rafmacalaba/datause-encoder-data huggingface dataset:
Cerulli and Paper is a lit review of the current state of Impact Poti(2008) Evaluations in R&D Reviews papers and classifies them in three groups: 1. Use of Structural Models, 2. Use of non-structural models, 3. Exploiting cross-section or longitudinal data.

Ozcelik and Technology Development Foundation of Turkey (TTGV, in Non-parametric matching. Firms are matched on the Taymaz the Turkish acronym). Study the effect of direct subsidies propensity score (the probability to receive R&D (2008) support), which is estimated by a logitmodel manufacturing industry.

- Hussinger Sample selection correction. The selection equation is (2008) R&D investment per employee and new product sales in estimated as a probit model on the probability of German manufacturing receiving public R&D funding. A tobit model is applied to test for robustness if the amount of funding is used as the endogenous variable. Newey’s and Robinson’s estimators are combined with the two different intercept estimators by Heckman to estimate ATET.

Aerts and In Germany public R&D funding relies largely on direct Parametric matching. Schmidt R&D funding; fiscal measures, like R&D tax credits, do not (2008) exist. In Flanders, accelerated depreciation for R&D capital assets and R&D tax allowances are available through the federal Belgian government.

Almeida and Explore asymmetric effects of patents on R&D in Panel data methods (not specified which one) Teixeira accordance to the level of GDP (2007) Simeonova Review of R&D policies that can be potentially evaluated Review of existing methods to conduct surveys and data (2006) in Bulgaria0 collection from firms demanding R&D support. Aralica and Rank Croatia’s achievements in innovation policy against Mostly qualitative evaluation based on European Bacic (2005) EU and Central and Eastern Europe countries (CEEC) Innovation Scoreboard (EIS) 47
> century (1820-70), the considerably smaller increase of 7 percent for East Asia reflects a

> decline of 6 percent in China's already large population partially offset by increases in

> the smaller populations of India, Indonesia, and Japan.

The significance of a region's population gains perspective when its relationship to economic development is considered. Maddison (2001) points to two possible causes of the accelerated population growth in the last millennium: increased fertility and reduced mortality, with the latter, in his opinion, being predominant. While acknowledging that increases in life expectation are not captured in GDP measures, he has found there to be "significant congruence, over time and between regions, in the patterns of improvement in per capita income and life expectation."<sup>12</sup> Improved economic development generally leads to longer life expectation, which in turn leads to increased population size.<sup>13</sup> Frank (1998) is of the opinion that despite the lack of production and income estimates for the period he covers (1400-1800), "it stands to reason that this much faster population growth in Asia can have been possible only if its production also grew faster to support its population growth."'1<sup>4</sup> He cites the literature of other economic historians, including Ho Ping-ti (1959), Gilbert Rozman (1981), Immanuel Wallerstein (1989), and others, as confirmation that "Asia and various of its regional economies were far more productive and competitive and had far and away more weight and influence in the global economy than any or all of the 'West' put together until at least 1800." 15 He goes on to say that this was made possible in part because of Asia's technology and economic institutions. Although hard data for this period is difficult to obtain, he supports his argument by referring to GNP estimates for at least the end of the period (1750-1800) as cited by Braudel (1992), who in turn cites Bairoch (198 1).16 These are indicated in Table 1.3 below.'<sup>7</sup> 4
||**Home**<br>**stimulation**|**Environment**|**Attitudes**|**Teaching**<br>**practices**|**Materials**|**Adherence**|
|---|---|---|---|---|---|---|
|Male|0.035|0.020|0.030|0.033|0.047|0.012|
||(0.047)|(0.056)|(0.053)|(0.058)|(0.060)|(0.061)|
|**Child age (in months)**|0.000|0.004|-0.008|0.016**|0.021***|0.013*|
||(0.003)|(0.006)|(0.007)|(0.006)|(0.006)|(0.006)|
|**Constant**|-0.310|-1.026*|0.274|-1.410*|-1.520**|-0.933|
||(0.270)|(0.516)|(0.543)|(0.543)|(0.497)|(0.553)|
|**N (obs.)**|1308|1302|1308|1302|1302|1302|
|**R-sq.**|0.233|0.181|0.086|0.078|0.090|0.081|
|**P-val. model**|0.000|0.000|0.000|0.000|0.000|0.101|
|**P-val. Moth. ed.**|0.014|0.175|0.556|0.028|0.110|0.282|
|**P-val. Fath. ed**|0.064|0.785|0.633|0.272|0.407|0.417|
|**P-val. Moth. oc.**|0.617|0.108|0.328|0.018|0.072|0.003|
|**P-val. Fath. oc.**|0.005|0.085|0.996|0.085|0.291|0.359|

