rafmacalaba/datause-encoder-data huggingface dataset:
# **Is the baby to blame?**

# **An inquiry into the consequences of early childbearing**<sup>**1**</sup>

Joao Pedro Azevedo<sup>2</sup> Luis F. Lopez-Calva<sup>3</sup> Elizaveta Perova<sup>4</sup> JEL codes: J13, I30, D00.

Keywords: teenage pregnancy, natural experiment, poverty traps, welfare.

Sector Boards: POV, GEN 1 This study is a background paper for the regional study „Teenage Fertility Decisions, Poverty and Economic Achievement: Evidence and Policy Lessons from LAC”. The authors would like to thank comments received from Adriana Camacho and Laura Chioda, as well as participants at the _International Society for Equity in Health_ Conference in Cartagena, Colombia.

2 The World Bank 3 The World Bank 4 The World Bank
# **Table 2**

# **Infrastructure and Economic Development:  Correlation Analysis**

||||S<br>|ynthetic Infrast<br>|ructure Index<br>||
|---|---|---|---|---|---|---|
||Economic|Gini|Infrastructure|Quantity|Infrastructu|reQuality|
|**Variables**|Growth|Coefficient|[IK1]|[IK2]|[IQ1]|[IQ2]|
|Infrastructure Quantity (IK1)|0.3397|-0.4667|1.0000||||
|(_synthetic index_)|(0.000)|(0.000)|||||
|Infrastructure Quantity (IK2)|0.3384|-0.4649|0.9962|1.0000|||
|(_synthetic index_)|(0.000)|(0.000)|(0.000)||||
|Infrastructure Quality (IQ)|0.4213|-0.5613|0.7287|0.7269|1.0000||
|(_synthetic index_)|(0.000)|(0.000)|(0.000)|(0.000)|||
|Infrastructure Quality (IQ2)|0.348|-0.5681|0.6286|0.6361|0.9328|1.0000|
|(_synthetic index_)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)||
||||S<br>|ynthetic Infrast<br>|ructure Index<br>||
||Economic|Gini|Infrastructure|Quantity|Infrastructu|reQuality|
|**Variables**|Growth|Coefficient|[IK1]|[IK2]|[IQ1]|[IQ2]|
|Access to Sanitation|0.3553|-0.3763|0.8299|0.8275|0.6418|0.6046|
|_(% population with access to sanitation)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Access to Safe Water|0.4370|-0.3023|0.7442|0.7423|0.6152|0.5317|
|_(% population with access to safe water)_|(0.000)|(0.003)|(0.000)|(0.000)|(0.000)|(0.000)|
|Access to Rural Roads|0.4732|-0.4929|0.7992|0.7957|0.6496|0.5867|
|_(% population with access to rural roads)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Access to Electricity|0.3494|-0.3651|0.6929|0.7122|0.2078|0.1219|
|_(% households with access to electricity)_|(0.022)|(0.047)|(0.000)|(0.000)|(0.330)|(0.915)|

_Note: The numbers in parenthesis below the correlation coefficients represent their corresponding p-values. Correlations between growth, infrastructure quantity and infrastructure quality are computed over country averages in the period 1990-2005 Access information is generally not available prior to 2000; hence the correlations of access measures with the other variables refer to averages over 2000-2005._ 37
The pro-growth scenario is essentially meant to be illustrative, but the assumptions and their impacts are consistent with other empirical work in this area. For example, Dessus, Fukasaku, and Safadi at the OECD (1999) estimate a macro relationship between openness—as measured by the export plus import to GDP ratio—and per capita GDP, and they derive an elasticity of 0.09. This is only about one-fourth of the elasticity implied by this work (0.4).

However, once one adjusts for the share of agriculture and manufacturing in GDP and the share of labor in value added (because the measure of productivity does not apply to services and is only labor augmenting), the skill- and sector-specific elasticity of 0.4 implies a macro trade to GDP elasticity<sup>27</sup> below 0.1, which is about the same as that of the OECD work.

