rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3 Logit Regressions for the Incidence of Civil Wars (PRIOCW)** **<u>(1965-2005)</u>**

||(1)<sup>1</sup>|(2)<sup>2</sup>|(3)|(4)<sup>3</sup>|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|LGDPPC|**-0.37***|**-0.52****|**-0.44***|**-0.45***|-0.38|-0.46|**-0.46**|
|LPOP|**0.37****|**0.42****|**0.39****|**0.46*****|**0.39*****|**0.42****|**0.42*****|
|PRIMEXP|0.34|0.31|-0.29|0.25|-0.44|-0.11|-0.08|
|MOUNTAINS|0.01*|0.01|0.01|0.00|0.01|0.01|0.01|
|NONCONT|0.35|0.60|0.63|0.75|0.74|0.68|0.68|
|DEMOCRACY|0.00|0.02|0.12|-0.05|0.08|0.14|0.14|
|ETHFRAC|1.10*|-0.06|0.01|-0.03|-0.06|-0.30|-0.33|
|ETHPOL||**2.34****|**2.47****|**2.13****|**2.24****|**2.42****|**2.55****|
|RELFRAC||||-4.52|-2.56|-2.32|-2.35|
|RELPOL||||3.18*|2.04|1.37|1.38|
|Int_nh|||**1.32*****||**1.33*****|0.02|0.32|
|Int_nh˟ RELPOL||||||**2.16***|**2.19****|
|Int_nh˟ ETHPOL|||||||-0.56|
|Intercept|-5.74**|-6.10**|-6.41**|-7.23**|-7.12***|-6.63***|-6.69***|
|N|937|937|946|937|946|946|946|
|McFadden’s R<sup>2</sup>|0.116|0.138|0.174|0.150|0.179|0.188|0.189|
|McFadden’s Adjusted R<sup>2</sup>|0.097|0.117|0.150|0.124|0.151|0.157|0.155|

1 Column (1) shows results for the specification of Montalvo and Reynal-Querol (2005), shown in column (1) of Table 1 in their paper, with the data set extended to 2005.

2 Column (2) shows results for the specification of Montalvo and Reynal-Querol (2005), shown in column (3) of Table 1 in their paper, with the data set extended to 2005. 3 Column (4) shows results for the specification of Montalvo and Reynal-Querol (2005), shown in column (8) of Table 1 in their paper, with the data set extended to 2005.

# _B. The MENA Effect_

This section investigates the robustness of the results to the inclusion of regional dummies. This way we address the relationship between geographical heterogeneity and civil conflict. In Montalvo and Reynal-Querol (2005) all countries not located in Asia, Sub-Saharan Africa, and Latin America are included in the reference (base) region. The MENA countries therefore are included in the reference region along with all developed countries and the rest of the world. The inclusion of the MENA countries with the base group poses a problem given the substantially higher incidence of civil wars and foreign military interventions in MENA compared with the rest of the world (Table 1). In line with the fact that conflict incidence is higher in MENA than in the rest of the world regions, the coefficient on the MENA dummy is large, positive, and significant, while all other regional dummies remain statistically insignificant (see column (2) of Table 4). Furthermore, the inclusion of MENA reduces the magnitude and significance of the coefficient on 24
method’s overall performance. The confidence intervals for LASSO-PMM predictions and actual classifications of households into these categories overlap in general. These tests further support the accuracy of LASSO-PMM to predict welfare dynamics using cross-sections.

Overall, the sensitivity analyses and robustness exercises show that the LASSO-PMM approach for predicting poverty and mobility approximates actual welfare changes. The method performs well considering changes in parameters, estimation procedure, direction of the prediction, sample, covariates, and weighting schemes. The two main conclusions from this validation are: i) changing parameters to make the method more computationally intensive provides only marginal changes; and ii) models with greater explanatory power may improve LASSO-PMM’s predictions. While we focus on generalizability by using a reduced set of harmonized controls to ensure comparability across countries and years, the determinants of poverty vary across countries and over time. This provides researchers in specific countries more leeway since they can use other variables in the models, such as ethnicity, and other retrospective and time-invariant correlates of welfare. As previous studies have shown, machine learning methods tend to work best when the model has high predictive power. While we show that the LASSO-PMM approach works well with a core-set of harmonized explanatory variables, our results suggest that poverty and mobility predictions may improve if the model is chosen adequately. An underlying model with a better fit will produce more accurate neighbors along a larger proportion of the welfare distribution and, therefore, the original distribution will be better replicated. Given these results, we now apply the procedure to harmonized cross-section data to predict poverty, mobility, and income changes in 43 countries across the world.

