rafmacalaba/datause-encoder-data huggingface dataset:
magnitude of the effect is not large. The average predicted probability of fever setting the education of all mothers to zero is 44 percent whereas it is 41 percent setting the education of all mothers to 6 years, i.e., only about a three percentage point differential for 6 years of schooling. The mother's husband's (or partner's) education is insignificantly related to the incidence of fever.

**Table 8. Probit estimates of the relationship between fever, wealth and background characteristics**

||_WesternandC _<br>|_entralAfrica_|_Easternand_|_SouthernAfrica_|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Wealthindex|-0.024<br>|-0.029<br>|-0.011<br>|0.004<br>|
||(1.33)|(1.60)|(0.62)|(0.22)|
|Wealth indexsquared|-0.000<br>|0.004<br>|-0.003<br>|-0.002<br>|
||(0.01)|(0.54)|(0.91)|(0.61)<br>|
|Clustermeanwealthindex||0.017<br>||-0.057<br>|
|||(0.72)||(2.17)**|
|Cluster wealthindexsquared||-0.016<br>||0.001|
|||(1.61)||(0.10)|
|Clusterproportionwithfever||0.794||0.585|
|||(9.07)**||(8.68)**|
|1=Male|_0.055_<br>|0.055<br>|0.030<br>|0.028<br>|
||(2.48)**<br>|(2.46)**|(1.56)|(1.45)|
|Age(months)|0.065<br>|0.066<br>|0.065<br>|0.066|
||(12.35)**|(12.55)**|(16.88)**|(16.93)**|
|Age squared(months)|-0.002<br>|-0.002<br>|-0.002|-0.002|
||(12.33)**|(12.51)**|(16.97)**|(17.02)**|
|Mother'sschooling(yrs)|-0.001<br>|-0.001<br>|-0.012<br>|-0.010<br>|
||(0.18)|_(0.15)_|(3.03)**|(2.64)**|
|Husband'sschooling(yrs)|-0.000<br>|-0.001<br>|0.001<br>|0.001<br>|
||(0.04)<br>|(0.22)<br>|(0.38)<br>|(0.37)<br>|
|1=Drinking water covered|-0.003<br>|0.010<br>|0.024<br>|0.024|
||(0.08)<br>|(0.28)<br>|(0.93)|(1.03)|
|1=Toiletflushorpitlatrine|0.057<br>|0.055<br>|0.004<br>|0.011<br>|
||(1.44)<br>|(1.65)*<br>|(0.13)<br>|(0.38)<br>|
|1=Urban|-0.085<br>|-0.069<br>|0.007<br>|0.057<br>|
||(1.63)<br>|_(1.55)_<br>|(0.19)<br>|(1.65)*<br>|
|Constant|-0.781|-1.084|-0.207|-0.582|
||(7.08)**|(11.27)**|(1.21)|(4.00)**|
|Modelincludesdummyvariables<br>|forsub-regionan<br>|dconstantterm<br>|(notreported)<br>||
|Observations|38,916|38,828|29,035|28984|
|Jointtests(p-values)<br>|||||
|Own wealth|0.13|0.20|0.04**|0.76|
|Clusterwealth<br>||0.27<br>||0.01**<br>|
|Clustervariables(wealthand<br>fever)||0.00**||0.00**|

_Note:_ Model includes dummy variables for region (not shown) and a dummy variable for husband's data available. Robust z-statistics in parentheses. * significant at 10%; ** significant at 5%.

> _Source:_ Author's calculations from pooled and weighted DHS. T-statistics, adjusted for clustering are reported in parentheses.

_17_
This paper aims to assess the effect of TFP growth which can be explained by broadband use on labor demand and wages. Since the estimation process imposes a structured autoregressive law of motion on the evolution of TFP which is affected by past broadband use, this exercise can be performed directly by regressing the variables of interest on the estimated TFP. De Loecker (2013) points out that methods which do not consider an endogenous TFP process that depend on past exporting behavior tend to underestimate the relationship between exporting and productivity. Under the assumption that there are similarities in the decision‐making process between the adoption of broadband technology and entering exporting markets, I conjecture that not considering an endogenous TFP process which depends on broadband use is likely to bias the relationship between ICT use and productivity. To assess the nature of the bias I will conduct robustness exercises using the productivity estimator of D. A. Ackerberg et al. (2015) and the De Loecker (2013) procedure which models the productivity law of motion as a function of past exporting status.

