rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: Effects of the share of immigrant classmates with low prior exposure to Dutch

||Nati|ves’|Natives’|
|---|---|---|---|
||languag|e score|math score|
||(1)|(2)|(3)<br>(4)|
|**Treatment 6:Share of recent immigrants**|-3.66**<br>|-3.68**<br>|-0.872<br>-0.856<br><br>|
|<br>**with low prior exposure to Dutch**|(1.470)|(1.450)|(1.271)<br>(1.226)|
|**Treatment 7:Share of other immirants**|-1.56|-1.48|-0.608<br>-0.618|
|**g**<br>**with low prior exposure to Dutch**|(1.375)|(1.362)|(1.206)<br>(1.290)|
|Grade-level controls||✓|✓|
|Grade effects|✓|✓|✓<br>✓|
|School-by-year effects|✓|✓|✓<br>✓|
|Number ofgrade-level observations|12,053|12,053|12,053<br>12,053|
|_Notes._ ***: signifcant at the 1% level, **:|signifcan|t at the 5%|level, *: signifcant|
|at the 10% level. Robust standard errors cl<br>parentheses. Each row reports the coeffcie<br>of the corresponding treatment on natives’ av|ustered at <br>nts from s<br>erage test|the school <br>eparate reg<br>scores. Co|level are reported in<br>ressions of the effect<br>ntrols for grade-level|
|characteristics include: the share of students<br>female students in the grade, the share of disa<br>weighting system, the share of students that <br>the grade, teacher’s years of experience, the t<br>square. Controls for linear school trends are a<br>for linear school trends are quantitatively sim|by level o<br>dvantaged<br> repeated a<br>otal numb<br>lso includ<br>ilar and av|f parental e<br>students a<br> grade, the <br>er of studen<br>ed. Estimat<br>ailable upo|ducation, the share of<br>ccording to the Dutch<br> average class size in<br>ts in the grade and its<br>es that do not control<br>n request.|

53
country had two national universities, three regional universities (with 20 university members), 76 independent universities and academies and 119 colleges. Among these, there were two semi-private and 16 private universities, and four semi-private and three private colleges. During academic year 2004 – 2005 the number of universities and colleges had grown to 230 (93 universities, and 137 colleges) (MOET, 2005). Tertiary education gross enrollment was 8.6% in 2001, up from 1.5% in 1992 and a total of 1.3 million graduates have attended technical training institutes<sup>3</sup> .

Over the last few years, the infrastructure and facilities of the university system have been upgraded significantly. Some laboratories have reached the regional standard. The system of electronic libraries and electronic communication networks such as LAN, have been strengthened, and websites have been set up. Expenditure for the R&D activities of universities is about 4% of total investment for national science and technology. Apart from the government budget, which satisfies about 50% of the need for science and technology activities, many universities have been contracted by other organizations and received non-government sources of funding: 29.2% from enterprises; 6.7% from other organizations and 48.8 % from international sources.

# **FIRMS AND UNIVERSITIES: DEMAND VERSUS SUPPLY**

Looking at firms, there is a mixture of various kinds of actors in the Vietnamese economy. A majority of local firms are still _small and medium enterprises_ (SMEs). In an innovation survey (Nguyen Vo Hung & Nguyen Thanh Ha, 2004) covering the mechanics and food processing industries, a majority of firms reported having innovation ideas from their own activities (82.4%) and from 7
A Climate-Fiscal Policy Mix to Achieve T ̈urkiye’s Net-Zero Ambition under Feasibility Constraints<sup>∗</sup> Christian Schoder<sup>†</sup> Remzi Baris Tercioglu<sup>‡</sup> World Bank World Bank

**Keywords:** Climate-fiscal policy, net-zero pathway, low-carbon transition, Bayesian estimation, T ̈urkiye

**JEL Classification:** Q43, Q48

> ∗We would like to thank J ́anos Varga, Charl Jooste, and Florent McIsaac for valuable comments and suggestions.

