rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5** : Probability of care that meets minimum quality standards (%)

||(1)<br>|(2)<br>Probability of<br>|(3)<br>Care Readiness<br>|(4)<br>|
|---|---|---|---|---|
|_Condition_|As observed|Assume<br>available<br>drugs|Assume<br>provider<br>presence|No. of<br>facilities|
|Diarrhea|51.9<br>(49.2-54.5)|57.7<br>(54.9-60.4)|61.7<br>(58.9-64.5)|7,856|
|Pneumonia|39.3<br>(36.9-41.7)|50.9<br>(48.4-53.5)|46.7<br>(44.1-49.3)|7,854|
|Malaria|62.5<br>(59.6-65.4)|77.9<br>(75.3-80.6)|71.6<br>(68.7-74.5)|4,790|
|Neonatal asphyxia|58.8<br>(56.2-61.5)|-|70.5<br>(67.6-73.4)|7,863|
|Post-partum hemorrhage|33.7<br>(31.5-35.8)|43.2<br>(40.8-45.7)|39.5<br>(37.1-41.8)|6,438|
|Child care|27.9<br>(25.9-29.9)|36.4<br>(34.1-38.6)|33.1<br>(30.9-35.4)|7,854|
|Neonatal & maternal care|29.0<br>(27.0-30.9)|37.1<br>(34.9-39.4)|34.2<br>(32.0-36.4)|6,438|
|Child, neonatal & maternal care|13.7<br>(12.5-14.9)|19.1<br>(17.6-20.6)|16.6<br>(15.2-17.9)|6,436|

**Notes** : Column (1) reports the estimated probability of care readiness that meets minimum quality standards for different conditions (or set of conditions). Column 2 reports the estimated probability of care readiness that meets minimum quality standards, assuming essential drug(s) (oral rehydration salts or rehydration therapy for diarrhea; antibiotics for pneumonia; artemisinin-based combination therapy (ACT) for malaria; and oxytocin for postpartum hemorrhage) for treating the condition(s) are available. No essential drugs data were collected for neonatal asphyxia. Column 3 reports the estimated probability of care readiness that meets minimum quality standards, assuming no absenteeism. Child care includes two conditions (diarrhea and pneumonia). Neonatal & maternity care includes two conditions (post-partum hemorrhage and neonatal asphyxia). Child, neonatal & maternity care includes four conditions (diarrhea, pneumonia, post-partum hemorrhage, and neonatal asphyxia). The estimates in the tables are (unweighted) mean outcomes across countries, with the country means calculated using countryspecific sampling weights, with 95% confidence intervals in parenthesis (see text for details). Data are from clinical vignettes, unannounced visits, and visual inspections from Kenya (2018), Madagascar (2016), Mozambique (2014), Nigeria (2013), Niger (2017), Sierra Leone (2018), Tanzania (2016), Togo (2014), and Uganda (2013), with number of facilities reported in column 4. The malaria vignette was not used in the Kenya (2018) survey.

30
**TABLE A8: Direct investment regressions are robust when using IRR as a measure of performance.** Specifications are identical to those in the referenced table in the main text, but use IRR as a dependent variable. The IRR is undefined for 138 investments that were written off and have zero or trivial positive cash flows. For the purposes of this table the IRR is coded as -100 in these cases. Constant, R-squared, and number of observations not reported for brevity. *** p<0.01, ** p<0.05, * p<0.1

