rafmacalaba/datause-encoder-data huggingface dataset:
# **<mark>Table 6. Regression of the probability of having a wage job with interactions between child schooling and demographic variables of children and women (probit models)</mark>**

|**child schooling and dem**<br>Explanatoryvariables|**ographic**<br>Model 1|**variables o**<br>Model 2|**f children**<br>Model 3|**and wome**<br>Model 4|**n (probit m**<br>Model 5|**odels)**<br>Model 6|
|---|---|---|---|---|---|---|
|Child care attendance|0.786***|0.564***|0.693***|0.595***|0.713***|0.786***|
||(0.112)|(0.168)|(0.127)|(0.112)|(0.122)|(0.112)|
|Child care attendance * age|-0.004<br>(0.003)||||||
|Child care attendance *||0.012**|||||
|schooling years||(0.005)|||||
|Child care attendance * ethnic|||-0.076*||||
|minority|||(0.045)||||
|Child care attendance *||||0.078***|||
|mother||||(0.027)|||
|Child care attendance * boy|||||-0.061*<br>(0.033)||
|Child care attendance * birth||||||-0.006|
|order||||||(0.021)|
|Age|-0.010|-0.008|-0.009|-0.007|-0.010|0.001|
||(0.015)|(0.015)|(0.015)|(0.010)|(0.015)|(0.015)|
|Age squared|<br>0.130|<br>0.074|<br>0.086|<br>0.010|<br>0.102|<br>-0.014|
||(0.224)|(0.227)|(0.228)|(0.134)|(0.227)|(0.226)|
|Ethnic minority|<br>-0.133***|<br>-0.133***|<br>-0.110***|<br>-0.186***|<br>-0.134***|<br>-0.126***|
||(0.022)|(0.022)|(0.027)|(0.016)|(0.022)|(0.022)|
|Number of years of schooling|0.021***|0.018***|0.021***|0.017***|0.021***|0.019***|
||(0.004)|(0.004)|(0.004)|(0.003)|(0.004)|(0.004)|
|Dummy year 2010|_Reference_||||||
|<br>Dummy year 2012|<br>0.042*|0.047*|0.043*|0.115***|0.044*|0.048**|
||(0.024)|(0.024)|(0.024)|(0.018)|(0.024)|(0.024)|
|Dummy year 2014|0.034|0.038|0.035|0.124***|0.036|0.040|
||(0.026)|(0.026)|(0.026)|(0.020)|(0.026)|(0.026)|
|Dummy year 2016|0.036|0.043|0.038|0.125***|0.039|0.043|
||(0.029)|(0.029)|(0.029)|(0.022)|(0.029)|(0.029)|
|Generalized residuals|-0.422***|-0.381***|-0.411***|-0.648***|-0.415***|-0.403***|
||(0.111)|(0.113)|(0.112)|(0.148)|(0.111)|(0.111)|
|Women (mother=1, father=0)||||-0.190***|||
|||||(0.016)|||
|Boy|||||0.024<br>||
||||||(0.022)||
|Birth order of children||||||-0.042***|
|||||||(0.014)|
|Observations|3,863|3,863|3,863|7,603|3,863|3,863|
|R-squared|0.103|0.104|0.103|0.104|0.103|0.106|
|Note: This table reports the coef|ficients of chil|d care attendan|ce and interact|ions between c|hild care attend|ance and other|
|<br>explanatory variables in probit r<br>child care attendance on the instr<br>(Wooldridge, 2015). Then we<br>generalized residuals, interaction<br>replications. Standard errors are<br>*** p<0.01, ** p<0.05, * p<0.1.<br>Source: Estimation from VHLS|<br>egressions of t<br>ument and oth<br>estimate a pr<br>s, and other ex<br>corrected for s<br> <br>S 2010, 2012, 2|<br>he probability o<br>er explanatory v<br>obit model of<br>planatory varia<br>ampling weigh<br>014 and 2016.|<br>f women work<br>ariables using<br>maternal emp<br>bles. Standard<br>ts and cluster c<br>|<br>ing in wage-pa<br>probit, and esti<br>loyment using<br>errors are estim<br>orrelation at th|<br>ying jobs. We<br>mate the genera<br>the child car<br>ated using boo<br>e commune lev|<br>first model the<br>lized residuals<br>e variable, the<br>tstrap with 200<br>el.|

