values across choices. Alpizar et al. (2003) Hanley et al. (2001), Hensher et al. (2005), and Hoyos (2010) provide reviews of the choice experiment methodology.

# **_Survey Instruments_**

The survey for this particular study presents respondents with opportunities to express preferences over hypothetical REDD+ contracts that have the following attributes: Contract payment denominated per household, duration of the contract, percentage of the payment going to the household (as opposed to the community), required reduction in fuelwood and the required reduction in grazing. The exact list of attributes was refined after studying the REDD+ literature and analyzing results from 20 focus groups. The attribute levels and the descriptions for the attributes are provided in Table 1.

Once an initial list of attributes was developed, we conducted focus groups with potential survey respondents. The final survey instrument contains background information about the REDD+ program, a description of the attributes and the levels, seven sets of binary choice question sets, and a small demographic questionnaire. Appendix A provides an example of one of the choice questions and the actual background information document used. These documents were pretested in the field before launching full implementation. For each of the choice sets the respondents choose between the two given alternatives and the status quo option.

# **_Empirical Design_**

We created an efficient experiment design that incorporates both main effects and interaction effects (Adamowicz et al. 1997, Adamowicz et al. 1998, Carlsson and Martinsson 2003, Louviere et al. 2000). The design for the choice experiment was generated following Kuhfeld (2010)<sup>3</sup> and achieved a 100% D-efficiency. The REDD+ survey design resulted in 84 9
# **Summary**

Since 1989, there has been a sharp increase in private capital flows to developing countries. The similarity between conditions in the early 1980s and those in 1994-an increase in interest rates in the industrial countries after several years in which developing countries received sizable capital inflows, and the recent Mexican crisis -has reopened the question of the sustainability of these flows and the appropriate policy responses to an adverse external shock to avoid a reversal of flows. In other words, the recent Mexican experience highlights the risks of a generalized reversal of flows and reopens the question of sustainability at a time when several developing countries remain highly indebted and are still in the process of completing some necessary economic reforms. Within this context, a generalized reversal of flows could seriously disrupt the economies of these countries and threaten their economic reform process.

The sustainability problem concerns the forces that have driven these flows in recent years, their relative importance, and the possibility that they could move adversely in the future. This research addresses these issues, particularly that of identifying the driving forces behind the surge in private flows. Because the surge in private capital inflows since 1989 has coincided with a period of low international interest rates and domestic policy reform in the developing world, there is a debate about whether the surge is driven primarily by domestic (pull) or external (push) factors. Under the pull hypothesis successful domestic policies are the key to ensuring sustainable capital inflows in the future, while under the push hypothesis an increase in international interest rates would cause a reversal of these flows (back to the industrialized world).

This research attempts to advance our understanding of the causal factors behind the recent surge in private capital inflows by including both pull and push factors in a model of the determinants of private capital inflows. In particular, it uses a partial adjustment model in which both domestic and external explanatory variables are defined, to explain the private capital flows to developing countries. The model is estimated using panel data for 1986-93 for 22 developing countries, including countries that have benefited from the new wave of private capital flows and countries that have not received capital flows. In addition, we compare the means of several economic indicators for two groups of countries: those that have received significant capital inflows in recent years and those that have not.

Since the start of 1994, the U.S. Federal Reserve Board has raised short-term interest rates six times, from 3 percent to 6 percent. Recall that the debt crisis was triggered when Mexico declared a moratorium in its extemal debt in 1982.

i
Eaton, J. & Kortum, S. 1996. Trade in ideas: patenting and productivity in the OECD. _Journal of International Economics,_ 3(4): 251-278.

Habib, M. & Zurawicki, L. 2002 Corruption and foreign direct investment. _Journal of International Business Studies,_ 33: 291-307.

Hausmann, R. & Rodrik, D. 2003. Economic development as self-discovery. _Journal of Development Economics,_ 72: 603-633.

Heeley, M.B., Matusik, S.F. & Jain, N. 2007. Innovation, appropriability, and the underpricing of initial public offerings. _Academy of Management Journal,_ 50(1): 209-225.

Kaufmann, D., Kraay, A. & Mastruzzi, M. 2005. Governance matters IV: governance indicators for 1996–2004, World Bank Policy Research Working Paper 3630, Washington, DC.

Keller, W. 2004. International technology diffusion. _Journal of Economic Literature,_ 42(3): 752-82. Klapper, L., Laeven, L. & Rajan, R. 2006. Entry regulation as a barrier to entrepreneurship. _Journal of Financial Economics,_ 82: 591-629.