Table 4 shows the estimated production function coefficients for each the methods used in this paper. My preferred method’s coefficients for labor and capital fall within the range of values of OLS and fixed effects estimations. Suggesting that both coefficients are biased upward under an OLS estimation. On the contrary, the materials coefficient is higher than either OLS or fixed effects. Table 5 shows that the correlation of TFP across the three methods is very high.

**Table 4 Production function estimated coefficients**

|Method|Labor|Capital|Materials|
|---|---|---|---|
|DLW (Broadband Use)|0.466 ***|0.126 ***|0.529 ***|
|ACF|0.458 ***|0.119 ***|0.539 ***|
|DLW (Exporting Status)|0.408 ***|0.112 ***|0.588 ***|
|OLS|0.492 ***|0.199 ***|0.381 ***|
|Fixed effects|0.373 ***|0.076 ***|0.418 ***|

Author’s estimations. Standard errors for DLW obtained via bootstrapping with 100 repetitions.

**Table 5 Correlation of TFP estimates**

|DLW (Broadband Use)|ACF<br>DLW (Exporting Status)|
|---|---|
|DLW (Broadband Use)<br>1||
|ACF<br>0.8257|1|
|DLW (Exporting Status)<br>0.9779|0.7849<br>1|

Source: EAM‐DANE. Author’s calculations.

DLW estimation is performed under the assumptions that: 1‐Variable input labor demands are a monotonic function of productivity. 2‐Input demand responds differently for firms that use broadband internet and those that do not. 3‐TFP evolves as a function of the previous period productivity and the previous period use of broadband internet. I now provide evidence that these assumptions hold.

9
# **Reference**

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Diamond, D., and Z. He, 2014, A theory of debt maturity: The long and short of debt overhang, _Journal of Finance_ 69, 719-761.

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Gambetti, L., and A. Musso, 2017, The macroeconomic impact of the ECB’s expanded asset purchase program (APP), ECB Working Paper Series No 2075.

29
# **6. Conclusions**

Developing countries have actively participated in the expansion of debt markets that took place since the 1990s. We use their activity over this relevant period to tackle an old debate in economics: whether developing country firms borrow short term and, if so, what is the source of the short-termism. Despite the importance of this question and the attention devoted in the literature, little evidence exists on the actual maturity at which firms from developing countries borrow. To fill this void in the literature, we use a large data set of transaction-level capital raising activity in domestic and international primary bond and syndicated loan markets. Developed countries are used as the benchmark for these comparisons.

The evidence in this paper shows that firms from developing countries access corporate bond and syndicated loan markets at maturities similar to those from developed countries. Thus, overall, these markets cannot be considered a source of short-term borrowing for developing countries. However, there is a substantial heterogeneity across several dimensions, such as instruments, market location, use of proceeds, and firm size, indicating that composition matters. In corporate bond markets, international issuances are particularly important in the provision of long-term financing to developing countries as most of the proceeds are raised abroad and at longer maturities than in domestic markets. Syndicated loan markets are also important for the long-term funding of developing countries, whose firms borrow in these markets at longer-term maturities than firms from developed countries. Firms from developing countries use syndicated loans in part to fund infrastructure projects, which have relatively longer maturities.

But not all firms borrow in these relatively long-term markets. Large corporations from developing countries are those that issue bonds and syndicated loans. Among this group of large firms, the largest ones typically issue debt at the longest maturities, and have better access to international 23
**The** expectations of this review are modest in view of four factors:

1. The wide variations in statistical methods for recording maternal health data make it difficult to compare data sets.

2. New projects started immediately after the conference are unlikely to have recorded expenditures in time to be included in this review.

3. The absence of international uniformity on the criteria for the initiative and the wide range of activities with direct and indirect maternal health effects means that much of the data were based on interpretations.

4. The relatively brief duration of the study.

# B. **_Amwtpton_**

1. The study looks at financial flows rather than cost-effectiveness, and does not assume that magnitude of investment is directly correlated to improvements tn maternal health.

2. Because there is no coniprehensive global system to monitor health expenditures by developing countries or exter'; .l sources, the data would have to be obtained from each financial source.

3. Because of variations in statistical recording systems and in definitions of health, full comparability would be difficult to assure.

4. It would be premature to expect identifiable new financing specifically addressing the initiative. Official statistical systems have not been adjusted to identify SM components. As a result, the data reflect unofficial estimates derived from multiple sources. The author, not the sources, is responsible for the- composite estimate.

