rafmacalaba/datause-encoder-data huggingface dataset:
the sample mean (median) of the natural logarithmn of GDP per capita in 1970<sup>4</sup> the marginal effect of a change in the Gini on the natural logarithmn of GDP per capita is equal to -1.13 (-0.97). Its standard error is 0.42 (0.41). These numbers should be read as a one percentage point increase in the Gini coefficient reducing GDP per capita over a five year period by around 1 percent. The longrun effect is larger and amounts to about 4 percent.

Figure 2 displays graphically how the marginal effect of a change in the Gini coefficient on the natural logarithmn of GDP per capita varies across countries' initial GDP per capita. The x-axis displays values of initial GDP per capita that fall in between the sample minimum and maximum. As can be seen, for low values of initial GDP per capita the effect of income inequality on GDP per capita is positive, while for high and intermediate values of initial GDP per capita it is negative. The marginal effect of the GINI becomes zero at around $665 PPP-adjusted U.S. dollars per capita in 1970 (equivalent to log of GDP per capita of 6.5).

# _5.2. Robustness_

Table 3 shows that our baseline estimates are robust to the use of alternative income inequality data. We use the inequality data discussed in Section 3 for our baseline regressions because these data provide us with the largest number of country-year observations. In column (1) of Table 3, we present instrumental variables estimates based on the Gini coefficient provided by WDI (2014); the remaining columns use the shares of income accruing to the 1<sup>st</sup> , 2<sup>nd</sup> , 3<sup>rd</sup> , 4<sup>th</sup> , and 5<sup>th</sup> quintiles. The estimate on the Gini coefficient is positive and significant at the 1 percent level; the interaction term between the Gini coefficient and initial GDP per capita is negative and significant at the 1 percent level. Looking at the income shares, we see that the estimated coefficients on the income shares accruing to the 1<sup>st</sup> , 2<sup>nd</sup> , 3<sup>rd</sup> , and 4<sup>th</sup> quintiles are negative while the coefficients on the interaction terms between the 1<sup>st</sup> , 2<sup>nd</sup> , 3<sup>rd</sup> , and 4<sup>th</sup> quintiles and initial GDP per capita are positive; the opposite

> 4 The sample mean (median) of the natural logarithmn of GDP per capita in 1970 is 6.82 (6.78).

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2050, the simulation suggests that it will have increased by more than 60 percent (to almost 3 years). These simulations (illustrated in Figure 7) make clear the complementarity between the reforms: combining them leads to substantially larger marginal impacts than either reform alone.

# 6. Discussion

Recent estimates suggest that the quality of human capital can explain a dominant share of world income differences (Jones 2014; Malmberg 2017) in part through its effects on economic growth (Hanushek and Woessmann 2015; World Bank 2018). Thus, the fact that many children in low-income countries learn little from attending school may be one of the most pressing development challenges. In this analysis, we focus on one component of the education production function—teachers’ knowledge of the subject they are teaching. While a growing literature has shown that teachers matter, much less is known about the link between specific teacher characteristics and student learning (Glewwe and Muralidharan 2015). Here we show that teachers’ content knowledge, or lack thereof, is an important part of the reason why primary school students in Sub-Saharan Africa are already well behind the expected curriculum—and their peers in other parts of the world—after only a few years of schooling. This implies a loss of potential human capital for numerous cohorts of students.

Our results have implications for both research and policy. Regarding the former, our results strongly suggest that research on how to improve teacher content knowledge should be a priority—there are few well identified studies on how to improve the quality of pre-service training (Glewwe and Muralidharan 2015). Moreover, we show that the marginal gain from collecting matched student and teacher data for one additional grade is large. Specifically, with additional data on teacher knowledge in other lower primary grades, one can recover the structural parameters of interest under the assumption that the cumulative education production function is fully characterized by two parameters, α and γ.