Klinger, B. & Lederman, D. 2006. Diversification, innovation, and imitation inside the global technological frontier, Policy Research Working Paper 3872, World Bank, Washington, DC. Klette, T. J. & Kortum, S. 2004. Innovating firms and aggregate innovation. _Journal of Political Economy,_ 112(5): 986-1018.

Lederman, D. & Saenz, L. 2005. Innovation around the world, 1960-2000, World Bank Policy Research Working Paper 3774, The World Bank, Washington DC.

Luo, Y. 2001. Determinants of entry in an emerging economy: a multilevel approach. _Journal of Management Studies,_ 38: 443-472.

Luo, Y. 2002. Product diversification in international joint ventures: performance implications in an emerging market. _Strategic Management Journal,_ 23(1): 1-20.

MacGarvie, M. 2006. Do firms learn from international trade? _The Review of Economics and Statistics,_ 88(1): 46-60.

16
and the border dummy becomes significant, consistent with the other results in the literature. The coefficients of the other gravity variables have the expected coefficients. Log Income and Log per Capita Income coefficients are close to unity as in the previous section. Sharing a common language and being an island increase trade by 52%<sup>15</sup> and 38% respectively. On the other hand, 1% increases in distance and transportation costs decrease trade volumes by 0.55% and 2%, respectively.

The results for 2001 are reported in column 2. The main difference is the addition of the AGOA utilization variable. Recall that the AGOA status dummy was correlated with the distance variable and that led to counter-intuitive results in the previous section. However, the coefficient of the AGOA utilization variable is now positive and significant. AGOA utilization variable has a mean of 0.4% but the trade weighted average is 45.6%<sup>16</sup> . If we use the former to evaluate AGOA, we see that the program leads to only 5% higher trade for an average beneficiary. Andean Utilization variable’s coefficient is also significant and positive in 2001, especially compared to1997. This is probably due to the fact that the beneficiaries had time to adjust and learn to take advantage of the program in the preceding years. The net effect is that Andean program beneficiaries export 24% more than they would have without the preferences. The coefficients for FTA and CBI increased slightly but the average utilization are slightly lower so the average effect of both programs are the same in 2001. GSP coefficient is still negative but lower in value. Combined with the decline in the average utilization, the net negative effect of GSP is now 25%. All of the other gravity variables’ coefficient are similar in value to the results from 1997. The only exception is the transport costs which indicates trade became more sensitive to these costs.

We try an alternative specification in columns 3 and 4. Instead of various country specific gravity variables (such as log income, distance, language etc.), we include country dummies<sup>17</sup>

> 15This is calculated by exp(0.424)-1.

> 16This is due to the fact that the two largest export categories from AGOA countries, oil and apparel, mostly enter the U.S. under the AGOA preferences and there is very little exports in other categories.

> 17We could not include country dummies in the previous section with Preference Status Dummies since these are also country specific.

14
**Figure 34: Root Mean Square Error Before and After FH Approach (Poverty Headcount Ratio $5.5/day - Left; Average per-capita consumption 2011 PPP – Right)**

<!-- Start of picture text --> 0.12 4<br>3.5<br>0.1<br>3<br>0.08<br>2.5<br>0.06 2<br>1.5<br>0.04<br>1<br>0.02<br>0.5<br>0 0<br>Standard Error Fay‐Herriot Standard Error Fay‐Herriot<br>Standard Error No Correction Standard Error No Correction<br><!-- End of picture text --> An additional robustness check estimates FH models for each country individually, comparing the results with those using pooled data across countries. The results presented in figure (35) show that this makes little substantive difference in either the point estimates or in the root mean square error for within-sample districts. There is a very strong relationship between the estimates regardless of the pooled vs. individual approach. The relationship remains strong but weakens somewhat with respect to out-of-sample districts (figure 35 - right). Using the pooled approach likely improves on the outof-sample prediction: this method improves the training of the model with more data than is available at the national level.

