rafmacalaba/datause-encoder-data huggingface dataset:
instead of a given party‘s performance.<sup>17</sup> Indeed, when people vote on the basis of fixed characteristics (such as ethnicity), this significantly reduces the extent of political competition—support is guaranteed, regardless of performance—and increases the ability of politicians to gain support in exchange for rewarding targeted groups with jobs and other private goods (Horowitz 1985; Acemoglu and Robinson 2008).Evidence that identity politics still plays an important role in Sierra Leone can be found in the reported ―winner-takes-all‖ strategy followed by the ruling party (APC) after its victory in the 2007 elections. According to several sources, there has been a deliberate attempt by the current administration to exacerbate regional political rivalries by dismissing numerous southeasterners functionaries appointed by the previous administration and replacing them with APC-supporting and Temne-dominated northerners<sup>18</sup> , often with little regard for their competence, experience or qualification (Gberie 2010; ICGR 2008; Africa Confidential 2009: 5). These practices have important implication for the management and performance of civil service. More specifically, this situation creates a classic ̳politician‘s dilemma‘ (Geddes 1994) between the goal of improving performance of the bureaucracy and ensuring political survival. When the logic of political survival prevails, the competence and efficiency of the public bureaucracy is undermined. From a politician‘ perspective, the bureaucracy could represent a potential source of political opposition to patrons, with the consequence that bureaucrats are continually ―shuffled‖ so that they cannot conspire against the rulers, while appointments, promotions and remunerations are based on patronage rather than merit and professional qualifications for the job. The case of Sierra Leone is illustrative of such dilemma, and shows how this creates strong resistance to any reform efforts that aim to change the management of civil service and insulate the bureaucracy from the discretionary power of patrons and politicians.

―Political market imperfections‖ (Keefer and Khemani 2004) also influence the extent to which societal groups can collectively mobilize and create political pressures for changes. Post-conflict Sierra Leone mirrors the experience of many other

> 17 Evidence from the 2007 national elections suggests, however, that there is a ―swing vote‖ in the urban areas (in particular, Freetown and the Western Peninsula) that is increasingly responsive to government performance  (Kandeh 2008).

> 18 It is worth noting possible counterarguments; for example, that this move was prompted by the new government‘s need to ―balance‖ a civil service dominated by the (Mende-Krio) SLPP.

11
for financing a significant portion of the BoP current account deficit, which exceeded 9% of GDP in 2008. Looking forward, strong FDI inflows will be instrumental in ensuring continued macroeconomic stability and productivity growth. Therefore, continued improvement in the business environment remains critical.

**Figure 6: Trade Integration in SEE and CEE, (2001-2007)**

<!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Slovak Czech Hungary Bulgaria Slovenia Croatia Poland Romania Serbia<br>Republic Republic<br>Avg 2001-03 Avg 2004-07<br>% of GDP<br><!-- End of picture text --> _Note: Data is from World Bank Indicators database (DDP)_

**Figure 7: FDIs in SEE and CEE, (2001-2007)**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Bulgaria Serbia Romania Croatia Czech R. Slovak R. Poland Hungary Slovenia<br>Avg 2001-03 Avg 2004-07<br>% of GDP<br><!-- End of picture text --> _Note: Data is from World Bank Indicators database (DDP)_

**Examining the various component indicators of outward oriented policies sheds light on the sources of the gap between Croatia and OECD CEE countries.** These include explicit barriers to trade and foreign investments such as tariffs, discriminatory procedures against foreign firms and foreign ownership barriers, as well as regulatory barriers such as the absence of international harmonization or mutual recognition agreements. While Croatia has achieved great

9
where 1 stands for the vulnerability line. The latter is derived empirically at population level after setting the vulnerability index , the highest probability of falling into poverty that we are willing to vv accept before we no longer consider a household to be economically secure. For example, we might PP<sup>VV</sup> set the index at a value of 20 percent. If a household faced a probability of falling into poverty greater than 20 percent, we would consider this household to be vulnerable. Formally, the vulnerability PP<sup>VV</sup> line is then derived from this index as follows<sup>10</sup> : This approach contrasts with other approaches in the sense that the vulnerability line is not set arbitrarily, for example at 1.5 times the poverty line, but derived empirically from the vulnerability index. In practice, this requires a recursive algorithm that goes over the synthetic panel data to arrive at the vulnerability line that yields the desired vulnerability index. We acknowledge that setting the vulnerability index is still an arbitrary decision, but it is based on a transparent weighing of risks that evokes the insecurity that is inherent to vulnerability.

the level of precision of the estimates. The main reason for this is that the results already consist of bounds that incorporate uncertainty about the errors in our income model. The bounds are highly likely to contain the true value of the probability estimates, as was confirmed in other settings by Dang et al. (2014) and Hérault and Jenkins (2019). In addition, sample sizes for each round are large, so that the standard errors of our estimators are generally small.