38
# **Table (3c):  Augmented Dickey-Fuller Tests for Unit Roots: Levels of Real Data - Sample 1959 - 2013**

|Variable|t-adf|beta Y_lag|t-DY_lag|Maximum Lags|AIC|
|---|---|---|---|---|---|
|rm2|-1.956|0.93461|0.9578|2|-5.636|
|rgdp|-2.198|0.87358|1.337|2|-6.341|
|INFL|-1.693|0.80466|0.8232|4|3.115|
|Interestrt|-1.714|0.92756|0.2542|4|-0.7156|
|Disc|-0.5471|<br>0.96391|-0.6173|4|0.7336|
|rcons|-2.302|0.76491|0.1102|4|-5.783|
|Dexrtend|-3.731*|0.086784|0.8326|4|-3.747|

Constant and Trend Included - Critical Values; T=55; 5%=-3.49 1%=-4.13 P-values are in brackets;

*** Significance level at 1%; , ** Significance level at 5%; *Significance level at 10%

43
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24
# Table 3 Empirical Power of **the** Ratio of Variances Test with and without Structural Breaks (Percent)

|||||||||AR(|1) Mod|el*|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||**_p_**|**_= 0.8_**|**_5_**|||||||p|=0.9|6||||
|**Sample**||**No Brea**|**ks**|L|evel Br|eak|T|rend Br|eak||No Brea|ks|L|evel Br|eak|Tr|end Br|eak|
|Size**|<br>**1%**|<br>5%|10%|1%|5%|10%|1%|_5%_|10%|1%|5%|10%|1%|5%|10%|1%|5%|10%|
|64|4.5|19.0|32.8|2.1|11.3|21.7|3.3|18.3|32.8|1.2|5.8|11.8|0.9|5.1|10.4|0.9|6.7|11.5|
|128|21.3|<br>56.4|75.0|11.6|36.7|55.3|16.6|48.7|68.6|1.7|8.9|17.3|1.5|8.1|15.5|1.7|8.9|16.7|
|_2S6_|_86.5_|<br>98.1|99.5|65.1|92.1|98.9|77.5<br>|96.7<br>ARIMA|98.9<br>(l,1,1)|_5.4_<br>Mode|20.7<br>l|34.7|4.6|17.6|<br>30.5|4.9|20.9|32.3|
|||||_p_|_=0.8_|_5_|||||||p|=0.9|6||||
|Sam|<br>ple|No Brea|ks<br>|L|evel Bre|ak|T|rend Br|eak||No Brea|ks|L|evel Br|eak|Tr|end Br|eak|
|<br>Size|<br> <br>1%|5%|.__<br>10%|1I%|___<br> <br>5%|10%|1%|5%|[0%|1%|5%|10%|1%|5%|10%|1%|5%|10%|
|64|2.4|10.9|21.7|1.8|8.8|17.1|2.4|13.0|19.7|1.1|5.7|11.0|0.9|_5.0_|10.1|0.8|5.7|11.6|
|128|_5.5_|21.3|34.9|4.4|17.9|<br>30.9|5.6|20.4|34.5|1.5|7.1|13.9|1.3|6.8|13.5|1.7|7.1|13.8|
|256|18.3|<br>45.7|63.0|15.7|41.4|_58.1_|15.8|41.7|_58.5_|2.5|2.1|22.2|2.3|11.2|20.3|2.9|11.4|20.8|

