rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 7: Distribution of the ratio of self-assessed forgone days to PW days and ratio of**

# **forgone wages to PW wages**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>ratio of foregone days to PW days ratio of foregone wages to PW wages<br>round1 round2<br>1.5 2<br>1.5<br>1<br>1<br>density density<br>.5<br>.5<br>0 0<br><!-- End of picture text --> _Note_ : These distributions are estimated for all public works at the household level. _Source:_ Authors’ estimates based on BREGS Survey.

31
8 world trade totals. The crisis depressed import demand in Europe, thus contributing to depress global trade. From this point of view, if European economies recover, world trade growth should pick up again.

Alongside these cyclical factors, however, there may be other (structural) components of the global trade slowdown. Figure 4 shows that, while after the crisis most countries/regions have a stable import to GDP ratio, this “flatness” appears to pre-date the crisis for China and the US where import volumes as a share in real GDP have been roughly constant for nearly a decade. This fact may point to a structural change in the relationship between trade and income, at least for the two largest economies.

Visual inspection of the data of world trade (import volume) and world income (real GDP) indicates that the relationship between trade and income changed in the last decades (Figure 5). Specifically, the period 1986-2000, which we call the “long 1990s”, were different from the preceding and the subsequent period.<sup>4</sup> For the entire sample (1970-2013), trade and income experienced an expansion and trade grew more rapidly than income. However, the trade-income ratio remained stable over much of the period 1970-1985, then increased rapidly over the long 1990s, but has not grown much since 2001. This behavior creates a presumption of greater trade responsiveness over the period 1986-2000.

In the rest of the paper, we first test if this observation is supported by a formal statistical analysis. Then, we analyze the extent to which the changing trade-income relationship can explain the current global trade slowdown and the implications for its future prospects. Finally, we investigate the underlying reasons for any changes in the long-run trade-income

> 4 This terminology borrows from Eric Hobsbawm’s characterization of the “long 19th century” as the period between the years 1789 and 1914.
**Appendix 11: Multivariate recursive Probit estimation of effects of non-farm employment participation on activities on inputs purchases for farm households only.**

|||Fertilizerpurchase|||Inputspurchase||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)||||
|VARIABLES|Wage<br>employment|<br>Self-<br>employment|Fertilizer<br>purchased|Wage<br>employment|Self-<br>employment|Inputs<br>purchased|
|Wage employment participation|.||0.437***<br>[0.002]|.||0.271*<br>[0.053]|
|Self-employment participation||.|0.339***<br>[0.007]||.|0.237*<br>[0.051]|
|Age of the household head|0.002|0.002|-0.013***|0.002|0.001|-0.002|
||[0.722]|[0.743]|[0.008]|[0.761]|[0.775]|[0.679]|
|Male-headed household (0/1)|0.445***|0.031|-0.062|0.447***|0.026|-0.107|
||[0.003]|[0.795]|[0.585]|[0.003]|[0.826]|[0.315]|
|Highest level of formal education acquired by|||||||
|household head|||||||
|PSLC|0.199|0.075|-0.039|0.208|0.077|0.053|
||[0.274]|[0.629]|[0.793]|[0.255]|[0.623]|[0.717]|
|JCE|0.700***|0.208|0.189|0.698***|0.211|-0.085|
||[0.002]|[0.305]|[0.333]|[0.002]|[0.297]|[0.666]|
|MSCE|1.172***|0.272|0.077|1.169***|0.277|-0.140|
||[0.000]|[0.334]|[0.782]|[0.000]|[0.324]|[0.624]|
|Maximum level of formal education acquired in the<br>household|||||||
|PSLC|0.127|0.044|0.038|0.122|0.041|-0.016|
||[0.427]|[0.733]|[0.760]|[0.446]|[0.749]|[0.897]|
|JCE|0.064|-0.219|-0.133|0.068|-0.219|0.168|
||[0.736]|[0.188]|[0.393]|[0.724]|[0.187]|[0.275]|
|MSCE|0.335|-0.365+|-0.074|0.343|-0.361+|0.275|
||[0.175]|[0.115]|[0.737]|[0.166]|[0.119]|[0.213]|
|Number of infant (<5yo) in HH|0.030|0.087*|-0.083*|0.031|0.089*|-0.004|
||[0.607]|[0.067]|[0.070]|[0.590]|[0.063]|[0.932]|
|Number of children (5-14yo) in the household|0.094**|0.001|0.012|0.094**|0.002|0.011|
||[0.018]|[0.975]|[0.699]|[0.018]|[0.953]|[0.719]|

