rafmacalaba/datause-encoder-data huggingface dataset:
probability that higher-productivity firms enter the EU market whereas tariff rates do not affect entry decisions. Second, I find that this impact is more relevant for U.S. firms that were already exporters serving developing countries than for firms entering the export activity. Third, I show that this impact is negative for the intensive margin of trade: the change in the value of U.S. goods that are already exported to the EU within surviving trade relationships, e.g., the same firm exporting the same product to Europe throughout the time span. Overall, the empirical findings suggest that EU product standards harmonization contributes significantly to explain the export entry patterns observed in my sample of U.S. manufacturing firms.

These results have an important policy implication. The U.S. National Export Initiative laid down in 2010 seeks to double exports over the next five years by enhancing small and medium sized firms’ ability to enter export markets and by actively reducing barriers to trade. The results obtained in this paper suggest that working towards a reduction in the differences of product requirements across markets could be a supportive policy to encourage the entry of small and medium size firms into export markets.

# **References**

Anderson, James E. and Eric van Wincoop. 2003. “Gravity with Gravitas: A solution to the Border Puzzle”, American Economic Review, Vol. 69, pp. 106-16 Baldwin, Richard E. 2000. “Regulatory Protectionism, Developing Nations, and a Two-Tier World Trade System” in Susan Collins and Dani Rodrik (eds.). Brookings Trade Forum 2000, Washington, D.C. Brookings Institution Press.

Baller, Silja. 2007. “Trade Effects of Regional Standards Liberalization: A Heterogeneous Firms Approach”, Policy Research Working Paper No. 4124, The World Bank.

Bernard, Andrew B., J. Bradford Jensen and Peter K. Schott. 2009. “Importers, Exporters and Multinationals: A Portrait of Firms in the U.S. that Trade Goods”, in T. Dunne, J.B. Jensen and M.J. Roberts (eds.), Producer Dynamics: New Evidence from Micro Data (University of Chicago Press, 2009) Bernard, Andrew B., Stephen Redding and Peter K. Schott. 2010. “Multi-product Firms and Product-Switching”, American Economic Review 100:70-97 (March 2010).

Blind, Knut, 2010. “The Economics of Standards: Theory, Evidence, Policy”, Published by Edward Elgar Publishing.

Chaney, Thomas. 2008. “Distorted Gravity: The Intensive and Extensive Margins of International Trade”, American Economic Review 2008, 98:4, 17071721.

Chen, Maggie X. and Aaditya Mattoo. 2008. “Regionalism in Standards: Good or Bad for Trade?”, Canadian Journal of Economics, 41(3), 838-863.

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# **Looking into the Performance-Based Financing Black Box: Evidence from an Impact Evaluation in the Health Sector in Cameroon***

Damien de Walque, Development Research Group, The World Bank Paul Jacob Robyn, Health Nutrition and Population, The World Bank <mark>Hamadou Saidou, Health Nutrition and Population, The World Bank and University of Paris Dauphine</mark> Gaston Sorgho, Health Nutrition and Population, The World Bank Maria Steenland, Department of Global Health and Population Harvard School of Public Health _Keywords_ : Performance-based Financing; Health; Africa _JEL classification_ : I15 J13; O15.

* We are extremely grateful for a fruitful collaboration with the Ministry of Health of Cameroon and in particular with his Excellency Andre Mama Fouda, Minister, Enandjoum Bwanga, Emannuel Maina Djoulde, Victor Ndiforchu, and all the Regional Health Delegates, Performance Purchasing Agency Managers and staff and from the NorthWest, South-West and East Regions. The baseline and endline data were collected by the Institut de Formation et de Recherche Démographiques (IFORD) with special thanks to Gervais Beninguisse, Didier Nganawara, Evina Akam and all staff implementing the baseline and endline surveys. This impact evaluation is funded by the Health Results Innovation Trust Funds (HRITF) at the World Bank. We are grateful to Emanuela Di Gropello, Guenter Fink, Gyuri Fritsche, Elisabeth Huybens, Gil Shapira, Jean Claude Taptue Fotso, Robert Soeters and Omer Zhang for useful comments and discussions. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
where di is a censoring indicator. xi = [X1,i, X2,c, κ, Dj] are covariates for project _i_ in sector _j_ , country _c_ . X1,i is project-specific characteristics per contract which includes procurement type (competitive bidding or negotiated contract), the share of private investment in the contract, and the supports from international financial institutions. The majority of contracts in the sample involve a significant share of private sponsorship, but only 13% of them involved multilateral institutions.

X2,i includes macro shock variables such as change in real per capita GDP growth and terms of trade during the contractual period t∈[t0, t1] defined as ∆g= gt1 −gt0 and ∆TOT= TOTt1 −TOTt0. Two measures capture improvement in the economic situation and foreign trade term which is likely to create buffers to reduce BOC risk.