# _Source:_ Authors’ calculations

_Notes:_ *p<0.05; **p<0.01; ***p<0.001. Standard errors clustered on the school level.

In Figure 7 we specifically explore the relationship between home inputs (home stimulation) and pre-primary inputs, based on a local polynomial (triangle kernel). The correlations between home stimulation and pre-primary inputs are modest, with home stimulation not strongly correlated with quality of pre-primary environments. The strongest correlation (0.17) is with the environment, followed by teacher attitudes (0.13), materials (0.10) and teaching practices (0.08). Adherence to the curriculum is not correlated with stimulation at home (-0.02). Generally, students with more stimulating home environments are experiencing slightly higher quality pre-primary inputs.

21
erature has shown that these non-health costs created by air pollution can be very considerable, with an increase in 1 μg/m<sup>3</sup> leading up to a 0.8 percent reduction in GDP (Dechezleprˆetre et al., 2019). For all these reasons, our loss estimates should be interpreted as lower bounds.

# **5 The future cost of drought-induced excess air pollution**

With an average commissioning year of 2003 and a lifespan of up to 60 years (Mills et al., 2017), the negative externality created by the combustion power plants in our sample could continue for four more decades. In this section, we quantify the possible paths of losses by combining our estimates with climate projections from 22 climate models from the CMIP6 under three Shared Socioeconomic Pathways (SSPs): SSP1-2.6 (low emissions), SSP2-4.5 (intermediate emissions), and SSP3-7.0 (high emissions). The SSPs are a set of scenarios and provide projections on several outcomes, including runoff, demographic changes, and economic growth.

In the first exercise, we quantify whether the hydropower plants are more likely to experience hydrological droughts, and we document the sub-regional heterogeneity in drought exposure. Specifically, for every climate model and SSP, we compute the percentage change in the average FHD between 2020 and 2059 relative to the FHD observed between 2000 and 2019. The result of this exercise for LAC is plotted in Figure 9 panel A. We summarize this information using box plots because we have 66 possible future paths (3 SSP _×_ 22 models). Each box plot corresponds to one of the SSPs, and the variability of the box plot is determined by the distribution of projections given by the 22 climate models. The triangle marker represents the ensemble mean from the 22 models. For the region, most models indicate an increase in the fraction of hydropower generation affected by hydrological droughts. The ensemble mean suggests that the FHD will likely increase between 22 and 24 percent. The ranking in the FHD increase is consistent with higher-emission SPPs leading to a higher drought exposure. Still, the differences across SPPs will remain small by 2060.

