rafmacalaba/datause-encoder-data huggingface dataset:
only the variable Number of Political Parties in Government is significant at the 10 percent level. Consistently with Persson and Tabellini (2003, 2004), we find that presidential systems are associated with smaller government. These results are robust to several exercises to address problems of outliers and multicollinearity, such as excluding countries one at a time, or randomly selecting small groups of countries to exclude, excluding a decade at a time, and excluding control variables one at a time or in groups.

There are several alternate political variables that have been examined in the literature as having effects on fiscal policy which may be driving the correlation we find between coalitions and government size. We consider each of these in turn.

Mueller and Stratmann (2003) have shown that greater voter turnout in elections is associated with larger government. If greater turnout is more likely to be driven by greater turnout of coresupporters, which in its turn is likely to be correlated with instances of single-party government, then excluding the turnout variable might be providing a spurious negative correlation between coalitions and government size. We use elections turnout data from the Institute for Democracy and Electoral Assistance (IDEA) which has been the source for measuring voter turnout in Mueller and Stratmann (2003) and in other work on re-election prospects of governments (Brender and Drazen, 2006).<sup>24</sup> We include turnout rates in our specification and report the results in Table 4-6.

We find all the indicators of coalitions retain their negative sign, and all but one remains statistically significant at least at the 10 percent level.<sup>25</sup> We find increasing voter turnout to be robustly and significantly associated with larger spending and tax revenues, a result consistent with the results of Mueller and Stratmann (2003).

As mentioned earlier, our fiscal variables only capture budgetary spending and tax revenues of central governments, and do not include local or provincial governments and extra-budgetary government accounts. Not accounting for fiscally decentralized or federal structures may produce a negative correlation between coalitions and central government spending because federal systems have smaller central governments and greater political contestation between parties. We include an indicator variable for whether a country has lower tiers of government with fiscal authority from Persson and Tabellini (2003) to test the robustness of our estimates.

Ideology of governments, whether they are right or left wing, might drive the correlation between coalitions and government size, if right wing governments spend less and are more likely to come to

> 24 The IDEA web-site from which we downloaded the data is: <u>http://www.idea.int/vt/index.cfm</u>

> 25 Only the Number of Parties indicator is not significant in predicting total spending in column 1 of Table 6, although it _is_ significant in predicting lower taxes in column 2 of Table 6.

18
# **Figure 10. Share of Revenue Sources in Total Receipts**

<!-- Start of picture text --> 100.0%<br>Grants from The Centre<br>90.0%<br>80.0% Share in Central Taxes<br>70.0% Own Non Tax Revenue<br>60.0%<br>50.0%<br>40.0%<br>30.0% O wn tax Revenue<br>20.0%<br>10.0%<br>0.0%<br>Own tax Revenue Own Non Tax Revenue Share in Central Taxes Grants from The Centre<br>Percent<br>1990/91 1991/92 1992/93 1993/94 1994/95 1995/96 1996/97 1997/98 1998/99 1999/2000 2000/01 2001/02 2002/03 2003/04 2004/05RE)<br><!-- End of picture text --> _Source_ : Government of Tamil Nadu budget documents.

Tamil Nadu achieved a number of positive outcomes since the reform launch in the late 2001. A large portion of arrears (2 percent of GSDP) was cleared in 2002/03. Furthermore, the consolidated fiscal deficit was reduced from the peak of 6.7 percent of GSDP in 1999/00 to 3.3 percent in 2003/04 (Table 2).<sup>25</sup> The fiscal adjustment was largely attributed to an increase in the ratio of the state’s own tax revenue to GSDP (from 8.6 percent in 1999/00 to 9.4 percent in 2003/04) and a reduction in the ratio of salaries to GSDP (from 6.5 percent in 1999/00 to 4.7 percent in 2003/04). Unfortunately, capital outlays and net lending suffered a reduction equal to 0.4 percent of GSDP due largely to lack of financing.

The financial performance of the state power utility also improved mainly due to improvements in operational efficiency, bill collection, and anti-theft measures. Estimates suggest that the earnings before interest, taxes, depreciation and amortization (EBIDTA) improved from negative Rs.12.07 billion in 2001/02 to negative Rs.4.98 billion in 2003/04 and the net loss before subsidy decreased from Rs.18.54 billion to Rs.3.90 billion over the same period. The financial performance of the state transport units also improved, with their net loss reducing by 98.6 percent between 1999/00 and 2002/03. Outstanding guarantees fell from 7.5 in 1999/00 to 6.4 percent of GSDP in 2003/04, lower than those of many Indian states.