The second approach is based on a method for analyzing vulnerability introduced by Chaudhuri (Chaudhuri et al. (2002); Chaudhuri (2003)).<sup>11</sup> Here, we again conceive of vulnerability as a certain probability of future poverty, with the important feature that a household that is currently poor may not necessarily be vulnerable: its characteristics could make the likelihood of poverty in the next period low, but it could have an income below the poverty line this period due to some shock. A where it is assumed that the error term of the regression follows a normal distribution. The probability that household income h will be below the poverty line , given household characteristics h , is then yy zz XX

> 10 Recall that we are assuming a positive correlation between the errors in our income model, which ensures that 2 2 1 1 1) is decreasing in 1 . 11 One reason for including this approach is that the World Bank uses it across the East Asia Pacific region to PP (yy ≤zz | zz < yy ≤vv vv define vulnerability lines based on a consistent methodology, see Krah et al. (forthcoming).

6
B1 and B2, together with details on the estimation sample and the MSE-minimizing weights. The next two ��� **�** columns of Tables 2 and 3 report our upper-bound estimate of the variance of the individual effect, �� and its contribution to steady-state inequality, and the final two columns report the lower bound and the ����� ����� average over time of the upper bound on the mobility parameter, ���� and ���� .

Figure 3 provides a useful visual summary of our estimates. In the top panel, we plot our preferred estimate of � (on the vertical axis) against log real GDP per capita (on the horizontal axis). The red circles correspond to our estimates for developing countries, while the blue squares correspond to advanced economies. The bottom panel plots the over-time average of the mid-point of our range for ���� , i.e. ���������� −����������/2 (on the vertical axis) against log real GDP per capita (on the horizontal axis). Our estimates of persistence � tend to be higher in the WID data than those based on the PovcalNet data – the mean estimate across countries of � is 0.86 in the WID sample, while it is only 0.63 within the PovcalNet sample. It is difficult to say whether this reflects an actual tendency for persistence to be lower in the developing countries covered in PovcalNet when compared with the largely OECD countries in the WID sample. An alternative interpretation is that our OLS estimates of � , as well as the MSE-minimizing combination of the OLS and bias-corrected OLS estimates, have greater small-sample downward bias in the PovcalNet sample where the available time series is much shorter than in the WID sample. However, within each group, there is a tendency for persistence to be lower in richer countries, and this correlation is significant at the 10 percent level within the WID sample of countries. The bottom panel of Figure 3, which plots the mobility parameter ���� against log real GDP per capita, looks broadly similar to the top panel, with the exception that the negative relationship with per capita income is less pronounced in the PovcalNet sample for ���� as it is for � in the top panel. Recall that ���� is a function of � and the share � �� of the variance of the individual effect in overall inequality, � ~~.~~ The similarity between the top and ���� � bottom panels of Figure 3 suggests that cross-country differences in ��� is much smaller than cross���� country differences in � .

Figure 4 plots the evolution over time of the bounds on ���� for the United States derived from the WID data. The bounds are displayed as a grey-shaded region. The lower bound of this range is the � point estimate of ��= 0.66 , while the upper bound varies over time with ���� . As in Figure 1, we also plot the mid-point of the range as a dashed line. Finally, we superimpose on this graph the mobility estimates based on the PSID micro data, for the 1997-1997 period. Although the bounds on mobility displayed in Figure 4 are based on very different data than the PSID (tabulated tax records versus a 18
Rajan, R., and A. Subramanian, “What Undermines Aid’s Impact on Growth?”, _NBER Working Paper No. 11657_ Roodman, D. (2003), “The Anarchy of Numbers: Aid, Development, and Cross-country Empirics”, Center _for Global Development Working Paper_ .

Staiger, D., and J.H. Stock (1997), "Instrumental Variable Regression With Weak Instruments", _Econometrica_ , May,Vol. 65, 5, 557-586.