> †E-Mail: cschoder@worldbank.org

> ‡E-Mail: rtercioglu@worldbank.org
20

# **Table 1: Summary statistics**

||S|alary|Non-p|oor bonus|Equal|bonus|Poor|bonus|Joint F-<br>test|
|---|---|---|---|---|---|---|---|---|---|
|Observations||96||99|7|9|1|05||
|Age (Years)|36.94|(8.22)|36.04|(5.82)|35.66|(5.37)|35.77|(5.51)|0.507|
|Female (=1)|0.63|(0.49)|0.63|(0.49)|0.71|(0.46)|0.56|(0.50)|0.247|
|Income (CFA)|145269.30|(121536.60)|188445.00|(360636.50)|128890.50|(89564.30)|134650.30|(56199.54)|0.161|
|Antipoverty preference (donation to poor<br>school / total donation)|0.65|(0.19)|0.63|(0.22)|0.63|(0.19)|0.66|(0.21)|0.616|
|Number of non-poor cases observed|3.00|(2.33)|4.36|(2.32)|3.15|(2.37)|2.73|(2.19)|0.000|
|Number of poor cases observed|1.91|(1.97)|1.11|(1.82)|1.84|(2.03)|2.66|(2.37)|0.000|
|Proportion of poor cases observed (% of<br>total)|0.42|(0.40)|0.21|(0.33)|0.39|(0.39)|0.49|(0.40)|0.000|
|Accuracy rate (%) in non-poor cases|0.40|(0.21)|0.44|(0.17)|0.41|(0.16)|0.45|(0.20)|0.256|
|Accuracy rate (%) in poor cases|0.45|(0.21)|0.43|(0.24)|0.42|(0.19)|0.43|(0.19)|0.833|
|Score in ability round (Max: 16)|8.07|(1.77)|7.57|(2.04)|7.49|(1.74)|7.48|(1.88)|0.088|
|Time taken in ability round (in seconds)|1275.93|(508.71)|1244.86|(470.69)|1280.39|(601.62)|1293.23|(649.01)|0.940|
|Effort cost ratio, poor/non-poor cases in<br>ability round|0.56|(0.14)|0.57|(0.16)|0.54|(0.12)|0.55|(0.13)|0.658|
|Time cost ratio, poor/non-poor cases in<br>ability round|0.49|(0.07)|0.50|(0.07)|0.50|(0.07)|0.50|(0.06)|0.615|

Note: Standard deviations in parentheses.
_Table 5: Effect of CCT on Incidence of Non-Partner Domestic Violence (NPDV)_

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Incidence of|Incidence of|Incidence of|Incidence of economic|
||physical NPDV|sexual NPDV|emotional NPDV|NPDV|
|RD Effect|-0.011|-0.003|-0.112**|-0.056|
||(0.69)|(0.29)|(0.02)|(0.14)|
|FDR q-values|0.69|0.38|0.09|0.28|
|Observations|564|564|564|564|
|Control mean|0.043|0.000|0.114|0.075|
|P-values in pare|ntheses: * p<0.1 *|* p<0.05 *** p<0.01|||

_Note:_ All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all NPDV regressions.

_Table 5a: Effect of CCT on Incidence of Non-Partner Domestic Violence (NPDV) by relationship to_ _perpetrator_

||(1) <br>NPDV by<br>mother/mother-|(2) <br>NPDV by<br>father/father-in-|(3) <br>NPDV by<br>sibling/sibling-in-|(4)<br>NPDV by other<br>(aunt/uncle/cousin)|
|---|---|---|---|---|
||in-law|law|law||
|RD Effect|-0.018|-0.021|-0.102***|-0.016|
||(0.25)|(0.42)|(0.00)|(0.69)|
|FDR q-values|0.50|0.56|0.01|0.69|
|Observations|518|517|537|527|
|Control mean|0.020|0.020|0.061|0.039|
|P-values in paren|theses: * p<0.1 ** p<|0.05 *** p<0.01|||

_Note:_ All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all NPDV regressions.

_Table 6: Effect of CCT on Incidence of Non-Domestic Violence (NDV)_

||(1)<br>Incidence of|(2)<br>Incidence of|(3)<br>Incidence of|(4)<br>Incidence of|
|---|---|---|---|---|
||physical NDV|sexual NDV|emotional NDV|economic NDV|
|RD Effect|0.000|-0.003|0.015|-0.010|
||(0.92)|(0.32)|(0.65)|(0.45)|
|FDR q-values|0.92|0.86|0.86|0.86|
|Observations|564|564|564|564|
|Control mean|0.011|0.000|0.029|0.007|
|P-values in parent|heses: * p<0.1 ** p<|0.05 *** p<0.01|||

_Note:_ All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all NDV regressions.

16
broad property sector, including housing, and purchase of securities account for nearly 45% of the outstanding stock of commercial bank lending. These are the sectors which are vulnerable to changes in asset prices and have a high concentration of non-performing loans and therefore less productive.