23
**Table 2.1: People Ever Migrating in Brazil, by Source and Destination**

||Migrating|FROM:||||||
|---|---|---|---|---|---|---|---|
|<br>**Migrating**|<br>**North**|NE|**Southeast**|**South**|**Center**|**Foreign**|**Total**|
|TO:||||||||
|**North**||||||||
|(1)|685,678|709,162|234,771|169,559|407,640|27,391|2,234,201|
|(2)|2%|2.1%|1%|<br>_0.5%_|1%|<br>0.1%|6.7%|
|(3)|34.9%|6.6%|2.0%|3.5%|12.7%|2.8%|6.7%|
|**Northeast**||||||||
|(1)|488,148|3,026,405|2,656,383|113,007|427,722|35,437|6,747,102|
|(2)|1%|9.0%|8%|0.3%|1%|<br>0.1%|20.1%|
|(3)|24.8%|28.0%|22.8%|2.3%|13.3%|3.7%|20.1%|
|**Southeast**||||||||
|(1)|300,535|5,902,227|5,732,500|1,995,336|1,049,890|590,886|15,571,374|
|(2)|0.9%|17.6%|17.1%|6.0%|3.1%|1.8%|46.4%|
|(3)|15.3%|54.7%|49.2%|40.7%|32.6%|61.4%|46.5%|
|**South**||||||||
|(1)|96,581|194,943|1,580,652|2,062,362|338,730|243,819|4,517,087|
|(2)|0.3%|0.6%|4.7%|6.2%|1.0%|0.7%|13.5%|
|(3)|4.9%|1.8%|13.6%|42.1%|10.5%|25.3%|13.5%|
|**Center**||||||||
|(1)|395,375|957,907|1,450,508|561,689|993,726|65,477|4,424,682|
|(2)|1.2%|2.9%|4.3%|1.7%|3.0%|0.2%|13.2%|
|(3)|20.1%|8.9%|12.4%|11.5%|30.9%|6.8%|13.2%|
|**Total**||||||||
|(1)|1,966,317|10,790,644|11,654,814|4,901,953|3,217,708|963,010|33,494,446|
|(2)|5.9%|32.2%|34.8%|14.6%|9.6%|2.9%|100%|
|(3)|100%|100%|100%|100%|100%|100%||

_Note: (1)_ Total head of households that migrated, (2) percentage share of total migrants, (3) percentage share of migrants from total migrants from a state. The PNAD does not provide information about emigration, as the respondent would have to be present in Brazil. _Source:_ Author's own calculations based on PNAD 1999.

The major inter-regional migration route is from the NE to the SE (NE-SE). About 18 percent of all Brazil's migrants and 55 percent of migrants from the NE have taken this route. The second most important migration route is from the SE to the NE (SE-NE); 8 percent of all migrants and 23 percent of migrants from the SE chose this route. Other important migration routes are: South to SE, SE to South, Center to SE, and SE to Center. The SE region has clearly been the most important sender and receiver of migrants in Brazil. Migration from the North region has been least important in absolute magnitude, but the North is also the least-populated region in Brazil.

In the last decade a slightly different migration pattern emerges (Table 2.2). A total of 11.2 million people in Brazil migrated over the last ten years. The largest share of recent migrants came from the SE (35 percent), followed by the NE (29 percent); this is roughly the same pattern as found for lifetime migration (compare Tables 2.1 and 2.2). The SE is still the main migrant-receiving area. Its positive value was about 0.6 million individuals during 1996-2000, down 7 percent in 10 years (census 2000). NE has grown

**5**
choices would allow policymakers to expand the existing set of policy alternatives in considering the optimal design of a given program. While this is true in principle, in order for the two approaches to complement each other in the context of a specific program, the consistency of the results produced by the two approaches would be an important consideration. The concerns about the validity of assumptions that underlie the _ex ante_ behavioral models make it all the more important that their predictions are checked for consistency with the results of _ex post_ experimental evaluations. Any evidence to suggest that _ex ante_ approaches yield results that are at least broadly consistent with those from methodologically sound experimental evaluations would strengthen the case for using such approaches to complement _ex post_ evaluations of similar programs in future, where the relative ease of employing _ex ante_ approaches can be particularly useful in evaluating alternative design options before a program is implemented.

Our paper represents an attempt to generate evidence on the consistency between the two approaches in the context of CCT programs by comparing _ex ante_ predictions with results of _ex post_ experimental evaluations, following the suggestion of Bourguignon and Ferreira (2003). Specifically, we compare the school enrollment, poverty and inequality impacts of a CCT program generated _ex ante_ , using the behavioral model suggested by BFL, with the corresponding impacts estimated _ex post_ from a randomized experiment involving the same program. The exercise is conducted for a number of CCT programs, of which the results of two exercises are presented in detail.