**Figure 35: Comparison of Individual Models vs. Pooled Model (Only in sample - Left; Including out of sample - Right)**

<!-- Start of picture text --> 1 1<br>0.9 R2 = 0.9852 0.9 R2 = 0.9216<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0 0<br>0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1<br>Individual Country Maps Individual Country Maps<br>Pooled Pooled<br><!-- End of picture text --> 68
# FDI, Market Power, and Markups

## Evidence from Vietnam

Yue Li, Ryan Kuo, Mauricio Pinzon-Latorre, and Mark Albertson<sup>1</sup> Keywords: Foreign Direct Investment, Competition Economics, Market Power, State-Owned Enterprises JEL classification: F21, F23, L11

> 1 We are grateful for feedback and comments from Trang Tran, <mark>Jesica Torres Coronado, and Seidu Dauda.</mark>
3.3. Model Specification To estimate the impact of the PSI-PMI program on student learning outcomes, we estimate the following model on the sample of the matched program and non-program students: j 0 1 j 2 j j (1) jj Where j is the performance outcome of student yy = ββ + ββ CC + ββ XX + εε in school on test . Our main outcome variables are standardized mathematics scores on the WASSCE and a dummy variable for having jj yy ii jj kk satisfactory scores<sup>9</sup> in mathematics and English on the WASSCE. The variable is the treatment variable, which has value 1 if the student is in the treatment group and 0 if otherwise. _X_ is a vector CC of covariates that includes the student- and school-level variables, and j is the idiosyncratic error term. In our estimations, we also control for any preexisting differences between the εε treatment and control groups. We estimated the program effect in equation 1 using OLS and directly through the propensity score matching estimator.

The student-level variables used as controls include the following: (i) students' performance in mathematics, English, and science three years earlier in grade 9; (ii) whether the student has repeated a grade; (iii) socioeconomic status, as measured by household asset ownership (TV, fridge, and car); and (iv) gender. The school-level variable is administrator capacity, as measured by the principal's level of education and experience. The standard errors are clustered at the school level.

Grade 12 students can take up to nine subjects in the WASSCE. However, only mathematics and English are compulsory for all students. This students’ choice of subjects taken could introduce a potential bias, since the choice of subjects will not be random. Hence, we focus on mathematics and English scores for the impact of the PSI-PMI program, although the program is designed for mathematics and science subjects (biology, chemistry, and physics).

> 9 A satisfactory score is defined as a score of at least 50 percent. On the WASSCE scale, this corresponds to a score of at least between 1 and 6, inclusive.

18
US$300 million (with a World Bank guarantee) was oversubscribed by 70 percent. The average interest rate on KPLC’s total borrowings, which is a blend of commercial and concession debt, is around 6 percent for the period under analysis.

**Table 6. Summary of financial indicators against global comparators**

||Kenya|Global<br>comparator*|
|---|---|---|
|Net profit (loss) margin|7%|–12%|
|EBITDA margin|11%|6%|
|Current ratio|0.98|0.79|
|Debt service coverage ratio|0.85|–4.56|
|External Financing Index Ratio<sup>14</sup>|0.02|6.92|
|Investment as a percent of revenues|45%|18%|
|Average borrowing rate|6%|7%|
|Government transfers as a share of utility revenue|3%|6%|
|Net capital cost recovery ratio<sup>15</sup>|44%|16%|

Note: Sample average from the Rethinking Power Sector Reform Project data observatory and includes data from Tanzania, Senegal, Peru, Colombia, Pakistan, the Philippines, Vietnam, Kenya, Uganda, Tajikistan, the Indian states of Rajasthan, Odisha, and Andhra Pradesh, and Egypt

# _3.3.2 Institutions_

The structural reforms initiated in the 1990s left Kenya’s power sector partially vertically unbundled, with private sector participation in generation and distribution (table 7). The government has made commitments to further unbundle transmission and distribution, but this seems unlikely in the short-term.

**Table 7. Comparing the extent of utility restructuring in Kenya and comparators, 2015**<sup>**16**</sup>

||**Kenya**|**Senegal **|**Tanzania **|**Uganda**|**International**<br>**Benchmark**|
|---|---|---|---|---|---|
|**Utility Restructuring**|**25%**|**0%**|**0%**|**55%**|45%|
|_Vertical Unbundling_|50%|0%|0%|60%|55%|
|_Horizontal Unbundling_|0%|0%|0%|50%|34%|
|**Pvt sectorparticipation**|**16%**|**11%**|**3%**|**38%**|24%|
|_PSP in Generation_|25%|34%|10%|53%|41%|
|_PSP in Distribution_|25%|1%|0%|63%|16%|
|_PSP in Transmission_|0%|0%|0%|0%|14%|

Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform PSP = private sector participation.