As a part of X2,i, each country’s political institutions, such as democracy (defined as Democracy=1[Polity IV>0]) and ideology of the incumbent government (right wing government or not), are included in the regression. Contractual terms (X1,i and Ai) are expected to differ by sector and region. As the omission of sectoral and regional affiliations will bias my point estimates for these variables, regional fixed effects κ and a vector of dummies for sectoral affiliations D<sup>13</sup> need to be included in the likelihood function. j Assuming that f(Ai|xi; θ) follows the Weibull distribution, the hazard function λ can be estimated with my MLE, θ<sup>�</sup> , which is defined as Eq. (2).

where α is the measure of duration dependence. I start regressions using the whole sample which includes both PPI and UNCTAD data sets.

# _5.1.1. Decomposing Commodity Price Shocks_

> 13 Dummies for sectoral affiliation are coded using ICSID’s industrial classification which categorizes each project into one of the following sectors: (1) agriculture, fishing, and forestry, (2) oil, gas, and mining, (3) electric power and other energy, (4) water, sanitation, and flood protection, (5) construction, (6) tourism, (7) transportation, (8) information and communication, (9) finance, and (10) other industry.

12
present mixed evidence, with either a positive, a negative, or an insignificant effect of variety of inputs on the growth rate of total factor productivity.

However, not all the existing evidence supports the hypothesis of a negative impact of natural resources on economic development. Davis (1995) compares the longrun economic development indicators of minerals-based economies and non-mineralsbased developing economies.<sup>11</sup> He finds that the minerals-based economies as a group significantly outperform the non-minerals-based economies. More recently, Rigobon and Manzano (2001) have found that Sachs and Warner’s results are not robust to small changes in econometric procedure when panel data are used. They specifically analyze the impact on growth of natural resources exports as a share of GDP. They find that when the model is estimated on panel data using fixed effects, the negative impact of natural resources on growth vanishes, but that it remains in the cross-sectional estimations. Rigobon and Manzano argue that the high prices on commodities during the 1970s led developing countries to use them as collateral for debt. During the 1980s, commodity prices fell sharply, leaving developing countries with massive debts and a reduced flow of foreign resources with which to pay it back. Finally, Lederman and Maloney (2002) find that their preferred measure of natural resource abundance appears to be positively correlated with economic growth, and that export concentration reduces growth. They use as a measure of resource abundance the net exports of natural resources per worker.

# **4.2 Empirical Methodology and Results**

We estimate the main empirical implications of our model using panel data for the period 1970-90. The data used in the regressions are from the Penn World Tables, the Barro and Lee (1994) Educational Data Set, and the World Tables from the World Bank (1993-96). We describe the data and their sources in more detail in appendix B.

We regress the growth rate of GDP per capita on various explanatory variables, using random and fixed effects to test the robustness of our measures of natural resources.<sup>12</sup> We also use instrumental variables in order to overcome the possible

> 11These indicators include life expectancy at birth, infant mortality, and share of the population with access to safe water and sanitation.

> 12Data limitations prevent estimation by some other procedures recommended in the literature,

16
Estimates from the RLMS show that informal employment broadly increased through the first half of the 2000s (2001-2004), levelled off in the period 2005-2010, and had a subsequent upward trend since 2010. Estimates show that the second half of the 2000s was marked by a temporary stagnation of informal employment, possibly as a consequence of the financial crisis. Informality started to increase again after 2010. The latest estimate for 2016 puts informal employment at 16.9 percent, up from 14.5 percent in 2010, and from 10.7 percent in 2000 (Figure 2).<sup>11</sup> The RLMS trend is broadly consistent with the official trend published by Rosstat in that informal employment increased substantially in the early 2000s, came slightly down around the financial crisis period, and then continued to increase again. Rosstat estimates show that informal employment increased from 12.5 percent in 2001 to 17.6 percent in 2005, then slightly decreased to 16.4 percent in 2010 but increased significantly to 21.2 percent in 2016.

LFS microdata are only available for a shorter period of 2010-2015, but the share of informally employed is invariably increasing, which is consistent with the trend from RLMS as well as Rosstat. Our estimates show that informal employment was at 15.1 percent in 2010 and 19.3 percent in 2015 (Figure 3), which again, is a significant increase over a relatively short period of time. Our definition is focused on measuring informality in workers’ primary jobs which yields a slightly lower estimate compared to the official Rosstat measure which is also based on LFS.

Additional estimates of informal employment from the LiTS and ESS show a slight decrease between 2006 and 2010, followed by a marked increase through 2016 (Figure 4 and Figure 5). Using the LiTS, the share of informal workers decreased from 16.1 percent in 2006 to 11.7 percent in 2010, then rose to 15.1 percent in 2016. Using the ESS, that same share started out at 12.8 percent in 2006, stood at 11.2 percent in 2010, but then recorded a large increase to 19.2 percent in 2016. In fact, the increase estimated from the ESS between 2010 and 2016 is the largest among all surveys.