Our simulation results suggest that reforms that ensure that upcoming cohorts of teachers have greater mastery of the content they are expected to teach, combined with incentives to increase time spent teaching, could make a big improvement to student learning outcomes—almost doubling them. But the (aggregate) impact of such reforms would take time to materialize—working through these channels alone it would take on the order of 30 years to double learning outcomes. For that reason, it is also important to experiment with and roll-out shorter-term approaches. A number of interventions have been shown to produce promising results, including programs to supplement current teachers with additional 30
variables include the export shares by trade regimes and firm ownership types, calculated at the product-country level. The first regression is based on the subsample with positive GAP, while the second columns are for the subsample with negative or zero GAP. HS4 product and country fixed effects are always included. As expected, (t-r) has a positive and significant effect on indirect export share only for products with positive GAP that indicates a higher chance of export underreporting at Chinese border. This implies that the underreporting behaviors through domestic intermediaries may be associated with cross-border evasion through underreporting export values. The magnitude of the coefficient of (t-r) for the positive GAP subsample is very similar to that reported in Column (1) of Table 1 for the whole sample (0.667 vs. 0.673). The coefficients of other variables also have expected signs: non-SOEs (especially foreign firms) and processing firms are less likely to use intermediaries than SOEs and normal firms.

# **5.3. A transaction level analysis**

Our third hypothesis states that the evasion motive is stronger for larger transactions. To test it, we need to carry out a transaction level analysis. In Table 4, we report the results from regressions at the transaction level. Here the dependent variable is a dummy indicating whether the final exporter in a transaction is a production firm (0) or trading firm (1), based on the conservative classification. (t-r) is still measured at HS8 product level.<sup>33</sup> log(value) is the logarithm of the value of an export transaction, where value is measured in current million U.S. dollars. Since a firm may exports multiple products at the tariff line level and each product may be exported through multiple transactions in a year, the number of observations at transaction level is extremely large, up to nearly 14 million in the regression. To avoid intensive computation, we adopt a linear probability model (LPM) rather than a logit or probit model. To prevent the variable (t-r) from being dropped, we include HS 4-digit fixed effects rather than HS 8-digit fixed effects. We also include country fixed effects to control for the unobserved heterogeneity at country level. In regression (1), we include only

> 33 Because the dependent variable is a dummy, which is on average much smaller than the indirect export share in percentage (ranging from 1 to 100), the coefficients can be much smaller than what we got from the product-level analysis. To avoid very small estimated coefficients of (t-r) and its interaction with log(value), we divide (t-r) by 100, so that it is now measured in percent, not in percentage as in our product-level analysis.

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of this conditional distribution provides a natural estimate of the precision of this aggregate governance measure for each country.

We illustrate our approach with reference to three fundamental aspects of governance: rule of law, government effectiveness, and graft. We group 31 indicators constructed in 1997 and/or 1998 into three clusters corresponding to these three concepts of governance, and compute aggregate indicators spanning 166, 156 and 155 countries respectively. In our companion paper documenting the governance database, we construct similar indices for several other aspects of governance.

Although the unobserved components methodology we use is quite standard, we find its application to the construction of composite govemance indicators interesting.' One of our major findings is that the aggregate governance indicators we construct are rather imprecise, despite the high correlations observed between various sources of governance data. In particular, a 90% confidence interval around the point estimate of governance for a typical country spans almost the entire interquartile range of the distribution of estimated govemance. This implies that although it is possible to robustly identify twenty or so countries with the best and worst govemance in the world, it is much more difficult to identify statistically significant differences in govemance among the majority of countries.

Our results are based on three key assumptions: (1) that the measurement errors in individual indicators of govemance are uncorrelated across indicators; (2) that the relationship between unobserved govemance and observed indicators is linear, and (3) that the distribution of unobserved governance across countries is normal. Relaxing the first assumption is difficult to do in practice, simply because without this assumption we cannot determine whether the correlation of observed scores across indicators is merely due to correlated perception errors or whether it reflects the common concept of governance being measured. However, under the likely altemative that perception errors are correlated across sources, the measures of precision we report will be biased downwards. As a result, the standard errors we report should be interpreted as a lower bound on the precision of aggregate govemance indicators. We consider the 1Unobserved components models were pioneered in economics by Goldberger (1972), and the closelyrelated hierarchical and empirical Bayes models in statistics by Efron and Morris (1971, 1972).

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have argued that the period after weaning from breast milk until age 24 to 36 months may also be “critical,” as protection from the mother during pregnancy and breastfeeding ends (Maccini and Yang, 2009). Second, this approach helps to check the validity of the research design by testing for differential trends between program receiving and non-receiving communities.

