rafmacalaba/datause-encoder-data huggingface dataset:
explanatory variables are strictly in line with previous estimates, this provides indirect evidence that the estimates can be trusted.

# _8.1 Robustness_

Lastly, it might be the case that some of the missing values, which we have every reason to believe are true missing values, are in fact zero. If so, then a failure to account for the heteroskedastic residuals, which arise from numerous zero values in the regressor, might lead to inconsistent results. Table 4 provides a summary of how many missing values exist in every decade.

**<u>Table 4 % Missing values in sample 1, 1960-2000</u>**

|**Decade**|**% Missing**|
|---|---|
|**1960**|**27**|
|**1970**|**18**|
|**1980**|**13**|
|**1990**|**12**|
|**2000**|**9**|

Source: Author‟s Calculations.

**<u>Table 5 Pooled Regressions to test for robustness</u>**

|<br>**Dependent Var.: Log Exports**||<br>**Migran**|<br>**t Links**||
|---|---|---|---|---|
||**Sample 2**|**Sample 1**|**Sample 3**|**Sample 4**|
|||**No 1960**|||
|**Log immigrants**|-0.005|-0.012|-0.025**|-0.024*|
||(0.013)|(0.016)|(0.013)|(0.014)|
|**Log emigrants**|-0.018|-0.009|0.016|0.020|
||(0.012)|(0.017)|(0.016)|(0.020)|
|**RTA**|0.439***|0.498***|0.380***|0.429***|
||(0.068)|(0.067)|(0.069)|(0.081)|
|**Common Currency**|0.522***|0.506***|0.458***|0.380***|
||(0.146)|(0.112)|(0.118)|(0.129)|

22
|GDP growth|-0.167***|-0.166***|-0.197***|-0.159***|1.336***|1.337***|1.537***|1.255***|
|---|---|---|---|---|---|---|---|---|
||(0.031)|(0.031)|(0.038)|(0.032)|(0.310)|(0.310)|(0.351)|(0.304)|
|GDP per capita|0.003***|0.003***|0.003***|0.003***|-0.003|-0.003|-0.004|-0.001|
||(0.000)|(0.000)|(0.001)|(0.000)|(0.004)|(0.004)|(0.005)|(0.004)|
|Deposit growtht-1|0.005|0.005|0.007|0.005|||||
||(0.004)|(0.004)|(0.006)|(0.004)|||||
|Interest expenset-1|||||0.263|0.279|0.202|0.274|
||||||(0.271)|(0.272)|(0.319)|(0.263)|
|Equity|0.010|0.011|||-0.139|-0.142|||
||(0.011)|(0.011)|||(0.134)|(0.135)|||
|Equity* Liabilities over GDP||-0.231**||||0.959**|||
|||(0.100)||||(0.460)|||
|Liquidity|||-0.015**||||0.026||
||||(0.007)||||(0.064)||
|Liquidity* Liabilities over GDP|||-0.205**||||0.867*||
||||(0.093)||||(0.471)||
|Return on assets||||-0.036||||0.790***|
|||||(0.024)||||(0.303)|
|Return on assets* Liabilities over|||||||||
|GDP||||-0.386**||||3.145**|
|||||(0.180)||||(1.270)|
|Constant|0.169***|0.167***|0.194***|0.155***|-0.170|-0.167|-0.054|-0.153|
||(0.011)|(0.011)|(0.014)|(0.016)|(0.113)|(0.113)|(0.129)|(0.112)|
|N|9136|9136|5568|9141|10750|10750|6524|10755|
|R-sq|0.664|0.665|0.711|0.664|0.050|0.051|0.059|0.053|
|Panel B: Big0.1 included<br>Big0.1|-0.002|0.004|0.006*|0.001|-0.031**|-0.078***|-0.074**|-0.039***|
||(0.001)|(0.003)|(0.003)|(0.002)|(0.013)|(0.028)|(0.032)|(0.015)|
|Equity*Big0.1||-0.098**||||0.806**|||
|||(0.047)||||(0.367)|||
|Liquidity*Big0.1|||-0.089*||||0.786**||
||||(0.047)||||(0.391)||