Did the regulatory authorities try to slow down credit growth? The Central Bank did introduce financial restraint in March 1997 in the form of ceilings on risky asset holdings, but these measures may have come too late. The Bank issued guidelines on lending to the broad property sector and for the purchase of shares and unit trust funds. Under these guidelines, loans to the broad property sector were subject to the limit of 20% of a banking institution's total loan outstanding, while loans for the purchase of stocks and shares were subject to a 15% of a banking institution's total loans outstanding. These guidelines were not very successful in slowing down the credit growth. Exemptions were given to several types of loans including construction of residential houses, development of infrastructure projects, public utilities and amenities, and for the purchase and construction of industrial buildings. How one should design a "negative" directed lending is another policy issue? This would need to be examined in the broader context of, for example, the design and role of deposit insurance scheme, greater disclosure of information, and the potential role for applying different statutory reserve requirements on individual banks depending on their risk profile and growth characteristics.

# **VII** CONCLUSION

Malaysia has maintained an exceptional growth record over the last quarter century. There is broad agreement that this was largely driven by the growth in physical capital stock. Although the TFP levels have been respectable, its relative contribution to growth has been modest. Moreover, there are signs emerging that the TFP growth may have slowed down in the late 1990s. Sustaining high output growth rates in the future will require placing increased emphasis on productivity improvements.

One lesson that we can draw from the Malaysian experience is that policies that encourage inflow of FDI and improve access to imported capital goods contribute to productivity growth. However, there are other policies that may have slowed down improvements in productivity growth. We find that rapid growth in bank lending relative to GDP is one such factor. While Malaysia managed to mobilize savings (both domestic and foreign) and the rapid growth in bank lending contributed to capital accumulation, it did not help raise productivity growth. Financial restraints, introduced in the form of negative directed lending, speed limits, and ceilings on lending to risky sectors, were not effective. More detailed industry level studies are needed on how financial systems influence TFP growth.

# **References**

Bank Negara Malaysia, 1998, Annual Report.

11
33

# Footnotes

- 1I See Cornia, Jolly, and Stewart (1987), Taylor (1987).

- 2/ See World Bank (1986), Huang and Nicholas (1987), IMF (1986) and, more recently, Heller et al (1988). Kanbur (1987) is an exception. He develops practical measures to measure poverty at the household level using expenditure survey data.

- 3/ The financial sector is in the tradition of Tobin (1969), Branson (1979). The real sector is in the CGE tradition (Dervis et al, 1982) and income distribution is modelled as in Adelman and Robinson (1976) but in less detail. All markets are assumed to clear in the representative period and there are no lags. The model does not address the short-run dynamics of adjustment as in e.g. Khan and Zahler (1983).

- _41_ Note that all sectors except primary export and informal nonagriculture use capital and that the ownership of capital across socioeconomic groups is the same for all sectors (and does not exhaust non-labor income because of retained earnings). This distributive sssumption is the result of oar desire to calibrate the model so that capital ownership shares are consistent with the savings rates by socioeconomic groups at the bottom of table 3. (By consistent is meant that the distribution of capital across socioeconomic groups would remain constant if the share of savings allocated to capital remained equal to its base year value.)

- 5/ Figures for modern labor in table 3 refer to the case of full employment in the modern labor market. In case of unemployment of modern labor because of fixed wages, rationing falls on modern workers and not on capitalists. In that case, the correaponding shares in table 3 become endogenous.

- 6/ A desirable step in specific country applications would be to combine of the shelf parameter selection with econometric estimates for elasticities deemed crucial in that particular application.

- 7/ For a description of calibration procedures see Dervis et al. (1982) (appendix B).

- a/ The calibration is achieved by iterations involving at each step the recalculation of incomes inclusive of interest earned (paid) based on assumed values for prices, interest rates and expectations for the pre-simulation year. At each iteration, the calibrated parameter values and constants for technology and consumption behavior are maintained, but those for portfolios are recalculated until the desired portfolio holdings (given by the ratios in table 3) are the desired ones for the read in initial values for prices and quantities.
Table 1. Growth, Inflation, and Fiscal Balances in Argentina and Brazil, 1983-97