29
- 40 -

Table 6.3

- Sample range: 1983(1) 1989(4); number of observations: 28

|Series<br>Mean|S.D.|Maximum|Minimum|DW|
|---|---|---|---|---|
|DLWAGE<br>0.1031135|0.1352018|0.4486949|-0.0153958|1.164384|
|DLCPI<br>0.1165299|0.1880734|0.9145840|-0.0054556|0.316326|
|DLPPI<br>0.0870261|0.1242070|0.4987389|0.0000000|1.358728|
|DLPRO<br>0.0108672|0.0280915|0.0927196|-0.0444890|1.724336|
||Covariance|Correlation|||
|DLWAGE,DLCPI|0.0177138|0.7224306|||
|DLWAGE,DLPPI|0.0148153|0.9149054|||
|DLWAGE,DLPRO|0.0009634|0.2630659|||
|DLCPI,DLPPI|0.0169464|0.7523111|||
|DLCPI,DLPRO|-0.0008803|-0.1727925|||
|DLPPI,DLPRO|0.0009282|0.2758756|||

The estimation reported in Table 6.4 relates wage changes to consumer and producer prices, productivity, a dummy being 1 for all quarters of 1987, and the correction term '. The constant is suppressed. The first quarter adjustment structure for the key variables prompts fourth differencing of the data. Unlike for Hungary, both consumer and producer prices enter jointly and significantly and their coefficients sum to near unity. Producer price increases that translate into consumer price increase appear to have direct and full transmission through to the wage. The productivity term has a rather high Table 6.4 Dependent Variable is D4LWAGE - Sample range: 1983(1) 1989(4); number of observations: 28

|VARIABLE|COEFFICI|ENT STD. ERROR|T-STAT.|2-TAIL SIG.|
|---|---|---|---|---|
|D4LCPI|0.374573|8<br>0.1421613|2.6348511|0.015|
|D4LPPI|0.617899|0<br>0.2258889|2.7354108|0.012|
|D4LPRO|0.640817|9<br>0.2495559|2.5678332|0.017|
|ECMP(-4)|-0.859755|1<br>0.2577528|-3.3355802|0.003|
|DUM87|-0.097691|2<br>0.0286817|-3.4060427|0.002|
|R-square|d|0.975054<br>Mean|of dependent|var<br>0.342996|
|Adjusted|R-squared|0.970716<br>S.D.|of dependent|var<br>0.303656|
|S.E. of r|egression|0.051964<br>Sum|of squared re|sid<br>0.062105|
|Durbin-W|atson stat|1.461380<br>F-st|atistic|224.7482|
|log likel|ihood|45.82553|||

' See Appendix 6 for actual and fitted values.
The parameters are tested to replicate historical paths of the Philippines during 197485.13 E general, we find that the values, a = 0 and ,B = 2, are reasonable. These values imply a linear adjustment cost function similar to Bruno and Sachs (1985). They generally will add a 10.5% adjustment cost to investment expenditures at the steady-state. In the consumer utility function, we set v = 0.90 and calibrate p from the state-state condition (A27). Taken together, the parameters indicate a stronger intertemporal substitution in the supply side relative to consumption.

We follow the usual procedure in solving an infinite-horizon growth model by requiring that a steady-state is reached at some future terminal period. For as long as the transversality conditions (A18 and A19) in the maximization of the objective function and the value of the firm are satisfied, the sums of various infinite series (pertaining to the utility function, the value of the firm, the present value of depreciation allowances, and the shadow price of capital) will be finite and solvable. A sufficient condition is that the discount rate and the rate of time-preference are positive and greater than the balanced-growth rate.<sup>14</sup>

# 3 External Shocks

In this section, we examine the effects of an import price shocks in a middle-income country like the Philippines using simulations from the dynamic CGE model. WVe take the case in which foreign lending is not constrained and forelgn debt is endogenous. The shocks to

> 13This step is implemented in Go (1988).

"The model is soved by using a high-level modeling language called General Modeling System (GAMS) with the Minos5 as the solution al8orithm. See Brooke, Kendrick, and Meeraus (1988) for a full exposition. The verion of GAMS used in thi study will actually solve for 30 periods but the computin§ time will increae geometrically. For the purpose of this study, 20 periods are used most of time. This is not a serious constraint - given reasonable parameters in the adjustment coot function, moat of the adjustments are exhausted within 10 to 15 years. Moreover, to prevent rounding errors and small data infeasbilities in the base-year from blowing up by exponential growth, the variables and solutions are detrended by the balanced-growth rate itE Detrending also allows us to check if the model generates and replicates the bae-year data in a steady-state solution.

