rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Placebo test: initial export share for Poland, Russia, Ukraine, Lithuania and France

||Po|land|R|ussia|Uk|raine|Lith|uania|Fr|ance|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|B. Dep. Variable: log TFPR|||||||||||
|Post*country’s export share|0.002|-0.079***|0.009|-0.047**|0.005|-0.044***|-0.021|-0.031**|0.039*|-0.042|
||(0.018)|(0.021)|(0.016)|(0.018)|(0.013)|(0.014)|(0.015)|(0.011)|(0.021)|(0.033)|
|Post*Germany’s export share||0.098***||0.086***||0.085***||0.050***||0.085***|
|||(0.019)||(0.023)||(0.022)||(0.013)||(0.028)|
|N (obs.)|134174|134174|134174|134174|134174|134174|134174|134174|134174|134174|
|N (frms)|18355|18355|18355|18355|18355|18355|18355|18355|18355|18355|
|R-squared|0.104|0.107|0.104|0.106|0.104|0.106|0.105|0.106|0.105|0.106|
|B. Dep. Variable: log revenue|||||||||||
|Post*country’s export share|0.021|-0.136***|0.022|-0.098***|0.015|-0.089***|-0.042**|-0.063***|0.074|-0.131**|
||(0.048)|(0.024)|(0.037)|(0.031)|(0.030)|(0.024)|(0.020)|(0.018)|(0.045)|(0.062)|
|Post*Germany’s export share||0.189***||0.185***||0.182***||0.110***||0.212***|
|||(0.043)||(0.047)||(0.045)||(0.030)||(0.069)|
|N (obs.)|134174|134174|134174|134174|134174|134174|134174|134174|134174|134174|
|N (frms)|18355|18355|18355|18355|18355|18355|18355|18355|18355|18355|
|R-squared|0.106|0.115|0.107|0.116|0.106|0.115|0.108|0.115|0.108|0.115|
|D. Dep. variable: log export|||||||||||
|Post*country’s export share|0.031|-0.12|0.058|-0.037|0.000|-0.114|-0.12|-0.172*|0.137**|-0.097|
||(0.088)|(0.109)|(0.093)|(0.082)|(0.046)|(0.070)|(0.108)|(0.097)|(0.056)|(0.185)|
|Post*Germany’s export share||0.196**||0.177***||0.227***||0.188***||0.239|
|||(0.077)||(0.05)||(0.074)||(0.058)||(0.181)|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|N (obs.)|63981|63981|63981|63981|63981|63981|63981|63981|63981|63981|
|N (frms)|13245|13245|13245|13245|13245|13245|13245|13245|13245|13245|
|R-squared|0.109|0.11|0.107|0.11|0.108|0.113|0.108|0.113|0.109|0.11|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. All regressions include firm and year fixed effects. Export shares are measured in the period 1994-2004. TFPR is estimated using the method in Levinsohn and Petrin (2003). Results using TFPR based on ACF method are shown in Table A.9. Standard errors in parentheses clustered at 2-digit industry level. *10% level, **5% level, and ***1% level.

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SUMMARY AND CONCLUSION 58. This paper has examined the relative effectiveness of 214 national public (government) schools, local public schools and private schools on secondary science achievement and attitudes in the Philippines. Significant differences were found among these three types of schools in terms of student achievement and available resources; peer effects and the context for learning were examined for their mediating effects on school type differences.

59. First, with SES, age and gender held constant, significant differences were observed in the achievement and attitudes of students in the three types of schools:

* Students in local schools had lower achievement (1.25 points lower in science and 1.61 points lower in mathematics) and less positive attitudes than their counterparts in government schools.

* Students in private schools outperformed students in government schools (.88 points higher in mathematics).