_5._ To accomplish the study in the foul-mr.onth period prior to the June 1989 Meeting of Interested Parties, direct interviews with the major bilateral and multilateral financial sources were considered preferable to risking a low response rate to a mailed questionnaire. For developing countries. however, the sources of information were resident WHO and UNDP representatives.

## C. **_Methodology_**

1. The study drew on data sets for 1985 to 1989 in order to permit a comparison of trends before and after the Nairobi conference.

2. The data were examined for characteristics that would identify components.

3. Where there were no specific components within a health loan or primary health care program, the respondent was asked to estimate the amount of financing for SM. Information from interviews was supplemented by the official annual reports of bilateral and multilateral agencies.

4. For purposes of comparisorn, each financial source was asked to use common definitions and to include all major categories of ODA for the health sector as follows:
Rangel, A. (2000); Parasites and Predators: Guerrillas and the Insurrection Economy of Colombia, Journal of International Affairs, 53(2), Spring, pp. 577-601.

Reuter, P. (1999); Drug Use Measures: What Are They Really Telling Us?, National Institute of Justice Journal, April, pp. 12-19.

Reuter, P. and V. Greenfield (2001); Measuring Global Drug Markets: How good are the numbers and why should we care about them?. World Economics, Vol. 2 (4), October-December.

----- (2001); The Limits of Supply-Side Drug Control, The Milken Institute Review, First Quarter.

Rocha, R. (2000); Algunos mitos y datos de la economía de la droga, Webpondo, August.

Available at: <u>http://www.webpondo.org/files/Revista%20del%20Rosario.pdf</u> Substance Abuse and Mental Health Services Administration (SAMHSA) (2005); The DASIS Report, January 28.

Available at: http://oas.samhsa.gov/2k5/CocaineTX/CocaineTX.pdf Saffer, H. and F. Chaloupka (1999); The Demand for Illicit Drugs, Economic Inquiry, 37(3), pp. 401-411.

Sherret, L. (2005); Futility in Action: Coca Fumigation in Colombia, Journal of Drug Issues, Winter, pp. 151-168.

48
# **3.3 Robustness checks on the procyclicality of lending**

Private banks can be domestic or foreign-owned. Foreign-owned banks tend to be subsidiaries of international banks. This potentially enables them to obtain additional funding from the international parent bank in case their local profitable lending opportunities expand. Thus, with a more elastic supply of funds, foreign banks may be able to expand their lending relatively more during economic upswings. To test this, we re-estimate regressions 1 and 2 of Table 2 after including a foreign bank variable and its interaction with GDP per capita growth. The results are reported as regressions 1 and 2 of Table 5. In the two regressions, the interaction of GDP per capita growth and the foreign bank variable obtains coefficients of 0.009 and 0.008 that are significant at 1%, implying that lending by private, foreign banks is more procyclical than lending by private, domestic banks. In both regressions, the interaction of the GDP per capita variable and the state bank variable obtains a coefficient of -0.008 that are significant at 5%, indicating that lending by state banks is less procyclical than for private, domestic banks. Regressions 3 and 5 include interactions of the GDP per capita variable with alternatively the state bank, domestic bank, and foreign bank variables. In both regressions, the three interaction terms are estimated with coefficients with significance of at least 10%. Estimated coefficients confirm increasing procyclicality of lending from state banks to private, domestic banks, and to private, foreign banks. Specifically, a 1% increase in GDP per capita growth leads to a credit supply growth of 0.5-0.6% for state bank, 1.1-1.2% for private, domestic banks and 2.1-2.2% for private foreign banks.

Next, we consider whether the relative procylicality of lending by state banks depends on the level of economic development. In particular, we estimate regressions 1-4 of Table 2 separately for the samples of high-income countries and developing countries (and emerging markets), using the World Bank classification. The results are reported in Table 6. In the highincome countries regressions 1 and 2, the interaction of GDP per capita and the state bank variable is estimated with negative and significant coefficients.<sup>8</sup> Interestingly, point estimates