> 14 _External financing index ratio_ measured as net cash flow from financing divided by the net cash flow from operations

> 15 _Net capital cost recovery ratio_ is the percent of full capital costs that can be recovered through tariff revenues net of operating costs

> 16 For a more detailed look at the indices used in this section, see the Annex

30
POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA

# **8.** **_References_**

- <mark>Asian Development Bank. 2008. “Research Study on 2005 International Comparison Program in Asia and the Pacific for Selected Countries in Asia and the Pacific - Poverty-specific Purchasing Power Parities”.</mark>

- <mark>Deaton, Angus and Olivier Dupriez. 2011. "Purchasing Power Parity Exchange Rates for the Global Poor."</mark> _<mark>American Economic Journal: Applied Economics</mark>_ <mark>, 3(2): 137-66.</mark>

- <mark>Dupriez, Olivier</mark> _<mark>.</mark>_ <mark>2007. “Building a household consumption database for the calculation of poverty PPPs: Technical notes ”</mark>

- <mark>Food and Agriculture Organization of the United Nations. 2015. “Agricultural Growth in West Africa, Market and policy drivers.”</mark>

<mark>Nielsen. 2014. “Getting to Know the Diverse African Consumer.” Available at:</mark>

- <mark>http://www.nielsen.com/ssa/en/insights/news/2014/getting-to-know-the-diverse-africanconsumer.html</mark>

- <mark>Summers, Robert. 1973, “International Price Comparisons Based on Incomplete Data”,</mark> _<mark>Review of Income and Wealth</mark>_ <mark>, 19(1), 1–16.</mark>

- <mark>United Nations Statistical Commission. 2016. “Report on the forty-seventh session (8-11 March 2016).” Available at: https://unstats.un.org/unsd/statcom/47th-session/documents/Draft-report-on-the47th-session-of-the-statistical-commission-Rev1-E.pdf</mark>

- World Bank. 2015. “Operational Guidelines and Procedures for Measuring the Real Size of the World Economy: 2011 International Comparison Program”. Washington, DC: World Bank. Available at: http://www.worldbank.org/en/programs/icp/brief/2011-operational-guidelines

21
European Commission, IMF, OECD, UN, and World Bank. 2009. System of National Accounts 2008.

Gollin, Douglas. 2002. Getting Income Shares Right. Journal of Political Economy 110 (2): 458‐474.

IMF. 2016. Guinea: Article IV Consultation. Country Report No. 16/261.

IMF. 2017. Balance of Payments Statistics. International Monetary Fund.

IMF. 2017. Investment and Capital Stock Dataset, 1960‐2015. International Monetary Fund.

Reinert, Kenneth A. and David W. Roland‐Holst. 1997. Social Accounting Matrices. In Joseph F. Francois and Kenneth A. Reinert (eds.). Applied Methods for Trade Policy Analysis: A Handbook. Cambridge University Press. New York: W.W. Norton and Company.

Rodrik, Dani. 2016. Economics Rules: The Rights and Wrongs of the Dismal Science. New York: W.W. Norton.

Round, Jeffrey. 2003. Constructing SAMs for Development Policy Analysis: Lessons Learned and Challenges Ahead. Economic Systems Research 15 (2): 161–183.

World Bank. 2017. International Debt Statistics.

World Bank. 2017. World Development Indicators.

‐25‐
# **Annex 2: Structure of variables in Dataset**

# **Legend:**

string data exists as a string ( _abcde_ ) in database int data exists as an integer in database float data exists in double precision float ( _0.00_ ) format in database scale response is expected to fit into a predetermined scale ( _0-6 = x, 6-12 = y, etc_ ) percentag _e_ response is expected in percentages (i.e. _0.79 = 79%_ ) number to be interpreted as a number ( _1,2,3,4, etc_ ) indicating quantity m<sup>3</sup> response to be interpreted in cubic meters of volume km<sup>2</sup> response to be interpreted in square kilometers of area variables are arranged chronologically by question number. (e.g Variable #1 = Question #1, Variable #11 = Question #2 ,..... Variable #223 = Question #47 )

# **General Data**

Variable #1| Question #1| **basinname** | name of basin | string

Variable #2| Question #1| **country** | name of country | string

Variable #3| Question #1| **rboname** | name of river basin organization | string

Variable #4| Question #1| **address** | river basin organizations’ address | string

Variable #5| Question #1| **contactperson** | contact person | string

Variable #6| Question #1| **position** | organizational position of contact person | string

Variable #7| Question #1| **telephone** | contact telephone information | int

Variable #8| Question #1| **fax** | contact fax information | int

Variable #9| Question #1| **email** | contact email information & website for river basin organization | string