60. Second, local public schools had fewer resources than either national public or private schools. In particular, fewer local public schools had laboratories and teachers in local schools were less educated and experienced.
> - 28 -

advantage. 20/ Finally, questions relating to the cost of nontariff protection are of particular importance. Many empirical studie3 have documented the fact that nontariff barriers involve major trade, employment and welfare costs for both developed and developing countries (see the survey provided by Laird and Yeats (1988)), and that more efficient (less onerous) measures ace often available to achieve desired policy objects. These studies, coupled with the fact that extensive increases in nontariff protection were shown to occur over 1966-1986, indicate that major and economic and social costs were incurred due to the imposition of new trade barriers over this 20 year period.

- 20/ Over 1966-1987 developing country exports of manufactures grew from $10 billion to $193 billion in terms of 1986 prices. While this expansion is impressive, the relevant comparison for assessing the effects of protection would be between actual trade growth rates and potential trade that would occur in the absence of restrictions. A further point is that newly industrialized countries like Korea, Taiwan and Hong Kong have in the past been able to avoid the effects of new protection somewhat by shiftine exports from restricted products (like textiles and clothing) to variou. labor intensive goods that were still traded relatively freely. Yeats (1S88) provides empirical evidence on the magnitude and direction of this shift.
four stages of development for the groundnut crop: initial, crop development, mid-season and late season. Because the mid-season and late season can be grouped as the flowering phase, the insurance contract in 2006 for Chitedze considered only three phases. The insurance scheme, trigger and claim payouts are based on the accumulated amount of rainfall for each of the three phases. The following trigger events are set in the 2006 contracts (Table 3 and Figure 3):

|Table 3: Upper a|nd lower claim triggers f<br>Uppertrigger(mm)|or each phase (in mm)<br>Lowertrigger(mm)|
|---|---|---|
|Phase1|35|30|
|Phase2|35|30|
|Phase 3|220|20|

<!-- Start of picture text --> Germination phase: Loan reduction as a Flowering phase: Loan Reduction as a function of<br>function of rainfall (Dekads 1-3) rainfall (Dekads 7-14)<br>300 300<br>270 270<br>240 240<br>210 210<br>180 180<br>150<br>150<br>120<br>120<br>90<br>90<br>60<br>60<br>30<br>30<br>0<br>0<br>0 5 10 15 20 25 30 35 40 45 50<br>0 5 10 15 20 25 30 35 40 45 50<br>Kwacha Hundreds Kwacha Hundreds<br>Mvula Rainfall (mm) Mvula Rainfall (mm)<br><!-- End of picture text --> Fig. 3: Trigger events for phase one and two (germination and crop development phase) on the left, and phase three (flowering phase) on the right side. Source: Osgood, 2006.

Each phase is further subdivided into _dekads_ of 10 days _._ The contract also contains a “no sowing condition,” which triggers a full loan payout if a minimum level of rainfall is not received in order for the farmer to successfully sow the plant during the contract’s initial stages. If rain is above a certain level (e.g. 60 mm during germination stage), then there is no payout. If rainfall is insufficient for the crop to survive (e.g. less than 30 mm during germination), then insurance pays back the entire loan. If rainfall lies in between, interpolation defines what portion of the loan is paid by the insurance company. Because excessive rainfall in one dekad does not contribute to the growth in other dekads the rainfall amount for each dekad is capped at 60 mm per period. Thus, this contract can be viewed as essentially two contracts, one for catastrophic events (first 2 phases) and one for more frequently less dramatic losses (third phase). Figure 4 illustrates the difference of the payout for (i) capped and (ii) non-capped rainfall data between 1961 and 2005.

9
Training System (PADETS). For the most part, the packets were sold on credit after a 10 to 35 percent down payment (DSA, 2006). Credit was extended through the Commercial Bank of Ethiopia through cooperatives, local authorities and micro-lending institutions, which also handle record keeping and the collection of interest and principal.

