rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix B: Econometric Methods**

**Panel Unit Root Test:** In the first step, Pesaran’s (2007) panel unit test is applied to examine the time-series properties of variables. The unit root test is carried out using the following standard ADF regression augmented by including both lagged levels and first differences: ∆Xit = αi + bi Xi,t-1+ + + uit .. (A1) ∑_(jj= 0)^pp▒ΨΨ"iijj"   ̄CC tt−1 ∑_(jj= 1)^pp▒λλ"iijj"  " ΔΔ"  ̄CC tt Where Δ is the difference operator,  ̄x t is the cross-section average and P is the lag order. The test for unit root is conducted by assuming null H0: bi= 0 against the alternative Ha: bi< 0 for at least some i.  The average cross-sectionally augmented IPS (CIPS) test depends on the average t-ratio is given by: ...(A2) CCP GG= ∑_(tt= 1)^PP▒(ttii(PP, SS))/PP

**Panel Cointegration:** In the second step, the Westerlund (2007) panel cointegration test is carried out using the following error correction model to establish a long-run relationship between the Infrastructure Index and its determinants:

where μi is the error correction term and D is the difference operator. Four-panel tests (two group statistics and two-panel statistics) were developed by pooling the error term to test panel cointegration. PT and Pa are panel statistics and written as: The null and alternative hypothesis is tested as: H0: μi = 0, H1: μi< 0 for at least some i.

Gα and GT are group statistics and can be written as: Correspondingly, the null and alternative hypothesis is for group statistics tested as H0: μi = 0, H1: μi< 0 for at least some i.

34
The 1985-1994 Global Real Estate Cycle. Page 21

- Finance has been the driving force during the global cycle by inducing rapid shifts in effective

- demand. The supply response has been strongly affected by the extent and rigidity of land and development controls. Urbanized land being the most inelastic input is expected to show the greatest degree of volatility. An hypothesis that could not be explored is that _land price volatility_ was particularly great in countries where pre-liberalization controls were the most rigid. This point has been extensively debated in the UK; and, as Figure 10 shows, its price volatility was very high and leading residential prices in terms of timing. Higher land supply inelasticity could also be the case for Finland compared to Norway. It must be noted that Germany which did not significantly deregulate its financial sector also relies on strict land use controls. (Seele 11982], Bulwien 11994]). The Netherlands also have some of the most systematic public land use controls anywhere. In that coimntry, administered land prices are very uniform. (Needham [1992]). Dutch real estate volatility may have been low and paralleled the German experience, no data were obtained on that country.

Figure 10: High Volatility of Land Prices in the U.K.

<!-- Start of picture text --> Unlbd Kingdom: Residential Building Land and Housa Pi1e Indexes<br>(1980 * 100)<br>t1000<br>go4-00 --- - - - - - - - - - - - - - - -_-- - -- -- -- -- - - - -- - -- - -- - - - -~ - - - - - - -<br>300 -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -7- -<br>200 -- - - - - - - - - - - - - - _, ~ -; - - - - - - - - - - - - - - - - - - - - - - - - - - - _<br>100<br>1980 1981 1982 1983 1984 1985 1986 1987 1968 1969 1990 1991 1902<br>| -Land Priessindox _4Hlou*P;;s IndWx<br><!-- End of picture text -->

Source: Savills, Financial Times, 1993.
- 10 -

Figure 3. Density Distribution of Size of Water-Sector PPP Contracts in the World <!-- Start of picture text --> 0 100 200 300 0 50 100 150<br>Amount of water sold (in million m3) Amount of sewage water collected (in million m3)<br>0 .2 .4 .6 .8 0 .2 .4 .6<br>Number of water connections (in million) Number of sewage connections (in million)<br>0 500 1000 1500 2000 2500 0 50 100 150<br>Number of full time employees Operating expenses (in million US$)<br>Source: PPIS Database.<br>.4 .4<br>.3 .3<br>.2 .2<br>Fraction Fraction<br>.1 .1<br>0 0<br>.4<br>.4<br>.3<br>.3<br>.2<br>Fraction Fraction .2<br>.1<br>.1<br>0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>Fraction Fraction<br>.1 .1<br>0 0<br><!-- End of picture text -->

