rafmacalaba/datause-encoder-data huggingface dataset:
_of those in the experiment are surveyed, and such tests should not be used to decide which variables to control for in treatment regressions._ The common practice of testing for significant differences between the two groups is otherwise testing whether something that we know was due to chance was due to chance. Researchers should control for variables believed to strongly influence the outcome of interest, regardless of whether the difference between treatment and control groups is significant or not.

- _6) Acknowledge that the different goals of randomization can conflict with one another in small samples._ The idea of randomization as a valid basis for inference (through permutation analysis), the desire for comparable groups, and the fairness and transparency involved in one-off public assignment present trade-offs for the researchers in terms of choice of randomization method.

# **References**

Aickin, Mikel (2001) “Randomization, balance, and the validity and efficiency of designadaptive allocation methods”, _Journal of Statistical Planning and Inference_ 94: 97119.

- Altman, Douglas A. (1985) “Comparability of randomized groups”, _The Statistician_ 34: 125-36

- Andrabi, Tahir, Jishnu Das, Asim Khwaja and Tristan Zajonc (2008) “Do Value-added Estimates Add Value? Accounting for Learning Dynamics”, <u>www.leapsproject.org</u>

- Ashraf, Nava, James Berry and Jesse M. Shapiro (2007) “Can Higher Prices Stimulate Product Use? Evidence from a Field Experiment in Zambia”, Poverty Action Lab Paper No. 41.

Ashraf, Nava, Dean Karlan and Wesley Yin (2006a) “Tying Odysseus to the Mast:

- Evidence from a Commitment Savings Product in the Philippines”, _Quarterly Journal of Economics_ 635-672

- Ashraf, Nava, Dean Karlan and Wesley Yin (2006b) “Deposit Collectors”, _Advances in Economic Analysis and Policy_ 6(2), article 5

- Banerjee, Abhijit (2007) _Making Aid Work_ . The MIT Press, Cambridge, MA.

- Banerjee, Abhijit, Shawn Cole, Esther Duflo, and Leigh Linden (2007) “Remedying Education: Evidence from Two Randomized Experiments in India”, _Quarterly Journal of Economics_ 1235-1264.

- Bertrand, Marianne, Simeon Djankov, Rema Hanna and Sendhil Mullainathan (2007) “Obtaining a Driver’s License in India: An Experimental Approach to Studying Corruption”, _Quarterly Journal of Economics_ 1639-76.

- Björkman, Martina and Jakob Svensson (2007) “Power to the People: Evidence from a Randomized Field Experiment of a Community-Based Monitoring Project in Uganda”, _World Bank Policy Research Working Paper_ 4268

- 34 -
- 40 -

**Figure 12. Energy savings rate and energy consumption**

<!-- Start of picture text --> 50<br>Office<br>45 Hospital<br>School, library, museum, institute<br>40 Community center, hotel, welfare facility<br>Field, pool, gym<br>35 Multiple facilities<br>30<br>25<br>20<br>15<br>Annual savings<br>= JPY80 million<br>10<br>JPY40 million<br>5 JPY20 million<br>JPY10 million<br>0<br>0 200 400 600 800 1000<br>Annual energy spending (JPY million)<br>Source : Author’s survey data.<br>Energy savings rate (target, %)<br><!-- End of picture text -->

**Figure 13. Distribution of imputed payback periods in the sample**

<!-- Start of picture text --> 0 10 20 30 40<br>Payback period (year)<br>Source : Author’s survey data.<br>.15<br>.1<br>Density<br>.05<br>0<br><!-- End of picture text -->