Questions included in the 2004 survey about PADETS and other extension programs show the reach of the government programs and provide anecdotal evidence that constraints, rather than limited profitability, prevent farmers from adopting more productive technologies. Households participating in the 2004 wave were asked if they participated in an extension program and how productivity gains from the package are compared with traditional practices. More than 64 percent of the sampled households participated in a program and 95 percent of the households found the new technology more profitable than the traditional one. More than 50 percent of the households responded that production increased by 50 percent while 20 percent of households reported production increase of more than 50 percent. Only a small proportion of households (7 percent) felt that the extension package was riskier than the traditional practices and only 11 percent of the households opted out of the extension package program.

Although fertilizers, pesticides, seeds and land-management practices are all important components of high-yielding technologies, the price of fertilizer plays a dominant economic role in the cost of competing technologies. Data from the 2006 wave of the survey shows that, among farmers that used fertilizer, the cost of chemical fertilizers averaged around 18 percent of the total value of outputs while the share of all remaining purchased inputs (including improved and traditional seeds, hired labor, transportation, rented oxen and tractor) averaged around 9 percent.

As Table 4 indicates, chemical fertilizer was not the only nutrient employed by farmers in the survey. Surveyed farmers often applied animal manures too, especially in 2006. The table also reveals differences in how the two types fertilizers are marketed. For most farms, chemical fertilizers reached the farm through formal markets – that is, they were generally purchased with cash or on credit. In contrast, almost 80 percent of organic fertilizer reached the farm through informal channels; most organic fertilizer was recovered from animals owned by households, or was given freely by neighbors. Some land owners provided manure to their tenants, accounting for another 13.5 percent.

# Empirical Results

Estimation results from the panel selection models, with and without group means, are given in Table 5. In general, the model fits well and differences between the two versions of the model are minor. The signs on the determinants are consistent with expectations, as are tests of significance for most of the individual parameters. The correlations between the error-components of the two model equations, reported in the final rows of the table, are statistically significant, which is consistent with our selection specification.

17
- 41 -

TABIZ 233 BASIC PARAMETERS OF CAPITAL GOODS ENTPRISES

||N|PC||Support Coefficients|<br>VA/Outp|ut (I)|
|---|---|---|---|---|---|---|
||Inout<br>|Output|<br>EPC<br>|Nominal Effective|Int.Prices|Dom. Prices|
|tGroups Average<br>|1.69<br>|1.31|1.01|n.a.|56|43|
|Median|1.65|1.33|1.02|n.a.|55|42|
|2ndGroup Average<br>|1.79<br>|1.58|1.40|n.a.|55|58|
|Median|1.76|1.56|1.42|n.a.|56|51|
|d Group Average<br>|1.77|1.56|1.61|<br>1.89<br>2.43|46|41|
|Median|1.76|1.47|1.22|<br>1.72<br>1.62|40|39|

TABLE 248 STRUCTURE OF VALUE ADDED IN CAPITAL GOODS ENTERPRISES (in **_Z_** of Output Value in Domestic Prices)

|||Enterprise Groups||
|---|---|---|---|
|Components|First|Second|Thirdla|
|Labor<br>|15.0|20.0|29.0|
|Depreciation|5.0|4.0|4.0|
|Interest|6.5|8.5|13.5|

/a Average structure for cash-losing CPEs in Capital Goods.

77. The combination of these variou **s** ets of parameters leads to the following three basic enterprise models which represent reasonably well the three groups identified earlier.
net profit margin, while BENECO shows a small negative margin<sup>18</sup> (loss) and the sample average has a large negative margin. The EBITDA (earnings before interest, taxes, depreciation, and amortization) is positive for both MERALCO and BENECO (as well as for the sample average) but may be misleading because it omits expenses related to the cost of capital. Importantly, neither Philippine utility receives government transfers, while the sample average has transfers averaging 5 percent of revenue.