The results of the health index, cognition index, and economic index are shown in Figure 2 to Figure 4 for males and females separately. The coefficients for males are strictly positive for age of exposure less than or equal to 0 years, while it is slightly positive until age 2 years (but statistically insignificant) and zero for age greater than 5 years. The graphs thus indicate that prenatal exposure (especially around conception) to midwives is the most beneficial in terms of adult outcomes, while the impacts are minimal if exposed 4 years after birth. The graphs for females show flat stretches around zero, indicating a negligible impact of midwives in adult outcomes for females. Thus, the evidence from the event study is consistent with the results presented in Table 2, Table 3, and Table 5.

The results also help in validating the research design. The flat stretches of the diagram for any exposure in late childhood lessen the concern that the estimates are confounded by differential cohort trends between program receiving and non-receiving communities. The graphs are also consistent with the fact that the results presented in Table 2 to Table 5, are not driven by the disproportionate impact of the 1997 financial crisis on older cohorts living in the program areas.

# **6 Discussion and Additional Results**

This section extends the previous analysis to examine the robustness and sensitivity of the results to alternative specifications. Moreover, it analyzes plausible mechanisms behind the results and explores the results with different sub-samples.<sup>33</sup>

> 33Additional regression analyses to provide suggestive evidence on the likely pathways through which early-life midwife exposure affects adult economic well-being are shown in the Appendix S7. Moreover, while the main focus is on pregnancy inputs, birth spacing, and fertility, the child outcomes could improve because of an improvement in maternal health.

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region-year that register due to the reform. In addition, mean employment of newly entering firms is smaller, but the coefficient is not statistically significant.

The coefficient in column 3 of table 7 suggests that the reform lowered the average size of new firms, but this effect is not statistically significant. Similarly, we find no significant effect of the reform on the one-year employment growth rate.<sup>6</sup> We do find, however, that the reform increased the one and two-year survival rates of new firms (columns 4 and 5 of table 7). The coefficient in column 4 suggests that going from the lowest level of distrust (0.43) to the highest level (0.77) increases the one-year survival rate by 3.8 percentage points (11.17*(0.77-0.43) =3.8), compared to an average survival rate of 89.8% (table 5). The corresponding increase in the twoyear survival rate is 8.3 percentage points, compared to an average survival rate of 81.4%.

**Table 7: Effect of the Reform on New Firms**

||Log number<br>of new firms<br>registered|Dep<br>Log number<br>of new firms<br>entering|endent variable:<br>Log mean<br>employment<br>in new firms|1-year<br>survival rate<br>of new firms|2-year<br>survival rate<br>of new firms|1-year<br>employment<br>growth rate of<br>new firms|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|*|0.861***|0.842***|-0.652|11.17*|24.40*|-32.92|
|ReformDistrust|(0.268)|(0.274)|(0.977)|(6.758)|(13.74)|(31.48)|
|Observations|108|108|108|96|84|96|
|R-squared|0.792|0.860|0.502|0.750|0.791|0.132|
|Number of regions|12|12|12|12|12|12|

Notes: All regressions include year and region fixed effects. The outcome variable in column 1 assigns new firms to the year they report as their registration year. The outcome variable in column 2 assigns new firms to the year when they first appear in the database. Employment, survival and growth in columns 3 and 6 are based on year of entry, not year of registration (since they are only observed once a firm enters the dataset). The growth rate in column 6 is calculated as emp� �emp��� growth� � �emp� �emp����/2 at the firm level. After that, we take the simple average of growth rates in a region-year cell. Bootstrapped standard errors clustered at the region level in parentheses; *** p<0.01, ** p<0.05, * p<0.1 <u>Sources: Data from Serbia’s Financial Accounts Registry and 2006 Life in Transition Survey</u>

> 6 As a robustness check, we also calculate an employment growth rate that is weighted by firm employment when taking the average within court-year cell. We also find no statistically significant effect here.