> 25 The consolidated deficits in 2000/01 and 2001/02, which were respectively 5.8 percent and 5.2 percent of GSDP, underestimated the real deficits as they did not reflect the accumulation of non-pension arrears equivalent to 1.7 percent of GSDP in 2000/01 and 2 percent of GSDP in 2001/02.

18
In the PNG sample, using PPP we see no significant change in income from migrating, whereas using the actual exchange rate the gain in income is again in the order of US$740-1100 per week, comparable to the gain for both New Zealand and Tongan top students. As discussed above, we believe the estimates using the exchange rate are more accurate. Note however that the PNG estimates are based on 149 individuals working in PNG and only 11 current migrants working abroad, so that this small sample size of migrants should make one very cautious in these estimates. Nonetheless, again they suggest that there is a large average gain in income from migrating.

Moreover, the fact that we get similar (and large) estimates of the income gain from migration both with and without controlling for education, and from both the regression and self-assessed methods suggests that selectivity biases are unlikely to be having a first-order effect on the estimates.

Of course in addition to estimating the average gain in income from migrating, it would be of interest to examine the returns to different qualifications at home and abroad. However, small sample sizes limit the extent to which we can do this – not to mention the fact that migration is linked to the attainment of many qualifications. For the New Zealand sample, which has the largest sample size, the difference in incomes is greatest for those without graduate education, and least for those with PhDs, suggesting the incentive to migrate (or at least remain abroad after getting education) for income gains should be largest for those with the least education among these top students. The current migrants with only undergraduate education are typically in management consulting, IT, and hedge funds, which are all very lucrative occupations abroad.

The top of Table 7 presents the results of quantile regressions of equation (4) to examine the heterogeneity in income gains from migrating. The bottom of the table presents quantiles of the self-assessed change in income, which we only have for current and return migrants. The quantile regression estimates show the income gains are still sizeable at the 25<sup>th</sup> percentile, and are very large for the 75<sup>th</sup> percentile and above.<sup>16</sup> Both methods suggest income gains for the top 10 percent in the order of 1500 to 2000 USD per week, or 75,000-100,000 USD per year!

> 16 The small number of current migrants working in the PNG sample makes the quantile regression estimates jump around a lot for PNG.

- 25 -
|||**Clusters**|**R^2**|**midline)Beta5**|**midline)Beta6**|**midline)Beta7**|**Beta6#**|**Beta7#**|
|---|---|---|---|---|---|---|---|---|
|**Handwashing k**|**nowledge**||||||||
|Important<br>before eating|4,674|250|.0592|.104**<br>(.0448)|.107**<br>(.0418)|.106**<br>(.0496)|.01<br>(.924)|0<br>(.959)|
|Important after<br>defecation|4,674|250|.0398|.0473<br>(.0418)|.0545<br>(.0455)|.0733<br>(.0444)|.03<br>(.867)|.39<br>(.534)|
|||||**Practice**|||||
|Soap used<br>after<br>defecation|4,674|250|.0131|.048<br>(.0416)|.064<br>(.0433)|.0405<br>(.045)|.12<br>(.735)|.02<br>(.878)|
|Soap used<br>before eating|4,674|250|.0549|.138**<br>(.0651)|.133**<br>(.0609)|.137**<br>(.0643)|.01<br>(.93)|0<br>(.984)|

_Note:_ * significance at the .1 level, ** significance at the .05 level, *** significance at the .01 level. # F-values and P-values (reported in parentheses). DID = difference-in-differences.

The results of the adjusted DID specification reported in table A.13 are similar to those of the unadjusted model. When we compared results for subgroups of children from households with and without access to toilets at the baseline (tables A.15 and A.14), we observed a large positive impact on awareness of the importance of handwashing before eating in both subgroups, though not statistically significant across all arms. Similarly, the project’s impact on awareness of the importance of handwashing after defecation was positive in both subgroups, but statistically significant only for arm 3 in the subgroup of children from households with toilets at baseline. The use of soap before eating among children from these households also increased significantly, by 15 to 18 percentage points, across treatment arms relative to the control arm. However, the impact on use of soap after defecation was relatively small and statistically insignificant in arms 2 and 3, and significant at the 10 percent level in arm 1. For children belonging to households without access to toilets at the baseline, the use of soap before eating increased between 9 and 12 percentage points relative to the control group across treatment arms, but the coefficients were not statistically significant. The project’s impact on the use of soap after defecation in this sub-group was relatively small and insignificant in arms 1 and 3, but was found to be significant at the 5 percent level in arm 2, which witnessed a 13 percentage points’ improvement relative to control post-completion of the project.