**Table 8: Summary of Financial Indicators against Sample Average**

||**MERALCO**|**BENECO**|**Global**<br>**comparator**|
|---|---|---|---|
|Netprofit (loss) margin (%)|8|-2|-12|
|EBITDA margin (%)|13|2|6|
|Current ratio|0.84|2.00|0.79|
|Debt service coverage ratio|10.33|-1.38|-4.56|
|External financing index ratio<sup>19</sup>|-0.40|-0.35|6.92|
|Investment as apercent of revenues (%)|10|6|18|
|Average borrowingrate (%)|4|4|6|
|Government transfers as a percent of utility<br>revenue (%)|0||6|
|Net capital cost recovery ratio<sup>20</sup>(%)|72||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

# _3.4.2 Institutions_

The regulator, ERC, was created through the EPIRA Act. This Act also contains, among other issues, its goals, functions, composition, and budget. ERC’s objectives are, among others, the following:

- To promulgate and approve rules, regulations, guidelines and policies.

- To enforce rules and regulations, including issuance of permits and licenses.

- To resolve cases (rates and other cases) and disputes.

- To promote consumers’ interest.

The performance of the regulatory system is evaluated from two standpoints. First, an evaluation is made of the de jure performance by comparing the laws, rules, and regulations in place with

> 18 The net profit of the ECs is misleading because their CAPEX collection is not reflected as revenue while they are required to declare depreciation as an expense.

> 19 _External financing index ratio_ measured as net cash flow from financing divided by the net cash flow from operations.

> 20 _Net capital cost recovery ratio_ is the percent of full capital costs that can be recovered through tariff revenues net of operating costs.

45
Hoynes, Hilary, and Diane Whitmore Schanzenbach (2018): “Safety net investments in children .” Brooking Papers on Economic Activity, Conference Draft, Spring.

- Lakner, Christoph, Mario Negre and Espen B. Prydz (2014): “Twinning the Goals: How Can Shared Prosperity Help to Reduce Global Poverty?” World Bank Policy Research Working Paper No. 7106.

- Lipscomb, Molly, Ahmed Mushfiq Mobarak and Tania Barham (2013): “Development Effects of Electrification: Evidence from the Topographic Placement of Hydropower Plants in Brazil,” _American Economic Journal: Applied Economics_ , **5** (2): 200–231.

- Lee, Kenneth, Edward Miguel and Catherine Wolfram (2016): “Experimental Evidence on the Demand for and Costs of Rural Electrification”, NBER Working Paper No. 22292.

- Okun, Arthur M. (1975): _Equality and efficiency, the big tradeoff_ . Washington, DC: The Brookings Institution.

- Parker, Susan W. and Tom Vogl (2018): “Do Conditional Cash Transfers Improve Economic Outcomes in the Next Generation? Evidence from Mexico.” NBER Working Paper No. 24303.

Pop‐Eleches, Cristian and Miguel Urquiola (2013): “Going to a Better School: Effects and Behavioral Responses.” _American Economic Review_ , **103** (4): 1289‐1324.

- Ravallion, Martin and Shaohua Chen (2003): “Measuring Pro‐Poor Groth.” _Economic Letters,_ **78** (1): 93‐ 99.

Rawls, John (1971): _A Theory of Justice._ Cambridge, Mass.: Belknap Press.

Rosenblatt, David and Tamara J. McGavock (2013): “A note on the simple algebra of the shared prosperity indicator.” World Bank Policy Research Working Paper No. 6654.

- Van de Gaer, Dirk (1993): “Equality of opportunity and investment in human capital.” Ph.D. Dissertation, Katholieke Universiteit Leuven.

- World Bank (2015a): _A Measured Approach to Ending Poverty and Boosting Shared Prosperity: Concepts, Data, and the Twin Goals_ . Policy Research Report. Washington, DC: World Bank.

- World Bank (2015b): _Global Monitoring Report 2014/2015: Ending Poverty and Sharing Prosperity_ . Washington, DC: World Bank.

- World Bank (2016a): _Global Monitoring Report 2015/2016: Global Development Goals in an Era of Demographic Change_ . Washington, DC: World Bank.