61
<!-- Start of picture text --> II.8 European banks are participants in most of the II.9 ...The share of dollar-denominated deals with<br>EMDE syndicated loans, but their involvement has European involvement fell significantly, during times<br>declined during financial stress periods with other their dollar funding conditions had deteriorated<br>banks stepping in... markedly<br>New Syndicated Loan Volumes to EMDEs European Market Share of Synd. Loans to EMDEs<br>$ millions (2006Q1-2012Q3) % of total volume (2000Q1-2012Q3)<br>80,000 110%<br>70,000<br>100%<br>60,000<br>90%<br>50,000<br>40,000 80%<br>30,000<br>70%<br>20,000<br>60%<br>10,000<br>0 50%<br>Non-EU lenders EU lenders Dollar denominated Other currencies<br>Source: Dealogic Source: Dealogic<br>II.10 Trade finance deals to EMDEs with European II.11 Average maturities of deals with European<br>bank participation have fallen significantly since participation have been lower than those of deals<br>2010... without any European banks and fell in 2010-2011<br>but have recovered<br>New Trade Finance Volumes to EMDEs Avg. Maturity of New Syndicated Loans to EMDEs<br>$ millions (2006Q1-2012Q3) Days<br>18,000 4,000<br>16,000<br>3,500<br>14,000<br>12,000 3,000<br>10,000<br>2,500<br>8,000<br>6,000 2,000<br>4,000<br>1,500<br>2,000<br>0 1,000<br>Non-EU lenders EU lenders Non-EU lenders EU lenders<br> Source: Dealogic Source: Dealogic<br>2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3<br>2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3<br><!-- End of picture text -->

# **III. European Bank Deleveraging Outlook**

**For now, acute systemic deleveraging pressures have abated** in the wake of massive and repeated ECB intervention in the form of long-term refinance operations (LTROs) and the Outright Monetary Transactions (OMT) sovereign bond buying program which averted extreme, fire sale, funding and breakup scenarios. Promising first steps towards further European integration have also been made. However, going forward, deleveraging needs could intensify

12
substantive explanation for these regional differences, it is unclear whether these results should generalize to our analysis of the 2011 CPIA, which is limited to the cluster D ratings.

# _Question effects_

Some questions cover more extensive issues or sub-criteria than others, so we would expect their write-ups to be longer on average. Questions have multiple components that are each assigned a sub-rating, and each component in turn lists several sub-criteria to address in the write-ups. Questions 12 and 15 each have three components, while question 16 has four and question 14 has only two. We should therefore observe longer write-ups for question 16 and shorter ones for question 14, relative to questions 12 and 15.

# **5. Write-ups: Word Counts**

Regressions presented in Table 1 show partial correlations of write-up length with the variables described in section 4. The baseline specification in equation 1.1 shows results for the full sample of 136 countries and 544 questions. Standard errors (in this and subsequent tables) are adjusted for clustering by country, as errors for the four observations for each country are not likely to be independent.

As hypothesized, write-ups for IDA countries are significantly longer than for non-IDA countries. The IDA dummy coefficient estimate of 0.15 implies that IDA country write-ups are about 16% longer on average, other things equal. Length of write-ups for benchmark and nonbenchmark countries do not differ significantly, although the coefficient for the benchmark dummy is positive as hypothesized.

Write-ups for ratings increases are also about 16% longer, other things equal. Ratings decreases, on the other hand, are not accompanied by longer justifications. The index of press freedoms, population, per capita income, and the dummy for in-country CMU are also not associated with significantly longer (or shorter) write-ups, contrary to predictions.

Coefficients for the regional dummies are interpretable relative to the omitted category, LCR. Other things equal LCR has the shortest write-ups on average. Coefficients in equation 1.1 are positive for the other five regions, and statistically significant for AFR, EAP and ECA. Relative to LCR, write-ups are longer on average by 19% for AFR, 32% for EAP, and 24% for ECA. Other than LCR, the next-shortest write-ups are for MNA, but the difference between MNA and the other four regions is significant only for EAP.