14
||(0.821)|(0.809)|(1.523)|(1.477)|
|---|---|---|---|---|
|Firm experienced|1.131|0.872|-0.148|-0.094|
|losses due to crime|||||
|Y:1 N:0|||||
||(0.765)|(0.738)|(1.040)|(1.054)|
|Access to finance:|-0.371|-0.360|-0.245|-0.303|
|Obstacle severity (0-4<br>scale)|||||
||(0.235)|(0.227)|(0.317)|(0.313)|
|Tax rates: Obstacle|0.272|0.248|-0.177|-0.141|
|severity (0-4 scale)|||||
||(0.246)|(0.245)|(0.362)|(0.358)|
|Firm has own website|0.732|0.559|-1.721*|-1.821*|
|Y:1 N:0|||||
||(0.571)|(0.563)|(1.010)|(0.992)|
|Fertility rate|0.129|31.492**|-0.836|34.653**|
||(1.016)|(12.415)|(1.155)|(16.622)|
|Gender parity in|0.079***|0.191|0.014|0.033|
|workplace laws|||||
||(0.024)|(0.194)|(0.026)|(0.223)|
|Gender parity in|2.625|-87.208|34.682*|-239.445|
|primary education<br>enrollment|||||
||(16.712)|(196.672)|(19.460)|(211.525)|
|Freedom from|0.208|0.296|1.369*|1.392*|
|business regulations|||||
||(0.572)|(0.572)|(0.732)|(0.762)|
|Urbanization rate (%)|0.016|0.011|-0.050|-0.059|
||(0.043)|(0.042)|(0.054)|(0.056)|
|Women in Parliament|0.091|0.080|0.177**|0.163*|
|(%)|||||
||(0.062)|(0.062)|(0.087)|(0.090)|
|Paternity leave (logs)|-0.223|0.150|0.563|0.782|
||(0.475)|(0.475)|(0.636)|(0.658)|
|Square of Maternity||0.096||-0.742|
|leave (logs)|||||
|||(0.604)||(0.690)|
|Maternity leave||<br>-6.036**||<br>-9.172**|
|(logs)*GDP per capita<br>(logs)|||||
|||(2.640)||(3.610)|
|Maternity leave||0.627**||1.428**|
|(logs)*Number of|||||
|workers (logs)|||||
|||(0.290)||(0.688)|

73
)

||**_Service_**||**_Number o_**|**_f Countries_**||
|---|---|---|---|---|---|
||Public Service|Purely Central<br>Function:|Central<br>Government<br>Involvement<br>(Other):|Purely Local<br>Function|Sample Size|
|SocialServices|Primary and Preschool<br>Education|12|9|12|33|
||SecondaryEducation|13|8|10|31|
||PublicHealth|9|14|8|31|
||Hospitals|11|12|4|27|
|Transportation|Urban Highways|7|5|17|29|
||Urban Transportation|6|4|12|22|
|Utility Services|Drinking Water and<br>Sewerage|8|16|6|30|
||Waste Collection|0|2|27|29|
||ElectricPowerSupply|8|13|4|25|
|Other Services|FireProtection|0|5|4|9|
||Public Orderand Safety|1|1|0|2|
||Police|14|10|5|29|

Source: Author’s calculations based on World Bank Qualitative Decentralization Indicators.

<u>(http://www1.worldbank.org/publicsector/decentralization/qualitativeindicators.htm</u> Quite a large number of central governments are involved in local functions. Out of a sample of 33 countries for which details on the assignment of local functions are available, primary education is the sole responsibility of the center in 12 countries and in additional 9 countries central government is involved in this service along with local governments (see Table 3 for details on central government involvement in local functions).

In areas of shared responsibility such as education, health and social services, policies of various levels of government are typically uncoordinated. While social services expenditures tend to be less important in developing countries than industrial countries, the local government role in these functions is more important in the latter. These are also the functions that are in some countries mandated by the constitution to be provided universally and free. In transition economies the central governments have often attempted to shift social expenditures downward to regional and local governments without providing additional finances. These largely unfunded mandates have therefore 16
or exercise books available at home, there are no significant gains in program schools (Panel B), although all six point estimates in the panel are positive, providing suggestive evidence in favor of some increased parental investments. Further evidence is provided by a specification that pools all cohort 1 and 2 girls and boys in Busia district, and finds an increase of 0.26 additional new exercise books or textbooks at home in program schools (standard error 0.19, regression not shown).