otes: 'pea e AR(l) model is as wter) tILe ARIM(IJ,I) 6e is scifted as v.=Y,+z,. wtter ZZZ1 +., and P- FRU.Mand where **_Y,=py.. +14_** and A-4N(0,1). Each set of rows corresponds to an independent and separate experiment based on 10.000 replications. Breaks of size 2i, for the level and 5% for the trend were introduced at the midpoint of each sample. (**) For 64 observations, we compute m-8, 16, **and** 32 autocorelations; for 128 and 256 observations, we compute m= **16, 32,** aid 64 sulocorrelations.
for that, it needs support from financial institutions. But the financial institutions may not take letters of credit from the buyer at face value, because are aware of all sorts of glitches – quality or other – that may emerge down the line. Anecdotal experience suggests that, in Sub-Saharan Africa, the banks’ response will typically be ‘no’ irrespective of the “proofs of profitability” that the exporter shows, and the trade relationship with the US buyer will end before it had a chance to bear fruit. However, if several Senegalese firms already sell t-shirts on the US market, the same financial institutions may be more easily convinced about the chances of success of this venture and better evaluate the potential risks involved in this transaction. If this scenario is representative, the synergy effect should be stronger for products that are especially dependent on external finance than for others as initial financial constraints would be more binding in these sectors. In order to test this conjecture we interact our variable identifying the synergy effect with the measure of dependence from external finance proposed by Rajan and Zingales (1998).<sup>15</sup> We construct our _rp_ variable at the product level by using concordance tables between ISIC3 and HS6 classification, and assigning to each HS product the Rajan-Zingales index of the ISIC code to which that product belong. Column (2) of Table 5 shows that the interaction term _rp ×_ ln _npdt_ is positive and significant.

As an alternative way of getting a handle on the degree of dependence from finance, we use a proxy for ‘asset tangibility’ proposed by Braun (2003).<sup>16</sup> The idea that firms with more tangible assets presents lower risks as these provides real guarantees for bank loans, and information asymmetries (adverse selection or moral hazard) are less important with good collateral, so synnergy effects should play a lesser role. In accordance with this conjecture, in column (3) of Table 5 we show that the interaction of asset tangibility ( _rp_ )

> 15Rajan and Zingales’ measure of financial dependence is an industry-level variable calculated for 27 3-digits ISIC industries and nine 4-digits ones using compustat data for the US. Let _k_ be capital expenditure and _x_ operational cash flow at the firm level. Rajan and Zingales’ index for industry _j_ , _rj_ , is the median value of ( _k −x_ ) _/k_ across all compustat firms in industry _j_ . Index values, given in Table 1 of Rajan-Zingales (1998), range from -0.45 for tobacco (ISIC 314) to 1.49 for drugs (ISIC 3522).

> 16Braun proxies asset tangibility by the ratio of net property, plant and equipment to market value at the firm level, using US compustat data. The industry-level variable is constructed, like in Rajan-Zingales, by taking the industry median at the ISIC 3-digit level. Index values, given in Table 1 of Braun (2003), range from 0.09 (leather products) to 0.67 (petroleum refineries).

27
# **Table 6**

# **Asset betas during the 2000s in EM and AM**

This table presents betas from country-by-country estimations of dlog(EM Asset) vs dlog(Global Asset) during the 2000s. EM assets are MSCI country indexes, exchange rates and EM sovereign credit spreads. Global assets are the S&P, the Broad Dollar Index and the US HY corporate spread. Dlog changes were estimated at different frequencies. For numbers in bold, t-test for means were performed. The hypothesis is that betas from the late period were lower than betas from the early period. *,**, and *** denotes significane at the 10, 5 and 1% level. Source: Bloomberg, MSCI, GEM World Bank, US Treasury.

