rafmacalaba/datause-encoder-data huggingface dataset:
income plus pensions (MYPP) have a smaller impact on poverty, and the indirect taxes have a similar effect (Figure 3 panel b and table A.1 in annex).

**However, similar to the previous results, the fiscal system is not unambiguously poverty-increasing, as it can still reduce poverty when measured using lower poverty lines, capturing that households in these lower-income deciles continue to be net beneficiaries of the fiscal system.** The overall fiscal system reduces poverty by using 3.8 percentage points when using the upper-middle countries' poverty line (US$6.85 2017 PPP per day) (Figure 3, panel a). Bulgaria performs favorably compared to other countries with similar income levels.<sup>18</sup> This is due to the direct taxes and transfers that lower poverty by 4.6 percentage points, which outweighs the impact of the indirect taxes and transfers that raise poverty by 0.8 percentage points. The net effect is reducing poverty from 9.2 percent to 5.4 percent. The difference in poverty impacts is because the AROP line - an upper line - captures more net payers into the fiscal system as a higher line. In contrast, the US$6.85 line captures more households who are net recipients of the fiscal system. This is consistent with the fact that the net cash position of households varies across the income distribution, with lower-income deciles being net beneficiaries and higher-income deciles being net contributors to the fiscal system.

# **Figure 3. Poverty Changes, Pre vs. Post Fiscal Incomes, by CEQ income concept**

_Panel a. Percentage Change in Poverty rate due to Fiscal Policy using AROP (at-risk of poverty) and UMI (Upper-Middle Income) Poverty Lines, 2018 vs. 2020._ <!-- Start of picture text --> 3.8<br>AROP<br>2.2<br>-3.8<br>UMIC<br>-2.4<br>-5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0<br>2020 2018<br><!-- End of picture text -->

> 18 Bulgaria performs favorably in reducing poverty when using the international poverty threshold of USD 5.5 PPP 2011 per person per day for upper middle-income nations (Annex 2, figure A.1 panel b).

20
**Table A4 continued**

|Dimension|Element|Scoringoptions|Notes|
|---|---|---|---|
|Openness|3. Download options|1 point if all published data has a bulk download<br>option and an API or user-selectable download<br>option.<br>0.5 points if some published data has an API, bulk<br>download, or user-selectable download options.<br>0 points if no published data have any download<br>options|A bulk download is defined at the indicator level<br>as: The ability to download all data recorded in<br>ODIN for a particular indicator (all years,<br>disaggregations, and subnational data) in one file,<br>or multiple files that can be downloaded<br>simultaneously. Bulk downloads are a key<br>component of the Open Definition, which requires<br>data to be “provided as a whole . . . and<br>downloadable via the internet.”<br>User-selectable download options are defined as:<br>Users must be able to select an indicator and at<br>least one other dimension to create a download or<br>table. These dimensions could include time<br>periods, geographic disaggregations, or other<br>recommended disaggregations. An option to<br>choose the file export format is not enough.<br>API stands for Application Programming Interface.<br>Ideally, APIs should be clearly displayed on the<br>website. ODIN assumes APIs are available for the<br>NSOs entire data collection used in ODIN, unless<br>clearly stated. ODIN assessors do not register for<br>use or test API functionality. Scores are given by<br>data category,not indicator.|
||4. Metadata available|1 point if all published data have complete metadata.<br>0.5 points if some published data have complete or<br>incomplete metadata, or all published data have<br>incomplete metadata.<br>0 points if all published data have no metadata.|ODIN classifies metadata into three categories: (1)<br>Not Available, (2) Incomplete, and (3) Complete.<br>The following must be available to classify<br>metadata as complete:<br>• Definition of the indicator, or definition of key<br>terms used in the indicator description (as<br>applicable), or how the indicator was calculated.<br>• Publication (date of upload), compilation date<br>(date on front of report is not sufficient), or date<br>data set was last updated.<br>• Name of data source (what agency collected the<br>data).<br>If the metadata only have one or two of the above<br>elements, they are scored as incomplete|

**<mark>Table A4 continued</mark>**

42
survey is even greater, 93 percent behind Croatia (0.61+0.32=0.93). The TFP gaps with other leading East European countries are as big or bigger.