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**Table 3: Sample descriptive statistics**

||Mean|Proportion|Proportion|Mean|Proportion|
|---|---|---|---|---|---|
||Age|Women|with|Household|Living in an|
||||no|Size|Urban Area|
|||(%)|Education|||
||||(%)||(%)|
||(1)|(2)|(3)|(4)|(5)|
|Comoros|29.6|36|22|6.6|n/a|
|Côte d’Ivoire|24.7|42|49.0|6.1|93|
|Djibouti<br>|33.4|100|82.4|6.9|100|
|Urban DRC<br>|32.6|42.7|n/a|6|100|
|Rural DRC<br>|34.2|38.1|26.1|6.6|0|
|Egypt,<br>Arab<br>|27.6|82.2|1.5|n/a|n/a|
|Rep.||||||
|Community||||||
|Egypt,<br>Arab<br>|n/a|0.43|25.1|4.5|25|
|Rep.||||||
|Infrastructure||||||
|Ethiopia<br>|33|57.6|24.9|3.9|100|
|Lao PDR|33.3|100|41|5.9|n/a|
|Sierra Leone|27|33|52|n/a|49.6|
|Tunisia|41.5|49.8|60|n/a|n/a|

Notes: Most of these characteristics are obtained using the control sample at baseline (except Sierra Leone, where we use the information from program participants). Tunisia and Egypt do not have a regular baseline and hence we used data from the control group in the 1<sup>st</sup> follow-up survey. The Urban DRC study reports median household size. n/a – not available.

# **4.3 Follow-up surveys and key outcomes**

All studies administered at least one follow-up survey after the start of the intervention to collect detailed data across the treatment and control groups. Four studies (Comoros, Côte d’Ivoire, Djibouti, Lao PDR) fielded follow-up surveys to measure impacts during the public works intervention. All studies, except Comoros, conducted at least one post-intervention follow-up survey to estimate impacts after the program ended. However, there is substantial variation in the timing of the post-program follow-up surveys, ranging from 1 month after the program in the Egypt Community and Sierra Leone programs to 66 months in the Tunisia program. Only four studies (Côte d’Ivoire, Djibouti, Egypt Community, and Tunisia) fielded two follow-up surveys to examine the evolution 15
# **Understanding the Global Drivers of Inflation:** **_How Important Are Oil Prices?_**

Jongrim Ha, M. Ayhan Kose, Franziska Ohnsorge and Hakan Yilmazkuday<sup>1</sup>

**_Statistically Keywords_** : Inflation; oil prices; global shock; domestic shock; FAVAR; exchange rates. **_JEL Classification_** : E31, E32, Q43

 Ha: World Bank, Prospects Group; jongrimha@worldbank.org. Kose: World Bank, Prospects Group; Brookings Institution; CEPR, and CAMA; akose@worldbank.org. Ohnsorge: World Bank, Prospects Group; CEPR; CAMA; fohnsorge@worldbank.org. Yilmazkuday: Florida International University; hyilmazk@fiu.edu. We would like to thank Zidong An, Carlos Arteta, Menzie Chinn, Kevin Clinton, Zolt Darvas, Alistair Dieppe, Laurent Ferrara, Graham Hacche, David Hendry, Gene Kindberg-Hanlon, Wee Chian Koh, Catherine Kyrtsou, David Papell, Xuguang Simon Sheng, Koji Takahashi, Christopher Towe, Kozo Ueda, Lei Sandy Ye, Kamil Yilmaz, and several seminar and conference participants for their helpful comments on earlier versions of this paper. Heqing Zhao provided excellent research assistance. We gratefully acknowledge support from World Bank PHRD and KCP Funds. The findings, interpretations and conclusions expressed in this paper are those of the authors and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.
These examples show that ultimate cash-flow and control rights are described both by their level, and by the type of shareholder. More complicated cases which involve crossholdings of companies within the group, and longer chains of control through pyramiding are discussed in Claessens et al. (1998b). For the purposes of the subsequent analysis, we assume that any corporation which is at least 20% controlled by the owner of the business group is an affiliate. This cut-off level is identical to the one used in La Porta et al. (1999b), and allows us to create a dummy variable for business group membership.