|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|
|||Quan|tile Regre|ssion||
|OLS|0.1|0.25|0.5|0.75|0.9|
|IRR|IRR|IRR|IRR|IRR|IRR|
|Panel A of Table 6: GDP per capita and legal origins||||||
|Ln(GDP per capita)<br>-5,492.7|0.7|3.3|1.0|1.5**|4.3**|
|(5,540.6)|(2.0)|(3.0)|(0.7)|(0.7)|(2.1)|
|British legal origins (=1)<br>-8,806.0|7.9|-1.4|0.1|0.2|2.8|
|(8,736.3)|(24.0)|(7.1)|(1.5)|(1.7)|(4.0)|
|Socialist legal origins (=1)<br>-2,940.0|36.2**|5.2|2.7*|3.2|14.7*|
|(2,819.4)|(14.2)|(5.4)|(1.5)|(2.4)|(8.4)|
|Panel A of Table 7: Other country-level risk factors||||||
|Political Risk (PRS Group)<br>-12.8|-11.3|-5.9|-1.5|-2.7|-3.5|
|(14.6)|(30.5)|(5.5)|(2.2)|(2.9)|(4.4)|
|Corruption Perception (Transparency Int’l)<br>20.8|4.6|4.8|1.5|5.2|5.0|
|(26.9)|(19.4)|(7.3)|(2.9)|(3.9)|(4.4)|
|Economic Freedom (Heritage Foundation)<br>-17.8|-5.7|2.2|-0.7|-3.2|-9.5*|
|(17.7)|(17.6)|(6.4)|(2.3)|(2.7)|(4.9)|
|Panel A of Table 8: Capital market openness and develop|ment|||||
|De-jure fnancial openness index<br>-226.4|0.8|0.3|0.2|-1.4|-3.5*|
|(193.6)|(3.9)|(1.8)|(1.7)|(1.6)|(1.9)|
|Private sector credit (% of GDP)<br>9.5|-2.2|-3.0|-4.3**|-6.2**|-2.1|
|(38.4)|(8.7)|(3.9)|(1.7)|(2.7)|(3.5)|
|Panel A of Table 10: Changes in macroeconomic variables|before f|rst cash|fow|||
|Real GDP<br>-35.2|0.5|0.5*|0.2|0.1|0.2|
|(43.4)|(0.4)|(0.3)|(0.2)|(0.2)|(0.4)|
|Local currency GDP defator<br>77.5|-0.1|-0.0|-0.0|-0.1|0.1|
|(55.2)|(0.1)|(0.1)|(0.0)|(0.1)|(0.2)|
|Local currency units per U.S. dollar<br>-51.9|0.4***|0.1|0.0|0.1|0.1|
|(42.6)|(0.0)|(0.1)|(0.0)|(0.1)|(0.3)|
|Panel A of Table 11: Changes in macroeconomic variables|over life|of invest|ment|||
|Real GDP<br>19.7|1.4|0.7*|0.5*|0.9*|0.8|
|(17.2)|(2.7)|(0.4)|(0.2)|(0.5)|(2.1)|
|Local currency GDP defator<br>15.2|1.4**|0.9***|0.7***|1.1***|2.0**|
|(13.6)|(0.6)|(0.4)|(0.2)|(0.2)|(0.9)|
|Local currency units per U.S. dollar<br>-14.6|-1.6**|-1.2***|-0.8***|-1.2***|-2.0**|
|(12.9)|(0.7)|(0.2)|(0.2)|(0.2)|(0.9)|

53
marginal contribution to inequality of each contribution is practically zero. Concerning poverty, the combination of all contributions has a poverty-increasing effect in percentage terms.

In contrast, at the individual level, only the employer pension contributions have a significant effect, around one percentage point. Finally, the progressivity of fiscal interventions regularly maintains the same sign. However, this scenario has a higher degree of progressivity for direct transfers compared to Kakwani's coefficient for the PDI scenario. The impact of transfers is more significant because there is a larger population group with near-zero market income, excluding pensions and direct transfers. At the same time, the degree of concentration of direct transfers on lower-income families is the highest of all fiscal interventions. As a result, these benefits are even more concentrated concerning the PDI scenario.

**Table A.2: Summary of indicators for PGT scenario**

||**Size**<sup>**+**</sup>|**Concentration**<br>**Coefficient**|**Kakwani**<br>**Coefficient**|**Redistributive**<br>**Effect**<br>Marginal<br>Contribution|**Poverty**<br>**Reduction**<br>**Effect**<br>Marginal<br>Contribution<br>(Moderate<br>poverty)|
|---|---|---|---|---|---|
|**Disposable Income**|**1.0279**|||||
|Contributory pensions (Per capita)|0.1459|-0.0961|0.7123|0.0297|0.0680|
|All contributory pensions|0.1459|-0.0961|0.7123|0.0297|0.0680|
|Direct transfer: Noncontributory pension (Per capita)|0.0321|-0.4419|1.0581|0.0239|0.0395|
|Direct transfer: other (Per capita)|0.0009|-0.2252|0.8414|0.0005|0.0009|
|Direct transfer: salario familiar (Per capita)|0.0009|-0.0086|0.6248|0.0007|0.0014|
|Direct transfer: unemployment benefits (Per capita)|0.0016|-0.0974|0.7136|0.0011|0.0015|
|Direct transfer: BPC (Per capita)|0.0140|-0.4943|1.1105|0.0105|0.0174|
|Direct transfer: abono salaria (Per capita)|0.0051|0.2885|0.3277|0.0019|0.0046|
|Direct transfer: Bolsa Familia (Per capita)|0.0075|-0.6035|1.2197|0.0094|0.0055|
|All direct transfers excl contributory pensions|0.0622|-0.3944|1.0106|0.0486|0.0708|
|All direct transfers incl contributory pensions|0.2080|-0.1853|0.8015|0.0852|0.1471|
|Direct taxes (Per capita)|-0.0462|0.7933|0.1771|0.0163|-0.0001|
|Direct tax: IPTU/IPTR (Per capita)|-0.0025|0.4723|-0.1439|0.0000|-0.0016|
|Total employer contribution to pension system (Per capita)|-0.0660|0.6359|0.0197|0.0027|-0.0135|
|Contributions to pensions INSS (Per capita)|-0.0416|0.6608|0.0446|0.0030|-0.0068|
|<br>Employer contribution to FGTS (Per capita)|-0.0239|0.6192|0.0030|0.0003|-0.0043|
|<br>All direct taxes|-0.0487|0.7770|0.1608|0.0163|-0.0017|
|All contributions|-0.1315|0.6407|0.0245|0.0068|-0.0238|
|All direct taxes and contributions|-0.1801|0.6776|0.0614|0.0209|-0.0249|