In column 2 of Table 1 we show an estimation of the production function which uses the same PICS-BEEPS dataset as the country-averages estimation but with dummy variables included for each country. The coefficients of the country dummies capture countryaverage TFP. This approach is known in econometrics as the “within approach”:<sup>8</sup> while the dummy variables are estimates of average firm TFP in a country, the coefficients of labor, capital, ownership and the investment climate variables explain the variation of productivity “within” the countries. The estimated difference in TFP levels between Serbian manufacturing firms in the BEEPS 2002 survey and Croatian firms is 80 percent<sup>9</sup> , and 108 percent using the 2003 Serbia PICS survey. These differences are statistically highly significant.<sup>10</sup> These cross-country differences in TFP partly reflect the differences in GDP per capita between the countries, as is apparent in the figure. But lower TFP cannot be blamed on having too little capital: holding capital and labor constant, Serbia is generating much lower output than Croatia, Slovenia, or the other East European countries that have joined the EU. Serbia will need to invest in new fixed capital. However, to catch up with these countries, it will also need to invest in improving the investment climate, and in changing incentives for firms—including privatizing those that are still socially- or state-owned.<sup>11</sup>

**Figure 1: GDP per capita and TFP, country-survey-averages regression**

<!-- Start of picture text --> 1<br>Bulgaria2002Latvia2002Peru2002 Lithuania2002Estonia2002 Slovenia2002<br>Poland2003<br>5 Moldova2002 Indonesia2003 Morocco2000Turkey2002 Brazil2003Slovakia2002Hungary2002<br>Ecuador2003<br>Croatia2002<br>0 Kyrgyzstan2002 Zambia2002 Uzbekistan2002 Bangladesh2002 India2000 Azerbaijan2002 India2002Pakistan2002Armenia2002Georgia2002 Bolivia2000 Philippines2003Belarus2002Algeria2002Romania2002Poland2002 Czech2002<br>Kazakhstan2002Guatemala2003<br>China2002<br>Honduras2003<br>-.5 Cambodia2003 Tanzania2003 Moldova2003 UkraiAlbaSerbia2002 n ia2002e2002China2003Montenegro2003BiH2002 Russia2002<br>Nicaragua2003<br>Serbia2003<br>Ethiopia2002 FYROM2002<br>Tajikistan2002 Kyrgyzstan2003<br>-1<br>Uzbekistan2003<br>Tajikistan2003<br>-1.5<br><!-- End of picture text --> <!-- Start of picture text --> TFP (between regression residual) Fitted values<br><!-- End of picture text --> 6
destination country. They include policy variables that are amenable to change in the short to medium term, as well as country fundamentals such as per capita GDP or population size. While these indicators may not be under the control of policy makers, their inclusion nevertheless has an important policy dimension to insert a reality check into sectoral FDI strategies. For instance, countries may find that policy initiatives intended to promote FDI in a certain industry are doomed to fail if country fundamentals are detrimental to the development of such an industry in the country. In other cases, there might be short-term initiatives that policy makers can consider to compensate for shortcomings on particular fundamentals, such as a more open immigration regime to make up for skills shortages or spatial solutions to reduce transport costs and disadvantages related to remote location.

_Table 1. Indicators categories_

|Category|Sub-category|
|---|---|
|DEMAND|Demand|
|PRODUCTION FACTORS|Labor and skills|
|PRODUCTION FACTORS|Geographyand natural resources|
|PRODUCTION FACTORS|Existingcapabilities|
|INPUTS|Energy|
|INPUTS|Transport|
|INPUTS|Finance|
|INSTITUTIONS|Regulatorybarriers and taxation|
|INSTITUTIONS|Rule of law andpropertyrights|
|INSTITUTIONS|Market contestability|
|INSTITUTIONS|Macroeconomic andpolitical stability|

# Empirical Design and Approach

# Data preparation

The initial country indicators data list included 594 potential indicators for 217 countries, collected over the 1960-2019 period. Not surprisingly, the country coverage is limited until the most recent periods. Due to large number of missing data points per individual indicators, we try to maximize the trade-off between including the maximum number of indicators while maintaining adequate country data coverage. After thorough data examination and cleaning, the final balanced data set includes 190 potential indicators which are available for 116 countries over the 2010-19 period, the period which matches the availability of FDI data while being representative in terms of the number of countries selected (Annex 3 provides the full list).