There is no convincing evidence of any positive or negative impacts on attitudes toward education, for instance, thinking of oneself as a “good student”, or preferring school activities to nonschool activities, based on survey responses (Tab le 8, Panel C).

Overall, then, there is evidence of increased student and teacher effort (reflected in school attendance, and increased textbook use at home), perhaps suggestive evidence of increased parental educational investments, but no evidence of the changes in attitudes emphasized by psychologists.

# **<u>5.3 Regression Discontinuity Estimates of the Impact of Winning the Scholarship</u>**

We explore within-school impacts of the program in 2002 using a regression discontinuity method. This method, in effect, compares the 2002 outcomes of girls who barely won the scholarship (their 2001 test score was slightly above the winning threshold) to girls who barely lost out, in order to estimate the impact of winning. In practice, 2001 test score polynomials (linear, quadratic, and cubic terms) are included to control for any smooth underlying relationship between the 2001 test score and 2002 outcomes, and an indicator variable for having a 2001 test score above the threshold then captures the impact of winning the scholarship. By including students in both program and comparison schools, we are able to estimate impacts on both scholarship winners and non-winners in program schools, in addition to estimating differences between non-winners across program and comparison schools.

The coefficient estimate on the interaction term between the program school indicator variable and the indicator variable for scoring above the scholarship threshold captures the impact of winning the scholarship (coefficient _f_ in equation 5): 25
8 are fed by retaining earnings of UIA holders in periods of high returns on investment. Similar arrangements help the IIFS protect the principal of UIA holders. A special risk investment reserve is used for compensating a loss of principal resulting from poor investment results.

The use of profit equalization and risk investment funds raises issues pertaining to the governance of these funds and the protection of UIA holders’ rights.<sup>20</sup> First and foremost, smoothing of returns to these depositors as currently practiced is a significant obstacle to transparency. By maintaining a stable return to this category of depositors, managers automatically send the signal that the firm is healthy and profitable, while the reality may be otherwise. Smoothing of returns therefore introduces a veil of opacity between depositors and the firm. This problem is heightened by the limited transparency on the use, size and allocation of these funds. An informal survey of 13 IIFS shows that of the 4 IIFS that admitted resorting to reserves, only 2 provide information in their financial statements and annual reports on the share of funds transferred from or to these reserves.<sup>21</sup> Limited disclosure does not provide comfort to UIA holders on their fair treatment. Second, these depositors lack the rights to influence the use of such resources and verify the degree of risk of management’s investments. Such reserves are considered retained earnings, at least in the AAOIFI definition, and reinvested in profit-bearing activities.<sup>22</sup> Third, individual UIA holders may not be able to opt out of their participation to the accumulation of these reserves. A UIA holder with a long-term investment perspective may find it useful to delegate the inter-temporal allocation of his income to a financial intermediary.<sup>23</sup> However, a UIA holder with a high discount rate may be negatively affected by the imposition of this practice. Finally, a UIA holder who

> 20 We are not here concerned with the fact that the existence of such funds may be contrary to the theory of Islamic financial intermediation, in that it creates a de facto insurance against market risk. 21 For the sample used please refer to footnote 54.  The only two banks that disclose use of PER are Bank Muamalat and Dubai Islamic Bank.

> 22 AAOIFI FAS 16

> 23 According to a recent study by Allen and Gale (2004) financial intermediaries appear to be as efficient as markets in inter-temporal consumption smoothing.
Where, _Di,t_<sup>_k_=1</sup><sup>_{_t-</sup><sup>_Gi_=k</sup><sup>_}_is defined as an event study dummy variable which takes the value</sup> of 1 if the unit i is k periods away from the start of the treatment period t, and zero otherwise. _Di,t_<sup>_<−K_</sup> and _Di,t_<sup>_>L_are defined comparably, such that,</sup><sup>_D_</sup> _i,t_<sup>0= 1 if the unit is first treated at time t, and</sup><sup>_D_</sup> _i,t_<sup>1= 1</sup> if one period has passed since the time of the first treatment (treatment lags) etc. Alternatively, we have _Di,t_<sup>_K_equal to 1 if the unit i is treated K times from t (treatment leads).Consequently, k=-1 is</sup> the reference period, _φs_ and _ωt_ represents state and time fixed effects and _ε_ st is the error term. In this paper, Equation 1 is estimated using both realized FDI and Domestic Direct Investment (DDI) data, with K and L limited to 4 lags and 5 leads respectively since this is quarterly data; and planned FDI data, with and Ł limited to 13 lags and 10 leads since this is monthly data.