Question effects in equation 1.1 are consistent with intuition. Relative to the omitted category of question 12, write-ups are significantly shorter on average (by about 13%) for question 14, and longer for questions 15 and 16 (by about 15% and 35% respectively). Question 12 has more subquestions (three) than question 14 (two), but fewer than question 16 (four). Question 15 also has three sub-questions, consistent with the finding that its write-ups are longer than question 12’s but shorter than question 16’s. Until it was revised for the 2011 CPIA, question 15 had four subquestions. The write-ups each year are not entirely re-written in most cases, but edited from the 11
41 (Table Consumption 2). During the period 1960-65 average annual growth in per capita consumption was 6 per cent. By the late sixties, 1965-70, this increase had reached 17 per cent per annum. The sharpest decrease in average annual growth in per capita private consumption took place during the early seventies with average annual growth decreasing from 17 per cent (1965-70) to 10 per cent (1970-75). This large reflects the slow down of growth in the low income Asian countries in the early seventies. In fact, all three South Asian countries in the sample, i.e. Bangladesh, India and Pakistan, experienced negative per capita growth during this period. Note that these countries enjoyed considerable Iiiprovement in performance since the late seventies while that of countries in Africa and in Latin America and the Caribbean (LAC) deteriorated substantially.

For instance, among all the countries (see Table Consumption 1) with consumption data, the proportion of Afrlcan countries with negatsLi growth in average annual growth in per capita private consumption increased from onethird (10 out of 30) in 1960-65, to over half (18 out of 34) in 1980-85. In LAC, this proportion increased from a little over one-fifth (3 out of 20) to over four-fifths (19 out of 23) during the same periods. All the Asian countries enjoyed positive growth in per capita consumption during the late sixties and eighties. However during the early seventies about one-third (5 out of 15) Asian countries had negative growth in per capita consumption. While positive growth in per capita consumption was more or less the norm for the countries of EKEEA and Eastern Europe during the sixties and seventies, some of the countries (Algeria, Syria, Poland, Yugoslavia) experienced negative per capita consumption during the eighties.
Peqository, ail Statin MaS, E.eardi Triange Park, Nbth Carolira 27711, USA.

MM, (1986a) "WM Expert Omittes on Malaria," 18th RPort, Techi. lhport No. 735, World Health **G** eva.rganizantio, WD, (1985), "Ihe Cmtrol of S o niasis," IRqt of a VW Expert Cnwmittee, Tech. Report No. 728, World Health Organization, Geneva.

OCP, (1984) flTIm Years of Qr r maiasi.s Cmtrol in West Africa," Report No. OCP/84.3, World Health Organizaticn,, Geneva. Smith, A. and Gratz, N.G., (1984), "Urban vector arid rodent cMntrol services," WHD unpublished doctment No. VBC/84.4, World Health Onization, TER, (1982), "Report of an Infornml Yesting on Glosina Trapping," Report No. TIR/¶MY(AF)/GP/Brazz/82.3, World Health Orgaization, Geneva.
**<u>Table 2: ITT panel sample: z-scores</u>**

||Weight for|age z-score|Height for|age z-score|Moderate U<br>(-2|ndernutrition<br>SD)|Moder<br>(|ate Stunting<br>-2SD)|
|---|---|---|---|---|---|---|---|---|
||OLS|Community<br>Fixed<br>Effects<sup>†</sup>|OLS|Community<br>Fixed<br>Effects|OLS|Community<br>Fixed<br>Effects|OLS|Community<br>Fixed<br>Effects|
|Program*year 2004|0.218***|0.149***|0.119**|0.094*|-0.075***|-0.052***|-0.025|-0.030*|
|_Sd_|_0.046_|_0.049_|_0.050_|_0.056_|_0.017_|_0.018_|_0.016_|_0.017_|
|Program|-0.220***||-0.173***||0.080***||0.046**||
||_0.038_||_0.049_||_0.014_||_0.014_||
|Year: 2004|0.022|0.063**|-0.041|-0.003|-0.019*|-0.037***|0.025*|0.018|
||_0.025_|_0.028_|_0.031_|_0.035_|_0.010_|_0.011_|_0.010_|_0.011_|
|No. obs.|29,911|23,333|29,211|22,828|29,795|24,279|28,675|23,368|
|R2|0.031|0.027|0.047|0.043|0.021|0.018|0.033|0.028|

Note Standard deviations based on Huber-White robust standard errors clustered at the community level. Significantly different at * 90%, ** 95%, *** 99%. Subsumed regressors are age and birth order dummies, gender, education level of the mother (and for OLS rural indicator). †The community fixed effects specification is based on a pscore weighted and trimmed in the [0.05, 0.95] interval sample.

23
9 more academic. In the standard expected utility approach, risk aversion is low when intertemporal substitution is high, because the relevant elasticities are each other's inverse. The consequence of this is that whenever the uncertainty effect is important, the direct anticipation effect is not and vice versa. This result is reversed in the non-expected utility approach, as we found out: the two effects are complemer;cary in thb case where the direct anticipation effect is important.