We average the data for indicators over two non-overlapping five-year periods (2010-2014 and 201519). The first period (2010-2014) is used for computation of the indicators to avoid any potential reverse causality issues (the lagged indicators are predetermined relative to the FDI and trade flows which are computed over the 2015-19 period). We do not use the information on indicators from the second period in estimations, but only for calculation of the thresholds for sector benchmarking exercise (see section below). Since we are interested in analyzing the key structural and macroeconomic factors associated with FDI and trade success from the perspective of the recipient country, we only use country level indicators and do not include any global or "push" factors.

11
Challenges and Solutions, with an Application”, _Journal of Economic Perspectives_ 31(4): 73-102.

- BBVA Research (2018) “Situación Automotriz 2018 Colombia”, BBVA Research, March.

- Bloom, Nicholas, and John Van Reenen (2007). "Measuring and Explaining Management Practices across Firms and Countries" _Quarterly Journal of Economics, 122_ (4), 13411408.

- Bloom, Nicholas, Benn Eifert, Aprajit Mahajan, David McKenzie, and John Roberts (2013). "Does Management Matter? Evidence from India" _Quarterly Journal of Economics, 128_ (1), 1- 51.

- Bloom, Nicholas, Aprajit Mahajan, David McKenzie, and John Roberts (2018) “Do Management Interventions Last? Evidence from India”, _American Economic Journal: Applied Economics_ , forthcoming.

- _”_

- Bloom, Nicholas, Raffaella Sadun, and John Van Reenen (2016) “Management as a Technology , NBER Working Papers 22327, National Bureau of Economic Research, Inc..

- Bloom, Nicholas, Erik Brynjolfsson, Lucia Foster, Ron Jarmin, Megha Patnaik, Itay SaportaEksten, and John Van Reenen (2018) “What Drives Differences in Management Practices?”, _American Economic Review_ , forthcoming.

- Bold, Tessa, Mwangi Kimenyi, Germano Mwabu, Alice Ng'ang'a and Justin Sandefur (2018) “Experimental Evidence on Scaling Up Education Reforms in Kenya”, _Journal of Public Economics_ , 168: 1-20.

- Brooks, Wyatt, Kevin Donovan and Terence Johnson (2018) “Mentors or Teachers? Microenterprise Training in Kenya”, _American Economic Journal: Applied Economics_ , 10(4): 196-221.

- Bruhn, Miriam, Dean Karlan, and Antoinette Schoar (2018) “The Impact of Consulting Services on Small and Medium Enterprises: Evidence from a Randomized Trial in Mexico", _Journal of Political Economy_ , 126(2): 635-87.

- Cai, Jing and Adam Szeidl (2018) “Interfirm Relationships and Firm Performance”, _Quarterly Journal of Economics_ 133(3): 1229-1282.

- Carrillo, Paul, Dina Pomeranz, and Monica Singhal (2017) “Dodging the Taxman: Firm Misreporting and Limits to Tax Enforcement.” _American Economic Journal: Applied Economics_ , 9(2): 144-64.

- Chatterji, Aaron, Solene Delecourt, Sharique Hasan and Rembrand Koning (2018) “When does Advice Impact Startup Performance?”, _Strategic Management Journal_ , forthcoming.

- Dalton, Patricio, Julius Rüschenpöhler, Burak Uras and Bilal Zia (2018) “Learning Business Practices from Peers: Experimental Evidence from Small-Scale Retailers in an Emerging Market”,

- Giorcelli, Michela (2019) “The long-term effects of management and technology transfers”, _American Economic Review_ 109(1): 121-52.