The selection of leads and lags is based on the time period of the data at the time of the analysis. Thus, if we have 13 months of data following the reform for planned investment for instance, then we select the pre-policy period to be an equivalent time period for comparison. The results will first show the average effect, then the effect period by period.

The primary identification issue is using aggregate data to measure the impact of a policy made at a more granular level. As discussed in the data section, while the reform is at the KBLI 5-digit, the unit analysis is at KBLI 2-digit due to data availability. For example, there is no restriction for manufacturing modern ships. However, traditional ship manufacturing is restricted to the domestic market only. Despite the difference in technology and capital intensity, these two manufacturing sectors belong to the same 2-digit KBLI sector. The estimation may pick up various sub-sector shocks (at 5-digit KBLI for example) leading to higher/lower FDI inflow that may not be related to the reform. These potential shocks cannot be dropped using FE estimation used since we are using KBLI 2 digit as the unit of analysis.

This is addressed in two ways. First, we present results in FDI regardless of whether the sectors were liberalized or not (but maintaining non-commodities focus). By doing so, the problem oof identification between the reform and the data is avoided entirely. These results will still tell how investors reacted to the reform even if their sectors were not necessarily fully liberalized. Investors may still decide to increase investment or commence new projects in anticipation that 11
exception of the Baltics, data prior to 1991 were not available for the FSU countries so they are excluded from the regression samples and scatter plot figures.

Data presented in Table 4.4 was adapted from a variety of sources. In most cases, the number of old persons receiving means-tested or flat pensions was divided by the 65+ year old population for the year the data was available. Estimates based on survey data from the cited Pan American Health Organization reports was used for Costa Rica, Trinidad and Tobago, and Argentina. Survey samples are described in the PAHO reports. The non-agricultural wage used as the denominator in the last column were taken from the UNIDO data on wages and should have been included under sources.

Annex Table I below presents the ratio of pension spending to GDP available by year and source. Out of 109 observations, 49 were based on ILO data, 18 on World Bank reports, 7 from OECD/EEC data and the rest from other sources. Data for all five Nordic countries came from the NOSOSO reports published with data on a three-year lag. The following supplementary country notes are included in order to highlight certain decisions made regarding the data and changes since the publication of the book.

> 1 Although not shown here, pension spending as a share of government spending uses line 82 from the IMF IFS Statistics in the denominator. This variable appears on the STARS data diskette or can be calculated by dividing the total pension spending figures below by government spending that year.

**_6_**
17 survey we employ a three-way city size breakdown, distinguishing between cities larger than 500,000 persons, large towns with a population between 100,000 and 500,000, and towns with fewer than 100,000 inhabitants. For reasons of data availability our index captures only food and housing price differentials. The reference basket employed in our Laspeyres index is based on the consumption patterns of the second quintile of the national urban per capita consumption distribution.

Table 6 presents our Laspeyres spatial price index based on the cost of food and housing in urban Brazil. Relative to the reference region of metropolitan São Paulo, the cost of living in other regions and city size categories of Brazil is generally lower, often a good deal lower. In the regions of the North, South and Center West there is evidence of lower cost of living as conurbations become smaller. This pattern is less clear-cut in the North East, where the cost of living appears particularly high in large towns, and the South East, where those living in small towns appear to face the highest cost of living in the region (outside metropolitan São Paulo).

We next take the price indices reported in Table 6 and apply them to the smallarea based estimates of per capita consumption for each household in the population census. We then re-calculate poverty rates across region and city-size categories. Does adjustment for cost of living in the Brazil data overturn our conclusion that urban poverty in smaller towns is significantly higher than in the large and metropolitan areas? Table 7 indicates that correcting for price variation does attenuate the “gradient” between poverty and city size somewhat, but is far from sufficient to negate or overturn our broad finding. In Brazil, it remains the case that the incidence of poverty in the smallest towns is roughly three times higher than in Metropolitan centers.

Our second rubustness check investigates whether the finding of a negative gradient between poverty and city-size is somehow an artifact of the focus, in this paper, on income poverty as opposed to a broader conceptualization of deprivation. While the broad pattern of higher poverty and lower access to services in small towns was found to be quite robust across our eight countries, an important potential caveat to this assessment concerns health outcomes. It has been suggested in the literature that health outcome indicators in large cities in the developing world may lag behind those in smaller towns. For example, Chattopadhyay and Roy (2005) demonstrate for India that a variety of