rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Principals heterogeneity on the impact of the school shutdowns on students’ learning, fifth-grade (2007-2009)

|**Efects of school shutdow**|**ns on the **|**locally-man**|**aged network**||||
|---|---|---|---|---|---|---|
||Math|||Portugues|e||
||DiD|DiD|DiD|DiD|DiD|DiD|
||(1)<br>|(2)|(3)|(1)|(2)|(3)|
|H1N1|-2.75**|-3.62|-2.69|-0.54|-0.34|-0.13|
||(1.23)|(2.04)|(1.62)|(0.95)|(1.77)|(1.38)|
|H1N1 versus||1.56|||0.01||
|principal managerial skills||(2.15)|||(2.30)||
|H1N1 versus|||0.1|||-0.33|
|program to reduce dropout|||(0.66)|||(0.58)|
|N. schools|3912|3838|3857|3912|3838|3857|
|Adj. R2|0.8|0.8|0.8|0.8|0.8|0.8|
|**Efects of school shutdow**|**ns on the **|**state-mana**|**ged network**||||
||Math|||Portugues|e||
||TD|TD|TD|TD|TD|TD|
||(1)|(2)|(3)|(1)|(2)|(3)|
|H1N1|-4.25***|-8.25***|-4.12***|-2.73***|-5.71***|-1.83*|
||(1.06)|(2.10)|(1.28)|(0.90)|(1.69)|(1.11)|
|H1N1 versus||5.34**|||4.12**||
|principal managerial skills||(2.47)|||(2.01)||
|H1N1 versus|||-0.04|||-0.75|
|program to reduce dropout|||(0.61)|||(0.53)|
|N. schools|5329|5227|5239|5329|5227|5239|
|Adj. R2|0.7|0.7|0.7|0.6|0.6|0.6|

_Notes_ : The authors’ estimate is based on data from _Prova Brasil_ , Census of Education, and IBGE. Wild bootstrap p-values. 95% CI in brackets. ***, **, and * indicate significance at the 1, 5, and 10% critical levels. Standard error in parenthesis and clustered at the municipality level. All regressions are weighted by fifth-grade enrollment at the school level. Math performance on a scale from 0 to 350 (SAEB scale). reading performance on a scale from 0 to 325 (SAEB scale). Columns DiD show the estimates of differences-in-differences as described in subsection 4.0.1 and Columns Triple D show the estimates for triple difference-in-differences as described in subsection 4.0.2. The sample of municipalities and schools included in the analysis are detailed in Table 2.

23
**Figure 5**

**Convergence between EM and G7: Alphas and Per capita GDP** This figure presents alphas from estimations in column 4 in Table 3.1 for individual countries and their relationship with initial (1993) GDP per capita PPP. Source: IMF IFS Statistics, IMF WEO

<!-- Start of picture text --> 8%<br>7% IN y = -0.015ln(x) + 0.1554<br>R! = 0.46406<br>6%<br>PH<br>5%<br>PE<br>4% ID<br>SG<br>3% PL<br>AR KR<br>2% TH ZA IL<br>1% TR RU TW HK<br>0%<br>MX<br>-1%<br>-2%<br>0 5000 10000 15000 20000 25000<br>Initial (1993) GDP Per Capita PPP<br>Alpha<br><!-- End of picture text -->
to income growth is responsible for increased equity in public expenditure incidence during the period; though both factors were undoubtedly instrumental. What is clear is that much of the distributional gain was captured by the urban poor. The Malaysia case study attempts to understand causality by supplementing the incidence analysis with regression analysis. Malaysia's policy of ethnic targeting is credited with the success in the education sector, while propoor improvements in the health sector are attributed to the private sector's increasing ability to attract wealthier households. The latter also appears to have been a factor in propoor changes in urban Indonesia.

# 3.2.2 Lessons from behavioral approaches

There is less to say here as the literature concerned with allowing for behavioral responses remains at an early stage and has yet to develop proven patterns in its empirical results. Heavy data requirements (such as on prices) and methodological pitfalls (simultaneity, endogeneity and self selection biases) have made progress slow. The research, which has focused on estimating the effects of price changes on behavior and welfare, has for the most part found that in the aggregate the demand for public services tends to be price inelastic. More importantly from an equity viewpoint however, the studies also find that elasticities vary across income groups and that the poor are invariably more price responsive (Gertler and van der Gaag 1990).