Stock, J.H, J.H. Wright, J.H, M. Yogo (2002), "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments", _Journal of Business and Economic Statistics_ , October, Vol.4, 20, 518-529.

- Svensson, J. (1999), “Aid, Growth and Democracy”, _Economics and Politics_ , Vol. 11, 3, 275297.

Tavares, J. (2003), “Does foreign aid corrupt”, _Economics Letters_ , Vol.79, 1, 99- 106.

Windmeijer, F. (2005),"A Finite Sample Correction for the Variance of Linear Efficient TwoStep GMM Estimators", _Journal of Econometrics_ , 126, 25-51.

38
public use sample of the 1960 Mexican Census: the Gini of household income, the Gini of years of schooling accumulated for males and females aged 15-20, and the average levels of years of schooling accumulated for males and females aged 15-20. Spearman rank-order correlation tests do indeed indicate some significant correlations between the 1924 migration rates and some of these controls: states with higher historic migration rates had higher average rates of schooling and lower inequality in schooling in 1960. This might represent the influence of migration over the 1924-60 period, or the effects of concomitant trends, and so we prefer to include these 1960 education inequality and levels as controls. Even after controlling for these variables, historic migration rates remain a powerful predictor of current community migration prevalence, with a first-stage F-statistic of 28.

A final threat to the validity of this instrument is the possibility that the historic community migration network has a direct effect on educational attainment through changing the incentives to acquire education. We argue that the incentive effects should be much stronger if children have a household member who has previously migrated than if they merely have someone in their community who has migrated, so that the direct effect of the community network is likely to be secondorder in the education decision. As a check on this assumption, we split states into those above and below the median migration rate in 1924, and then regress years of schooling on a dummy variable for being in a high migration state for children in nonmigrant households. Table 2 shows the effect of the community network is insignificant for three out of the four groups, and has a small _positive_ effect on school attainment of 12 to 15 year-old females. This provides us with further confidence in our instrument and suggests that a finding of migration lowering education rates is not a result of the community network directly lowering education rates.

12
**Table** **<u>3: Regressions for log household consumption per capita</u>**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|**VARIABLES**|**FHH**|**MHH**|**All rural **|**Allurban **|
|Widowed female head|--|--|-0.128**|-0.081**|
||||[-2.23]|[-2.52]|
|Age of household head|0.005<br>|-0.009**<br>|-0.007*<br>|0.001<br>|
||[1.24]|[-2.52]|[-1.80]|[1.30]|
|Age squared|--|0.0001**<br>|0.00005<br>|--|
|||[2.16]|[1.35]||
|Head married polygamous|-0.091|0.070***|--|--|
||[-0.43]|[3.00]|||
|Head is widowed|-0.246**|-0.045|--|--|
||[-2.29]|[-0.71]|||
|Head is divorced/separated|-0.306<br>|-0.001<br>|--|--|
||[-1.48]|[-0.01]|||
|Head chronically ill in past year|0.251*|0.079***|0.084***|0.095**|
||[1.92]|[3.69]|[3.53]|[2.56]|
|Share of others > 15 yrs ill|-0.611|0.112|0.096|0.083|
||[-1.39]|[1.51]|[1.12]|[0.73]|
|Log household size|-0.500***|-0.410***|-0.371***|-0.430***|
||[-6.95]|[-22.02]|[-16.99]|[-21.15]|
|Share of members women 56+|0.396|-0.105|-0.083|-0.052|
||[0.55]|[-1.08]|[-0.67]|[-0.41]|
|Share of members men 56+|0.576|0.019|-0.901**|0.660**|
||[1.28]|[0.05]|[-2.03]|[2.14]|
|Share of members women 15-55|0.464**|0.157***|0.177**|0.314***|
||[2.09]|[2.79]|[2.53]|[4.60]|
|Share of members men 15-55|0.237|0.158**|0.074|0.323***|
||[1.07]|[2.59]|[1.04]|[4.07]|
|Share of members girls 0-6|-0.038|-0.059|-0.055|-0.032|
||[-0.15]|[-0.88]|[-0.68]|[-0.27]|
|Share of members boys 0-6|0.054|-0.127**|-0.123*|-0.038|
||[0.19]|[-2.01]|[-1.67]|[-0.36]|
|Years of schooling of head|0.048***|-0.043***|-0.041***|-0.037***|
||[2.68]|[-5.28]|[-2.93]|[-3.65]|
|Years schooling squared|<br>--|<br>0.008***|<br>0.008***|<br>0.007***|
|<br>||[10.15]|[4.77]|[8.13]|
|Non-agr. landownings (100s m<sup>2</sup>)|0.017|0.001***|0.001***|0.009***|
||[0.73]|[5.30]|[5.41]|[2.66]|
|Agr. landownings (hectares)|-0.0001|0.002|0.002|-0.003|
||[-0.00]|[1.20]|[1.23]|[-0.89]|
|No. large livestock owned|0.007|0.005***|0.005***|0.006**|
||[0.47]|[5.96]|[5.92]|[2.12]|
|No. small livestock owned|0.003|0.001**|0.002***|0.0002|
||[0.85]|[2.54]|[2.73]|[0.18]|
|House exterior made of cement/tile|0.209<br>|0.354***<br>|0.264***<br>|0.388***<br>|
||[1.64]|[10.11]|[5.08]|[9.95]|
|Rural|<br>-0.209|<br>-0.108*|<br>--|<br>--|
||[-1.08]|[-1.91]|||
|Constant|11.988***|13.343***|12.437***|13.117***|
||[15.25]|[98.38]|[31.79]|[92.85]|
|Observations|283|4,048|2,782|1,549|
|R-squared|0.727|0.651|0.587|0.553|