The comparison presented in this paper is a validation test of the strongest hypothesis of the BFL model: that the cross-sectional income effects estimated _ex ante_ with a representative household sample would coincide with the _ex post_ estimates of income effects generated by the program. The _ex ante_ simulation of the impact of CCTs on enrolment rate and poverty is conducted using the same baseline surveys that are used for the experimental evaluations of _PROGRESA_ in Mexico and _Bono de Desarrollo Humano_ (henceforth BDH) in Ecuador. The full set of _ex ante_ predictions are then compared with the highly credible and accepted _ex post_ results presented in Skoufias and Parker (2001) and Schultz (2000) for _PROGRESA_ and Schady and Araújo (2008) for BDH. The choice of these programs for the exercise is partly to do with the fact that they represent large and well-known CCT programs that contain many of the typical features of such programs. Equally important for our purposes is that for both these programs, the randomization of program implementation allowed researchers to conduct 4
- 24 -

**Figure 4. Intermediation of Corporate Foreign Borrowings**

<!-- Start of picture text --> 14000<br>12000<br>1 0000<br>8000<br>6000<br>E 4000<br>0 2000<br>E 0<br>U) -2000<br>-4000<br>-6000<br>O N ) t to10 (D t- co 0 0 N CO It 0) (D<br>0o 0 0) C ) co 0o 0c 0C co OD 0) 0 0 0 0 0)<br>Other Loans Total Banks<br><!-- End of picture text --> <!-- Start of picture text --> Source: Bank of Thailand.<br><!-- End of picture text -->

- The average numbers also hide changes in the relative importance of the other nonbank capital inflow categories during the 1990s. First, during 1988-92 foreign direct investment accounted for a much larger proportion (42 percent) of nonbank capital inflows (excluding other borrowing) than during 1993-96 (17.5 percent). The Bank of Thailand believes that part of this decline was due to a "significant rebooking of FDI though BIBF" (i.e., the refinancing and new borrowings of overseas affiliates of FDI companies, previously classified as FDI, through BIBF). Second, the relative importance of portfolio flows, especially debt instruments, increased from 14 percent to 44 percent between the two periods.

**_Increased_** _Reliance_ **_on_** _Foreign Capital_ **_and_** _the_ **_Shortening_** _of_ **_the Maturity Structure._** During the 1990s, the Thai economy increased its reliance on foreign capital which is reflected in an increase of the share of foreign debt to total debt from 59.1 percent in 1988 to 94.1 percent at the end of 1997. At the same time, changes in the composition of capital inflows during the 1990s have increased the proportion of shortterm and potentially more volatile inflows in total private capital. Important aspects of this were the large increase in BIBF inflows, the decline in FDI both in absolute and relative terms, and the increase in portfolio flows. Funds intermediated through N/R Baht accounts, believed to be mainly invested in short-term liquid domestic debt instruments, as well as in the stock market, have remained importan **t** hroughout the whole period.

Similarly, these trencds also led to a rapid build-up of private short-term debt. The Bank of Thailand estimates that short-term external debt quadrupled between 1990 and 1995, from US$ 10 billion to US$ 41 billion (Table 8), and doubled as a ratio to GDP to 24 percent. The increasing importance of bank internediation of capital inflows in Thailand and the role played by banks in the short-term debt build-up, in particular since the establishment of the BIBF in 1993, are also evident: commercial bank debt as a share of total private external debt rose sharply from 23 percent to 63 percent between 1990 and 1995, while the US$ 30 billion increase in short-tenn debt is fully explained by bank borrowing. The financial system increased its reliance on foreign funding for their
# **<mark>Table 3. The effect of child care attendance on women’s employment</mark>**