**Figure 14. Distribution of unit cost of energy savings in the sample**

<!-- Start of picture text --> 0 50000 100000 150000 200000 250000<br>Unit cost of energy savings<br>for 10 year period (JPY/tCO2)<br>Source : Author’s survey data.<br>2.0e-05<br>1.5e-05<br>Density 1.0e-05<br>5.0e-06<br>0<br><!-- End of picture text -->
|||Romania|||||All ECA|||
|---|---|---|---|---|---|---|---|---|---|
|Employ\Age|1‐5|6‐15|>=16|Total|Employ\Age|1‐5|6‐15|>=16|Total|
|Micro|0.36|0.63|0.01|26,556|Micro|0.25|0.64|0.10|269,735|
|Small|0.17|0.81|0.02|20,608|Small|0.19|0.68|0.13|225,957|
|Medium|0.11|0.73|0.15|5,195|Medium|0.11|0.63|0.25|76,951|
|Large|0.31|0.49|0.20|1,010|Large|0.09|0.45|0.46|20,678|
|Total|13,944|37,720|1,706|53,370|Total|121,983|384,250|87,087|593,320|
|||EU8|||||EU10|||
|Employ\Age|1‐5|6‐15|>=16|Total|Employ\Age|1‐5|6‐15|>=16|Total|
|Micro|0.18|0.69|0.13|124,967|Micro|0.21|0.68|0.11|166,805|
|Small|0.18|0.71|0.11|88,097|Small|0.18|0.73|0.09|121,066|
|Medium|0.11|0.67|0.21|25,969|Medium|0.12|0.68|0.20|32,964|
|Large|0.17|0.48|0.35|4,501|Large|0.18|0.50|0.32|5,938|
|Total|42,038|167,904|33,592|243,534|Total|61,424|227,221|38,128|326,773|

**Table 9 Firm Growth in Comparator Countries**

||Russia|Poland|Ukraine|Romania|All_ECA|EU8|EU10|Turkey|
|---|---|---|---|---|---|---|---|---|
|Small|‐0.068|‐0.069|‐0.094|‐0.085|‐0.058|‐0.057|‐0.058|‐0.14|
||(0.024)***|(0.021)***|(0.023)***|(0.023)***|(0.011)***|(0.019)***|(0.016)***|(0.039)***|
|Medium|‐0.069|‐0.058|‐0.088|‐0.070|‐0.059|‐0.053|‐0.052|‐0.155|
||(0.017)***|(0.020)***|(0.032)***|(0.047)|(0.011)***|(0.019)***|(0.017)***|(0.033)***|
|Large|‐0.075|‐0.094|‐0.122|‐0.165|‐0.071|‐0.059|‐0.071|‐0.114|
||(0.023)***|(0.037)**|(0.041)***|(0.053)***|(0.017)***|(0.037)|(0.031)**|(0.060)*|
|Age|‐0.003|‐0.001|‐0.001|‐0.003|‐0.002|‐0.002|‐0.002|‐0.003|
||(0.000)***|(0.001)|(0.001)|(0.001)***|(0.000)***|(0.001)***|(0.001)***|(0.001)**|
|Foreign|0.378|‐0.003|0.046|‐0.126|0.014|0.014|0.012|‐0.032|
||(0.108)***|(0.024)|(0.053)|(0.097)|(0.015)|(0.017)|(0.020)|(0.072)|
|Gov_Own|0.059|‐0.026|0.157|0.121|‐0.010|‐0.026|‐0.019|0.037|
||(0.072)|(0.029)|(0.059)**|(0.105)|(0.018)|(0.041)|(0.037)|(0.045)|
|Exporter|‐0.048|0.044|0.029|‐0.053|0.022|0.036|0.028|0.018|
||(0.040)|(0.027)|(0.023)|(0.072)|(0.011)**|(0.016)**|(0.015)*|(0.033)|
|Const|0.115|0.095|0.075|0.211|0.107|0.067|0.072|0.321|
||(0.037)***|(0.039)**|(0.054)|(0.041)***|(0.033)***|(0.050)|(0.031)**|(0.089)***|
|Obs|754|248|622|288|7392|1452|1918|668|
|R<sup>2</sup>|0.249|0.150|0.250|0.193|0.113|0.120|0.113|0.201|

Robust standard errors clustered by region and 2‐digit industry are in parentheses. Additionally control for 2‐digit industry and region fixed effects. Regressions for EU8, EU10 and All_ECA include controls for countries. *** p<0.01, ** p<0.05, * p<0.1.