Note: Dependent variable is log household consumption per capita. Robust t-statistics in brackets, clustered at the community level. *** p<0.01, ** p<0.05, * p<0.1. The regressions also control for location fixed effects and interview months.

Source: 2006 ELIM.

33
program may lead to higher earnings for non-participants. Or a road improvement project in one area might improve accessibility elsewhere. Depending on how important these indirect effects are thought to be in the specific application, the “program” may need to be redefined to embrace the spillover effects. Or one might need to combine the type of evaluation discussed here with other tools, such as a model of the labor market to pick up other benefits.

An extreme form of a spillover effect is an economy-wide program. The classic evaluation tools for assigned programs have little obvious role for economy-wide programs in which no explicit assignment process is evident, or if it is, the spillover effects are likely to be pervasive. When some countries get the economy-wide program but some do not, cross-country comparative work (such as growth regressions) can reveal impacts. That identification task is often difficult, because there are typically latent factors at country level that simultaneously influence outcomes and whether a country adopts the policy in question. And even when the identification strategy is accepted, carrying the generalized lessons from cross-country regressions to inform policy-making in any one country can be highly problematic. There are also a number of promising examples of how simulation tools for economy wide policies such as Computable General Equilibrium models can be combined with household-level survey data to assess impacts on poverty and inequality.<sup>10</sup> These simulation methods make it far easier to attribute impacts to the policy change, although this advantage comes at the cost of the need to make many more assumptions about how the economy works.

In both assessing impacts and understanding the reasons for those impacts, there appears to be scope for a “meso” level analysis in which theory is used to inform empirical analysis of what would appear to be the key mechanisms linking an intervention to its outcomes, and this is done in a way that identifies key structural parameters that can be taken as fixed when estimating counterfactual outcomes. This type of approach can provide deeper insights into the factors determining outcomes in _ex post_ evaluations and can also help in simulating the likely impacts of changes in program or policy design _ex ante_ .

Naturally, simulations require many more assumptions about how an economy works.<sup>11</sup> As far as possible one would like to see those assumptions anchored to past knowledge built up

> 10 See, for example, Bourguignon et al. (2003) and Chen and Ravallion (2004).

> 11 For a useful overview of _ex ante_ methods see Bourguignon and Ferreira (2003).

23
Table 7: Heterogeneous impact of PAL on learning by indigenous ethnicity

|_Outcome =_|(1)<br>Math<br>(z-score)|(2)<br>Spanish<br>(z-score)|(3)<br>3rd<br>subject<br>(z-score)|
|---|---|---|---|
|In-kind|0.009|0.021|0.043|
||(0.069)|(0.070)|(0.065)|
|Cash|-0.118|-0.106|-0.086|
||(0.075)|(0.078)|(0.073)|
|In-kind x Indigenous household|-0.245|-0.207|-0.288*|
||(0.153)|(0.176)|(0.159)|
|Cash x Indigenous household|-0.206|-0.137|-0.221|
||(0.192)|(0.195)|(0.185)|
|Indigenous household|-0.291**|-0.375**|-0.199|
||(0.141)|(0.170)|(0.156)|
|State, Grade, & Year FE|YES|YES|YES|
|Pre-program controls|YES|YES|YES|
|Observations|11,006|11,006|10,432|
|**_Effect size:_ In-kind - Cash**|0.13|0.13|0.13|
|**_H 0 :_ In-kind = Cash (p-value)**|0.06|0.06|0.05|
|**_Effect size: In-kind x indigenous - Cash x indigenous_**|-0.04|-0.07|-0.07|
|**_H 0 :_ In-kind x indigenous = Cash x indigenous(p-value)**|0.81|0.61|0.61|

Notes: *** p<0.01, ** p<0.05, * p<0.1 (1) Standard errors are clustered at the village level.