Attempts to measure the impact of public spending investments on outcomes (such as those by Pitt and others, Deolalikar, and Alderman and others in PSP), find that this can be a noisy relationship and one that is arduous to isolate. Deolalikar's results indicate that marginal changes in Indonesia's provincial health spending have a positive effect on children's utilization of facilities and on their health outcomes though both results apply to a greater degree for children of well-off households. This leads him to conclude that Indonesia's public health care services are poorly targeted. Alderman and others establish that girls and children in a poor region are relatively 26
Economic Growth in Ghana: Determinants and Prospects<sup>_∗_</sup> Anna K. Raggl<sup>_†_</sup>

**Keywords:** Economic growth, natural resources, oil production, panel growth models, Ghana, Sub-Saharan Africa.

**JEL Classification Codes:** O11, O13, O55, Q43

**Sector Board Code:** EPOL

> _∗_ This report serves as a background study for the World Bank’s _Policy Note on Long Run Growth in Ghana (2014)_

> _†_ WiC - Wittgenstein Centre for Demography and Global Human Capital, WU - Vienna University of Economics and Business. Address: Welthandelsplatz 1, 1020 Vienna, Austria. Email: anna.raggl@wu.ac.at. The author gratefully acknowledges valuable contributions from Jes ́us Crespo Cuaresma, Leonardo Garrido, Santiago Herrera and Mathias Moser.
held assets worth more than three-quarters of a billion dollars, only two did not offer pensions at this time.<sup>1</sup> 3 And of the 200 corporations with the most financial assets, eighty-seven had formal pensions.

Nearly all of these plans were noncontributory-they started as pay-as-you-go plans, which gave full credit for past service to all current workers regardless of age. None of the large companies required their workers to contribute to their pension. Of all workers covered, 95 percent wvere covered by noncontributory plans.

Most pension plans included all employees. Only fifty-six plans out of almost 400 excluded some workers, and half of these excluded only officers, "higher employees," or office workers. There appears to have been no exclusions in the larger firms. There were approximately 60,000 pensioners in 1927, more than half of whiom were former railroaders. The average pension was $605, although penisions varied widely by industry.'<sup>4</sup> Salary data for manufacturing show that the average replacement ratio (pension/worker income) was 35 percent in that industry.

_Funtidinig Reserves and Noncontributory Pensions_ W\lile many firms were beginning to confront the issue of funding by the time of the Depression, there was still much progress to be made.'5 Insurance companies and actuarial organizations were attempting to educate management, but many firms probably underestimated the costs because they underestimated . General Motors and Standard Gas and Electric were the two exceptions-General Motors because it was too new and Standard Gas and Electric because it offered an informal plan.

_1_ More precisely, 21.2 percent of the average pensions by industry were in the $400 to $499 range; 33.8 percent, $500 to $599; and 28.9 percent, $600 to $699. (Williamson 1992 p. 54).

'. Thirty percent of covered employees worked for companies that had set up a separate trust fund or contracted with an insurance company. The rest of the plans either carried reserves on their balance shieets (19 percent) or paid pensions out of current operating expenses (59 percent, mostly the railroads). (Williamson 1992, p.55).

10
# **3.2 Differential effects of EU accession on German acquisitions across sectors**

We proceed by providing a formal test for whether Polish firms in sectors in which Germany was more specialized were more likely to be acquired by a German shareholder. To do so, we adopt an event study approach, which does not, a priori, impose a treatment period and allows for the effects to vary over time: where _Gijt_ is a binary variable indicating whether firm _i_ in sector _j_ was acquired by a German shareholder at time _t_ , and the variable _Sj_ is a measure of Germany’s specialization in sector _j_ measured in the pre-accession period. The specification includes firm, _μ_<sup>_G_</sup> _i_<sup>,andyear,</sup><sup>_η_</sup> _t_<sup>_G_,fixedeffects.Weincludeonlyfirmsthatwere</sup> established prior to 2004, i.e. we ignore the entry of new German-owned firms. In this specification, _θt_ are time-specific coefficients that capture the effect of German specialization over time.