|**Country**<br>|**Period**||**Equity**|||**Spreads**|||**FX**||
|---|---|---|---|---|---|---|---|---|---|---|
|**Group/Statistic**||_Monthly_|_Quarterly_|_Annual_|_Monthly_|_Quarterly_|_Annual_|_Monthly_|_Quarterly_|_Annual_|
||2000-2004|0.761|0.952|1.071|0.552|0.743|0.544|0.654|0.916|1.181|
|Mean-EM|2005-2008M02|**1.229*****|**1.281*****|**0.721**|**0.618**|**0.793**|**0.836****|**0.618**|**0.931**|**1.052**|
||2005-2010|**1.023*****|**1.159*****|**1.281****|**0.803*****|**0.973****|**0.994*****|**1.245*****|**1.403*****|**1.484**|
||2000-2004|0.766|0.976|1.021|0.553|0.784|0.567|0.632|0.858|0.896|
|Median-EM|2005-2008M02|1.223|1.312|0.645|0.581|0.712|0.792|0.550|0.843|0.991|
||2005-2010|1.041|1.163|1.236|0.786|0.946|0.967|1.324|1.500|1.540|
||2000-2004|1.063|1.001|1.199||||1.269|1.695|1.802|
|Mean-AM|2005-2008M02|**1.087*****|**1.277*****|**1.202**||||**1.437***|**1.642**|**2.050***|
||2005-2010|**0.941**|**1.036**|**1.216**||||**1.433**|**1.512**|**1.516**|
||2000-2004|0.834|0.971|1.077||||1.269|1.752|1.673|
|Median-AM|2005-2008M02|1.063|1.271|1.199||||1.407|1.595|2.249|
||2005-2010|0.938|0.993|1.141||||1.570|1.564|1.591|
agricultural output _Qijt_ to input allocation _Kijt_ , labor _Lijt_ , plot size _Aijt_ and risky crop choice _Rijt_ . The agricultural production function is assumed to be translog, in which the choice of crops affects output multiplicatively, Because it seems very likely that increasing the average risk in a crop portfolio is only beneficial to a certain extent, beyond which risk is simply too high to increase output, _g_ ( _Rijt_ ) is a non-linear function. The production function described in equation (13) can be estimated by log-transforming the data and controlling for shocks _Zijt_ , unobserved characteristics _γij_ and time effects _δt_ . Again, _g_ ( _Rijt_ ) is approximated by the level and the square of _Rijt_ : The production function is estimated in FGLS. The estimates suggest that households could significantly raise the value of their agricultural production if they were to increase the share of inputs allocated to riskier crops (c.f. Table 2). However, this is only true up to a certain level. The square of the risk index is statistically significant at the 5% level in all specifications. Based on the estimates in col. (4), predicted agricultural output reaches its maximum at a risk index of 0 _._ 41.<sup>33</sup> Beyond this point, a further increase in risk would reduce total agricultural output. Average risk levels in households’ crop portfolios are well below this value; in the survey round of 2007 the average risk index was 0.36 in the treatment group and 0.26 in the control group (c.f. Table 1).

As can be seen from columns (1) and (2) of Table 2, the estimates are not affected by the exclusion of labor from the agricultural production function. The analysis here is restricted to the survey round of 2007.<sup>34</sup> Columns (3) and (4) show estimates from the

> 33C.f. Figure D.4 in the appendix.

> 34I cannot control for labor in the panel data models, because time information was only collected in 2007 and not in 2009/10.

20
Figure 8: Spillovers from U.S. growth shocks (A) Output and investment growth in (B) Output and investment growth in other advanced economies EMDEs <!-- Start of picture text --> Percentage point Percentage point<br>4.0 Output Investment 4.0 Output Investment<br>3.5 3.5<br>3.0 3.0<br>2.5 2.5<br>2.0 2.0<br>1.5 1.5<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>-0.5 -0.5<br>On Impact 1 Year 2 Years On Impact 1 Year 2 Years<br><!-- End of picture text --> Sources: World Bank; Haver Analytics; OECD.

Note: Figures reflect cumulative impulse responses of weighted average other AE and EMDE GDP growth to a 1 percentage point increase in growth in real GDP in the United States. Growth spillovers are based on a Bayesian vector autoregression of global GDP growth excluding the United States and other AEs or EMDE, U.S GDP growth, the U.S. 10-year sovereign bond yield plus JP Morgan’s EMBI index and AE or EMDE GDP growth. The oil price is assumed to be exogenous. Bars represent medians, and error bars 16-84 percent confidence bands. The Sample for other AEs includes Euro Area (19 countries), Canada, Japan, and the United Kingdom and 19 EMDE for 1998Q1-2016Q2.

bilateral portfolio flows (Ehrmann, Fratzscher, and Rigobon 2011; Rose and Spiegel 2011). This makes U.S. monetary policy and investor confidence important drivers of global financial conditions (Ehrmann and Fratzscher 2009; Arteta et al. 2015; Rey 2015).

Because of its predominant use in global trade and financial transactions, broad-based U.S. dollar exchange rate movements have global implications. Episodes of U.S. dollar appreciation tend to coincide with bank deleveraging, tighter global financial conditions, greater incidence of financial crises and subdued EMDE growth.<sup>10</sup> Although the share of private and public debt denominated in foreign currency has declined since the 1990s, the exposure of some EMDEs to foreign currency movements is still high, especially in commodity exporters, as well as importers that have received large capital inflows after the global financial crisis (Arteta et al. 2016). If the U.S. dollar goes through a period of significant appreciation,

> 10See Bruno and Shin (2015a and b); IMF (2015a and b); Druck, Magud, and Mariscal (2015); Abbate et al. (2016).