- World Bank (2016b): _Poverty and Shared Prosperity 2016: Taking on Inequality._ Washington, DC: World Bank.

- World Bank (2018): _World Development Report 2018: Learning to Realize Education's Promise_ . Washington, DC: World Bank.

21
Figure 6. International trade policy, border processes, and logistics A. Tariff rates in AEs and EMDEs

# B. Tariff rates by different sectors

<!-- Start of picture text --> Percent<br>Advanced economies EMDEs<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1995 2000 2005 2010 2015 2020<br>C. Trade uncertainty for AEs and EMDEs<br>Percent Percent<br>8 40<br>6<br>30<br>4<br>20<br>2 Advanced economies EMDEs (RHS)<br>10<br>0<br>2 0<br>1995 1999 2003 2007 2011 2015 2019<br>E. Liner shipping connectivity index<br>Index, 100 = maximum in 2004 2006<br>60 2020<br>50<br>40<br>30<br>20<br>10<br>0<br>Advanced economies EMDEs EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> <!-- Start of picture text --> Percent Agriculture Manufacturing<br>18<br>15<br>12<br>9<br>6<br>3<br>0<br>1995 2020 1995 2020<br>Advanced economies EMDEs<br>D. Logistics performance index<br>Score 2007 2018<br>4<br>3<br>2<br>1<br>0<br>Advanced economies EMDEs EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

# F. Regional trade agreement participation

<!-- Start of picture text --> Number of RTAs 2000 2020<br>AEs<br>EMDEs<br>EAP<br>ECA<br>LAC<br>MNA<br>SAR<br>SSA<br>0 20 40 60 80 100<br><!-- End of picture text --> Sources: CEPII (Gravity database); Gurevich and Herman (2018); World Bank; World Trade Organization: UNCTAD.

Note: AEs = advanced economies; EMDEs = emerging market and developing economies. EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa. RTA = regional trade agreement.

A. B. Average tariff rates are computed as unweighted cross-country averages of applied weighted tariff rates. Sample includes up to 35 advanced economies and 123 EMDEs. Primary tariffs are used as a proxy for agriculture tariffs.

C. Proxy for trade uncertainty is the difference between the bound and applied tariff rates, as defined by the WTO. Data through 2020. Sample includes up to 27 advanced economies and 97 EMDEs.

D. World Bank’s logistics performance index is a summary indicator of logistics sector performance, combining data on six core performance components into a single aggregate measure. The indicator is available for a sample of 160 countries. Sample includes 36 advanced economies and 123 EMDEs.

E. UNCTAD’s liner shipping connectivity index is an average of five components and captures how well countries are connected to global shipping networks. The index value 100 refers to the country with the highest average index in 2004. Sample includes up to 30 advanced economies and 118 EMDEs.

F. Regional trade agreements are reciprocal agreements between two or more partners and include both
poverty lines for Russia, Ravallion and Lokshin (2002) find some striking differences in the properties of the equivalence scale. The objective poverty lines had an elasticity of 0.8 to household size, while the subjective indicator called instead for an elasticity half this size.<sup>21</sup> Subjective data have thrown new light on the long-standing debate on whether poverty is ―absolute‖ or ―relative.‖ The position one takes on this issue is crucial to whether one believes that economic growth reduces poverty. If the poverty line has an elasticity of zero to the poverty line then growth tends to reduce poverty, but this ceases to be true at an elasticity of one (Ravallion, 2003). However, one finds little support for the idea of a relative poverty line set at a constant proportion of the current mean income. Poverty lines calibrated to subjective welfare tend to rise with mean income but with an elasticity less than unity, suggesting that they are more like the ―weakly relative poverty lines‖ defined by Ravallion and Chen (2011). Hagenaars and Van Praag (1985) estimated an elasticity of 0.51 for eight European countries. For the US, Kilpatrick (1973) estimated an elasticity of about 0.6 for subjective poverty lines and De Vos and Garner (1991) found an own-income elasticity of the US subjective poverty line of 0.43.