# **3.3 Differential effects of EU accession on firm performance across sectors**

We then investigate whether Polish firms in sectors where Germany was more specialized improved their performance using the following specification: where _Yijt_ is a measure of performance of firm _i_ in sector _j_ in year _t_ ; _Postt_ is a dummy variable that takes value 1 for the post-accession period (i.e. from 2005 to 2014). Finally, _Xjt_ indicates controls at the sector level. In particular, we control for both output and input tariffs to capture possible changes in Poland’s wider trade relationships. These tariffs would correspond to Poland’s external tariffs before accession, which were already similar to EU tariffs, and to the EU external tariffs after accession. In addition, we also control for EU external tariff for the entire period. The main coefficient of interest is _β_ , which corresponds to the interaction between the post-accession dummy and the pre-determined sector-level measure of Germany’s comparative advantage. Equation (4) can be viewed as a difference-in-differences specification, in which all firms are considered treated after EU accession, but with a different intensity of treatment given by their industry’s exposure to Germany’s capabilities. We consider different measures of firm performance: a revenue-based measure of total factor productivity (TFPR), total 7
OZIER - REPLICATION REDUX – p. 14 of 41 “contamination” in the design of a clinical trial, brought about by what could be termed an “indirect effect,” “spillover,” or “externality.” The upshot is that in an individually‐randomized deworming trial, the “treatment” and “control” groups may not be what the researcher meant for them to be, so any differences that deworming medication could bring about – in health, in schooling, or in anything else – could be substantially muted when simply comparing these two groups.

Starting in 1998, for a period of several years, Miguel and Kremer (henceforth, MK) did things differently. Rather than randomizing at the individual level, they assigned treatment at the level of the school: if the school was assigned to be “treated,” everyone who came to school on deworming day received medication. If the school was assigned to be “control,” nobody received such medication (at least not until a few years later). This has advantages and drawbacks. A great advantage, in light of the reinfection dynamics described above, is that within the immediate vicinity of the school, the reinfection spillovers do not dampen the intensity of effects in the study. In treatment areas, children who receive deworming medication gain both the direct benefit of treatment and the indirect benefit of living around others who are less likely to spread worm eggs into the environment. In control areas, neither the direct nor indirect effect is present – at least, if the schools are far enough away from one another. A drawback of this “cluster‐randomized” design is that the statistics must eventually be adjusted for the inherent correlations in outcomes among children living in the same neighborhood and attending the same school, so the study must be quite large in order to precisely measure effects; MK’s study thus involved tens of thousands of pupils.

MK found that deworming reduced worm infections (as one would hope), improved self‐ reported health status, and improved school attendance: in the simplest analysis, the likelihood of attending school on a given day was increased by 5.1 percentage points, against a background of a 20 to
The second approach to dealing with consumption data which differ in their level of comprehensiveness is to predict unknown expenditure components. For example, suppose we have two time periods, _F_ and T, with food and food plus non-food expenditure data, respectively. A food share equation can be estimated using the data in period T. The estimated relationship between food and total expenditure can then be used to calculate a _predicted_ value of total expenditure for each household in period _F_ based on that household's food expenditure in time _F._ Finally a poverty measure based on predicted total expenditure for time period _F_ can be compared to one based on actual total expenditure in T. Using this approach, one can base comparisons on a common, price-adjusted, final poverty line, as is common practice, without recourse to food poverty lines (on price adjustments in this context see Datt, 1995, and Ravallion and Subramanian, 1996). The procedure would be more relevant when making a comparison of poverty between two time periods within a country, where the assumption of a stable set of demand functions is most tenable, rather than across countries.