47
head and its squared term, and the gender of the head (female=1) as regressors. The human capital of the household is measured by four di ff erent categories of education of the member of the household with highest level of education. In addition, we included the number of male and female household members with education above primary level as separate explanatory variables. These additional education variables are introduced to capture the role of educated members of the households other than the person with highest level of education. The regressions also control for the household’s non-liquid assets. This asset variable includes all types of assets such as house, land, business assets and other durable goods. We include dummies to indicate household head’s main sector (agriculture, manufacturing, services) and type (private wage employment, self-employment) of employment. We included similar variables for the household head’s spouse but they turn out to be statistically insigni fi cant and are dropped from the regression. The HIES 2000 and 2005 collected community level information for the rural psus. There is, however, no information on the characteristics of the urban psus, barring us to use these variables as controls. Instead, we include dummies (U=1 or I=1) to capture any mean di ff erences in the overall infrastructure across regions.

Appendix Table A.3 reports summary statistics for the dependent and explanatory variables for di ff erent consumption quantiles for the I and LI regions. A number of variables exhibit interesting patterns. For instance, the percentage of households receiving foreign remittances increases with an increase in per capita real consumption in both regions. Households own larger amount of assets in the I region relative to the LI region. Similarly agricultural employment is relatively less important in the I region. The percentage of household head with education above secondary level increases with an increase in LRPCE. The relationship between LRPCE and education above secondary level is however similar in both I and LI regions. Overall, the di ff erences between the household characteristics across regions do suggest presence of some locational sorting of households. We also check the di ff erences in household attributes across the rural and urban areas (appendix Table A.4). These di ff erences are relatively larger compared with the I-LI region di ff erences. This suggests a larger role of locational sorting in explaining the urban-rural di ff erences in welfare.

14
The results for business environment variables are presented in Table 3, Panel A, and the results for governance indicators are presented in Table 3 Panel B. GDP per capita is significantly positive in all specifications, suggesting that the overall level of development is a significant predictor of subsequent business registration. An important caveat is that our dataset only includes new formal, limited liability firms – yet the entry of individuals into informal and formal self-employment is likely higher in lower-income countries with fewer employment opportunities. For instance, according to the World Value Survey<sup>13</sup> , 28.4 percent of individuals report being self-employed in Sub-Saharan Africa and 21.3 percent in South Asia, while only 6.6 percent of individuals in North America and 8.0 percent in Europe report being self-employed. Although we cannot directly compare our flow of new limited-liability firm registration to the stock of informal and formal self-employment, the data suggest that self-employment is higher in lower-income regions. In other words, we find that higher economic development is related to a more dynamic formal private sector, with greater potential for high-growth entrepreneurship; but our results do not suggest that entrepreneurship, per se, leads to greater economic growth.

Next, we find that Starting Costs, Starting Days, and Number of Procedures have a negative and significant relationship with the subsequent growth of new businesses: a faster and simpler registration process results in greater new business creation. Employment Rigidity is negative, but not significant. Financial Development is positive but not significant despite the strong unconditional correlation.<sup>14</sup> Finally, the tax rate has important implications for new business registrations. We find that the higher the tax rate, the less formal business registration. This is expected, since taxes represent the main cost of becoming registered, versus operating in the informal economy. The higher these costs, the less incentive firms have to register.

> 13 Calculations by the authors.  Complete data is available at:  http://www.worldvaluessurvey.org/.

> 14 If log GDP per capita is excluded from this regression, we find that financial development is positive and strongly significant at 1% level. The rest of the results in Table 3 and 4 are not sensitive to exclusion of log GDP per capita.