To summarize, estimates of long-term trends suggest that informal employment in Russia has continued to grow in recent years. A comparison of trends from different surveys reveals that regardless of the data source used, informal employment appears to have increased in the first half of the 2000s, then briefly leveled-off or even fell, but then continued to increase. The deceleration in the relative size of informal employment in second half of the 2000s coincides with the global financial crisis.

> 11 This 6.2 percentage point increase in the rate of informal employment between 2000 and 2016 is due to an increase of 3.6 percentage points in salaried informals (who grew from 3.1 to 6.7 percent of employment) and a 2.6 percentage points increase of self-employed informals (who grew from 7.6 to 10.2 percent of employment).

11/67 Informal Employment and Worker’s Wellbeing in Russia
It is important to keep in mind that the relative price series is calculated with price indexes, set to unity in 2006:12, and that it does not say anything about the actual price levels. Moreover, the stationarity of the relative price series does not imply that world and domestic prices will converge, only that domestic food prices adjust when relative prices drift apart.

Table 2: Cointegration analysis of the external sector, 1999:3-2008:11

||Rank test|
|---|---|
|Null hypothesis|r=0<br>r≤1<br>R=≤2|
|Eigenvalues|0.204<br>0.082<br>0.014|
|Trace statistic|38.51<br>11.76<br>1.67|
|Probability-value|0.003<br>0.17<br>0.196|
||Standardized eigenvector<br>_i_<sup>β</sup>|
||p<br>Wfp<br>pc|
||1.00<br>-0.66<br>-1.28|
||[0.00]<br>[0.22]<br>[0.33]|
||Standardized adjustment coefficients<br>_i_<br>α|
||-0.125<br>-0.018<br>-0.007|
||[0.025]<br>[0.024]<br>[0.016]|
|Likelihood ratio test|for restricted cointegrated vector ;<br>1<br>2<br>3<br>0<br>β<br>β<br>β<br>−<br>−<br>=<br>2(2)<br>1.11 [0.57]<br>χ<br>=|

_Note:_ The VAR includes two lags on each variable and centered seasonal dummy variables. Standard errors are in brackets.

The log of the non-food relative price is also depicted in Figure 7. It is measured with non-food CPI, _pnf_ , the Birr-Euro exchange rate, _e,_ and the EU producer prices, _wp_ . The reason is that the EU is Ethiopia’s largest trading partner: in 2007 roughly 40 percent of total exports went to the EU. Moreover, this relative price is easy to calculate, transparent, and works well empirically. We also tested alternative specifications, the Birr-US$ exchange rate, US wholesale prices and the real trade weighted (effective) exchange rate, calculated with weights for the ten largest trading partners. The real effective exchange rate is in principle the most adequate one, but it works very much like the Birr-Euro exchange rate. Although there are some differences in the series, they nonetheless provide the same information for our purposes.<sup>18</sup> As the figure shows, _e_ + _wp_ − _pnf_ is clearly non-stationary, which is also shown by the Johansen cointegration test (not reported). We thus tested for

> 18 The use of the official Birr-US$ exchange rate weakens the results in the sense that the t-values are lower. This is probably partly due to Ethiopia’s trade pattern, but also partly due to the management of the Birr, since the exchange rate changed very slowly between 2002 and 2007. The effective real exchange rate was used in an earlier version of the paper, but it has not been updated. The real effective exchange rate and the real Birr-Euro exchange rate produce similar results.

23
||_Table 4.1 _|_Static Deco_|_mposition: _|_Contributio_|_n to the E_|_xplanation o_|_f Earning_|_s Inequality _|_(%)_||
|---|---|---|---|---|---|---|---|---|---|---|
|||1988|1|992||1996|2|000|2|002|
|Variable|Gross|Marginal|Gross|Marginal|Gross|Marginal|Gross|Marginal|Gross|Marginal|
|Education|20.3|20.8|26.9|21.6|29.3|21.2|27.6|19.6|30.3|21.4|
|Age|5.4|8.3|7.2|6.1|6.6|6.2|5.9|4.6|5.9|4.4|
|Economic Sector|2.7|8.1|4.0|5.2|6.8|5.9|5.5|4.5|6.1|3.5|
|Status|12.8|11.2|13.7|8.9|13.7|7.4|13.9|8.4|13.7|7.4|

The other variables considered seem to be much less important. All three of them, but particularly economic sector and status in the labor market, display an upward trend in their gross contribution and a declining trend in their marginal contribution. This can be interpreted as evidence that the interaction between these variables and education has become more intense. That is, the workers’ skills are becoming increasingly more relevant to the determination of their type of participation in the labor market as well as to their position across different economic segments of the economy. The same pattern holds when number of hours worked instead of sector is considered.