Because the comprehensiveness of the data being compared across time periods or countries is, in principle, identical using this procedure, all poverty indicators may be used. However, in practice, caution is warranted. Households may deviate from the expenditure predicted for them based on the assumption that Engel's Law holds for the population as a whole. For example, a given household may purchase relatively little food given its income level. To the extent that this is true, welfare measures based on a small subset of expenditure components will be a poor proxy for true household welfare. In addition, all components of surveyed expenditure data are subject to measurement error. The quality of the welfare measure is likely to be particularly poor when the data are sparse. (These issues are discussed in detail in the following section.) Since FGT measures are more sensitive than the headcount to errors, the use of these indicators should be restricted to situations where all of the consumption data being compared are reasonably comprehensive. " '° What may appear to be a straightforward solution to the problem of inconsistent consumption data is to discard some of the available information and base comparisons only on those components of consumption which are common to all countries or time periods. While simple, there are, however, a number of drawbacks to this approach. First, discarding data is likely to worsen the measure of household welfare. Second, if one is interested in a cardinal magnitude for the poverty rate, rather than ordinal comparisons, then the appropriate basis is total expenditure. As shown in Section 11, FGT (a2 1) measures of poverty, for example, would have a lower values when food expenditure alone is used in their calculation. Finally, this approach may be impossible to implement, either because detail about sub-categories of expenditure are unavailable or because there are no common elements in the sub-categories used in the different datasets.

16
Since there is no savings in the model, the current account deficit is equal to the inflow of mobile capital. Expressing this inflow as a fraction of GDP, we obtain: where Pk is the price of a unit of mobile capital.<sup>8</sup> These definitions provide the linkage between model and data.

# **2. The International Transmission of Disturbances**

The theory developed above can be used to illustrate how domestic and foreign shocks influence growth rates and the behavior of the current account. In this section, we derive the stochastic processes for the GDP growth rate and the current account for each country and discuss their properties. We start however by looking at the world economy as a whole.

## **World Aggregates**

> **8** Given the nature of preferences and the assumptions of free trade and perfect competition, the price of a unit of mobile capital must equal the expected net present value of its marginal product, which is simply pp RA p

1 0
# **_Including China_**

The baseline results exclude China from the regressions, since Chinese trade data are reported jointly for January and February 2020. In addition, China does not report information feeding the Google mobility data. As a robustness check we combine trade flows data on January-February for our 28 countries and measure Covid-19 related shocks with the industrial production variable to be able to include China as exporter in our regressions. That is, monthly growth rates that were previously computed for February only are now computed for January and February combined. Work mobility changes are replaced with annualized IPI growth in the exporting countries and retail mobility with annualized IPI growth in the partner countries. The results are reported in Table 6.

**Table 6: Supply, demand and third-country shocks and bilateral export growth, the role of sector characteristics,**

**including China, OLS regression results**

|FRAMEWORK|VARIABLES|(1)<br>**_growth_**_ijkt_|(2)<br>**_growth_**_ijkt_|(3)<br>**_growth_**_ijkt_|(4)<br>**_growth_**_ijkt_|
|---|---|---|---|---|---|
|**Exporter supply**|**_ipiit*remoteik_**|**1.309*****|||**0.772***|
|**sector**||(0.440)|||(0.469)|
||**_ipiit*gvcil_**|**2.104*****|||**2.571*****|
|||(0.616)|||(0.638)|
|**Partner demand**|**_ipijt*durablek_**||**0.475*****||**0.382****|
|**sector**|||(0.161)||(0.167)|
||**_ipijt*gvc partnerjl_**||**-0.197**||**-0.239***|
||||(0.122)||(0.127)|
|**Third country**|**_competition shockijkt_**|||**-0.549*****|-0.244|
|**supply shock**||||(0.141)|(0.158)|
||**_upstream shockilt_**|||**1.804****|**2.400*****|
|||||(0.727)|(0.789)|
||**_global outputmt_**|**0.851*****|**0.710*****|**0.754*****|**0.611*****|
|||(0.143)|(0.148)|(0.143)|(0.153)|
||**Constant**|**0.840*****|**0.657*****|**0.813*****|**0.971*****|
|||(0.030)|(0.018)|(0.069)|(0.085)|
||Observations|561,693|510,641|588,245|487,264|
||R-squared|0.433|0.433|0.433|0.433|
||Exporter-Time FE|Yes|Yes|Yes|Yes|
||Importer-Time FE|Yes|Yes|Yes|Yes|
||Exporter-Partner-Sector|<br>Yes|Yes|Yes|Yes|
||Sector-Time FE|Yes|Yes|Yes|Yes|
||Cluster|Exporter-Partner|Exporter-Partner|Exporter-Partner|Exporter-Partner|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Note: Sector-time fixed effects control for unobserved effects affecting aggregated sectors over time (see footnote 10). The variable _global outputmt_ additionally controls for global sector-time trends.