> 8 Similarly, Iannotta et al. (2011, Table 6) report that credit growth at state banks in Western Europe over the 20002009 period is relatively less procyclical, although this effect is not statistically significant. The difference in results could reflect a difference in sample size : 976 banks and 3439 observations in our high-income sample in regression 1 of Table 6, and 210 banks and 1541 observations in Iannotta et al. (2011). A further difference is that we define 13
**Table 34. Reactions to political risks: Sudden, adverse change in laws and regulations**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0274|-0.00383|||-0.0505|-0.00377|||
||(0.0660)|(0.0663)|||(0.106)|(0.112)|||
|Export % of sales|-0.000489|0.000636|-0.000585|0.000525|-0.00152|0.000722|-0.00149|0.000718|
||(0.000912)|(0.000946)|(0.000937)|(0.000976)|(0.00150)|(0.00160)|(0.00159)|(0.00171)|
|Share of sourcing via imports|8.55e-05|0.000803|0.000196|0.000699|0.000430|0.00178|0.000690|0.00160|
||(0.000957)|(0.000963)|(0.00100)|(0.00101)|(0.00156)|(0.00165)|(0.00167)|(0.00174)|
|High-income source country|0.0335|0.0498|0.0546|0.0627|0.0798|0.127|0.115|0.150|
||(0.0942)|(0.102)|(0.0959)|(0.103)|(0.148)|(0.164)|(0.152)|(0.168)|
|>250 employees|-0.180**|-0.190**|-0.160**|-0.175**|-0.279**|-0.297**|-0.239*|-0.264**|
||(0.0785)|(0.0757)|(0.0764)|(0.0741)|(0.126)|(0.127)|(0.125)|(0.126)|
|Cumulative inv. >US$10M|0.123|0.106|0.153*|0.132*|0.199|0.176|0.249*|0.214|
||(0.0778)|(0.0779)|(0.0785)|(0.0791)|(0.126)|(0.132)|(0.130)|(0.135)|
|Years in country|0.000782|-1.87e-05|0.000330|-0.000445|0.00221|0.000676|0.00129|-0.000193|
||(0.00205)|(0.00202)|(0.00202)|(0.00201)|(0.00315)|(0.00327)|(0.00316)|(0.00334)|
|Level of foreign ownership (%)|-0.000535|-0.000315|-0.000683|-0.000399|-0.00127|-0.000998|-0.00127|-0.00100|
||(0.00138)|(0.00152)|(0.00137)|(0.00145)|(0.00215)|(0.00238)|(0.00224)|(0.00243)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.005|0.068|0.025|0.081|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How would the following situations affect your investments in this? Please tell us whether they would cause you to significantly delay an investment, to cancel a planned investment, to withdraw an existing investment, or to consider a delay or cancellation. [Sudden change in the laws and regulations with a negative impact on your company]". Higher numbers denote more adverse reactions. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

51
- all water use (withdrawal and consumption) and effluent discharges (BOD - Biological Oxygen Demand, COD - Chemical Oxygen Demand, sediment residue and inorganic discharge) are subject to pricing;

- different basic charges for type of use (withdrawal, consumption, and effluent discharges) were defined per unit of water (Table 4);

- several multiplier coefficients were established to take local specificities into account, such as type of water source (surface or groundwater), seasonality (wet or dry seasons), location (water source protection areas, etc.);

- the final charge (“basic charge” X coefficients) should have a ceiling (“maximum charges”) to avoid having significant impact on users (Table 4);

- all water users should be charged (first: industrial and municipal; five years later: irrigation and others).

**Table 4: Proposed Bulk Water Charges In São Paulo State**

|**Wt**|**Uit**<br>|**Water c**<br> <br>|**harges**<br>|
|---|---|---|---|
|**aer use**|**n**<br>**Basic**|**charges**<br>**Maxi**|**mum charges**|
||**R$**|**(US$)**<br>**R$**|**(US$)**|
|Withdrawal|m<sup>3</sup><br>0.01|(0.0038)<br>0.05|(0.0192)|
|Consumption|m<sup>3</sup><br>0.02|(0.0076)<br>0.10|(0.0385)|
|Effluent discharges||||
|_BOD_|kgDBO<br>0.10|(0.0385)<br>1.00|(0.3846)|
|_COD_|kg DQO<br>0.05|(0.0192)<br>0.50|(0.1923)|
|_Sediment residue_|liter<br>0.01|(0.0038)<br>0.10|(0.0385)|
|_Inorganic discharge_|kg<br>1.00|(0.3846)<br>10.00|(3.8462)|

* US$1=R$2.60 (April 2005)

Source: CORHI, 1997.