A number of papers have reported evidence of effects on subjective welfare that can be interpreted as indicative of ―relative deprivation,‖ meaning that self-assessed well-being tends to fall as social comparators become better off, at given ―own income.‖<sup>22</sup> Luttmer (2005) reports regressions for subjective welfare in the US that imply a particularly strong relativism, whereby own income does not matter to subjective well-being independently of income relative to the mean in the area of residence. (The next section points to some reasons to question this finding.) The bulk of the evidence has been for relatively rich countries. The work that has been done for developing countries has been less supportive. Senik (2004), Kingdon and Knight (2007) and Ravallion and Lokshin (2010) tested for relative deprivation effects in self-reported happiness in Russia, South Africa and Malawi respectively and found rather little support for the idea and even evidence of positive external effects of higher ―neighbors’ income,‖ rather than the negative effect predicted by the theory of relative deprivation.

> 21 Similarly, see Pradhan and Ravallion (2000), using data for Jamaica and Nepal, Bishop and Luo (2006), using data for urban China, and Rojas (2007) using data for Mexico. For a more general discussion of economies of scale in consumption in developing countries see Lanjouw and Ravallion (1995).

> 22 See Oswald (1997), Frank (1997), Frey and Stutzer (2002), Clark, Frijters, and Shields (2008) and Clark and Senik (2010). Reviewing the evidence, Frey and Stutzer assert that ―There is little doubt that people compare themselves to other people and do not use absolute judgments‖ (p.412). This would seem to be over-stated.

12
F. Ranking of Global Talent Competitiveness Index conducted by INSEAD. 1=best, 134=worst. Figure 7: ECA: Investment growth and needs

# A. Investment growth in ECA

<!-- Start of picture text --> Percent Investment growth<br>10 2000-21 average<br>5<br>0<br>-5<br>ECA ECA excl. RUS,<br>BLR, UKR<br>C. Investment growth in Central Europe<br>and the<br>Western Balkans<br>Percent Investment growth<br>15 2000-21 average<br>10<br>5<br>0<br>-5<br>-10<br>Central Europe Western Balkans<br>E. Estimated annual infrastructure<br>investment to<br>halve gap with euro area by 2030<br>Percent of GDP<br>15<br>10<br>5<br>0<br>ECA Central Western RUS and<br>Europe Balkans TUR<br>2000-10 2011-21 2020 2021 2022 2023 2000-10 2011-21 2020 2021 2022 2023<br>2000-10 2011-21 2020 2021 2022 2023 2000-10 2011-21 2020 2021 2022 2023<br><!-- End of picture text --> C. Investment growth in Central Europe and the <!-- Start of picture text --> B. Investment growth in the Russian<br>Federation<br>and Türkiye<br>Percent Investment growth<br>15 2000-21 average<br>10<br>5<br>0<br>-5<br>-10<br>Russian Federation Türkiye<br>2000-10 2011-21 2020 2021 2022 2023 2000-10 2011-21 2020 2021 2022 2023<br><!-- End of picture text --> D. Investment growth in Central Europe and the Western Balkans <!-- Start of picture text --> Percent<br>20 Western Balkans Central Europe<br>10<br>0<br>-10<br>2019 2020 2021 2022 2023<br>F. Estimated reconstruction costs in<br>Ukraine<br><!-- End of picture text --> versus post-WWII Marshall Plan for Europe <!-- Start of picture text --> Percent of GDP<br>800<br>600<br>400<br>200<br>0<br>Marshall Plan Ukraine<br><!-- End of picture text --> Sources: Board of Governors of the Federal Reserve; European Investment Bank; Global Infrastructure Hub; International Monetary Fund; Kyiv School of Economics; Three Seas Initiative; Ukraine Government; U.S. Bureau of Economic Analysis; World Bank.