# **III. ESTIMATING ECONOMIES OF SCALE IN PPP WATER OPERATIONS**

# **_Empirical model_**

Based on the traditional industrial organization literature (e.g., Nerlove, 1963; Christensen and Greene, 1976), the following cost function is estimated by a seemingly unrelated regression (SUR) technique:
# **3 Empirical Strategy**

A key advantage of using loan-level data is that we can set up our dataset at the country-sectoryear level and look at new loans in a given country-sector pair after an MDB has entered that country-sector co-financing a syndicate loan, controlling for time-varying unobservable factors both at the country and sector level. Thus, we evaluate whether the presence of an MDB attracts private capital by estimating the following equation: where _ycs_ , _t_ is the outcome variable in country-sector pair _cs_ at time _t_ . As anticipated in the previous section, in the paper we explore effects on different outcome variables defined at the countrysector-year level: 1) the number of syndicated loans; 2) the total size of syndicated loans, scaled by GDP; 3) the average number of banks involved per syndicated loan; and 4) the average loan maturity of syndicated loans, in years. When measuring the outcome variables, in the baseline analysis we focus on indirect mobilization and we always exclude the loans with MDB participation. In additional analyses, we exploit the richness of the dataset to disentangle direct and indirect mobilization effects by computing the number of banks and loan size including also the partners of the MDBs and the amount they lend in the loans with MDB participation; see Section 5.2.

We also include a lagged dependent variable, _ycs_ , _t−_ 1, as lagged syndicate lending in the countrysector _cs_ could be an omitted variable that is time-varying within sector and that represents a threat to our identification strategy.

Our key explanatory variable ( _MDBcs_ , _t_ ) is a dummy equal to one if there is at least one deal supported by MDBs in the country-sector pair _cs_ at time _t_ .<sup>12</sup> The coefficient _β_ 0 of _MDBcs_ , _t_ measures the contemporaneous MDBs’ (indirect) mobilization effect. However, to allow for the possibility that mobilization effects show up with a lag, we include up to two lags of _MDBcs_ , _t−k_ and then calculate the cumulative effect between year _t −_ 2 and _t_ ( _β_ 0 + _β_ 1 + _β_ 2). To rule out that there is an increase in the outcome variables before MDB participation, in the robustness section we also test for the presence of any anticipation effect including up to two leads of the MDB dummy variable.

We saturate the model with a large set of fixed effects to absorb unobserved factors which

> date, we compute the shares by allowing the “first” to begin in 1995, to leave at least two years with no syndicated loans before (under the assumption that there were no loans before 1993). If we relax this assumption, and we start considering any year between 1996 and 2000 as the “first” year, the results are qualitatively similar.

> 12However, we also employ a continuous indicator to measure the intensive margin of MDB participation and compute the MDB lending multiplier; see Section 5.1.

8
Table D3: Difference-in-Difference Regressions by Head-of-Household Gender [Dependent Variable: Food Insecurity] <!-- Start of picture text --> Fractional Index 3 Fractional Index 4<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>HHI 1 HHI 2<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>54<br><!-- End of picture text --> _Note:_ The figure plots difference-in-difference regression results with region and round controls and standard errors clustered at the household level (see Equation C.2). We display coefficients for the interaction of lagged income diversity indices (Indices 3-6) and a head-of-household gender indicator. Male-headed households serve as the comparison group. Horizontal lines represent 95 percent confidence intervals.
<!-- Start of picture text --> 20<br>°<br>2020 2030 2040 2050<br>-20<br>-40<br>-60<br>80<br>-100<br>Electricity demand Power sector CO2<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0% NORADOANMTNHORADOANANMTHOYORADYSNauanaanmrmaonmmrmmnamanmnwasse srrrrrrsry<br>NNNNNNNNNNNNNNNNNNNNNNNNoooooooooooosoooosoeoeoscoeecesesaeeecseNS<br>mSolar sOil hydro<br><!-- End of picture text -->
household’s recovery. While encouraging, these results show the joint effect of the basic program and top-ups, and not the isolated the effect of the latter.