37
INKTOoUanOt LXXw

**Table** AZ Assuimd **Anmus** Incre_ents to **Life** Expeetncy

|Initial<br>Life||**Mates**|||FamLes||
|---|---|---|---|---|---|---|
|tancy|Minima|Medium|_M"xima_|Minima|Mdi|Max<br>xi_mm_|
|40<br>45<br>50<br>55<br>60<br>65<br>70<br>75<br>80<br>**as**|0.14<br>0.15<br>0.16<br>0.16<br>0.15<br>0.13<br>0.10<br>0.07<br>0.03<br>--|0.34<br>0.38<br>0.39<br>0.39<br>0.37<br>0.32<br>0.26<br>0.18<br>0.08<br>--|0.55<br>0.61<br>n.63<br>0.62<br>0.59<br>0.52<br>0.'2<br>0.28<br>0.12|0.14<br>0.16<br>0.17<br>0.17<br>0.17<br>0.16<br>0.14<br>0.12<br>0.09<br>0.05|0.36<br>0.40<br>0.43<br>0.44<br>0.43<br>0.40<br>0.36<br>0.27<br>0.21<br> <br>0.11|0.58<br>0.65<br>0.69<br>0.70<br>0.66<br>0.69<br>0.57<br>0.47<br>0.34<br>0.18|

not **applicoble.** qui'quewnium in the same manner. This equation allows rates of change to converge toward the uniform pattern imposed for subsequent quinquennia.

For these subsequent quinquennia for all countries, the rate of change in life expectancy is constant at -.035 for both sexes. At this rate, the annual increments to life expectancy vary by initial levels **as** indicated by the medium patterns in Table A2. Minimum and maximum increments oor.esponding to the slow and rapid limits imposed on rates of change in the first three quinquennia ae also shown.

Future trends in infant mortality. Infant mortality is projected using a similar logistic fAnction. The rate of cbange for each of the first three quinquennia is obtained from the equation r, - .0275 + .5 r,., with the restriction that this rate must be in the interval [.024, .130]. These limits provide schedules of minimum and maximum annual decrements to infant mortality, varying by the initial rate, shown in Table A3. A set of medium decrements is also provided, repressnting the typical schedule of Tabte A3 _Ass,med_ Annual **Decrements** to Infant "ortaLity Rate

|Initial<br>infant<br>mortality<br>rote|Minimua|Medium|Maxima|
|---|---|---|---|
|150<br>140<br>130<br>120<br>110<br>100<br>90<br>80<br>70<br>60<br>SO<br>40<br>30<br>20<br>10|0.83<br>0.92<br>1.00<br>1.13<br>1.16<br>1.16<br>1.14<br>1.10<br>1.03<br>0.93<br>0.81<br>0.67<br>0.50<br>0.31<br>0.09|2.08<br>2.32<br>2.50<br>2.63<br>2.69<br>2.71<br>2.66<br>2.56<br>2.41<br>2.20<br>1.94<br>1.62<br>.26<br>0.84<br>0.36|4.02<br>4.47<br>4.81<br>5.03<br>5.15<br>5.16<br>5.07<br>4.86<br>4.56<br>4.16<br>3.65<br>3.05<br>2.35<br>1.36<br>0.68|
These results are similar to those of the World Economic Outlook (WEO, 2009). Considering 88 economies that experienced significant crisis over the last four decades, output was about 8 to 9 percent lower than it would have been without the crisis. Here the impact is much lower, but so was the impact of the crisis on GDP.

The previous exercise has two important limitations: a) it assumes a constant TFP growth in spite of decreased private investment; and b) it assumes that the public capital stock remains constant, and that the productivity of both private and public capital is the same. For the moment we wish to point out that in 1991, the fall in private investment coincided with a rise in public investment of an almost equivalent amount. Given the differences in the productivity of public and private capital estimated by Favero Giavazzi and Missale (2009) and Annex 4, the output drop would occur more from the change in composition than in the levels of capital stock.

TFP growth decreased as a result of the crisis. Empirical studies for Egypt show a negative association between increased public spending and productivity growth (Kheir El DinMoursi, 2007). International evidence (Bernanke-Gurkaynak, 2001) shows that there is a positive association between savings and growth. As a result of the crisis, both public spending increased and national savings rates decreased, and these existing empirical results provide us the only basis to quantify the impact on productivity in Egypt. Kheir El Din and Moursi show that increased public spending of 1 percent of GDP is associated with a fall in TFP growth of about 10 percent, and Bernanke and Gurkaynak estimate that a fall in savings of 1 percent of GDP leads to a similar fall in TFP. Figure 16 shows the deviation of GDP from the baseline given a 10 percent fall in productivity growth in 2010 and 2011, recovering the baseline growth of .7 percent in 2012. By 2020, potential GDP is around 0.25 percent lower than the baseline. The Figure also shows that decline in GDP resulting from a decrease in TFP growth seems to be long-lasting. And again, the higher the capitals share in output, the larger the resulting output loss.