These results have important policy implications. If the trade reform will not be reversed, but the Government cannot credibly communicate that to the private sector, consumers effectively trade off t'e wrong intertemporal prices. As a consequence, private savings will be suboptimally low; this justifies policy intervention to increase private savings, preferably through a temprorary increase in consumption taxes. If that is not feasible, a case can be made for temporary tariffs as a second best response; this would be equivalent to gradual rather than 'cold turkey" liberalization. This is a specia'l case of a moie beneral point made by Calvo (1988a,b): mistaken beliefs about future policy "ct like a distortion and therefore justify policy intervention in principle.

A magnifying impact could come about if the private savings response leads to such a large current account deficit that the trade reform itself does in fact get reversel, a case of self-fulfilling prophecy. 5/ This very real possibility further strengthens the case for policy intervention to increase private savings, and, arguably, for external support in the early periods of trade reform, possibly through institutions like the World Bank or IMF.

# References

- Calvo, C. (1988a), "Credibility and Economic Policy", presented at the conference: SRain and the EuroDean Monetary System.

- Calvo, G. (1988b), "Costly Trade Liberalizations: Durable Goods and Capital Mobility", IMF Staff Papers, Vol.35, pp.<sup>461</sup> -<sup>74</sup>

- Diamond, P. and J. Stiglitz (1974), "Increases in Risk and Risk Aversion", Journal of Economic Theory, Vol.8, pp.<sup>337</sup> -<sup>60</sup> .

- Dornbusch, R. (1987),"External Balance Correction: Depreciation or Correction?", Brookings Papers on Economic Activity, 1987, No.1, pp.249-71

- Dornbusch, R. (1989), "Credi ity and Stabilization", mimeo, MIT.

- Edwards, S. and S. van Wijnbergen (1987),"The Welfare Effects of Trade and Capital Market Liberalization", International Economic Review, Vol.27, pp. 141-149

- Razin, A. and L. Svensson (1983),"An Asymmetry between Import and Export

- 5 Dornbusch (1989) disc sses the possibility of such self-fulfilling

- equilibria in the context of szabilization programs.
<!-- Start of picture text -->
-.01 0 .01 .02 .03<br>Coefficient<br>IV-Lin, No Control IV-Lin, w/Control<br>IV-Het, w/Control IV-Het, No Control<br>800<br>600<br>400<br>Density of IV Coefficients 200<br>0<br><!-- End of picture text -->

FIGURE A19: COEFFICIENTS OF AID ON CONFLICT WITH RISK FROM EL NINO IN IV AND OLS FOR LINEAR AND NONLINEAR MODELS Notes: Kernel density plots of coefficients obtained from 100 simulations of the IV and OLS specifications of the linear and quadratic risk models with and without controls. The green line is the coefficient from the quadratic model not including a linear control for NINO intensity, the blue line shows coefficients for the quadratic model including a linear control for NINO intensity, the black line is the coefficients from the linear model not including a NINO control, and the red line shows the coefficients from the linear model with a linear control for NINO. Data are simulated except for NINO3 variable taken from (Hsiang, Meng, and, Cane, 2013).

79
54

# **Table** 7

## PANEL DATA: LoGIT RESULTS (ARGENTINA, BRAZIL, CHILE, COLOMBIA AND MEXICO)

## Dependent Variable: DUSUM

Method: ML - Binary Logit (Quadratic hill climbing) Sample(adjusted): 60 1272 Included observations: 815 Excluded observations: 398 after adjusting endpoints Convergence achieved after 6 iterations QML (HuberNVhite) standard errors & covariance

|Variable|Coefficient|Std. Error|z-Statistic|Prob.|
|---|---|---|---|---|
|NSPR103|1.481|0.176|8.407|0.000|
|NRINTRINBUBUS|-0.282|0.135|-2.098|0.036|
|NEQREIDEV|0.637|0.151|4.217|0.000|
|NDCRGHAT(-12)|-0.426|0.195|-2.184|0.029|
|<br>NDCRGHAT(-18)|0.530|0.143|3.696|0.000|
|NKFLO6HAT|0.408|0.210|1.938|0.053|
|NTOTDEV|0.659|0.107|6.150|0.000|
|NVOL24DLDCRHAT|0.367|0.227|1.614|0.107|
|NVOL24DLGTRLCRHA|T<br>0.600|0.301|1.996|0.046|
|DARG|-3.281|0.328|-10.006|0.000|
|DBRA|-3.465|0.429|-8.085|0.000|
|DCHL|-1.395|0.275|-5.070|0.000|
|DCOL|-1.079|0.226|-4.775|0.000|
|DMEX|-2.117|0.270|-7.843|0.000|
|Mean dependent var|0.189|S.D. depen|dent var|0.392|
|S.E. of regression|<br>0.324|<br>Akaike info|<br>criterion|0.706|
|<br>Sum squared resid|84.322|Schwarz cri|terion|0.787|
|<br>Log likelihood|-273.880|Hannan-Q|uinn<br>criter.|0.737|
|<br>Avg. log likelihood|-0.336||||
|Obs with Dep=0||Total obs||815|
|Obswith Dep=1|661.0||||