17
stock of capital in developing countries was financed by foreign savings consequent upon growing financial integration. They constructed a ―self-financing ratio‖ for the period 1991 to 2001 using a 10-year horizon for 47 developing countries and 22 OECD countries using data for the period 1981-2001. The first step was to derive an estimate of the capital stock in year t minus 10 using the national income accounts. The next step was to add the cumulative national investment over the 10-year period to this initial capital stock, and then divide this by the initial capital stock plus the cumulative national savings over the same period to obtain the selffinancing ratio, with suitable adjustments made for depreciation.<sup>5</sup> The purpose was to see how the self-financing ratio varied over the 1990s in response to growing financial integration and to compare the 2001 ratio with that in 1991.

APR‘s main findings were first, that the average self-financing ratio for developing countries did not change much over the 1990s, global financial liberalization notwithstanding. Second, there did not seem to be a growth bonus associated with greater external financing of the domestic capital stock. To the contrary, countries with higher self-financing ratios grew significantly faster. Third, more volatile self-financing ratios were associated with lower growth; but this result disappears when a proxy for the quality of institutions was included in the crosscountry regressions. But the strong, asymmetric effect of self-financing on growth remained: a rise in the self-financing ratio from 1.0 to 1.1 was associated with increasing the average per capita growth, from 2.8 percent to 4.4 percent, while a drop from 1.0 to 0.9 lowered per capita growth from 2.8 percent to 2.2 percent.<sup>6</sup> Self-financed growth is not a new idea. Feldstein and Horioka (1980) wrote a seminal paper asking how internationally mobile capital really was. In regressing national investment rates on national savings rates for 21 OECD countries using data from 1960 to 1974, they consistently found coefficients close to 1.0 and stable over time. These results were evocative of a closed economy, suggesting capital was not as mobile across borders as might have been assumed; if it were, one would expect a close to zero correlation between national saving and national investment. This finding became enshrined as the ―Feldstein-Horioka puzzle‖ and is evocative of home biases and self-financed growth.<sup>7</sup> In a paper related to APR, Prasad, Rajan and Subramanian (2007)—PRS—examined the links between capital flows and growth in the financially globalized 1990s. The starting point of

> 5 For details and caveats, see Aizenman, Pinto and Radziwill (2004, 2007).

> 6 If the country is on average running current account surpluses, the self-financing ratio would exceed 1. 7 It would of course be a genuine puzzle only if there were no legal restrictions on capital flows across countries.  In other words, F-H were testing de facto capital mobility.  See Obstfeld and Rogoff (1996) for further discussion of the various F-H interpretations.

4
Table 1: Summary Statistics

||Mean|Standard Deviation|Min.|Max.|
|---|---|---|---|---|
|Annual sales|1833.3|6811.6|1.0|164357.0|
|Price - Incentive (1,000 USD)|59.0|37.3|1.7|200.7|
|MSRP (1,000 USD)|62.4|36.6|6.9|202.1|
|Incentive (1,000 USD)|3.4|3.8|0.0|56.3|
|Number of EV chargers (1,000)|34958.2|60925.6|600.0|515908.0|
|Battery capacity (kWh)|25.7|21.2|4.4|100.0|
|Range (miles)|103.5|98.4|0.7|706.5|
|Vehicle size (m3)|13.0|2.8|6.3|26.7|
|Engine Horsepower|194.1|103.7|11.8|761.0|
|Indicator of Non-fnancial Incentives|0.7|0.5|0.0|1.0|
|Heavy-duty EV Stock|810.7|2557.5|0.0|10588.2|

_Notes:_ The unit of observation is country-year by model. The number of observations is 4,528. The data are from 2013 to 2020 for 13 countries. Price is MSRP (manufacturer suggested retail price) plus taxes.

26
linkages pertaining to services here but will instead focus on individual subsets of our social accounts data, region by region, with external accounts in each case aggregated to a rest of world.