The results of the decomposition of the variations in the Theil T index for different intervals of time are shown in Table 4.2. First, when the variables are considered alone, education made the highest gross contribution to the changes in earnings distribution. Second, both the allocation and the income effect were positive in all periods. This means that changes in the distribution of education and in the relative earnings among educational groups were always in phase with alterations in the earnings distribution. Namely, when the income profile related to education became steeper and the inequality of education grew, the earnings distribution worsened as in all periods below.

22
These intimations of change raise the question of whether a closer examination of the data may show clearer signs of the beginnings of a turnaround in the “missing girls” phenomenon in other parts of Asia other than South Korea. Here we look for such evidence, focusing in particular on the large populous countries of China and India, and the data suggest that their child sex ratios are peaking, and are beginning to trend towards less masculinization.

# **2.** **<u>National Trends</u>**

# **_Trends in the sex ratios at birth in East Asia_**

Sex ratios at birth rose significantly during the 1980s in both South Korea and China (Figure 1). In South Korea, they continued to rise sharply till the mid-1990s, and since then they have fallen so rapidly that by 2007 the ratios are within the biologically normal range.<sup>4</sup> In China they continued to rise through the 1990s and overtook South Korea, but between 1999 and 2007 the ratios seem to have fluctuated close to 1.20 — with no apparent secular trend either upwards or downwards. In both settings, the annual data fluctuate, partly in response to the desirability of bearing boys versus girls in different animal years in East Asia.<sup>5</sup> We are unable to provide a comparison with India, since it does not have data on sex ratios at birth of comparable quality.<sup>6</sup> The data for South Korea are from their official vital statistics data, which are of high quality. The data on sex ratios at birth and child sex ratios for China are taken from the official statistics from the National Bureau of Statistics (NBS) of China, without any adjustments.<sup>7</sup>

# **_Trends in the sex ratios of infant and child mortality_**

As is well known, sex ratios at birth do not capture the full extent of the removal of unwanted female children, since sex selection can take place after birth. This is especially the case in countries like China and India, where substantial parts of the population lack easy financial and/or physical access to sex-selective technology.

A large literature shows that postnatal sex selection has been common in both countries. In China, the literature focuses largely on infanticide, while in India it focuses largely on differentials in health care utilization in the first years of life.<sup>8</sup> This difference between the two countries is reflected in Table 1, which shows that the female disadvantage in childhood mortality is highest in China during the first year of life, while in India it is highest between ages 1-4 years. The proportion of children who die in the first year of life is, of course, much higher than that dying during ages 1-4 and thereafter.

The net result in both cases is a striking level of excess female under-5 mortality, which increased in both countries in recent decades<sup>9</sup> — even as levels of prenatal sex selection 3
coexist in the market. Nonetheless, little is known about what makes a program “good”—what is distinctive of high-quality programs—and, as result, SCP quality remains a “black box”.

This knowledge gap is largely due to a data gap, as researchers often lack information on student outcomes and, more importantly, on program practices (e.g., how programs relate to local employers) and inputs (e.g., facilities for practical training)—henceforth, quality determinants. Thus, in this paper we open the SCP quality “black box” to identify the quality determinants associated with good academic and labor market outcomes for students.<sup>4</sup> We adopt two complementary approaches. The first one is a regional (aggregated) analysis of program-level average academic and labor market outcomes for five countries in Latin America and the Caribbean (LAC). The second is a country-level analysis for two of those countries using individual-level data on graduation and labor market outcomes.<sup>5</sup> Underpinning both approaches is a new and unique survey, the World Bank Short-Cycle Program Survey (WBSCPS), which we designed and implemented in five countries in LAC, namely Brazil (in the states of São Paulo and Ceará), Colombia, the Dominican Republic, Ecuador, and Peru (for licensed programs). Together, these countries account for more than half of the SCP enrollment in LAC. The survey contains data from nationally representative samples of SCP providers for a total of 2,103 programs. It collects rich program-level information about quality determinants that are not reported in administrative datasets but might be associated with student outcomes (Bailey et al, 2015). We group them in six categories: training and curriculum (T&C),

> 4 Throughout this paper, “determinants” refers to practices and inputs that programs can choose. Other program or institutions characteristics (for example, institution’s age or public/private status) are not quality determinants because they are not equally flexible. 5 We initially set out to collect individual-level data for each of these five countries. However, most countries in the region either do not collect this type of data, merge datasets as needed (for example, those from higher education and social security), or make datasets available to researchers. Building good information systems and facilitating data access remains a key task in developed and developing countries alike.

3
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