In a similar vein, Itakura, Hertel, and Reimer (2003) develop a formula that links gains in efficiency to export performance, on the assumption that firms are heterogeneous and that exporters are more efficient than other firms: In this formula, δ represents the ratio of the technology index between export-oriented firms relative to domestic-oriented firms, _sE_ is the share of exports in production, _sD_ is the share of output supplied to the domestic market, and _E_ and _D_ represent exports and domestic supply (with hats used for percentage changes). Based on estimates from Bernard and Jensen (2004) for U.S. firms, they assume that the ratio of the technology indexes is 1.08—that is, exporting firms are on average 8 percent more efficient than firms oriented exclusively toward the domestic market. Here, formula (3) is inverted to derive the implied δ generated by the results reported in this chapter. In other words, the results from the model provide the change in export and domestic

> 27. For example, if manufacturing is 33 percent of output and labor has a 50 percent share, the macro elasticity would be 0.066.

17
|Net crop seller<br>2,855<br>-34<br>n.a.<br>94<br>n.a.|20,816*|
|---|---|
|_n.a._<br>_n.a._<br>_n.a._||
|Note: The coefficients measure the time fixed effects relative to 2007:Q1. The figures in italics are p<br>a test for whether there is a difference between the fixed effects between that year and the previou<br>Values are shown in the prices of 2007. Boldfaced numbers are statistically different from the previo|-values for<br>s year.<br>us year at|
|the 5% level; yellow cells indicate lower real expenditure per capita in 2009 than in 2008. * Informat|ion on who|
|is a net seller of crops is only available for 2007 and 2009, covering a total of 86,899 observations.||
|2009; however, their situation cannot be compared from one year to the next|because|
|information on income was only collected in 2007 and 2009.||

The numbers in Table 4 show the effects of the recession by gender, and by age. Each regression is weighted by the number of males, or children aged under 5, or other age category, in the household. This should thus measure the experience of these groupings of individuals during 2007-2010. So, for example, in looking at the effect on women, a household with no women would have no weight in the regression – i.e. the observation would be excluded – and a household with two women would have double weight, and so on. The maintained hypothesis, in the absence of other information, is that the consumption per capita for each household member reflects the average consumption per capita for a household.

**Table 4.  Measuring the Impact of the 2008-09 Recession on Real Expenditure per capita by gender and age**

||Real<br>expenditure|Change in r|eal expenditur<br>to 200|e per capita<br>7:Q1|compared|Number of|
|---|---|---|---|---|---|---|
||per capita|2007<br>_ba_|2008<br>_ht/quarter_|2009|2010|households|
|Males|4,042|-16|-103<br>_0.08_|**171**<br>_0.00_|**1**<br>_0.00_|152,455|
|Females|4,153|-93|-56<br>_0.78_|**100**<br>_0.00_|76<br>_0.63_|162,307|
|Age: under 5|3,093|-72|-67|30|-50|29,922|
||||_0.84_|_0.15_|_0.23_||
|Age: 5-9|3,184|-103|-122<br>_0.54_|**37**<br>_0.03_|-11<br>_0.52_|33,543|
|Age: 10-14|3,350|33|-51<br>_0.25_|**220**<br>_0.00_|**50**<br>_0.01_|42,222|
|Age: 15-19|3,874|117|**142**<br>_0.01_|153<br>_0.90_|268<br>_0.17_|33,469|
|Age: 20-29|4,722|-83|**562**<br>_0.00_|**200**<br>_0.00_|**689**<br>_0.00_|45,546|
|Age: 30-49|4,528|-31|**-179**<br>_0.02_|**145**<br>_0.00_|**-35**<br>_0.01_|113,544|
|Age: 50-59|4,518|-162|**-406**<br>_0.00_|**16**<br>_0.00_|**-254**<br>_0.01_|57,942|
|Age: 60-64|4,006|50|**474**<br>_0.00_|322<br>_0.30_|518<br>_0.18_|22,635|
|Age: 65-74|3,774|30|-43<br>_0.57_|145<br>_0.11_|75<br>_0.55_|30,096|
|Age: 75 and over|3,576|-70|32|106|148|20,119|