# **4.1 all countries**

Table 9 presents a cross-section, for year 2007, for services shares of exports on a gross value basis, direct value added, and total domestic value added. Two patterns stand out on this basis. One is that certain economies (the United States, Hong Kong, Singapore, and Europe) stand out as commercial services exporters, while lower income countries, at least in terms of direct exports, are lower down the ladder of relative importance for service exports. One exception is India, which has the highest share of commercial service exports on a gross basis among the developing nations, even surpassing the high income countries. Furthermore, on a value added basis, service exports are often much more important than on a gross basis. For high income resource abundant countries (Australia, Canada, New Zealand), there is a similar shift in the share from between 8.4 and 9.3 percent on a gross value basis to between 11.7 and 14.6 on a direct value added basis. For the United States, commercial service exports accounted for more than 20 percent of direct value added exports. For Hong Kong, this is a third. Again India stands out as an outlier with commercial service exports accounting for almost 40 percent on a direct value added basis.

We also calculated a revealed comparative advantage index based on forward linkages. These indexes are based on the sector share of value added exports. Table 10 presents these indexes for each region for the year 2007. The figures are in line with those presented in Table 9. Again the United States, Hong Kong, Singapore and Europe, but also India, stand out with their comparative advantage in commercial services.

# **4.2 Brazil, China, Korea, the United States**

We will next look at how value added structures, and services linkage to trade, have evolved over time. Our dataset is actually quite rich, and will allow (with some adjustments to bilateral allocation of goods trade) to study of cross border linkages 16
have an incentive to transfer older technologies to their subsidiaries in developing countries than to those in developed countries to avoid the risk of expropriation (Mans fi eld and Romeo, 1980). Others fi nd that technology transfers from parent fi rms to fully-owned subsidiaries are higher than those to minority-owned subsidiaries (Ramachandran, 1993). Alternatively, the international transmission of technology can occur through trade. If new knowledge is embodied in imported capital goods or intermediate inputs, we would expect importers to be more innovative than fi rms that source only in the domestic market. Importers can improve their technology by incorporating in their production processes these state-of-theart inputs or machinery, which may not be available domestically (Grossman and Helpman, 1991). Similarly, exporters can learn about new technologies or products through their interaction with more knowledgeable foreign buyers in external markets. Alternatively, they may be exposed to more competitive markets and hence be forced to improve their technology frequently. If the exposure to export markets is indeed a channel promoting innovations, we would expect exporters to be more likely to adopt new technologies than fi rms selling exclusively to the domestic market. Finally, fi rms can engage in the direct trade of knowledge, through technological licensing agreements, to acquire new technology. These channels are not necessarily used in isolation. For example, the use of licensing and FDI are often complementary channels for the international transmission of technology.

Perhaps the biggest difficulty in the analysis of the international transmission of technology is to gather good indicators on the adoption of new technology. How one exactly de fi nes a technological innovation depends on the speci fi cities of each fi rm and its production process. Most of the empirical work using micro data uses information on fi rm-level research and development (R&D) expenditures or the number of patented technologies.<sup>2</sup> However, the R&D activities of the fi rm are only one type of research inputs in the process of generating new technologies and they do not necessarily lead to a successful and applicable new technology. Moreover, the propensity to patent varies widely across industries and is likely to be more important for the creation of new knowledge than for the adoption and adaptation

> 2One exception is Comin et al. (2006) that use a cross-country dataset on the adoption of speci fi c technologies.