- Higuchi, Yuki, Vu Hoang Nam and Tetsushi Sonobe (2017) “Management skill, entrepreneurial motivation, and enterprise survival: Evidence from randomized experiments and repeated surveys in Vietnam”, Mimeo.

- Hsieh, Chang-Tai and Peter Klenow (2014) “The Life Cycle of Plants in India and Mexico”, _Quarterly Journal of Economics_ 129(3): 1035-1084.

- Lafortune, Jeanne, Julio Riutort and José Tessada (2018) “Role models or individual consulting: The impact of personalizing micro-entrepreneurship training”, _American Economic Journal: Applied Economics_ , 10(4): 222-45.

28
|**Oaxaca decomposition of log va**|**Table 7**<br>**lue of output per he**|**ctare: North (excl**|**uding the west)**|
|---|---|---|---|
|_A. Mean Gender differential_||||
|Mean Male Managedplot||11.38***||
|Agricultural Productivity||(0.0351)||
|Mean Femalemanaged plot<br>Agricultural Productivity<br>||11.34***<br>(0.116)<br>||
|Mean Gender Differential||0.0389||
|in agricultural Productivity||(0.121)||
|_B. Aggregate Decomposition_|_Endowment effect_|_Male structural_<br>_advantage_|_Female structural_<br>_disadvantage_|
|**_Total_**|-0.235***|-0|0.274***|
||(0.0875)|(0.00175)|(0.102)|
|Share of the Gender Differential|-604%|0%|704%|
|_C. Detailed decomposition_|_Endowment effect_|_Male structural_<br>_advantage_|_Female structural_<br>_disadvantage_|
|Age|0.0107|-0.385**|-3.808**|
||(0.0224)|(0.169)|(1.587)|
|AgeSquared|-0.00567|0.200**|2.290***|
||(0.0189)|(0.0864)|(0.872)|
|Years ofschooling|-0.0242|0.00626|0.00796|
||(0.0166)|(0.00622)|(0.0584)|
|Manager isnonmuslim†|0.00884|0.0107|0.343*|
||(0.0247)|(0.00995)|(0.193)|
|Manager has nonfarm activity†|0.00203|-0.00986|-0.156*|
||(0.00292)|(0.00674)|(0.0905)|
|Householdfemale adult size (age12-60)|0.0102|0.00370|0.392**|
||(0.00692)|(0.0143)|(0.177)|
|Householdmale adult size (age12-60)|0.0446***|0.000341|0.0264|
||(0.0163)|(0.0111)|(0.104)|
|Child dependencyratio|-0.000616|-0.00163|-0.0134|
||(0.00922)|(0.0106)|(0.0734)|
|Log [GPS-LandArea (HA)]|-0.432***|-0.00524|-0.0167|
||(0.0682)|(0.0113)|(0.221)|
|Log [GPS-LandArea (HA) Squared]|0.0631**|-0.0124|-0.0664|
||(0.0245)|(0.0106)|(0.164)|
|Plot distance to household|0.000780|0.000214|-0.0292|
||(0.000802)|(0.000199)|(0.0245)|
|Access to extension†|0.000883|0.000820|-0.00530|
||(0.00284)|(0.00293)|(0.0275)|
|Grows cashcrop†|-0.00600|-0.00524|-0.0960*|
||(0.00521)|(0.00384)|(0.0507)|
|Onlyone croponplot†|0.0255|-0.00725|-0.132*|
||(0.0174)|(0.00878)|(0.0784)|
|Log [Qty of fertilizer(kg)/ha]|0.0433***|-0.000227|0.00535|
||(0.0131)|(0.00551)|(0.0461)|
|Lo[Herbicide(k)/ha]|000427|-000378|-00512|
|g g|.<br>(0.00480)|.<br>(0.00462)|. <br>(0.0488)|
|Log [Pesticide (kg)/ha]|-0.00122|-0.00108|-0.0102|
||(0.00302)|(0.00158)|(0.0162)|
|Used purchased seed†|0.000461|-0.00454|-0.0828*|
||(0.00393)|(0.00462)|(0.0483)|

