rafmacalaba/datause-encoder-data huggingface dataset:
where **a** and Φ and the coefficient vectors. A vector of country-specific shocks, **u** _it_ , is assumed to be distributed serially uncorrelated with zero mean and a nonsingular covariance matrix, i.e. **u** _it ∼ i.i.d._ (0 _,_ Σ _ii_ ). With this specification, all variables of **x** _it_ are assumed to be endogenous in general, thus interactively determined within its economy.

There are variables that are determined outside the country of interest. The price of crude oil, which largely reflects the demand and supply conditions in the world market, is one such example. For a small-open economy, it is more likely that the oil price is exogenously determined. Thus we expand the VAR model and add such global variables, **d** _it_ , as follows: A VAR model with exogenous variables is called “VARX”.<sup>7</sup> The GVAR model consists of a set of county-by-country VAR models that includes a set of “country-specific foreign variables” **x**<sup>_∗_</sup> _it_<sup>,whichisconstructedbytakingtheweighted</sup> average across all the countries _j_ of the corresponding variable as follows: where the weights satisfy _ωii_ = 0 and<sup>∑</sup><sup>_N_</sup> _j_ =1<sup>_ωij_=1for</sup><sup>_i_=1</sup><sup>_, . . . , N_.Sincethisweight</sup> represents the closeness of the economic activities between the countries, the trade share, which is constructed by using the bi-directional trade flow data, is often used.

The VARX*( _p, q, r_ ) of country _i_ , which includes _p_ -th order lag of **x** _it_ , _q_ -th order lag of **x**<sup>_∗_</sup> _it_<sup>and</sup><sup>_r_-thorderlagof</sup><sup>**d**</sup><sup>_it_,isrepresentedasfollows:</sup>

> 7The treatment of the oil price may vary across countries. For a large-open economy, such as the US, it is plausible to assume that the oil price is determined domestically. Therefore it should be included in **x** _it_ .

9
# **_Agglomeration Economies/ clustering effects_**

For estimating Equation (6), we used the own industry concentration measure specified in Equation (1), which is the firm-size adjusted employment for each industry - city pair. The estimated parameters are statistically significant and positive for all broad sectors under examination. This suggests that, at this level of industrial and geographic aggregation, own-industry concentration or localization economies have a considerable impact on location decisions of firms. The results for low technology industries show that a 10 percent increase in own industry concentration will increase city’s attractiveness by 1.1 percent (or a doubling of own industry concentration increases investment attractiveness by 11 percent). The estimated effects are higher for medium (18 percent) and high (33 percent) technology firms.

Next, we estimate Equation (7) using IV techniques, and instrument own industry concentration with the choice of land revenue system. We performed the Durbin-WuHausman test to examine if endogeneity of own industry concentration would have adverse effects on OLS estimates. The test results reject the null hypothesis that industry concentration is exogenous, suggesting that IV estimates would be preferable to those from OLS.<sup>18</sup> While the binary variable is a crude measure, it still is a good predictor of industry concentration, and not associated with unobserved sources of natural advantage. Results from these estimations are reported in Table 5. We have also included the OLS estimates from Table 4 for reference. We find that own industry concentration is still a significant and positive determinant of firm location decisions, as reflected in the city-

> 18 Test results are provided in Table 5.

29
# 3.1.3 Incorporating behavioral responses in incidence studies

Behavioral approaches contain important lessons for benefit-incidence studies. All incidence studies are essentially comparisons of pre- and post-intervention distributions. The contribution of behavioral approaches in this context is to obtain a better measure of the distribution of welfare that would have been observed without intervention-the counterfactual. By measuring benefits net of behavioral responses the approaches thus try to work out the real impact of policy. Economic analyses-such as some of those discussed above-often try to do this in the aggregate for some "representative household". The next step is to determine impacts at the household level and to assign those impacts correctly in the distribution of welfare. The studies by Cox and Jimenez, Sahn and Alderman, and Ravallion and Datt in PSP provide interesting empirical examples of such an approach (also see van de Walle and others 1994). They illustrate how econometrically estimated parameters and simulation techniques allow what are basically benefit-incidence studies to be modified so as to incorporate incentive effects and thereby attain a more precise estimation of the distribution of a policy's net benefits across households.

In order to assess the real impact of a public employment scheme on poverty, Ravallion and Datt argue that the foregone incomes of participants must be netted out of the distribution of transfer benefits. Their task is to estimate the cumulative distribution of incomes that would have existed had the workfare option not been available, and to then compare that distribution with the one observed with the policy. To begin, they econometrically model individual time allocation across all potential activities (including self and wage employment, leisure, domestic work, and unemployment). The results are next used to simulate time use and incomes in the absence of public employment and to draw conclusions about the intervention's net welfare outcome.