Losers and Winners in Recession: Thailand 2008-09 18
# **How Can Public Spending Help You Grow? An Empirical Analysis for Developing Countries**<sup>1</sup>

## **Nihal Bayraktar Penn State University and World Bank and Blanca Moreno-Dodson World Bank**

**JEL codes:** H50, O11, O23 **Key words:** Public spending, productive expenditure, core spending, government budget, economic growth

> 1 The authors would like to thank Pierre Richard Agénor, Professor at the University of Manchester and Center for Growth and Business Cycles Research; Vito Tanzi, former IMF Fiscal Affairs Department Director; and Gilles Nancy, Professor at the University of Aix-Marseille II, for their helpful comments. This version also reflects the comments received when the paper was presented at the ECLAC Fiscal Policy Seminar for Latin America held in Chile in January 2010.
**Table 9: A Comparision of Out-of Sample Predictive Accuracy of Different Methods**

|**Method**|**Optimal Hyper-Parameter Values (via cross-validation)**|**Log-Loss**|
|---|---|---|
||**_Mozambique_**||
|Logistic Regression|None|0.640|
|Conditional Inference Decision Trees|Level of Significance = 0.95|0.653|
|Random Forest|Min. Node Size = 200 , Number of Var Tried = 6|0.642|
|Gradient Boosted Trees|eta = 0.05,max depth = 3,num rounds = 150,min child wt = 20|0.643|
||**_Malawi_**||
|Logistic Regression|None|0.635|
|Conditional Inference Decision Trees|Level of Significance = 0.90|0.646|
|Random Forest|Min. Node Size = 200 , Number of Var Tried = 9|0.636|
|Gradient Boosted Trees|eta = 0.05,max depth =2,num rounds = 300,min child wt = 60|0.633|
||**_Zimbabwe_**||
|Logistic Regression|None|0.592|
|Conditional Inference Decision Trees|Level of Significance = 0.95|0.597|
|Random Forest|Min. Node Size = 300 , Number of Var Tried = 10|0.592|
|Gradient Boosted Trees|eta = 0.05,max depth =2,num rounds = 150,min child wt = 10|0.591|
||**_Tanzania_**||
|Logistic Regression|None|0.616|
|Conditional Inference Decision Trees|Level of Significance = 0.95|0.644|
|Random Forest|Min. Node Size = 150 , Number of Var Tried = 8|0.617|
|Gradient Boosted Trees|eta = 0.4,max depth = 1,num rounds = 300,min child wt = 20|0.616|
||**_Zambia_**||
|Logistic Regression|None|0.645|
|Conditional Inference Decision Trees|Level of Significance = 0.90|0.655|
|Random Forest|Min. Node Size = 100 , Number of Var Tried = 5|0.640|
|Gradient Boosted Trees|eta = 0.1,max depth = 2,num rounds = 100,min child wt = 10|0.644|

Notes: The models were trained on 70 percent of each set of surveys and tested on the remaining 30 percent. The hyper-parameters were optimized using 10-fold cross-validation (log-loss metric). The table reports the optimized (i.e. minimum) log-loss for each model. The hyperparameter candidate sets are as follows. Conditional Inference Decision Trees: Bonferroni level of significance ( 0.90, 0.95). Random Forests ( R package Ranger): minimum node size (50, 100, 150, 200, 250, 300, 350), number of variables tried at each node (4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14), and number of trees (1000). Gradient Boosted Trees ( R package Xgboost): shrinkage (0.05, 0.1, 0.2, 0.3, 0.4), max depth (1, 2, 3, 4), number of rounds (50, 100, 150, 200, 250, 300) and min. child weight (10, 20, 30, 40, 50, 60).