15
We therefore split the sample across the poverty status in wave one ( w= 1). The variable Poori,l,t,w | non−poori,,l,t,w=1 takes the value 1 if a household i in the LGA l that was nonpoor in wave 1 switched to becoming poor in either of the subsequent waves and 0 if the household stayed nonpoor. Similarly, the variable Nonpoori,l,t,w | poori,l,t,w=1 takes the value 1 if a household that was poor in wave 1 became nonpoor in either of the subsequent waves and 0 if it stayed poor throughout. The variable λl denotes LGA fixed effects, and the rest of the variables are as previously noted. We use a linear probability model to estimate the specifications above. As previously stated, we cluster the standard errors at the LGA level.

Table C.2 displays transition probabilities in and out of poverty across all three waves. The variable of interest takes the value 1 if a household is poor in any given wave and the value 0 if the household is nonpoor. The poverty line used follows the international poverty line of US$1.90 per person per day (in 2011 PPP) (World Bank 2019), using the consumption aggregate.<sup>26</sup> This is also the poverty line used by the Nigerian government. We can see that there is strong persistence in being nonpoor: households that are nonpoor in wave 1 have an 83.7 percent probability of staying nonpoor during wave 2 and a 79.9 percent probability of staying nonpoor between waves 1 and 3. More variation exists among the initially poor: between wave 1 and 2, the probability of staying poor across the waves is 44.9 percent. Between waves 2 and 3, the persistence in poverty is higher at 59.6 percent.

Table 8 displays the results from estimating equations 2 and 3. Panel a shows the results of all conflicts and by each type of event, and panel b shows the results disaggregated by the type of perpetrator. Columns 1–3 display the model that uses the dummy for moving out of poverty (equation 3), and columns 4–6 display the model that has moved into poverty as the dependent variable (equation 2). Looking at columns 1–3 in panel a, we can see that households that have experienced conflict are marginally more likely to stay poor. Hence, conflict decreases the likelihood of moving out of poverty by 4 percent, and the result is significant at the 10 percent level. The results are stronger for the events related to violence (column 3) than for those related to property (column 2). A violent conflict event is associated with a decrease in the probability of becoming nonpoor by around 12.9 percent, a result that is significant at the 1 percent level.

> 26 The last official poverty estimate in Nigeria is from 2009. The international poverty line of US$1.90 per day (2011 PPP) corresponds to 133.5 naira per person per day in 2009 prices.

22
would be the case in the United States. The Philippines has also seen important economic changes in the last two decades, including a supply-push to make secondary education universal, volatility in various economic indicators, including aggregate GDP growth, and substantial differences in the rate of growth of output and productivity across sectors. These changes, many of which are not yet complete, may result in differences in the rate of return to education across levels and across years which are large, at least in the short run.

The rest of the paper proceeds as follows. Section 2 gives a brief review of education and labor markets in the Philippines, including a discussion of recent literature. Section 3 describes the data set. In Section 4, I discuss the econometric specification. Section _5_ presents results on the differences in rates of return across levels, and across years within levels. Section 6 concludes.

# 2. **Education** in **the Philippines**

Basic education in the Philippines is acquired in two levels: primary education (six years), and secondary education (four years). Graduates from secondary school can continue on to university (five years) or post-secondary vocational training (up to four years).<sup>4</sup> As in most countries, there are both public and private providers of education, and the latter are particularly important at higher levels of education: Private providers of education account for a very small fraction of total enrollment in primary school (7%), but much larger fractions for secondary school (22%), and a majority of tertiary 4Much of this section is based on Asian Development Bank and World Bank (1999).

4
# Figure 4: Share of plants with financial loans by <u>gender of employees and owners</u>

## A.  Organized sector using employee split

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>1989 1994 2000 2005 2010<br>Male majority Female majority<br><!-- End of picture text -->

## B.  Unorganized sector using employee split

<!-- Start of picture text --> 14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>1989 1994 2000 2005 2010<br>Male majority Female majority<br><!-- End of picture text -->

- C.  Unorganized sector using gender of business owner

<!-- Start of picture text --> 12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>1989 1994 2000 2005 2010<br>Male owned Female owned<br><!-- End of picture text --> Notes: See Table 9a.