# **3 The Pantawid Pamilyang Pilipino Program**

The 4P conditional cash transfer program was first introduced in 2008 by the Department of Social Welfare and Development (DSWD). After a very limited pilot in 2007, the program started its geographic rollout in March 2008. Over the course of that year, 4P started operating in 160 municipalities in 33 provinces representing all 17 regions. Geographic eligibility was based on the poorest municipalities in the 20 poorest provinces, the poorest provinces in regions not covered by this criterion, and some pockets of poverty in urban areas. In the first half of 2009, the program was expanded to cover all municipalities with a poverty index of 61.23 percent or more. This added another 28 provinces and 140 municipalities to the area under coverage. In 2010 the threshold for the municipal poverty index was dropped to 57.13 percent and in 2011 to 36.99 percent. In 2011, the program also experienced its biggest expansion by adding over 1.2 million households as beneficiaries as it extended to cover almost all provinces. After a further expansion in 2012 it finally covered all municipalities in 2013. To determine household eligibility, since 2010 it employs the proxy means test based _Listahanan_ registry. In addition to being considered poor according to _Listhanan_ , eligible households 9
is economically significant as well. For instance, in column (1), a 10-percentage point increase in _gov44_ leads to a 10.7 percentage point decrease in the share of short-term debt in overall debt. The marginal effect of _gov_board_ , which uses board attributes only, is also economically large at 8.3 percentage points for the same 10-percentage point increase.

# **Table 3. Effect of Governance on Short-Term Debt**

This table presents the estimates of the following regression.

_stditjk = β0 + β1 x govit + Ω x controlsi,t-1 +_ αj _+_ λt _+ θk + εitjk_ The dependent variable is short-term debt. Leverage is instrumented by lagged short-term debt and the predicted value of leverage is used in the regression. The first step of this 2SLS model is reported in Table A4 of the Appendix. Each column reports the results for a different governance measure. The definitions of the variables are in Appendix. We winsorize all variables at the 1% level except the governance variables. We use one-year lagged values of firm and country-level control variables. All specifications include country, year and industry fixed effects. The z-values from 2SLS approach are reported in the parenthesis. Robust standard errors are estimated by clustering at the firm level. *** ** * , , and indicate significance at 1%, 5%, and 10% levels, respectively.