32
19

# **The World Bank’s Agricultural Distortions database**

The database generated by the World Bank’s Agricultural Distortions project (Anderson and Valenzuela 2008), using a methodology summarized in Anderson et al. (2008), provides a timely opportunity to estimate global commodity trade and welfare reduction indexes for individual commodity markets. The database contains consistent estimates of annual nominal rates of assistance (NRAs) and consumer tax equivalents (CTEs) at the commodity level, for a set of agricultural products (called covered products). These products account for around 70 percent of total agricultural production in 75 countries (called focus countries), which in turn account for 92 percent of global agricultural GDP. The data cover a time period between 1955 and 2007 for the majority of countries but the country coverage is most complete for the years 1960 to 2004, so only those are used here. Global NRAs and CTEs for various commodities are estimated, using as weights the values of production and consumption, respectively, at undistorted prices.<sup>4</sup> The range of measures included in the Agricultural Distortions database NRA estimates is wide. By calculating domestic-to-border price ratios the estimates include assistance provided by all tariff and non-tariff trade measures, plus any domestic price support measures (positive or negative), plus an adjustment for the output-price equivalent of direct interventions on inputs. Where multiple exchange rates operate, an

> 44 Appendix Tables 1 and 2 report those estimates for 28 major products, whose NRAs and CTEs cover 77 percent of their global production on average.
page 24 Table 7. Cost-Effectiv;n Choice of an Integration Strategy for Various Parameter Estimates

|Estimates<br>of the<br>Average Impacr on<br>Healthy Life Days of:|Java<br>with<br>EPI<br>(1)|Java<br>Orig.<br>Assum.<br>(2)|Ghana<br>Theory<br>(3)|Ghana<br>Demand/<br>Obsrvd<br>(4)|
|---|---|---|---|---|
|.A vaccination<br>(parameter<br>b)|9.4|25.0|75.4|53.8|
|A VHW encounter<br>(parameter<br>a)|15.0|15.0|14.9|7.5|
|Ratio of b/a:|.6|1.7|5.1|7.2|
|Cost Function<br>Parameters<br>s andpf|||||

Constant Returns to Scale:

|(A) For s<br>1, If .<br>10.6|F<br>F|F<br>,|3.2<br>(0.5)|
|---|---|---|---|
|Increasing<br>Returns<br>to Scale:||-<br>j-<br>J||
|(B) For s -0.9,<br>Pf.<br>7.7|F<br>F|;<br>2.9<br>(0.5)|20 3<br>(0.05)|
|(C) Fors<br>0.8, hf<br>- 5.2<br>(D) Fors90.7,f- 3.1|F<br>F<br>**I**~<br>F<br>a<br>1.7<br>|<br>(1.4)|71.2<br>(0.01)<br>I<br>_Ji--_<br>-<br>*M|--<br>H<br>M|

NOTES: Column (1): The estimate of b is derived in the text. The estimate of a is based on Table 4. Column (2): The estimate of b is based on Table 2, that of a on Table 4. Columns (3) and (4): The estimates of b and a are from Tables 1 and 3. Rows (A) throough (D): The estimates of pf are from Table 6. The numerical entries in cells A4, B3, B4, C3 and D2 are the values of 13 which solve the equation: b hf a (2[3/(>1)]1<sup>)(hl)/B</sup> The values in parentheses are the values of the elasticity of complementarity, defined as 8 - _1/(-1)._ In these cells, if 13 is above the specified value (or if 6 is below the specified value in parentheses) then Strategy F is the cost-effective choice. Otherwise Strategy M is the cost-effective choice.
**Figure 5: Change in share of emissions by sector between 1990 and 2005.**

<!-- Start of picture text --> Albania Industry Energy Agriculture Ot her<br>Armenia<br>Azerbaijan<br>Belarus<br>Bosnia & Herzegovina<br>Bulgaria<br>Croatia<br>Czech Republic<br>Estonia<br>Georgia<br>Hungary<br>Kazakhstan<br>Kyrgyzstan<br>Latvia<br>Lithuania<br>Macedonia, FYR<br>Moldova<br>Poland<br>Romania<br>Russian Federation<br>Serbia & Montenegro<br>Slovakia<br>Slovenia<br>Tajikistan<br>Turkey<br>Turkmenistan<br>Ukraine<br>Uzbekistan<br>-100% -80% -60% -40% -20% 0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> Source: WRI (2011) and authors‘ calculations.