15
schooling outcomes of child _i_ who reside in time _t_ in village _j_ with program treatment status _B_<sup>_T_</sup> _j_<sup></sup> 0,1 _Nd_ , _t_ and neighborhood treatment frequency , are independent of that realized _N P d_ , _t_ treatment frequency when controlling for targeted neighborhood treatment frequency . Formally: Under this conditional independence property, comparisons of average outcomes across different levels of actual treatment frequency _NiB_ , _j_ , _t_ , for example, _n_ 1 _B_ and _n_ 2 _B_  _n_ 1 _B_ , at a _N P_ given level of potential treatment frequency _i_ , _j_ , _t_ , capture the causal effect of an increase _B B n n_ in actual treatment frequency from 1 to 2 . Formally (and omitting the indexes): As a validation test of the property depicted in equation (2), we use data from the baseline collected in October 1997 on children’s school participation as well as the full set of covariates that we employ in the empirical analysis and estimate equation (1) using those baseline characteristics as outcomes. This amounts to a test of the balancing of baseline covariates with respect to the variation in local treatment frequency generated by the randomized experiment. Table 2 reports the means and standard deviations for those variables (columns 1 and 2), along with the associated OLS coefficients of the _N B_ neighborhood treatment density term ( _j_ , _d_ , _t_ ). In column 3, we display the unconditional marginal effects which reveal the presence of systematic differences in observable characteristics across neighborhoods with different degrees of program frequency. Consistent with the targeting design of the program, treatment frequency correlates 15
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Among cohort 1 students, the restricted sample includes 73 percent of baseline program school students and 76 percent of comparison students in Busia district (Table 3, Panel D1). In Teso, however, only 54 percent of program students and 58 percent of comparison students remain in the restricted sample (Panel D1), thus attrition rates are much higher in Teso district than in Busia.

Differential attrition between program and comparison schools in Teso district is smaller among cohort 2 students than cohort 1. To understand why, recall that the 2002 district exams for Teso were canceled in the run-up to Kenyan national elections, and the NGO instead administered its own exam – modeled on standard government exams – in Teso in early 2003. Students did not need to pay a fee to take the NGO exam, unlike the government test, and this is likely to account at least in part for the low levels of attrition for cohort 2 in Teso district.

There is some evidence that the scholarship program led academically weaker students in program schools – who ordinarily would not have paid to take district exams – to take the exam, potentially biasing estimated program impacts downward, although it is difficult to test this given unobservable dimensions of student academic quality.<sup>12</sup> Students who did not take the 2001 exam (“attritors”) tended to be somewhat lower achieving students at baseline in both Busia and Teso districts (Table 4, Panel B). Examining the differences in 2000 baseline test scores between attritors and nonattritors shows that Busia program school students who did not take the 2001 exams scored 0.07 standard deviations lower at baseline than those who did take the 2001 exams (column (i)-(iv)). The difference is 0.56 standard deviations in Busia comparison schools (column (ii)-(v)), providing evidence that a greater proportion of low performing students attrited from the comparison school sample than from program school sample. This suggests that program impact estimates are likely to be lower bounds on true effects. Below we discuss the construction of bounded estimates that take into account potential attrition bias.

In reference to attrition due to schools pulling out of the program, Teso district students whose schools pulled out of the program, and were in program schools, were typically higher achieving students

> 12 Theoretically, the introduction of a scholarship could have induced poorer, but high-achieving students to take the exam, leading to an upward bias in the estimated effect of the program, but we do not find evidence for this.

17
9 Naturally if B 3 1 there is no bias against exports, but there isn't either a bias in favor of exports as the label EP usually implies. Third, and perhaps more importantly, neither of these regimes is necessarily related to a "hands off" or "laissez-faire" strategy. In fact an EP strategy can be achieved with a highly interventionist government that, on average does not distort the ratio (1+s+r)/(l+r+n+P). Moreover, an ultra export promoting strategy, where B > 1, may require significant government intervention. As discussed below, this fact has recently prompted some experts to make an additional distinction between a negtral trade regime and a liberal trade regime.