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|||D<br>|ependent variable<br>|:_std_<br>||
|governance measure(gov):|_gov44_|_gov_board_|_gov_indep_|_size_eff_|_ceo_chair_|
|gov|-1.071***|-0.803***|-0.441***|-0.068***|-0.034***|
||(-6.893)|(-6.836)|(-7.103)|(-4.474)|(-2.794)|
|_leverage_|-4.816***|-4.796***|-4.778***|-4.777***|-4.735***|
||(-17.147)|(-17.221)|(-17.283)|(-17.171)|(-16.313)|
|_size_|<br>0.040***|<br>0.043***|<br>0.036***|<br>0.032***|<br>0.020***|
||(4.896)|(5.175)|(4.512)|(4.013)|(2.622)|
|_asset_tangibility_|<br>0.453***|<br>0.449***|<br>0.443***|<br>0.453***|<br>0.403***|
||(5.170)|(5.145)|(5.099)|(5.186)|(4.494)|
|_asset_turnover_|0.015|0.016|0.012|0.013|0.017|
||(0.758)|(0.785)|(0.593)|(0.662)|(0.815)|
|_roa_|<br>-1.184***|<br>-1.179***|<br>-1.174***|<br>-1.178***|<br>-1.145***|
||(-13.367)|(-13.402)|(-13.394)|(-13.390)|(-12.684)|
|_mb_|0.034***|0.033***|0.031***|0.030***|0.027**|
||(2.989)|(2.891)|(2.765)|(2.681)|(2.345)|
|_gdp_growth_|0.005|0.009|0.010|0.008|0.010|
||(0.639)|(1.114)|(1.177)|(0.967)|(1.148)|
|_inflation_|<br>0.029**|<br>0.034***|<br>0.035***|<br>0.035***|<br>0.001|
||(2.387)|(2.787)|(2.933)|(2.916)|(0.104)|
|_deposits_|0.005***|0.005***|0.005***|0.005***|0.005***|
||(2.818)|(2.905)|(3.140)|(3.025)|(3.224)|
|_bonds_|-0.002|-0.002|-0.001|-0.001|-0.002|
||(-1.019)|(-0.766)|(-0.746)|(-0.459)|(-1.064)|
|_stockmarketcap_|<br>-0.004***|<br>-0.004***|<br>-0.005***|<br>-0.003***|<br>0.001|
|____|(-4.500)|(-4.168)|(-5.103)|(-3.184)|(0.760)|
|_stockmarketturnover_|<br>-0.000|<br>-0.000|<br>-0.001|<br>-0.000|<br>0.000|
|____|(-0.529)|(-0.640)|(-1.168)|(-0.822)|(0.688)|
|Constant|<br>2.605***|<br>2.233***|<br>2.288***|<br>1.770***|<br>1.311***|
||(7.846)|(7.267)|(7.428)|(6.106)|(4.117)|

19
and the standard deviation of regional nonfarm productivity levels are 398.5 thousand and 69.3 thousand cedis per worker, respectively. Moving from the actual to the efficient allocation of labor across sectors (that is, to where regional output wedges are eliminated) increases national nonfarm productivity to 448.5 thousand cedis per worker (a 111.6 percent increase from the level of national nonfarm productivity under the actual distribution of regional output wedges); in addition, it increases the standard deviation in regional nonfarm productivity levels to 169.3 thousand cedis per worker.

Table 2 presents analogous results regarding the effects on national aggregate labor productivity from the three types of simulations. National aggregate labor productivity is a weighted average of farm and nonfarm labor productivities. To obtain the effects on national aggregate labor productivity, apart from the simulation results on the effects on nonfarm productivity (presented in table 1), we use data from Phase II of the IBES to estimate productivity in farm enterprises, as well as GGDC data to estimate the farm employment share, the change in the nonfarm employment share, and the change in farm labor productivity.<sup>18</sup> As the table shows, under the actual distribution of regional output wedges, we obtain a national aggregate productivity of 164.4 thousand cedis per worker. Moving from the actual to the efficient allocation of labor across sectors increases national aggregate productivity to 316.7 thousand per worker (an increase of 92.6 percent from the level of national aggregate productivity under the actual distribution of regional output wedges).

As both Tables 1 and 2 show, the simulation results from adjusting regional output wedges in all regions or in the five richest regions suggest a strong negative association between the extent of disparity in regional output wedges and the level of national labor productivity. In addition, the results from adjusting output wedges in the five poorest regions suggest little or no association between the extent of variation in regional output wedges and the level of national labor productivity. These results suggest that the negative association is effectively driven by the five richest regions.

In addition, the effect of reductions in regional output wedges on the level of national nonfarm or aggregate labor productivity are nonlinear. Reducing output wedges in all regions by 50 percent increases nonfarm productivity to 217.9 thousand cedis per worker (an increase

> 18 Based on GGDC data, we obtain a farm employment share of 36 percent averaged across 2013 and 2014, an annual rate of increase in the nonfarm employment share of slightly over 1 percentage point, averaged over the period from 2000 to 2018, and an annual farm productivity growth rate of 3 percent, averaged over the period from 2013 to 2015. Farm productivity is estimated based on 567 farm enterprises (2.3 percent of the total surveyed sample of 24,400 enterprises) in Phase II of the IBES.