61
16

# Table 2: Com2arino OntImal Policies for Dirty and Clean industries (Policies that Maximize the Federal Welfare)

|Optimal Federal Inspection|.......<br>.......<br>.<br>....<br>.|....<br>...<br>. . ....<br>. ....<br>.. . ....<br>...<br>...<br>**:...**<br>. . . .. . . . .. . . . .|
|---|---|---|
|<br>Rate for Various Degree of<br> <br>|||
|Averson<br>to Polution OM|||
|noaversion|10%|15%|
|lowaversion|45%|100%|
|highaversion|100%|100%|
|Ootimapl<br>StteMonitoring Rate|||
|With Federal Intervention|||
|no federalaversion|52%|100%|
|low federal aversion|71%|100%|
|high federal aversion|100%|100%|
|With noFederalIntervention|4S%|40%|
|InvextMgnt<br>in Pollution Control|||
|withFederalInterventionand|||
|no aversion|_5.5%_|19.7%|
|low aversion|14.3%|33.2%|
|highaversion|30.0%|33.2%|
|with no Federal Intervention|0%|12.6%|
|Minimum State Monitoring Required for|||
|<br>a Non-zero Investment in Pollution Control|44.9%|3.4%|
|Pollution Level (as % of current)|||
|<br>with Federal Intervention**and**|||
|no aversion|88%|49%|
|low**aversion**|_58%_|I%|
|high aversion|10%|I%|
|with noFederalIntervention|100%|62%|
|Value Added (as % of current)|||
|withFederalIntervention and|||
|no aversion|97%|93%|
|low aversion|88%|75%|
|highaversion|68%|75%|
|with noFederal Intervention|100%|96%|
|Stats Net Revenue**(s**_Y_of current VATrevenuel|||
|<br>with Federal Intervention and|||
|no aversion|167%|75%|
|lowaversion|101%|_75%_|
|highaversion|87%|75%|
|withno Federal Intervention|186%|143%|
|Feder Net Revenue<br>**(as**% of current VAT revenu|||
|<br>with Federal intervention**and**|||
|no aversion|14%|15%|
|<br>lowaversion|32%|-24%|
|high aversion|-37%|-24%|
|withno FederalIntervention|0%|0%|
Our main result is robust to several alternative measures of efficiency that were discussed in Section 3. These include slack-based super efficiency, bias corrected efficiency, BCC efficiency, and FDH efficiency. All the efficiency measures considered assume an input-oriented model with variable returns to scale. Outputs and inputs are as stated in the previous paragraph. Table A1 in the Appendix provides more details on these efficiency measures.

# 4.2 _Main explanatory variables_

Our main explanatory variables are hotel size, volatility in market demand proxied by volatility in occupancy rate experienced by a hotel in the last year, and the interaction term between the two. For hotel size, we use the log of the number of permanent full-time workers employed at the hotel at the end of the last fiscal year. For volatility, we use the information provided in the ES on the highest occupancy rate (percentage) last year, the lowest occupancy rate last year, and the average occupancy rate last year. Specifically, our volatility measure equals the difference between the highest occupancy rate and the lowest occupancy rate divided by the average occupancy rate ( _Volatility in Occupancy_ ). Such range-based measures of a given phenomenon are typical in the related literature (see Ferrante et al. 2018, Lundtorp 2001). The mean value of _Volatility in Occupancy_ equals 0.81, and the standard deviation is 0.49. The focus of the empirical exercise below is on the interaction term between hotel size and volatility in occupancy rates (henceforth, volatility). This interaction term captures how the relationship between efficiency and volatility varies with the size of the hotel.