Interestingly, while the previous findings hold in terms of significance and direction, several differences stand out when comparing the results with results for the original country sample excluding China (Table 24
<!-- Start of picture text --> 3<br>2.16 2.19<br>2<br>1.74<br>1.49 1.55<br>1.42<br>2<br>1.03<br>0.90<br>1<br>0.62<br>0.37<br>1<br>0<br>-1 -0.29 -0.26<br>-0.40<br>-0.53<br>-1 -0.78-0.81<br>2030 2040 2030 2040<br>Net Zero Budget Neut.<br>Poor Middle Rich All<br>% change from BaU<br><!-- End of picture text -->

**Figure 11: Welfare impacts**

# 4 From sectoral to macro: A macrostructural approach

This section places the analysis at the aggregate level, focusing on the fiscal impact and including monetary dynamics. To do so, we complement the work with the CGE by focusing on these effects using the World Bank's macrostructural model MFMod, which has a more granular representation of monetary and fiscal issues.<sup>14</sup> The model assumes that households smooth their consumption paths in an optimal manner and that private investors use cost and return signals to allocate investment amounts. Monetary policy responds to deviations in inflation expectations from target inflation. The set of fiscal expenditure options include transfers to households, government compensation of employees, net acquisition of non-financial assets (investment) and use of goods and services. The model is an open economy characterization of Türkiye’s economy.

## 4.1 Mechanics of hybridization and assumptions

To support Türkiye's growth and development objectives over the next two decades, the RNZP scenario’s cumulative additional investments are of about USD 313 billion, while cumulative saving fuel expenditures amount to about US$182 billion (until 2040).

Based on the capital structure used in an application of MFMod to evaluate the macroeconomic risks from floods and earthquakes in Türkiye (Hallegatte, Jooste, & McIsaac, 2022), the model includes exogenously the additional investment needs from the sectoral scenarios, for the same four sectors (power, transport, building, and forestry).

For infrastructure capital (including buildings), we assume that 50 percent is funded by the government. The remaining 50 percent will be taken up by the private sector. This increase in

> 14 See (Burns, Campagne, Jooste, Stephan, & Bui, 2019) and (Hallegatte, Jooste, & McIsaac, 2022).

20
# **Table A2.2: Second Stage Regression Results (Table 4)**

||Depen|dent Variable|
|---|---|---|
||(1)|(2)|
||Ln Height<br>in 2004|Years of schooling<br>in 2004|
|Ln height at baseline|0.164||
||(0.217)||
|Years of school at baseline||1.130|
|||(0.933)|
|Mother deceased before child is 15 years old|-0.011*|-1.134**|
||(0.006)|(0.451)|
|Father deceased before child is 15 years old|-0.000|-0.339|
||(0.004)|(0.317)|
|6 years old||4.544|
|||(4.300)|
|7 years old|-0.013|4.720|
||(0.009)|(4.347)|
|8 years old|-0.019|4.501|
||(0.019)|(4.132)|
|9 years old|-0.024|4.211|
||(0.028)|(4.032)|
|10 years old|-0.023|4.217|
||(0.032)|(3.788)|
|11 years old|-0.033|2.571|
||(0.039)|(3.255)|
|12 years old|-0.032|1.766|
||(0.049)|(2.340)|
|13 years old|-0.032|1.487|
||(0.055)|(1.680)|
|14 years old|-0.047|0.379|
||(0.060)|(0.830)|
|15 years old|-0.045||
||(0.068)||
|Male|0.057***|0.566**|
||(0.003)|(0.223)|
|Household head years education|-0.000|0.149**|
||(0.001)|(0.059)|
|Household head male|-0.000|-0.257|
||(0.006)|(0.486)|
|Household head age|-0.000|0.009|
||(0.000)|(0.011)|

26