(2) Pre-program controls include gender, age, and indicators for whether the household head is married, whether the house has running water, and whether the closest school offers morning shift.

Table 8: The impact of PAL on health outcomes

|_Outcome =_|(1)<br>Person was eve<br>we|(2)<br>r sick in last 4<br>eks|(3)<br>Z score hei|(4)<br>ght for age|(5)<br>Z score wei|(6)<br>ght for age|(7)<br>An|(8)<br>emia|
|---|---|---|---|---|---|---|---|---|
|In-kind|-0.023|-0.015|0.025|0.031|0.026|0.020|-0.021|-0.020|
||(0.027)|(0.027)|(0.107)|(0.118)|(0.085)|(0.094)|(0.029)|(0.031)|
|Cash|0.001|0.002|-0.109|-0.082|-0.005|-0.001|-0.024|-0.022|
||(0.032)|(0.033)|(0.136)|(0.145)|(0.099)|(0.111)|(0.030)|(0.033)|
|State FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Pre-program controls|YES|YES|YES|YES|YES|YES|YES|YES|
|Restrc. to those with ENLACE|NO|YES|NO|YES|NO|YES|NO|YES|
|Observations|4,266|3,138|3,817|2,494|3,861|2,522|2,403|1,855|
|Outcome mean in control group|0.29|0.30|-0.32|-0.20|0.99|0.12|0.19|0.19|
|**_Effect size:_ In-kind - Cash**|-0.02|-0.02|0.13|0.11|0.03|0.02|0.00|0.00|
|**_H 0 :_ In-kind = Cash (p-value)**|0.38|0.55|0.23|0.32|0.69|0.80|0.91|0.93|
|**_H 0 :_ In-kind = Cash**|||||||||
|**(Randomization Inference p-**<br>**value)**|0.37|0.57|0.25|0.34|0.73|0.83|0.92|0.93|

Notes: *** p<0.01, ** p<0.05, * p<0.1 (1) Standard errors are clustered at the village level.

(2) Pre-program controls include gender, age, and indicators for whether the household head is married, whether the house has running water, and whether the closest school offers morning shift.

(3) Sample only includes individuals aged 6 or younger in 2003.

36
based apprenticeships, these results Indicated the 4esirability, on economic grounds, of re-balancing the training effort in the direction of informal, industry-based methods.

However, the papor emphasized the tentative nature of these findings as well as policy conclusions emanating from them, in view of the relatively short post-training time horizon on which they **were** based - **at** niost five to **six** years, which included **a** three year period of compulsory army service. This note of caution **was** felt to be particularly appropriate with regard to the two mainstream modes, vocational secondary school **and** apprenticeships. In Israel, the former courses differ from the latter in givint due emphasis to general academic studies and to the theoretical, in addition to the purely practical, aspects of training; they would therefore be expected to prepare these students better for long-run advancement in their vocations. As Borus concluded, 'such advancemerm with attendant earnings gains could not be identified in the presen; . Future studies should attempt a longer follow-up period." Th1-i ^aveat constitutes the point of departure for the research reported in the **present** paper, in which the original sample (broadened by the inclusion of an additional cohort of former trainees born in 1948) is followed over a further 6-year period of experience in the labor market. Our now results prove to be confirmatory of the earlier ones which related to a very much shorter follow-up period and, in particular, they confirm the cost effectiveness of the apprenticeship over the vocational secondary school mode of training.

The caveat also evoked an imaginative response from Victor Levine,<sup>3</sup> who attempted to take account of the effect on the earnings profiles of possible additional on-the-job training acquired after course 3/ Victor Levine, 'Evaluating Vocational Training Alternatives Using Single Period Earnings Datat A Technical Note", Comparative Educational Review, 23 (February 1979): 125-133.

3