# 6.3 Results from the ANCOVA Estimations

Estimates from the ANCOVA specifications are reported in table 6. We also estimated the model for the two subsamples: households with and without access to toilets at the baseline. The results are reported in tables A.16 and A.17, respectively. Since ANCOVA specifications used baseline values of the outcome variables as regressors, access to toilets as well as outcome variables that were conditioned on access to toilets could not be estimated for the subsamples.

30
ˆ where _R_ ( _q_ ˆ _h_ , _q_ ˆ _l_ ) = _<u>q</u>_ ˆ _<u>h</u>_ denotes the estimated quantile ratio. Therefore, the model to be estimated _ql_ through OLS becomes: with _h_ and _l_ equal to, respectively, 90 and 10 for the P90/P10 ratio, and to 80 and 20 for the P80/P20 ratio.

When looking at the right-end side of equations 3 and 5, FLFP is modeled as a dummy variable that takes value of 1 if the respondent was engaging actively in the labor market – either by working or looking for work – at the date of the survey, or zero otherwise. Besides the dummy variable representing FLFP, we also use controls for several factors in order to account for the role of some potential determinants of household income and its inequality. For this purpose, we opt for a list of LIS variables that are fully comparable across time and countries. Specifically, in the RIFregression of each pair country/year, we control for geography (regional and rural area dummies); household composition (number of household members, number of household members aged 65 or more, and number of household members aged 13 or younger); demographic characteristics (age of household head); educational attainment (a dummy representing three educational levels); and labor market information (an indicator variable for a three-category industry classification of the household head’s main job).

One of the key concerns with the estimation framework described above is the potential endogeneity of FLFP due to omitted variable bias. Household income may indeed lead women to seek employment, or unobserved factors such as ability could drive women’s decision in favor of work. To address these issues, the strong assumption of FLFP being exogenous is relaxed, and an instrumental variable (IV) strategy is used to recover a causal interpretation. This choice is motivated from the observation that standard OLS estimates could be biased and inconsistent to the extent that FLFP is endogenous.

As the proposed instrument, we chose women’s average wage by deciles from the past 10 to 30 years (depending on the country) for that income bracket. The idea to use this instrument follows the findings of Blau and Kahn (2007), who show how FLFP is conditioned by the wage women 12
Policy Research Working Paper 7406

# **Abstract**

This paper uses survey data from 120 developing countries to compare the role of institutions with firm characteristics at the time of creation of the firm in explaining the size, growth, and productivity of firms over their lifecycle. The study finds that firm-level characteristics have comparable, and sometimes even larger, power than institutional factors in predicting size and growth, but not productivity. In particular, size at birth plays a key role in predicting variation in firm size and growth since birth over the firm lifecycle, whereas country factors dominate in predicting variation in labor productivity over the firm lifecycle. The study also finds that older firms are larger, partly because of the selection of more efficient firms. The findings point to the importance of initial founding conditions in explaining variations in size and growth over the firm lifecycle across countries.

This paper is a product of the Development Research Group. 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://econ.worldbank.org. The authors may be contacted at Ademirguckunt@ worldbank.org, ayyagarim@gmail.com, and vmaksimo@rhsmith.umd.edu.

_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
# Inflation and Indivisible Investment in Developing Economies

## Maya Eden and Ha Nguyen<sup>_∗_</sup> World Bank

## JEL Classifications: E31, E22

> _∗_ The authors are at the Development Research Group of the World Bank. We thank Ben Eden, Aart Kraay, Lars Moller, Claudia Ruiz and Luis Serv ́en for very helpful discussions and feedback.
Estimated Equation for MPORK MPORK - 211.8 **+** 105.9 **Q1** + 44.49 Q2 + 165.1 Q3 - 229.2 EXR (95.63) (23.21) (20.80) (22.85) (76.96) + .5264 MPORK(T-1) (.1182) R<sup>2</sup> _ .736, R2 - .710, DW = 2.25, cy = 50.68, Data - 1974:1 - 1987:4 Estimated Equation for XBR XBR - 21.53 + 12.23 Q1 + 17.62 Q2 + 22.01 Q3 - 1.176 TPBR (56.51) (L<sup>4</sup> .<sup>8 6</sup> ) (14.81) (14.81) (.7373) + 115.5 EXR + .6010 XBR(T-1) (105.0) (.1287) R2 _ .601, R2 - .552, DW - 2.40, Cry - 38.79, Data - 1974:1 - 1987:4

# **Quarterly Exchance Rate Model**

The exchange rate equation is a simple partially reduced foim equation designed to reflect the effects on exchange rates of major changes in macroeconomic policy. Since the major macroeconomic policies of interest are monetary and fiscal policy, the two variables most commonly used as measures of the corresponding effects are included -- the real interest rate and the federal deficit.