36
# _iii) Labor and Skills_

It is a well documented fact that unit labor costs, defined as the total labor costs divided by either the value of output or value-added, tend to be higher in Africa than in other regions. It has been argued in several Investment Climate Assessments (World Bank, 2002; 2004c,d) that high unit labor costs, relative to international competitors, imply poor global competitiveness. Eifert, Gelb and Ramachandran (2005) have recently challenged this view, citing Eifert and Ramachandran (2004), who report that median unit labor costs are lower in Eritrea and Nigeria than in Uganda and Mauritius, and Cadot and Nasir (2001) who document higher average labor unit costs among firms in China than in Ghana, Lesotho, Madagascar, Mozambique and Kenya. The idea that Mauritian firms are less competitive than Nigerian ones, or that Chinese firms are less competitive than Ghanaian ones, seems counter-intuitive.

Some basic theory illustrates why it may be problematic to use the unit labor cost as a measure of competitiveness. Suppose the firm operates with a Cobb-Douglas technology and that factor prices – wages, the cost of capital etc. - are determined exogenously.<sup>7</sup> Suppose the firm seeks to minimize costs (or maximize profits), and consider the effects of an exogenous increase in the wage rate. Clearly a wage rise makes the firm less competitive, and so a useful competitiveness measure should reflect this. The theory implies, however, that if the firm can adjust its labor force without incurring adjustment costs, it will respond to the wage rise by reducing the number of employees by exactly the amount that leaves the unit labor cost unchanged. That is, even though the firm is in a weaker position after the wage increase, the unit labor cost measure fails to reflect this. An analogous result is obtained for changes in total factor productivity or in the demand for the firm’s products. In fact, the only way of generating a low unit labor cost in this model is to reduce the output elasticity of labor – i.e. make output _less_ responsive to changes in labor input. It is far from obvious that this would be an improvement in terms of competitiveness. The reason China has higher labor unit costs than Ghana could thus be that – for technological reasons - output is more responsive to labor input in China than in Ghana. This seems

> 7 The Cobb-Douglas specification, which is extremely popular in applied research, is written as log _Y_ = log _A_ + ∑ _j_ α _j_ ln _X j_ , where _Y_ is output or value-added, _A_ is total factor productivity, _X j_ is input _j_ and α _j_ is the elasticity of _Y_ with respect to _X j_ .

12
percent of total assets. But large exposures to major domestic groups and large domestic bond issuers have been substantially reduced, following the decision to diversify investments internationally.

ATP reports an average nominal rate of return of 11 percent over its entire life. However, it does not provide an indication of the average rate of inflation during this long period or of the volatility of nominal and real returns. On the basis on data on nominal returns provided by ATP and data on consumer price inflation contained in the IFS database, the following picture emerges regarding the behavior of real returns over time (Table 6).

It is striking how different the selected sub-periods look. While in its first 17 years of operation, which were the years of high inflation in the whole OECD area, ATP achieved a real rate of return of just 0.69 percent, in the 15 years ending in 2005, the years of low inflation, it registered an impressive real return of 7.43 percent. ATP achieved an even higher real rate of return during the 1980s, which was the decade of disinflation.

Large capital gains on bond portfolios were registered during the period of disinflation because of the steady decline of nominal interest rates. However, this episode highlights the shortcomings of focusing on asset returns without looking at the implications of financial developments on the value of liabilities. ATP, like pension funds everywhere, suffered from the rise in the value of liabilities, even though these losses were not reported because liabilities were not required to be marked-to-market.