**Figure 16 - Simulation 3: GDP Deviation from the Baseline Level arising from a decrease in TFP growth**

<!-- Start of picture text --> 0.00%<br>-0.05%<br>-0.10%<br>-0.15%<br>-0.20%<br>-0.25%<br>-0.30%<br>with capital share 40% with capital share 60%<br>-0.35%<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> _Source: Authors calculations. Figures shown are in percentage change with respect to the baseline scenario._ 28
**Table 9. Compendium of results by different hedonic model specifications**

|_Indicator_|_Region_|_Original_<br>_tit_||Adjusted<br>|Estimates<sup>/2</sup><br>||
|---|---|---|---|---|---|---|
|||_esmaes_|_Model 1_|_Model 2_|_Model 3_|_Model 4_|
||Metropolitan|624<br>(844.98)|4194.6<br>(949.65)|3374.8<br>(962.73)|3590.5<br>(1265.11)|4194.6<br>(949.65)|
||Lower Urban|805|3759.1|2998.0|3613.0|3759.1|
|||(914.72)|(715.03)|(763.22)|(712.83)|(715.03)|
|Housing services<sup>/1</sup>|Lower Rural|1419<br>(849)|3154.2<br>(680)|1371.0<br>(695)|1371.0<br>(695)|3154.2<br>(680)|
||Upper Urban|944|3783.4|3035.9|3711.9|3783.4|
|||(936.1)|(687.85)|(743.12)|(761.)|(687.85)|
||Upper Rural|1328|2508.6|1197.2|1197.2|2508.6|
|||(1056.7)|(627.35)|(964.12)|(964.12)|(627.35)|
||Metropolitan|16.5|10.5|11.4|11.4|9.6|
||Lower Urban|17.4|9.7|12.4|10.1|9.3|
|Poverty rates among|Lower Rural|15.6|12.0|15.6|15.6|12.0|
|OLD law tenants|Upper Urban|22.6|15.9|19.4|16.5|15.9|
||Upper Rural|46.5|46.7|46.5|46.5|46.7|
||Total|19.3|13.3|15.0|14.0|12.7|
||Metropolitan|13.3|12.1|12.4|12.4|13.3|
||Lower Urban|10.6|9.9|10.3|9.9|10.0|
|Poverty rates across|Lower Rural|16.1|16.3|15.6|15.6|16.4|
|all households|Upper Urban|24.8|23.9|24.4|24.0|24.0|
||Upper Rural|46.7|46.9|46.0|46.0|47.7|
||National|24.4|24.1|23.8|23.7|24.6|
||Metropolitan|0.179|0.162|0.165|0.165|0.161|
||Lower Urban|0.108|0.105|0.106|0.105|0.106|
||Lower Rural|0.072|0.072|0.071|0.071|0.072|
||Upper Urban|0.145|0.134|0.135|0.135|0.135|
|Atkinson Index (1)|Upper Rural|0.080|0.080|0.078|0.078|0.080|
||National|0.136|0.133|0.132|0.133|0.131|
||Within|0.114|0.108|0.109|0.109|0.108|
||Between|0.024|0.027|0.027|0.027|0.026|

Source: Own calculations using the HIECS 2013.

/1: Housing services are the interviewee’s declared rent as collected in the survey. Estimates from different models are based on predictions of hedonic models under different specifications. Averages are shown, and standard deviations are in parenthesis. /2: Model 1 results are based on one multi‐region regression as described in Table A1. Fixed effects for region, owners, owner* region, in‐kind dweller, in‐kind dwellers* region are included in the regression. Model 2 uses the same multi‐region specification but is estimated only with Metropolitan, Lower Urban and Upper Urban regions. Model 3 estimates three region‐specific hedonic models for the urban regions. No corrections are done for rural regions. Model 4 uses the estimates from Model 1 and further adjusts the housing services estimates to account for “owner’s pride”. All models include dwelling type fixed effects.

16
the Arellano-Bond test of first and second-order serial correlation, the Sargan test of overidentification, and the Hansen test of exogeneity of GMM instruments.