Having built the ultimate ownership structure of each firm in our data, we next group firms which are controlled by the same ultimate owner. The data requirements make it impossible to track all business groups in each country-there are 150 family-controlled groups in Thailand alone. Since our data are restricted to listed companies, many privatelycontrolled firms affiliated with groups do not appear at all. We hence focus on the largest twelve business groups in each country in terms of market capitalization.8 We define the diversification level of a firm as the number of industries in which the firm operates. This number count reasonably captures the breadth of the firm's activities. To determine firms' diversification levels, we employ segment information. Worldscope reports segmental sales breakdown and business description for the three latest available fiscal years. Relatively fewer companies are covered by the database in earlier years. To increase sample

> **8** The following large business groups are identified in the data, given by the name of the controlling owner: Hong Kong (Li-ka Shing, Nina Wang, Henry Fok, Stanley Ho, the Kwok brothers, Lee Shan Kee, Chen Din Hwa, Sir Adrian and Sir John Swrite, Cheng Yu-Tung, the Chuang brothers, and the Ng family); Indonesia (Salim, Sinar Mas, Astra, Lippo, Gudang Garam, Djarum, Dharmala, Bob Hasan, Barito Group, Argo Manunggal, and Bakrie); Japan (Mitsui, Mitsubishi, Yasuda, Sanwa, Dai-ichi, Industrial Bank of Japan, Tokai Bank, Daiwa Bank, Hankyu); Korea (Hyundai, Samsung, LG, Daewoo, Sunkyung, Ssangyong, Hanjin, Kia, Hanwha, Lotte, Kumho, Doosan), Malaysia and Singapore<sup>8</sup> (Lee Seng Wee, Kuok, Hong Leong, Amanah Raya, Pernas Charter, Yeoh Tiong Lay, Berjaya, Haw Par brothers, the Arab-Malaysian Fund, Harrison and Grosfield); the Philippines (Ayala, Soriano, Aboitiz, Concepcion, Gokongwei, Sy, Go, Ty, Lopez, and Rosario); Taiwan (Farmosa, Koumintang, Lee, Li, Chen), and Thailand (Sirivadhanabhakdis, Sophonpaniches, Lamsams, Techapaibuls, Wanglees, Chearavanonts, Phatrasits, Leophairatanas, Shinawatras, Kajanapas, Viriyapraikit, and Chirathivats).

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activity. On the one hand, banks mobilize primarily short-term deposits and they take term transformation risks by lending part of their portfolios long term. The risk is higher in countries with weak banking systems. In these countries, banks are subject to higher probabilities of runs, which, in turn, could trigger their bankruptcy. On the other hand, contractual savings institutions that have long-term liabilities are better positioned to lend long term. Since their liabilities are less liquid than bank deposits, the risk premium demanded for engaging in long-term lending should be lower than what would be asked by banks. In other words, contractual savings are likely to charge lower long-term interest rates for the same credit risk.

With the development of contractual savings, banks are likely to reduce their exposure to term transformation risks. This could happen in two ways: i) bank portfolios could be biased towards short-term loans, while contractual savings institution portfolios could be biased towards long-term and risky assets, and ii) complementarity between contractual savings and banks would also allow banks to provide more term financing by funding it through the placement of securities in the market. These securities could be bought, for instance, by contractual savings institutions.

The different specialization along the duration dimension of investment projects supports the view, confirmed in the literature, that contractual savings and other financial interrnediaries supplying liquid assets are complementary rather than substitute institutions. Available studies on the effect of stock market development on debt-equity ratios found that at low initial levels of stock market development, the development of stock markets increases the debt-equity ratios of the real sector implying that the banking sector expands. For high initial levels of stock market development, further development produces a reduction in the debt-equity ratios. In this later stage of development, an increasing number of larger and/or more mature firms find it optimal to raise equity funding through the stock market, so that the relative importance of bank financing decreases. 16

# Vulnerability to interest rate and demand shocks

Corporate financial structures with excessive leverage (low equity/debt ratio) expose firms to interest rates and demand shocks. Either of these shocks increases debt services relative to revenues in a material way which could worsen enterprises' liquidity, and ultimately, could trigger their bankruptcies. Again, the 1997 East Asia financial crisis was also due, in part, to an excessively leveraged enterprise sector. By increasing the demand for equity, contractual savings can improve the financial structure of enterprises (through higher equity to debt ratio), allowing them to better withstand interest rates and demand shocks.<sup>17</sup> 16 See Demirgut!-Kunt and Maksimovic (1996), and Demirgu,c-Kunt and Levine (1996). 1 7 On this issue see Impavido, Musalem and Tressel (2000).