**Table 6: Historical Nominal and Real Investment Returns, 1964-2005**

|Average Annual Rate<br>%|Nominal<br>Return|Inflation<br>Rate|Real<br>Return|
|---|---|---|---|
|1964-1970|6.18|5.95|0.22|
|1970-1980|10.96|9.84|1.02|
|1980-1990|18.67|5.91|12.05|
|1990-2000|10.48|2.14|8.17|
|2000-2005|7.97|1.97|5.88|
|1964-1980|8.97|8.22|0.69|
|1980-1990|18.67|5.91|12.05|
|1990-2005|9.86|2.23|7.43|
|1964-2005|11.44|5.44|5.69|

Source: ATP Focusing on investment performance in recent years, investment returns suffered during the first few years of the new millennium from the decline in interest rates and the collapse of equity prices between 2000 and 2002 (Table 7). Although equity prices recovered substantially between 2003 and 2006, the overall investment performance 14
<!-- Start of picture text --> 70 a b<br>30<br>ae ><br>‘S 60 € 25<br>2G<br>85<br>Ee= Siese 20 voc --<br>350 Sa ae<br>3 - 85 a<br>a oer 8 = 15 “<br>5 ae wo Ee “<br>a “ i 0 v<br>S 40 a Es 1 /<br>rc)5 ¢“ cS}<x 1<br>“ 5 4<br>“ of<br>30 Pae uo"<br>nn 0 Sse eee<br>rr | ee|<br>c 1.40 d<br>0.8 % 1.35<br>Tt<br>on<br>> 5B 1.30<br>aa oN go:<br>os 0.6 - s oo<br>ow \ \ 5 = 1.25<br>gs /‘ N‘ os#2<br>ow 1 \ $6 1.20<br>3604 ! ‘\ oa”<br>£= i \ £Y<br>Qe ! \ >= 115<br>=~ F \ £1<br>0.2 ; \ 551.10<br>/ re)<br>/ NS c<br>; =)<br>4 1.05<br>Se eee<br>0.0 o” 1.00<br>Le | Le|<br>2020 2030 2040 2050 2060 2070 2080 2090 2020 2030 2040 2050 2060 2070 2080 2090<br>Year information is revealed Year information is revealed<br>T*=1.7°C — T*=2°C<br><!-- End of picture text -->
number of public boys' primary schools as an additional instrument in the selection equation. In the presence of competitive schooling eects, private schools should be less likely to set up in villages where there are public boys' primary schools. Additionally, such schools are unlikely to aect the wage-bill of the entrepreneur directly since public school teachers are rarely, if ever, hired locally and because their wages are xed and centrally determined. While we remain cautious in pushing this instrument since primary schools for boys may be endogenously placed, it does serve as a robustness check on the identication based on non-linearities in the selection equation. Columns (2) and (4) use the functional form of the selection equation to achieve identication, and Columns (3) and (5) introduce the additional instrument. The results are similar to the OLS estimates, with estimates of 27 to 28 percent<sup>22</sup> suggesting that selection into the non-zero wage sample is of limited importance.

Columns (6) and (7) present tentative evidence that wage declines due to a GSS are larger in villages where labor markets for women are more restricted and localized, i.e., the interaction terms of GSS existence and the village progressivity indicator are positive. In Column (6), we look at the dierential eect of GSS construction on wages for more and less progressive villages using the female/male ratio for children under the age of 14 as an indicator of progressivity/gender bias. Arguably, villages with a lower female/male child ratio may be more conservative with fewer labor market opportunities for women outside the immediate vicinity of the house. Indeed, villages at the 25th percentile of the distribution (female/male ratio of 0.86) see a wage decline of 58 percent due to GSS construction, compared to essentially no decline for villages at the 75th percentile of the distribution (female/male ratio of almost 1).

In Column (7), we look at analogous results using households per capita with access to radios as an indicator of village-level development. While the results for the interaction term are only signicant at the 26 percent level in this case, the signs are in the expected

> 22 Since our dependent variable is log (wage), the coecients of about -0.32 on the GSS existence dummy represent a decrease of approximately 27 percent in average wages. For example, in Column (2), the coecient implies that, in villages with a GSS, wages change by a factor of e<sup>�0:3207</sup> (or 0.7256) , which is equivalent to a 27.44 percent decline.

26