In the same spirit, Sahn and Alderman examine how labor supply disincentive effects of foodbased income transfers result in a divergence between net public expenditure increments and net 16
Early research results based on VIIRS-DNB data has been encouraging. In an early application of the new data source, Small et al (2013) used the new data source to accurately detect urban areas, small villages, intra-urban road networks and inter-urban road networks with minimal blooming. Shi et al (2014) compared _GDP_ correlations at the provincial and prefectural levels of China with _SoL_ data generated by VIIRS-DNB and DMSP-OLS data from 2012. They found the VIIRS-DNB data performed better. Chen and Nordhaus (2015) investigated the potential gains from using VIIRS-DNB data to explain socioeconomic outcomes in low-density populations. They found that VIIRS-DNB data performed better than DMSP-OLS data in explaining a cross-section of gridded data for population and for economic output.

The first Suomi satellite will be followed by at least two others over the next several years. If all goes as expected, the new satellites will provide decades of improved data. NOAA is now offering the new data on a monthly basis and they expect to gradually progress to averaged quarterly and averaged annual data. This will make it possible to conduct new research comparing the growth of VIIRS-DNB data with economic variables.
**Table 5: Selection model estimation results with independent or correlated group effects**

||**Group-mean**|**s excluded**<br>**Stdd**|**Group mean**|**s included**<br>**Stdd**|
|---|---|---|---|---|
|**Selection equation parameters (adoption)**|**Coefficients**|**anar**<br>**Error**|**Coefficients**|**anar**<br>**Error**|
|Market performance|||||
|Fertilizer distribution center|0.270<sup>a</sup>|0.064|0.309<sup>a</sup>|0.062|
|Credit|0.363<sup>a</sup>|0.067|0.432<sup>a</sup>|0.066|
|Extension|0.852<sup>a</sup>|0.063|0.801<sup>a</sup>|0.062|
|Climate|0.001<sup>a</sup>|0.000|0.002<sup>a</sup>|0.000|
|Price risk|-0.121<sup>a</sup>|0.044|-0.015|0.044|
|Household capacity|||||
|Household size|0.111<sup>a</sup>|0.012|0.101<sup>a</sup>|0.012|
|Age of household head|-0.005<sup>b</sup>|0.002|-0.010<sup>a</sup>|0.002|
|Education of household head|1.494<sup>a</sup>|0.253|1.274<sup>a</sup>|0.232|
|Wealth|0.128<sup>b</sup>|0.063|0.150<sup>b</sup>|0.062|
|Village Council membership|0.476<sup>a</sup>|0.090|0.551<sup>a</sup>|0.087|
|**Selection-corrected regressionparameters(p**|**er hectare deman**|**d)**|||
|Relative price|-13.778<sup>c</sup>|7.61|-17.034<sup>b</sup>|7.822|
|Livestock density|0.532<sup>a</sup>|0.035|0.580<sup>a</sup>|0.034|
|Area|-0.839<sup>a</sup>|0.168|-0.913<sup>a</sup>|0.158|
|Household size|0.041|1.094|0.584|1.119|
|Age of household head|0.084|0.206|-0.044|0.211|
|Education of household head|26.953<sup>b</sup>|11.192|27.231<sup>b</sup>|11.714|
|Wealth|11.415<sup>b</sup>|5.510|9.896<sup>c</sup>|5.525|
|Weather|-0.025|0.016|-0.042<sup>b</sup>|0.017|
|Time effect|41.728<sup>a</sup>|6.838|41.848<sup>a</sup>|6.821|
|**Error structure**|||||
|Disturbance standard deviation, σ|0.008|0.538|0.009|0.565|
|Correlation between regression andprobit, ρ:|-0.155<sup>a</sup>|0.042|-0.151<sup>a</sup>|0.044|

Note: The superscripts a, b, and c indicate significance at the 1, 5, and 10 percent level, respectively. The models were estimated using LIMDEP version 9.0.

33
Table A.9: Robustness checks using diplomatic exchange at destination. Estimating the effect of migrants’ legal status on hourly wages after return, unconditional on return migration