Estimated Equation for EXR EXR = .1923 - .01144 Ql - .007962 Q2 - .007129 Q3 + .8346 EXR(T-1) (.0568) (.01022) (.010161) (.010157) (.0513) - .003491 RRS - .0007997 RSGP (.001389) (.0002767) R<sup>2</sup> = .951, R2 = .946, DW = 1.63, ay - .02687, Data = 1973:1 - 1986:4 24
|||_Table A- 3_|_: Firms that inn_|_ovate and gove_|_rnment-owned_|_banks_|||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|INNOV|INNOV|INNOV|INNOV|INNOV|INNOV|INNOV|INNOV|
|SOB|-0.2327|-0.1937|0.148|0.294|1.570|1.702|1.015|1.364|
||(0.2077)|(0.2397)|(0.728)|(0.975)|(1.629)|(1.711)|(1.327)|(1.657)|
|INTER|||-0.548|-0.679|-2.969|-3.079|-1.736|-2.084|
||||(1.191)|(1.461)|(2.950)|(3.053)|(2.051)|(2.399)|
|ROA||0.0129||0.022||0.029||0.047|
|||(0.0366)||(0.044)||(0.038)||(0.054)|
|LTA||-0.0199||-0.016||-0.010||-0.006|
|||(0.0871)||(0.089)||(0.088)||(0.090)|
|lsize|-0.0217|-0.0232|-0.022|-0.024|-0.024|-0.027|-0.026|-0.030|
||(0.0482)|(0.0489)|(0.049)|(0.050)|(0.048)|(0.049)|(0.050)|(0.052)|
|lage|-0.0146|-0.0135|-0.013|-0.012|-0.007|-0.006|-0.008|-0.005|
||(0.0721)|(0.0717)|(0.071)|(0.071)|(0.068)|(0.068)|(0.068)|(0.068)|
|SOE|0.5357|0.5394|0.550|0.551|0.573|0.565|0.606|0.601|
||(0.6922)|(0.6999)|(0.695)|(0.701)|(0.695)|(0.701)|(0.700)|(0.704)|
|FEM|-0.3773**|-0.3903**|-0.379**|-0.390**|-0.422**|-0.430**|-0.413**|-0.422**|
||(0.1578)|(0.1758)|(0.159)|(0.175)|(0.183)|(0.193)|(0.181)|(0.189)|
|Constant|0.5492|0.7164|0.565|0.678|0.742|0.780|0.730|0.714|
||(0.6066)|(1.1525)|(0.624)|(1.150)|(0.735)|(1.148)|(0.751)|(1.123)|
|Observations|2,117|2,117|2,117|2,117|2,117|2,117|2,117|2,117|
|R-squared|0.1194|0.1203|0.120|0.121|0.126|0.127|0.125|0.127|