It was argued in Section I that the notion of "trade liberalization" has changed significantly in the last decade. In the original NBER study liberalization was defined in a very general way as a more extensive use of the price mechanism. Anne Krueger has, in fact, maintained that position throughout the years. At the specific operational level, in the synthesis volume of the NBER project Krueger defined trade liberalization as a reduction in the premium resulting from the use of QRs. There is no mention of zero or even very low tariffs. In fact, it must be noticad that according to this definition we can have a "liberaliced" economy with very high tariffs. This, indeed, is a possibility that Krueger recognizes: Inspection of the definitions of bias and liberalization shows that there is no necessary reason, at least in theory, for a connection between the two. A regime could be fully liberalized and yet employ exceedingly high tariffs in order to encourage import substitution. The regime would then be liberalized and highly biased. (1978, p. 89, emphasis added) This definition of liberalization is indeed very different to the way in which the concept has been used (as synonymous with free trade) in the 3Krueger (1978, 1981, 1984).
_Empirical evidence: the linkage between TFP and economic reforms in Rwanda_ In an effort to explain the behavior of total factor productivity, the yearly estimates of TFP growth obtained above were regressed upon several measures accounting for trade, currency, and financial conditions in Rwanda. They were: CREDIT = (Private sector credit) / GDP EXCHG = Real effective exchange rate TAXES = (Tax revenue collected on international trade) / GDP TRADE = (Exports + imports) / GDP DUM94 = 1 for the year 1994, and 0 otherwise.

CAPITAL = (Imported capital) / GDP ODA = [Official aid * (labor force * e<sup>RS</sup> )] / GDP.

Given the above variable definitions, the following model was estimated: (5) TFPt = F(CREDITt, EXCHGt, TAXESt, TRADEt, DUM94, CAPITAL, ODA).

With the exception of ODA, all of the variables are frequently used in the development literature. ODA is a control variable that combines the interaction of human capital and official development assistance. The estimate of human capital (labor force * e<sup>RS</sup> ) applies improvements to education (e<sup>RS</sup> , where S = average educational attainment in years of schooling and R = average return to education) to the size of the labor force. The return to education is assumed to be 10 percent per year, and estimates of average educational attainment were provided by the Poverty Reduction and Economic Management Total Factor Productivity Toolkit compiled by Sandeep Mahajan in 2001 and available at the World Bank. Due to data restrictions the earlier 1961-1970 period 23
**24** **<u>│</u>**

observed on aggregate with domestic firms do not appear to be substantial, job creation also appears to be associated with seasonal dynamics; that is, business cycles and varying global macro conditions specific to the time period of the surveys.

The debate, however, tends to focus not just on any job creation, but on “good” jobs associated with high wages, labor market security, and quality of the working environment. And while there is no strong evidence that FDI also improves non-wage working conditions (OECD, 2008), there is evidence that foreign-owned firms pay higher average wages --10 to 70 percent higher, according to estimates.<sup>4</sup> This finding is corroborated across countries, development levels, sectors, skill level and over time, with developing countries enjoying much larger premia than developed countries.

Several factors <mark>justify higher wages by foreign multinationals, all related one way or another</mark> t <mark>o productivity advantages. Firms acquired or established through FDI may be more productive than other domestic firms, thus earning higher profits and paying</mark> higher wages. Second, foreign affiliates may pay more because their parent firms are relatively more productive (having had to overcome the liability of foreignness to undertake FDI), and thus have higher profits and can pay higher wages. Third, foreign firms might try to prevent technological spillovers through labor turnover by paying higher wages. Fourth, the FDI wage premium may compensate for higher labor demand volatility in foreign plants or for a higher foreign plant closure rate. Fifth, non-market forces, such as equity considerations, could also play a role (Bernard and Sjöholm, 2003; Egger and Kreickmeier, 2013; Fabri et al., 2003; Fosfuri et al., 2001; Heyman et al., 2007; Javorcik, 2014; Tomohara and Takii, 2011).

The World Bank Enterprise Surveys confirm the existence of substantial aggregate wage premia in the majority of developing countries (see Fig. 9). The levels of premia seem to be associated with intensity of skill use and existing wage gaps, ranging from high levels in countries where unskilled labor is more intensively employed in foreign multinationals to lower levels where skilled labor is abundant. A concentration of foreign MNEs in activities with higher returns or skill intensity could well explain aggregate differences between multinational and domestic firms; i.e., differences within sectors might be much less pronounced. The fact that average wages hide variations across skill remuneration in developing countries does not invalidate the findings of the literature; it rather shows that foreign multinationals’ wage advantages do not accrue automatically.

> 4 Heymanet et al. (2007) draw from the following studies in estimating this range: Aitken et al. (1996), Bandick (2004), Conyon et al. (2002), Doms and Jensen (1998), Driffield and Girma (2001), Girma et al. (2001), Griffith (1999), Görg et al. (2002), Haddad and Harrison (1993), Lipsey (1994), Lipsey and Sjöholm (2004), and Sjöholm and Lipsey (2006).