For robustness, we show that our main results hold for alternative measures of hotel size and volatility. For hotel size, we use the log of the number of rooms. For volatility, we use the 13
Table 12: Network regressions: log Average Output Pooled

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||logAvOut|logAvOut|logAvOut|logAvOut|logAvOut|logAvOut|
|SOE|-0.335***|-0.335***|-0.335***|-0.335***|-0.335***|-0.335***|
||(0.016)|(0.016)|(0.016)|(0.016)|(0.016)|(0.016)|
|Connect|0.079*|0.079*|0.042|-0.008|0.208***|0.110***|
||(0.047)|(0.047)|(0.042)|(0.060)|(0.034)|(0.040)|
|BigI|0.093*|0.047||0.096|||
||(0.054)|(0.057)||(0.084)|||
|HasBetween||0.093*||||0.229***|
|||(0.052)||||(0.070)|
|logDegree|||0.075***|.100***|||
||||(0.025)|(0.049)|||
|BigI_×_logDegree||||-0.046<br>(0.059)|||
|Politician|||||-0.113**|-0.002|
||||||(0.046)|(0.053)|
|Politician_×_HasBetween||||||-0.267***<br>(0.097)|
|Constant|9.402***|9.402***|9.402***|9.402***|9.402***|9.402***|
||(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|
|Observations|8,846,434|8,846,434|8,844,757|8,844,757|8,846,434|8,846,434|
|R-squared|0.444|0.444|0.444|0.444|0.444|0.444|
|Sector Dummies|✓|✓|✓|✓|✓|✓|
|Size Dummies|✓|✓|✓|✓|✓|✓|
|Year FE|✓|✓|✓|✓|✓|✓|
|Country FE|✓|✓|✓|✓|✓|✓|

Robust standard errors in parentheses clustered at the level of firm *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 36
curve,<sup>33</sup> Even if the annual pay-off from exporting were the same for young and old plants, the young ones would perceive less return to breaking into the market because they are less I ikely to survive.

Location matters as well, presumably because of transport costs. Bogota, the base category in the model, is land-locked in the Andes mountain range, and plants there are among the least likely to produce for foreign markets. Cali and Medellin are also inland, but less mountainous and closer to the coast. Nonetheless, we estimate that these cities are as unlikely to serve as a base for exporters as Bogota. Perhaps their locational advantage is offset by their lack of Bogota's agglomeration economies. Finally, the port cities of Cartagena and Baranquill **a** re most likely to host exporting plants.

Interestingly, neither wage rates nor export prices relative to domestic output prices are significant determinants of exporting behavior. This should _not_ be interpreted to mean that prices don't matter; time dummies have already controlled for general movements in relative prices, and the plant-specific price variables therefore reflect across-plant deviations from average trends that can result from local market conditions, measurement error, and differences in mput or output quality. Although we would expect increases in the export price to increase export market participation, we have no strong priors on whether changes in the cost of labor should make exporting more or less attractive than servicing the domestic market.

Unobserved Plat Heterogeneity and **Noise:** The final sources of vaniaon in export status are unobserved error components: persistant plant heterogeneity, a;, and transitory noise, w. _As_ shown in Table 3, .336 of the total unobserved variation is due to persistent heterogeneity, and this I A decline in the probability of failure as a plant ages has been found by Roberts (1994) for Colombi **a** nd by Tybout (1994) for Chile. Liu and Tybout (1994) also find tht failing plants in Colombi **a** re systematically less productive than surviving plants. Both patterns have been found in data from the U.S.. (see Evans (1987). Dunne. Roberts. and Samuelson (1989). and Bailey. Hulten and Campbell (1992)).

24