3
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== fiodelled incidence (%), adults 15-49, from Spectrum software weee= Prevalence (%), females 15-24 (source: national ANC reports) se ART coverage ()(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013 <!-- Start of picture text --> 3 0.9 3 80<br>25 . 0.8 25 | 70 _<br>OF s<br>= = 60<br>= 2 0.6 g = 2 50 oD<br>8 oo g Hi<br>@ 17 3 @ 157 40 9<br>=S ie er2 |$ -30 0(=)<br>o 1 io o 4 =<br>o o 20 &<br>0.5 0.2 0.5 <<br>0.4 10<br>0° 0 O° a)<br>7990 1995 2000 2005 2010 2000 20027 2004 2008 2008 2010 2012<br>ems Modelled incidence (%), adults 15-49, from Spectrum software<br>Prevalence (%), females 15-24 (source: national DHS/AIS reports)<br>am Prevalence (7), females 15-24 (source: national ANC reports)<br>ommmms Prevalence (7), young people, UNAIDS Indicator 1.6 (source: AlDSinfo database<br>om ART coverage (%)(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013<br><!-- End of picture text -->
to poverty statistics using estimation models.” (Yoshida et al. (2015)). It employs Computer Assisted Personal Interviewing technology to collect the data and carefully builds and estimates the model that is used for prediction. Both nested and non-nested double sampling approaches are considered.<sup>5</sup> While SWIFT is still relatively young, at the time of writing, it has already been applied 34 times in 27 countries. Assessments of the cost-precision trade-offs however are still limited. Ahmed et al. (2014) denotes an exception which provides a SWIFT-like application of double sampling to poverty estimation in Bangladesh, where a variety of different data collection scenarios are being considered. Their simulation results indicate that substantial cost savings could be achieved with a moderate loss in precision. It is unclear however how much of this may be attributed to the use of double sampling as, in addition to relying on predicted data, they also reduce the number of primary sampling units.<sup>6</sup> Pape and Mistiaen (2015) apply a double sampling approach to poverty measurement in Mogadishu. They extend the set of predictors by including a sub-set of consumption items. Ligon and Sohnesen (2016) explore a similar strategy and include an empicical illustration using data from Uganda, Tanzania and Rwanda. Both these studies build on the idea that was put forward by Lanjouw and Lanjouw (2001). This approach strengthens the correlation with total household consumption but also adds to the costs, although Pape and Mistiaen (2015) in their application to Mogadishu are able to keep the face-to-face interview time below 60 minutes.<sup>7</sup> Other popular examples of prediction-based poverty estimation include the “Simple Poverty Scorecard” (SPS) project and the “Poverty Assessment Tool” (PAT) developed by the IRIS Center for USAID, see Schreiner (2014a) for a comparison.<sup>8</sup> These approaches predict a household’s poverty status on the basis of a small number of questions, mostly relying on nonnested double sampling, and then aggregate these predictions to obtain estimates of poverty at the national level (and possibly other administrative levels). SPS and PAT have been applied to

> 5In its version of the non-nested double sampling approach infrequent conventional surveys, which collect both _y_ and _x_ for all households, are alternated with more frequent low cost surveys that only collect _x_ . The infrequent full surveys are used to estimate (and update) the prediction model which is then used to predict poverty into the subsequent low cost surveys that are conducted for the years in between the full surveys.

> 6Furthermore, they do not provide a full break-down of the costs involved; only selected cost components are reported which excludes transport costs for example.

> 7Security concerns denote an important motivation for reducing the time of data collection by means of faceto-face interviews.

> 8The poverty scorecard takes a more pragmatic approach to building its prediction model as it emphasizes simplicity and ease of implementation. See Schreiner (2014b) for further details on the Simple Poverty Scorecard.

4
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 15. Potential gains from greater operational efficiency**

||ICT|Power|Transport|WSS|Total|
|---|---|---|---|---|---|
|Over manning||0.7||0.0|0.7|
|Losses||0.8||1.0|1.8|
|Undercollection||0.6||0.6|1.1|
|Budget execution|0.0|0.2|0.0|0.3|0.5|
|Tariff cost recovery||2.3|9.0|1.7|13.0|
|Total|0.0|4.5|9.0|3.6|17.1|

Source: Derived from Foster and Briceño-Garmendia 2009.

Both water and power are priced below cost in Liberia. At $0.43 per kilowatt-hour, power tariffs still fall well short of the estimated total cost of $0.77 per kilowatt-hour. Similarly, water tariffs at $0.45 per cubic meter also fall well short of the $1.00 typical cost recovery benchmark for the sector. Nevertheless, the macroeconomic impact of under-pricing water and power is relatively small in Liberia. Under-pricing of power and water services each amount to foregone revenues of around $2 million annually—around 0.2 percent of GDP for each sector. This is much lower than what is found in the low-income fragile state benchmark group. However, to some extent this simply reflects the fact that access to services is exceptionally low in Liberia. Thus, while the subsidy figures look small in absolute terms and relative to GDP, they look much larger when one considers that less than 1 percent of the population receives power and less than 3 percent receives piped water.