|||_Table A- 4: firms_|_that invest in g_|_reen activities an_|_d government-_|_owned banks_|||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|GREEN|GREEN|GREEN|GREEN|GREEN|GREEN|GREEN|GREEN|
|SOB|0.1500|-0.2136|-0.277|-0.256|-0.312|-0.259|-0.604|-0.288|
||(0.1305)|(0.1864)|(0.790)|(0.811)|(0.856)|(0.879)|(1.395)|(1.437)|
|INTER|||0.581|0.059|0.816|0.082|1.056|0.106|
||||(1.081)|(1.128)|(1.519)|(1.585)|(1.968)|(2.053)|
|ROA||-0.3615**||-0.361**||-0.361**||-0.361**|
|||(0.1547)||(0.155)||(0.155)||(0.155)|
|LTA||-0.0900||-0.090||-0.090||-0.090|
|||(0.1017)||(0.102)||(0.102)||(0.102)|
|lsize|0.1145*|0.1261**|0.115*|0.126**|0.115*|0.126**|0.115*|0.126**|
||(0.0611)|(0.0621)|(0.061)|(0.062)|(0.061)|(0.062)|(0.061)|(0.062)|
|lage|0.0204|0.0260|0.020|0.026|0.020|0.026|0.020|0.026|
||(0.0952)|(0.0946)|(0.095)|(0.095)|(0.095)|(0.095)|(0.095)|(0.095)|
|SOE|-0.6658|-0.8934|-0.662|-0.893|-0.662|-0.893|-0.662|-0.893|
||(0.7170)|(0.7310)|(0.723)|(0.731)|(0.723)|(0.731)|(0.723)|(0.731)|
|FEM|0.0713|0.1013|0.076|0.102|0.076|0.102|0.076|0.102|
||(0.2343)|(0.1964)|(0.235)|(0.197)|(0.235)|(0.197)|(0.235)|(0.197)|
|Constant|-0.3893|0.7490|-0.389|0.750|-0.389|0.750|-0.389|0.750|
||(0.3628)|(1.0581)|(0.363)|(1.059)|(0.363)|(1.059)|(0.363)|(1.059)|
|Observations|1,491|1,491|1,491|1,491|1,491|1,491|1,491|1,491|

36
- 31 -

# FIGURE 4b DECOMPOSITION OF CHANGES INDOMESIC DEBI TO GDP RATIO

<!-- Start of picture text --> PORflAL<br>10-<br>5-<br>0-<br>-15 - l * . . . T- T l I<br>1980 1981 1982 1983 1954 1985 t19e 1W7 1988 ise<br>3 -<br>[|Non sb* t uPe"Lw E:R GNP<br>2. C4"Efo X Chmpin O*PW<br>IURM<br>.10<br>-15<br>1960 1961 1982 1963 1964 1965 1966 1967 1968 1969<br>Y<br>.~~~~~No NSC* P* E59 [MI*<br>1SC_ ER -4 Cownp in 0OdV<br><!-- End of picture text -->
empirical studies have focused on the technological explanation of concentration, underscoring the signi...cance of the cost curve (especially the minimum e¢ cient scale of production) and barriers to entry by new ...rms (Davies and Lyons, 1982). Implicitly these studies have emphasized the numbers equivalent component of concentration. This paper departs from mainstream practice by focusing on the observed inter-industry di¤erences in the dispersion of ...rm sizes–the variance equivalent component of concentration.

The paper invokes one of the fundamental welfare properties of contestable markets, which refers to the absence of any sort of ine¢ ciency in production in industry equilibrium. Any unnecessary cost, like any excess pro...t, would constitute an invitation to entry and render incumbents vulnerable to being displaced by more e¢ cient entrants (Baumol, 1982). Thus, in contestable industries with U-shaped average cost curves, we should observe a very low dispersion in the size distribution of ...rms–i.e., ...rms could stay in the market only if they produce at the minimum point of the average cost curve, or its neighborhood. In markets that are not contestable, on the other hand, ine¢ cient industry structures characterized by a large dispersion of ...rms around the minimum point could be sustainable even in the long run. Moreover, the extent to which an industry is contestable depends critically on the magnitude of sunk costs–i.e., the irreversible costs that must be borne by an entrant but are mostly bygone for the industry’s incumbent ...rms (Martin, 1989).

We recognize that the assumption of U-shaped average cost curves is far from being universally valid. Indeed, a widely accepted emprical fact is that the observed average cost curves in many industries appear to be linear for a wide range of output. However, as we argue below, although the implied e¤ect of contestability will be weaker in industries with U-shaped cost curves with a ‡at bottom or L-shaped curves, it will not entirely break down.

Our econometric model relates measures of ...rm size dispersion to proxies for industry-speci...c sunk costs. The results of the paper, which are based on a crosssection of four-digit US manufacturing industries, lend support for the hypothesis that the structure of markets depends continuously on the degree to which they exhibit imperfect contestability–smaller dispersions of ...rm sizes are observed in industries whose share of investment that is composed of sunk capital is low (i.e., industries exhibiting high degrees of contestability).<sup>3</sup> The paper’s empirical ...ndings are robust to reasonable changes in model speci...cation as well as to variations in the measures of ...rm size dispersion and sunk costs.

> 3 The ...nding that sunk costs in‡uence the size distribution of ...rms is also consistent with the predictions of the Stackelberg-Spence-Dixit model of entry deterrence. Sunk costs allow early entrants to exploit their headstart and limit the scale of entry of other ...rms by strategically investing beyond their steady-state Nash levels.

2