19
1 4 determinant of spreads. In both of the specifications<sup>political instability</sup> indicators are not found to be im'portant in determining<sup>the spreads.</sup> Overall, our investigation suggests that defaulters were<sup>charged higher</sup> spreads than non-defaulters. How important is the magnititude<sup>of this</sup> penaltv ? One simple approach to this question is<sup>to assume that the spread</sup> penaltv is applied each year on the servicing of the outstanding<sup>long term</sup> debt stock to financial creditors. For concreteness consider<sup>the penalty</sup> for defaults of the 1930s. Our estimations suggest that<sup>the ratio of the</sup> penaltv to interest pavments, were near 2-4<sup>percent during the 1970s.19</sup>

# 4. Discussion

The main finding of this paper is that defaulters<sup>were penalized by</sup> being charged worse credit terms than non-defaulters.<sup>In contrast, the</sup> established view appears to be that creditors<sup>have paid little attention to</sup> the debt histories of developing countries,<sup>as is evident in recent</sup> citations: "The empirical case for the pure reputation approach is also<sup>weak.</sup> Eichengreen (1987) and Lindert and Morton (1987)<sup>both show that,</sup> historically, past repayment records have<sup>had little bearing on a</sup> countrv's abilitv to borrow." (Bulow and Rogoff<sup>(1989, p 158,</sup> see also Bulow and Rogoff (1988, p.<sup>1 8</sup> ) for a **similar citation).** "..the major banks... did not discriminate between<sup>countries that</sup> had or had not defaulted in the past, or those that<sup>had or had not</sup> required rechedulings by charging the former higher<sup>premiums."</sup> Schwartz (1989, p 8-9).

The view that default historv is not important<sup>is based on two sets of</sup> studies: those that investigate a period of stagna:ion<sup>in lending</sup> following a a perird of widespread defaults,<sup>and those that investigate an</sup>
# 1 Introduction

Stabilization programs based on the use of the exchange rate as a nominal anchor have often been characterized by a boom-recession cycle, a real exchange rate appreciation, and persistent current account deficits. In the Southern Cone "tablita" experiments of the late 1970s in Argentina, Chile, and Uruguay, for instance, aggregate consumption increased in real terms by an average of ten percent in the first year following the implementation of the plan, before slowing down (see Calvo and V6gh (1994)). An expansion in investment (often associated with an increase in imports of capital goods) and a rise in labor supply have also been observed in some of these programs (Rold6s (1995)). The output and domestic absorption booms appear to have been observed in both successful and unsucessful exchange rate-based stabilization attempts.' Various theories have been proposed to explain the boom-recession cycle in exchange-rate based stabilization programs. One branch of literature, developed in particular by Helpman and Razin (1987), emphasizes the wealth effects of stabilization programs. A second approach is the temporariness hypothesis, developed by Calvo and Vegh (1993). A key feature of this approach is its emphasis on the interactions between the lack of credibility (modeled as a temporary policy change) and intertemporal substitution effects. A transitory reduction in the devaluation rate is equivalent to a temporary fall in present prices relative to the future, and induces an intertemporal substitution in consumption toward the present-leading to a rise in output, real exchange rate appreciation, and a current account deficit. However, evidence on the temporariness hypothesis is mixed. In particular, the econometric study by Reinhart and Vegh (1995) suggested that although it can explain the behavior of consumption in some of the programs implemented in the 1980s, it is less useful for the tablita experiments of the late 1970s in Argentina, Chile, and Uruguay. Essentially, the low intertemporal substitution parameters estimated for these countries suggest that nominal interest rates would have had to fall by substantially more than they actually 'Research by Easterly (1996) and Gould (1996) has shown, however, that output and absorption tend to rise at the inception of both money-based (MBS) and exchange-rate based (ERBS) stabilization programs. Gould (1996) for instance found that real output growth tends to increase in all the programs in his sample (except for the 1985 stabilization in Bolivia). Output growth, nevertheless, appears to be higher in the immediate aftermath of ERBS compared to MBS.

2