||(percen|t)|||
|---|---|---|---|---|
|_Country and indicator_|_1983-87_|_1988-90_|_1991-94_|_1995-97_|
|_Argentina_|||||
|Real growth (annual average)|1.77|-3.43|8.89|2.69|
|Inflation (annual average)|370|1,910|53|1.3|
|Overall balance to GDP|||||
|Central|-5.11|-0.87|0.27|-1.32|
|Subnational|-4.90|-1.99|-1.33|-0.88|
|Primary balance to GDP|||||
|Central|-3.44|-0.07|1.40|0.28|
|Subnational|4.84|-1.76|-1.16|-0.56|
|_Brazil_|||||
|Real growth (annual average)|4.27|1.61|2.73|3.52|
|Inflation (annual average)|186|1,670|1,350|30|
|Operational balance to GDP*|||||
|Central|||0.28|-1.69|
|Subnational|||-0.05|-2.16|
|Public enterprises|||-0.20|-0.46|
|Primary balance to GDP|||||
|Central|||1.68|0.24|
|Subnational|||0.75|_-0.51_|
|Public enterprises|||0.73|0.02|

* Does not include the inflation component of nominal interest.

_Source:_ Argentine and Brazilian Ministries of Finance, International Monetary Fund _GFS_ and staff estimates, and authors' estimates.

In Argentina in the 1980s, provinces accounted for at least half of the public sector deficits that were fueling hyperinflation. By the end of the decade, hyperinflation had greatly eroded the real value of domestic debt, giving govemments at all levels almost a clean slate on that side, although external debts remained substantial. Figure 1 shows the path of subnational debts following the stabilization in 1991. 2 Since then, the provincial debt has groWn little in pesos (or dollars) and has declined as a share of gross domestic product (GDP) and of provincial revenues. A large share of the debt is owed to the private sector and is being serviced. The result of reforms made at the time of stabilization, this effort has helped to

**Figure 1. Provincial Debt Stocks**

<!-- Start of picture text --> a-<br>U 15.00 _<br>"a 10.00 Argentina<br>= 5.00 - Brazil<br>0.00<br><!-- End of picture text --> motivate provinces to behave in a fiscally responsible manner.

_Source:_ World Bank and International Monetary Fund staff estimates. Argentina stock is calculated from 1996 base. Brazil stock is net domestic debt only.

2
of the study imply that green investments increase TFP at the aggregated level. Using a dataset of 5,498 manufacturing firms in Italy from 2000 to 2008, Leoncini et al. (2019) show the positive effect of green technologies on firm’s productivity growth is greater than that of non-green technologies.

There exists no consensus, however, on the relationship between the investment of green/clean technologies and productivity. Using the firm-level data from the Czech Republic, Horvathova (2012) finds a significant upfront innovation expenditure that led to firms’ lower productivity. However, this negative effect also tends to fade over time and transforms into a positive effect in the long run. Using cross-country sector-level panel data for 17 European countries between 1997 and 2009, Rubashkina et al. (2015) find no evidence of a positive relationship between pollution abatement and control expenditures, as a proxy for clean/green technology investment, and productivity. Analyzing the causal relationship between the growth of TFP and that of fossil and renewable energy consumption in the power sector in BRICS countries<sup>7</sup> during the 1992-2012 period, Tugcu and Tiwari (2016) find no remarkable causal link between renewable energy consumption and TFP growth. Instead, they find a positive relationship between non-renewable energy consumption and TFP in Brazil and South Africa.<sup>8</sup> Using firm-level data of selected firms collected through a survey in Austria, Germany, and Switzerland, Stucki (2019) finds significantly positive productivity effects of an investment in green energy technologies in firms with high energy costs but not in firms with medium energy costs. It finds significantly negative effects for firms with low energy costs.

# **3.2 Factors affecting the relationships between clean investments and productivity**

Several factors influence whether investments in green/clean technologies increase a firm’s productivity. These factors include the size of firms, their ownership, the presence of environmental regulation, and activities for investments (development vs adoption of technologies). Below, we briefly highlight these factors.

As discussed above, there are mixed findings on the relationships between green/clean investments and productivity. Firm size is one of the key factors that influence whether green/clean

> 7 BRICS refers to the group of countries including Brazil, Russia, India, China and South Africa.

> 8 Note, however, that expansion of renewable energy is happening more recently. Therefore, this finding is not

> surprising as the data used in this study were for the period when there was little expansion of renewable energy.