We first check cross-section dependency and stationary properties of all variables by using the Pesaran CD test (2004) and the second generation panel unit root model developed by Pesaran (2007). The CD test suggests the presence of cross-section dependence among variables and therefore, a panel unit that accounts for cross-section dependency is appropriate. The unit root test results using the CIPS test suggest that most of the variables (except FINFRA, and CRE) are non-stationary in their levels or I (1) process (Table-B1, Appendix –B). We then examine the existence of cointegration to establish the long-run relationship by using Westerlund's (2007) panel error correction model (see Appendix-B for details). The cointegration results reveal that the null of no cointegration is rejected by group tests (Gt and Ga) as well panel tests (Pt and Pa) level in favor of the existence of cointegration in the panel (Table-B2, Appendix –B).

# **5.1. Determinants of Physical Infrastructure**

The estimations of the relationship of the physical infrastructure index with its potential drivers are reported in Table 5. The estimations under the baseline model are presented in column 2 with the heading “Model 1”. The results under successive models (Model 2 to 8) with additional and alternative determinants<sup>12</sup> of the physical infrastructure index are presented in the rest of the columns. The results reveal that all the potential determinants with the exception of population density, have expected signs and significant effects on physical infrastructure. Infrastructure is highly path-dependent as its past values are significant in all specifications. This indicates that infrastructure development has an inertia effect and is procyclical.

The coefficient of the size of the economy or economic performance (GSDP) is positive and significant across models indicating that the size of the economy is one of the most important determinants of physical infrastructure. The bigger economy size, primarily due to better economic performance/economic growth, increases the demand for infrastructure facilities such as roads, power, and telecom and thereby increases supply (Sharma, 2012;

> 12 We use additional and alternatives variables in these 8 specifications to support the robustness of our results. Some of the variables are also corelated, like GSDP, per capita GSDP and population density which we use alternatively. Similarly, investment and capital expenditures are used alternatively. We have also included the lag of the dependent variable to capture the dynamic aspects.

20
12 The net benefits of Ml are

- _H, = Pf - P, C,_

where _Pfp_ is the average _PVAM_ price, P, is the average spot<sup>price, and C, is the unit</sup> cost of the _P VAM_ The benefits of the _P VAM_ over the<sup>average spot price derive from a</sup> disciplined strategy of forward sales and are unrelated to<sup>the forward premium. The</sup> net benefits are the gross benefits less the costs of operating the<sup>program and the</sup> additional maritime transport and direct domestic marketing<sup>costs incurred through</sup> the _bareme._ In terms of Table 1, _Pfl_ is the CIF price<sup>of 904 F/kg and C, corresponds to</sup> those of CAISTAB's operating costs that are directly<sup>related to the</sup><sup>_PVAM11._</sup> The price Pf, could not be directly observed'<sup>2</sup> over many years but<sup>has been</sup> calculated from a simulation study of the _P VAM_ (Marquet 1997)<sup>for 1979 through</sup> 1996. According to the study, the average simulated PVAM<sup>price would have been 1.9</sup> percent higher than the average spot CIF price of equivalent<sup>quality cocoa. We use</sup> that figure to calculate the gross benefits of public forward sales<sup>in M, (Table 3).</sup> In estimating the net benefits of the _PVAM_ we assume:

- Cote d'Ivoire sells 1 million metric tons at an average CIF price<sup>of</sup> US$1507 per metric ton; the latter price is roughly the<sup>average PVAMprice</sup> for 1997/98;

- The average _P VAM_ price is 1.9 percent higher than the<sup>spot CIF price (as</sup> calculated from Marquet 1997);

- The operating expenses of CAISTAB related to the _PVAM_<sup>are some US$</sup> 16.7 million (about 10 billion CFA francs at an average<sup>exchange rate of</sup> 600 CFAF/US$)'<sup>3</sup> ;

- The "excess marketing costs" are estimated at 5 percent<sup>of the FOB price,</sup> or some US$69 million; and

- The producer price is the 1997/98 minimum indicative price of<sup>455F/kg</sup> (US$758/mt).

> " Some two-thirds of CAISTAB's average operating costs<sup>of 49 CFAF/kg in Table I are for subsidies to industrial</sup> processing of cocoa beans and to repayment of debt to exporters<sup>accumulated during the price slump of the</sup> 1980s.

> 12 The price _Pf,_ is the weighted average of contract prices,<sup>where the weights are quantities sold by CAISTAB at</sup> each position. It cannot be observed for previous seasons because<sup>CAISTAB has not always reported the</sup> quantities sold by position.