DUSUM = Bubbles dummy; it is equal to I when durintrinbub = I (when rintrinbub > I standard deviation), or when dudiffnorm dummy = I [when the difference of the normalized price (gprlcr) - normalized Present value (K*Dividends)>1.3 **],** and 0 otherwise.

NSPRI 03 =Normalized Spread between the 10-year and 3 month US t bills NRINTRINBUBUS = Normalized residual of US intrinsic bubble NEQREIDEV = Normalize **d** eviations of the equity real estate variable NDCRGHAT(-12) =Normalized domestic credit growth in real terms at t-12 months NDCRGHAT(-18) = Normalized domestic credit growth in real terms at t-18 months NKFL06HAT = Normalized 6-month moving average capital flows proxy NTOTDEV = Normalized Terms of trade deviation NVOL24DLDCRHAT =Normalized 24 month moving window of a standard deviation of the differenced log of domestic credit in real terms.

NVOL24DLGTRLCRHAT = Normalized 24 month moving window of a standard deviation of the first difference of the log of the real global total return series DARG = Argentina dummy DBRA = Brazil dummy DCHL = Chile dummy DCOL = Colombia dummy DMEX= Mexico dummy. Note: Variables with the suffix HATP are fitted values of the variables used as instrumental variables
Policy Research Working Paper 10500

# **Abstract**

This paper studies how firms respond to differential, sizebased tax rates using administrative tax data in Lithuania. Exploiting a notch in the tax schedule faced by corporations, it documents strong behavioral responses to tax incentives— revenue elasticity is estimated at 0.35 and cost elasticity at −1.3, implying a large total profit elasticity of 7.4. It then leverages the panel structure of the data to provide insights on the dynamic effects of these tax incentives. Firms located close to but below the notch report systematically lower revenue growth in the short term, but the effects dissipate over time.

This paper is a product of the Development Impact Evaluation Group, Development Economics and the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pgarriga@worldbank.org and tscot@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
small firms, but as we have seen this does not appear to be the whole explanation for the relationship between firm size and wages in Africa. Kingdon et al. (2004) distinguish between labor market institutions and intra-firm organizational mechanisms as potential explanations for this result and labor market segmentation more generally. We summarize their arguments next.

As for the importance of labor market institutions, Kingdon et al. (2004) cite numerous studies providing evidence that unions play a central role in many African labor markets. The authors report estimates of the union premium ranging from 3 to 28 percent in Ghana, Kenya, Nigeria, South Africa and Tanzania, controlling for observed worker skills. The authors acknowledge, however, that systematic identification of the union effect is complicated by the factor that unionization is highly correlated with other leading explanations for labor market distortions including other regulations, monopoly power of producers, and firm size more generally. A related possible explanation is that there is rent-sharing as a result of a bargaining process between the firm and the workers. The idea is that when business is good the workers manage to bid up their wages by threatening to walk out if the firm refuses to accommodate their demands. Evidence of such rent-sharing effects on wages has been found for Ghana (Teal, 1996) and Zimbabwe (Velenchik, 1997).

It is also possible large firms pay relatively high wages in order to provide workers with incentives to exert effort as in efficiency wage models. Fafchamps and Söderbom (2004b) derive a theoretical model in which the size-wage relationship is generated by the cost of monitoring workers rising with firm size. Using worker-firm matched data across 10 African countries, and contrasting SSA with Morocco, their estimates indicate that a doubling in the number of production workers will increase the labor cost per unit of effort by 9 percent for Morocco and between 11 and 14 percent for SSA. This is the penalty large firms have to incur in order to motivate workers. This incentive problem appears to be more severe in SSA than in Morocco. The authors further find that an increase in total factor productivity leads to an _increase_ in worker effort in Morocco but a _decrease_ in SSA. High TFP firms in SSA hire fewer workers and supervisors (and produce less output) relative to what they would have done if the incentive structure had been similar to that in Morocco. Quantitatively, this effect on output is large: an increase in TFP by 1 percent increases output by 2.9 percent in Morocco but only by 1.3 percent in SSA. This is because a high TFP firm in SSA finds it more difficult than in Morocco to manage and supervise 14