12
at time t. Crisis de fi nition 1 is our benchmark de fi nition; it sets the beginning of a crisis when 9-year spreads are greater than a threshold, which is de fi ned as the average spread over the previous six months plus 300 basis points.<sup>29</sup> The end of the crisis is at the end of the fi rst four-week period in which spreads have remained below the threshold used to determine the beginning of the crisis. Crisis de fi nition 2 is similar to crisis de fi nition 1, but uses 400 basis points to de fi ne the threshold. Crisis de fi nitions 3 and 4 are similar, but use a one-week period instead of a four-week period to end the crisis.

Table 3 lists the crisis periods obtained with crisis de fi nition 1. All major crises are captured by the crisis de fi nition. For example, the Mexican 94-95 crisis affected Argentina and Brazil. (Note that our sample does not contain spreads for Mexico during that period.) The Russian crisis affected all countries in the sample except Uruguay, which had its own crisis after Argentina defaulted on its debt in early 2002. The Argentine crisis started when the government was forced to change its economic plan and the default became very likely. Brazil and Colombia were also hit by crises in 2002.

Then, we calculate excess returns at different maturities, splitting the sample between tranquil times and crises. The results are reported in Table 4 for all crisis de fi nitions. We report results using bonds with coupons of size 7.5 percent, although the results are similar for bonds with coupons of 5 and 10 percent. The results indicate that both excess returns and the term premium are very large during crises, and close to zero during tranquil times. For example, according to de fi nition 1, the difference between the average 12- and 3-year annualized excess returns (our measure of the term premium) is around 28 percent. Table 5 displays excess returns by country according to crisis de fi nition 1. The table shows that for most countries the risk premium and the term premium are large during crises and low during non-crisis periods. There are two exceptions, excess returns are negative during crises in Uruguay and, for short maturities, in Argentina. This is due to the fact that both countries have experienced actual defaults, namely, crises in which the situation continued to deteriorate rather than improve. In the case of Argentina, this is partly compensated by the Mexican crisis, when excess returns were very high.

In Table 6, we analyze more formally whether excess returns across maturities are predictable,

> 29 To classify the fi rst observations of the sample for each country, we repeat the fi rst price observed during the previous six months.

21
The variable for public expenditure on land registration is strongly significant in both cases and carries the expected sign. The land titling variable is significant with four and eight period lags, respectively; it is also significant when it is not lagged. However, it carries an unexpected negative sign, when it has no lag, but a positive sign as expected when lagged by four and eight periods, respectively. The unexpected negative sign is consistent with the results from the ECM model for the impact of land titling with one and two lags. Given that the land titling variable seems to carry negative signs at low order lags but positive signs at higher order lags, it would be useful to trace the dynamic path that is followed through this adjustment. We do so by undertaking regression analysis using the standard economic growth equation plus the two variables for land registration (land titling and public expenditure on land registration) in the same way as we have done to generate the results in columns 2 and 3 of Table 4. But this time, we undertake a separate regression analysis for each lag of the land titling variable, from zero to thirteen (13) lags. The results are summarized in Table 5.

**Table 5: Response of Economic Growth** (AlogY) **to Lags in Land Titles (logLRT)**

|Lags|LRT<br>Coefficient|T-ratio|Level of<br>Significance||
|---|---|---|---|---|
|0|-0.029114|-1.8170|******|0.49|
|1|0.055068|0.46100||0.50|
|2|0.10132|1.3117||0.53|
|3|0.11104|1.6190||_0.55_|
|4|0.13156|1.8818|******|0.59|
|_5_|0.13917|1.9896|******|0.57|
|6|1 0.15015|2.1903|**|0.59|
|7|0.13791|2.4970|******|0.65|
|8|0.15694|3.2849|***|0.70|
|9|0.17477|3.8323||0.74|
|10|0.19602|4.2924||0.77|
|11|0.15642|2.6376||0.75|
|12|0.16167|1.7874|******|0.65|
|13|0.13741|1.4382||0.58|

* * and *** * *** indicate significance at levels of 5 and 1 percent, respectively. Results obtained using Microfit 4.0 by Pesaran and Pesaran (1997).

With a zero lag, the land titling variable is statistically significant and carries a negative sign. But with one and more lags, the land titling variable assumes a positive sign although the variable is not statistically significant for one, two and three period lags. For higher lags ranging from 4 to 12, it becomes statistically significant, with lags 8 to 11 being strongly significant. Then the variable becomes statistically insignificant for lags higher than 12. This analysis of the response of economic growth to land titling clearly follows a J curve, first starting negative but slowly building, over 3 years, to a positive and strong response whose impact lasts for nine (9) years.

21