**Figure 16. Underpricing of water in Liberia relatively low but still material**

Financial burden of underpricing in 2008, as percentage of GDP <!-- Start of picture text --> Liberia<br>% GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia and others 2009.

A key question is whether full cost recovery tariffs for power and water would be affordable to a broader swathe of the population. To answer this question, the case of a household of five persons living on one dollar per capita per day is used as a point of reference. The monthly budget of this household would be $150. Based on a standard affordability threshold of 5 percent of budget for a water or utility bill, such a household could reasonably devote up to $7.50 per month on either service.

For such a household, subsistence power consumption of 25 kilowatt-hours per month (enough to power a couple of light bulbs and a small radio) would cost $19 per month at cost recovery tariffs— 30
# The Export-Productivity Link in Brazilian Manufacturing Firms<sup>1</sup>

X. Cirera<sup>1</sup> , D. Lederman<sup>1</sup> , J.A. Máñez<sup>2</sup> , M.E. Rochina<sup>2</sup> and J.A. Sanchis<sup>2</sup> 1 The World Bank Group 2 University of Valencia and ERICES

**Keywords:** TFP, export status, exogenous vs. endogenous Markov, semi-parametric approach;

self-selection, stochastic dominance, learning-by exporting, matching techniques.

**JEL Classification:** F1, L2, L6, O4

> 1 The authors gratefully acknowledge the financial support from The World Bank Group’s Brazil Country Office, under the aegis of its study on “Brazil’s Productivity Challenge.” This revised version benefitted from insightful comments provided by Roland Clarke, Mark Dutz, Claire Hollweg, and Peer Reviewers of the World Bank’s Brazil productivity study. In addition, the authors benefitted from comments from two anonymous referees and the Editor of the opensource Economics ejournal.
Table 2. Definitions of inequality measures and inequality possibility frontier for Theil (0), Theil (1) and standard deviation of logs <!-- Start of picture text --> Theil (0) or mean log Theil (1) or entropy Standard deviation of<br>deviation index logs<br>Formula for inequality T (0) = 1 n ∑ i ln y 1 i // mnn T )1( = 1 n ∑ i myi ln myi σ= �1<br>measure n �(lny−lnm) 2<br>i<br>Range of the 0 to infinity 0 to ln n 0 to infinity<br>measure<br>Inequality ln α Infinity ln α<br>possibility<br>frontier (with<br>s=given)<br>Figure 8. Actual (measured) inequality and inequality possibility frontier within Gini and<br>Theil (0) frameworks<br>Nueva EspañaChile<br>Hol1732<br>Maghreb Hol1561France NetherlandsEngl1867<br>Old Castiille<br>India-MoghulIndia-BritishSiam Eng1801USA1860<br>ByzantiumKenyaRoman EmpirePeruJava1880Brazil JapanFlorenceUSA1774Eng1688Eng1759<br>Eng1290<br>KenyaBihar Java1924<br>Nueva España<br>Kingdom of Naples Chile<br>Hol1732<br>China Maghreb<br>Engl1867<br>Old CastiilleFrance NetherlandsEng1801<br>ByzantiumKenyaIndia-BritishSiam USA1774<br>KenyaIndia-MoghulRoman EmpirePeruJava1880Brazil FlorenceEng1688Eng1759<br>BiharChinaKingdom of NaplesEng1290 Java1924<br>1000 2000 3000 4000 1000 2000 3000 4000<br>GDI per capita in 1990 PPP dollars GDI per capita in 1990 PPP dollars<br>100<br>80<br>200<br>60<br>150<br>Gini Theil<br>40<br>100<br>20<br>50<br>0 0<br><!-- End of picture text --> Note: Augmented sample of pre-industrial economies from Milanovic, Lindert and Williamson (2010).

18