53
||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Average<br>change<br>in sales<br>(%)|Average<br>change<br>in sales<br>(%)|Average<br>change<br>in sales<br>(%)|Average<br>change<br>in sales<br>(%)|Average<br>change<br>in sales<br>(%)|
|Tech Sophistication|5.38*|5.82**|6.23**|7.77**|5.38*|
||(2.75)|(2.75)|(2.72)|(3.14)|(2.76)|
|Increased use of digital platforms|6.80***<br>(2.34)|||||
|New investment in digital solutions||7.20**<br>(3.38)||||
|More work from home|||11.93***|||
||||(3.87)|||
|More online sales||||16.34***||
|||||(4.24)||
|Increased use of digital tech|||||6.60***|
||||||(2.24)|
|Medium|2.30|1.98|1.28|2.30|2.46|
||(2.68)|(2.67)|(2.72)|(3.09)|(2.65)|
|Large|0.85|1.34|1.12|-1.06|1.69|
||(3.34)|(3.45)|(3.50)|(4.00)|(3.37)|
|Age 6 to 10|-3.97|-4.36|-2.77|-0.28|-4.66|
||(3.30)|(3.32)|(3.38)|(3.83)|(3.30)|
|Age 11 to 15|-1.84|-2.31|-0.25|1.15|-2.59|
||(3.67)|(3.69)|(3.77)|(4.19)|(3.68)|
|Age 16+|-0.83|-1.43|0.42|2.39|-1.30|
||(3.65)|(3.67)|(3.77)|(4.27)|(3.68)|
|Export|3.90|3.53|3.54|2.18|3.19|
||(3.01)|(2.99)|(3.07)|(3.70)|(2.99)|
|Foreign owned|11.57**|11.34**|8.12*|6.81|11.27**|
||(4.68)|(4.67)|(4.76)|(5.70)|(4.69)|
|Change in mobility|4.91***|4.86***|5.05***|3.92**|4.90***|
||(1.52)|(1.55)|(1.55)|(1.89)|(1.50)|
|Work experience|-0.02|-0.01|-0.00|0.07|-0.01|
||(0.12)|(0.12)|(0.12)|(0.15)|(0.12)|
|Post-graduate degree|1.99|2.50|2.98|4.85|2.01|
||(3.74)|(3.73)|(3.88)|(4.45)|(3.70)|
|Studying abroad|-8.14**|-8.27**|-6.38*|-5.48|-8.27**|
||(3.76)|(3.76)|(3.85)|(4.83)|(3.77)|
|Experience in large business|2.69|2.40|2.12|2.44|2.24|
||(2.81)|(2.81)|(2.88)|(3.31)|(2.81)|
|Management Z-score|0.28|0.45|0.24|-1.60|0.47|
||(1.88)|(1.88)|(1.93)|(2.29)|(1.89)|
|Observations|885|891|853|605|892|
|R-squared|0.20|0.20|0.21|0.23|0.21|

Table B3: Regressions of sales on digitalization and tech sophistication. _∗∗∗p <_ 0 _._ 01 _, ∗∗ p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1. Robust standard errors in parentheses.

32
## I.INTRODUCTION

Argentina has had a sad economic history in recent decades. As of '89 real per-capita GDP stays at a level similar to that of the _arly 1960's. This means a<sup>quarter of a century</sup> without growth at a time when the rest of the world<sup>has seen one</sup> of the most glowing periods in terms of economic achievements. Per-capita GDP reached a historical level in 1974 and has never again been able to surpass this level in spite<sup>of reaching it</sup> again in 1977 and 1979. Since 1980 GDP per<sup>capita starts a steep</sup> decline that results in an accumulated fall<sup>of 23.5% in the ten</sup> year period 1979-88. During the same decade, the<sup>orice level</sup> increased by a factor of 3.36 million, equivalent<sup>to an annual</sup> compound rate of inflation of 349%.

It is not our objective to explain the reasons<sup>for the</sup> economic stagnation of Argentina but to concentrate<sup>on the role</sup> that public sector behavior may have had in that process.<sup>In</sup> particular, we shall be concerned with the role of government spending, taxation and deficit financing on the<sup>rest of the</sup> economy. We shall not be concerned with the effects<sup>of government</sup> reaulation, a topic that deserves a volume on itself.

FIGURE 1

# **CDP** PER CAPITA (1969 **PRICES)**

<!-- Start of picture text --> 75- - I I I I- I I I I I<br>7S . . . . . . . . . . . . . . . . . . . . . . ....<br>65 . . . . . . . .<br>. . . . . .<br>fig<br>55 . .<br>50 . .;<br>45<br>1969 1965 1978 1975 1989 9985<br><!-- End of picture text -->