|Dependent variables|Panel<br>|A. Short-term e<br>|ffects<br>|Panel B<br>|. Medium-term<br>|effects<br>|
|---|---|---|---|---|---|---|
||All children|Children<br>age 1-3|Children<br>age 3-5|All children|Children<br>age 1-3|Children<br>age 3-5|
|_Bivariate probit model (ma_|_rginal effects)_||||||
|Working|-0.110|-0.170|-0.128|-0.016|0.037|0.146|
||(0.126)|(0.144)|(0.090)|(0.110)|(0.060)|(0.124)|
|In wage-paying job|0.411***|0.490***|0.408***|0.377***|0.477***|0.333***|
||(0.010)|(0.033)|(0.021)|(0.024)|(0.038)|(0.087)|
|In self-employed|-0.103|-0.240**|0.070|0.043|-0.004|0.089|
|nonfarm work|(0.105)|(0.092)|(0.149)|(0.108)|(0.150)|(0.145)|
|In self-employed farm|-0.454***|-0.563***|-0.440***|-0.419***|-0.384***|-0.297***|
|work|(0.011)|(0.053)|(0.008)|(0.032)|(0.078)|(0.103)|
|In skilled work|0.108|-0.146|0.043|-0.055|0.187|-0.239|
||(0.835)|(1.260)|(0.238)|(0.384)|(0.143)|(0.157)|
|In a formal job|0.257***|0.172|0.264***|0.149|0.382|0.017|
||(0.035)|(0.229)|(0.077)|(0.206)|(0.349)|(0.296)|
|_2SLS_|||||||
|Log of monthly working|0.155|0.378|-0.009|0.293|0.489|0.206|
|hours|(0.209)|(0.358)|(0.255)|(0.312)|(0.470)|(0.463)|
|Log of hourly wage|0.572|0.948|0.141|-0.275|-0.104|-0.421|
||(0.460)|(0.649)|(0.568)|(0.478)|(0.511)|(0.842)|
|Log of wage for the last|0.525|0.951|0.113|-0.078|0.071|-0.286|
|month|(0.410)|(0.586)|(0.521)|(0.523)|(0.580)|(0.895)|
|Log of total wage for the|0.903*|1.165|0.645|-0.068|0.397|-0.527|
|past 12 months|(0.524)|(0.743)|(0.666)|(0.678)|(0.733)|(1.183)|

This table reports the marginal effects from the bivariate probit regression and 2SLS of dummy employment variables on child care attendance and the control variables of women. For the dependent variables of wages and working hours, the regressions are 2SLS. The observations in these regressions are women of children age 1-5. Heteroskedasticity-robust standard errors in parentheses. Standard errors are corrected for sampling weights and cluster correlation at the commune level. *** p<0.01, ** p<0.05, * p<0.1. Source: Estimation from VHLSS 2010, 2012, 2014 and 2016.

35
<!-- Start of picture text --> Cc mI)<br>Neng (1 —T’)<br>eng Qeng<br>i<br>/emech Nmecn(1 — T')<br>1\<br>\'<br>\ ;<br>h*(Mnecw Geng) h (Mechs Gnech) R Neng, % ng) h<br><!-- End of picture text -->
characteristics such as age and size that impact financial constraint, it can cause the omitted variable bias problem. To guard against this problem, we control for several covariates of corruption that may also affect whether the firm is financially constrained. The choice of control variables is motivated by existing literature. In the baseline model, the controls include all country specific features, all sector specific features, and several firm characteristics. We discuss the motivation for the controls before formally defining them.

At the country or macro level, studies have found several factors that determine the availability of credit and therefore financial constraint faced by firms. These factors cover various aspects of the macroeconomic, institutional, and financial environment in the country - see World Bank (2008) for an excellent overview of the related literature. Examples include legal structure and the overall law and order situation (Demirgüç-Kunt & Maksimovic, 1998) enforcement of creditor rights (Bae & Goyal, 2009), financial outreach or number of bank branches (Beck, 2005), banking regulations (Elliot & Willesson, 2018), competition in the banking industry (Petersen & Rajan, 1995), and macroeconomic stability (Boyd, Levine, & Smith, 2001). If these variables happen to vary systematically across countries with high vs. low levels of corruption, our results may suffer from omitted variable bias. The need for finance and its availability may also vary across sectors. For instance, Rajan and Zingales (1998) suggest that there is a technological reason why some industries depend more on external finance than others. They note that this reason may be related to differences across industries in initial project scale, the gestation period, the cash harvest period, and the requirement for continuing investment. Similarly, growth and profitable opportunities and therefore availability of external finance may also vary by industry.

13
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28