Studies carried out by CORHI in 1997, based on this proposal<sup>42</sup> , predict the potential annual revenue of water use and pollution charges in the Alto-Tietê River Basin to be R$178 million (US$68.46 million) (CORHI, 1997).<sup>43</sup> However, it is estimated that over a third would go to the other basins that provide water to Greater São Paulo: the Baixada Santista and, above all, Piracicaba-Capivari-Jundiaí. The draft law requires that another 10% be used to finance the state system. The potential revenues that could be used for investments in the Alto-Tietê basin is, therefore, approximately R$106.8 million (US$41.07 million) a year, based on the assumption that all collected charges do, in fact,

> 42 The simulation for the Alto-Tietê considered that industrial users, urban users and irrigators would pay. All the coefficients were equal to one.

> 43 Two other simulations were carried out later (1999 and 2001): one reached the same estimated revenues collected, but the second estimated only about R$110 million (US$42.31 million) per year (FUSP, 2002a).

- 39 -
|**Table A.6: Effort in thepublic an**|**dprivate secto**|**rs by checklist**|**item discrimin**|**ation terciles**||
|---|---|---|---|---|---|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|**Outco**<br>**Repr **|<br>**me variable: P**<br>**esentative sam**|<br>**ercentage of re**<br> **ple**|**commended ty**<br>**Dua**|<br>**pe of checklis**<br>**lpractice sam**|<br>**t items**<br> **ple**|
|Low|Medium|High|Low|Medium|High|
|discrimination<br>|discrimination<br>|discrimination|discrimination|discrimination|discrimination|
|**Panel A: SP and case fixed effects**||||||
|Is a private provider<br>10.982***|7.085**|1.760|10.519***|11.745***|5.122***|
|(3.281)|(2.875)|(2.143)|(2.404)|(2.360)|(1.746)|
|R-squared<br>0.144|0.175|0.238|0.279|0.236|0.318|
|Number of observations<br>662|662|662|330|330|330|
|Mean of public<br>21.770|13.975|10.197|28.254|14.592|10.020|
|Mean of private<br>32.966|21.322|12.235|41.104|28.800|15.234|
|Mean of sample<br>32.108|20.759|12.079|34.756|21.782|12.659|
|**Panel B: SP, case and market/district fixed effe**|**cts**|||||
|Is a private provider<br>11.290***|8.597***|1.594|10.594***|11.709***|5.077***|
|(2.609)|(2.535)|(1.969)|(2.358)|(2.381)|(1.751)|
|R-squared<br>0.253|0.256|0.300|0.301|0.247|0.322|
|Number of observations<br>662|662|662|330|330|330|
|**Panel C: SP, case and market/district fixed effe**|**cts**|||||
|Is a private provider<br>8.538***|7.317**|1.657|11.786***|12.518***|4.523**|
|(3.030)|(3.092)|(2.381)|(2.480)|(2.466)|(1.794)|
|Has MBBS<br>2.548|5.175|2.307||||
|(3.949)|(3.449)|(2.916)||||
|Has some qualification<br>2.300|4.764*|0.721||||
|(2.563)|(2.447)|(1.831)||||
|Age of provider<br>-0.151|-0.009|0.044|-0.069|-0.135|-0.042|
|(0.102)|(0.094)|(0.077)|(0.139)|(0.138)|(0.101)|
|Gender of provider (1=Male)<br>1.009|-1.353|-2.369|2.773|-2.792|-3.651|
|(4.541)|(5.383)|(3.361)|(3.586)|(3.565)|(2.593)|
|Patient load during visit<br>-0.041|-0.396|0.050|-0.501|-0.203|-0.211|
|(0.736)|(0.557)|(0.528)|(0.576)|(0.572)|(0.416)|
|R-squared<br>0.254|0.262|0.301|0.290|0.252|0.330|
|Number of observations<br>638|638|638|301|301|301|

Notes: *** Significant at 1%, ** Significant at 5%, * Significant at 10%. Robust standard errors clustered at the market level are in parenthesis. Observations are standardized provider-patient interactions. Checklist item discrimination parameters are estimated using the IRT methodology. The classification of items into terciles of difficulty is done within each case, but the results are robust to classifying the items jointly across all cases. Market fixed effects are used for the representative sample, and district fixed effects for dual practice sample.

66
# **Disparities in Labor Market Performance in the Philippines**

**Xubei Luo**<sup>*****</sup> The World Bank

**Key Words:** Employment, wage, disparities.

> * The author would like to thank Jehan Arulpragasam, Jean-Jacques Dethier, Swati Ghosh, Ulrich Lächler, Silvia Redaelli, Erwin Tiongson, and Nong Zhu for useful comments, and Takanobu Terada for excellent research assistance. Email: <u>xluo@worldbank.org.</u>