> 3 Debt service is excluded. It could be argued that it should<sup>be included as the cost of past errors in judgment in</sup> managing the forward sales program.
# **Figure 19. Investment and governance reform**

_Reform spurts are significantly associated with higher investment growth. Since 2011, improvements in the business climate have continued, but at a slower pace._

## **A. Growth differentials during reform spurts and setbacks**

<!-- Start of picture text --> Growth differential, in percentage points<br>6<br>3<br>0<br>-3<br>-6<br>Reform spurt Reform setback<br><!-- End of picture text -->

## **B. Distance to frontier of Ease of Doing Business**

<!-- Start of picture text --> Distance to frontier score<br>80 2004 2005-2010 2011-2016<br>60<br>40<br>20<br>0<br>Time Cost Procedure<br><!-- End of picture text --> Sources: Doing Business Report, World Bank; Worldwide Governance Indicators, World Bank; Haver Analytics; World Economic Outlook, International Monetary Fund.

A. The columns show the cumulative investment growth differential of economies during a reform spurt or setback episode, relative to those that experienced neither spurts nor setbacks. Spurt (setback) is defined by a two year increase (decrease) by two standard deviations in one or more of indexes of regulatory quality, government effectiveness, rule of law, and control of corruption. Differentials are based on estimates from a panel data regression with time and country fixed effects.The sample includes 75 reform spurt episodes and 71 reform setback episodes among 97 EMDEs over 1996‐2015. The growth differential during reform spurt episodes is significant at the ten percent level. See Annex 2E for more details. B. Indicates proximity in score to country with the highest‐ranking (best) scores for Ease of Doing Business across all time periods with available data. A higher distance to frontier score (DTF) indicates an easier business environment. Unweighted averages of 117 EMDEs. “Time” refers to the average DTF of the time to start a business, obtain construction permits, connect electricity, registering property, paying taxes, and enforcing contracts. “Cost” refers to the average DTF of the costs to starting a business, connect electricity, registering property, and enforcing contracts. “Procedure” refers to the average DTF of the number of procedures to starting a business, obtain construction permits, connecting electricity, and registering property. Blue column denotes the DTF level in 2004. The red and orange columns denote the change in DTF over the respective periods. Each year denoted refers to June of previous year to June of current year.

39
# **References**

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21
Finally, potential asymmetric effects over the business cycle are tested to determine whether pro-cyclical behavior differs between business cycle upturns and downturns. Upturns are defined as periods where the actual level of the output variable (GDP, GSP) exceeds the value of its trend.<sup>12</sup> Downturns are defined in the opposite way. The results presented in Table 7 suggest that the fiscal expenditures of the states are more procyclical during downturns than in upturns over the period of analysis. Personnel expenditures are also pro-cyclical during upturns, but to a lesser degree than they are in downturns. Total revenues, tax revenues, and ICMS all exhibit the same degree of procyclicality in both upturns and downturns.

Table 7: Cyclical Sensitivity of Fiscal Policy: Subnational Level – During Upturns and Downturns

|Variables|AllStates||Upturns||Downturns||
|---|---|---|---|---|---|---|
|**_Revenues:_**|||||||
|Total Revenue|0.273|***|0.212|***|0.228|***|
||[0.036]||[0.068]||[0.078]||
|Intergovernment Transfers|0.091||0.123||0.055||
||[0.066]||[0.087]||[0.099]||
|Tax Revenue|0.309|***|0.262|***|0.280|***|
||[0.030]||[0.063]||[0.062]||
|V.A.T. (ICMS)|0.312|***|0.196|***|0.294|***|
||[0.040]||[0.067]||[0.068]||
|**_Expenditures:_**|||||||
|Total Expenditure|0.305|***|0.213|**|0.490|***|
||[0.031]||[0.102]||[0.102]||
|Primary Expenditure|0.703|***|0.175|*|0.446|***|
||[0.057]||[0.103]||[0.104]||
|Maintenance and Capital|0.340|***|0.164||0.656|***|
||[0.088]||[0.152]||[0.162]||
|Personnel Expenditure|0.703|***|0.345|**|0.730|***|
||[0.056]||[0.144]||[0.147]||
|Interest Payments|0.516|***|0.412||0.584|*|
|***, **, and * denote signific|[0.126]<br>ance at the 1|%, 5%,|[0.322]<br>and 10% levels|, respe|[0.326]<br>ctively.||

***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. The dummy variable equals 1 during upturns, which are define as the positive difference between the actual value of the variable and its trend.   Estimation done with FGLS

> 12 The cyclical component was calculated using the Baxter and King (Band-Pass) filter.

15