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|---|
|Age|0.012***|-0.089***|-0.018***|0.000|
||(0.001)|(0.033)|(0.007)|(0.005)|
|Age squared|-0.000***|0.001**|0.000***|0.000*|
||(0.000)|(0.000)|(0.000)|(0.000)|
|No education|0.021***|0.509***|-0.035*|-0.446***|
||(0.004)|(0.114)|(0.021)|(0.026)|
|Primary or preparatory|0.031***|0.431***|0.038*|-0.420***|
||(0.004)|(0.124)|(0.022)|(0.025)|
|Secondary|0.017***|0.334***|0.027|-0.287***|
||(0.003)|(0.102)|(0.018)|(0.020)|
|Rural|0.013***|0.140*|-0.006|-0.029*|
||(0.003)|(0.075)|(0.014)|(0.016)|
|Oil prices|0.013***<br>(0.000)||||
|Diplomatic exchange||-0.153*<br>(0.090)|||
|Confict|||0.058***<br>(0.013)||
|Illegal status||||-0.202***<br>(0.072)|
|Legal status||||0.153***<br>(0.048)|
|Observations|14,879|1,960|2,071|8,054|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Non-mig.=1.|515; Illegal mig|.=1.348; Legal|mig.=1.745||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of Egypt in each of the countries of destination of Egyptian migrants at the time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, conditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

56
capital. On average, this scenario generates an additional 0.23 percentage point growth rate after becoming eective around 2020. This eect becomes stronger towards the end of the projection horizon, and reaches nearly 0.4 percentage points in 2050. However, is this sucient to oset the increase in education expenditures? The increase in public education expenditures does not deteriorate the public nances if a highly educated workforce enables the government to save from other public services. This is likely when a highly educated workforce enables the government to save on other public expenditures such as policing and hospital care. Empirical evidence shows that higher education is correlated with lower crime rates and more frequent practice of preventive care. Giving some credibility to this argument, our assessment is that a better educated workforce can help to make the public nances more sustainable in Bulgaria. Panel (e) shows that, on average, a more educated and productive workforce leads to a reduction in the primary decit by about 0.15 percentage points per year. As a result, the debt to GDP ratio decreases from 51 percent in 2050 under the baseline to near 41 percent. It should also be noted that, there are also benets in terms of higher living standards of the households as public services other than the education also increase because of higher per capita income.

There are, however, two issues that require further analysis in education: bringing more students to school may prove to be costly, and more schooling does not guarantee higher productivity. The education scenarios characterized here are based on certain assumptions. First, the projected increase in the school attainment is assumed to be feasible, e.g. not too costly. We assumed that bringing an additional child to school in the high education scenario will cost the same amount as an incumbent (children that would already be registered in the baseline scenario). In the case of Bulgaria, this assumption may be dicult to justify in the future. Certain minorities, especially Roma population, have high fertility but low school attainment rates. If this prole persists in the future, expanding school attainment may become increasingly costly. Second, we assume that school attainment in itself increases the productivity of the workforce. Again, this assumption may not hold in the case of a skill mismatch, e.g. the education system does not serve the business needs. Finally, the quality of education is at least as important as the quantity of graduates.

# 4 Conclusions

The ndings in this paper suggest that an aging and shrinking population threatens the living standards and sustainability of the scal policies in Bulgaria. The reductions in the work force are estimated to depress economic growth in the long run. In the mean time, 28
subsidy reform on any consumer item, be it electricity, food, petroleum products or water (Silva et al. 2013). Interestingly, in all countries, the (self-identified) lower middle income group is slightly less likely to oppose subsidy reform than the upper middle and wealthy group, and in three out of four countries the lower middle income group is slightly more willing to consider subsidy reform than the self-identified poor.

However, of those willing to consider subsidy reform (in the MENA SPEAKS survey’s Jordan sample), diesel was the most frequently cited candidate for reform, followed by subsidy on bread (figure 6). Electricity and LPG subsidies, which are much larger, appear to be even more politically sensitive than bread subsidies (removals of which have led to riots in the past): only 11 percent of respondents were open to LPG subsidy removals and a paltry 7% were willing to consider electricity reforms. These figures underscore the challenges the government faces. In fact, the government appears to be sensitive to such concerns as the November 2012 reforms did not fully eliminate LPG subsidies while eliminating the subsidies on other petroleum products. It is interesting that the opposition to reforming expensive and regressive energy subsidies is stronger than opposition to food subsidies (which tend to be less regressive). A likely explanation for this is the relative importance of these energy products in the consumption baskets of people.

**Figure 6: Preferred product for subsidy removal (assuming reform is** **<u>inevitable)</u>**

<!-- Start of picture text --> 35%<br>29%<br>30%<br>25%<br>20%<br>19%<br>20%<br>15%<br>15%<br>11%<br>10% 7%<br>5%<br>0%<br>Gasoline Water Electricity LPG / gas Diesel Bread /<br>cylinders flour<br><!-- End of picture text --> <u>Source: Silva et al. (2013) calculations using the MENA SPEAKS survey</u> These numbers beg the question as to how the November 2012 petroleum reforms went into effect without any significant public unrest. Silva et al. (2013) synthesize the vast literature in social protection to summarize the strategies that underscore successful reforms. First and foremost they mention right timing to be key to the success and failure of reforms. More specifically, they argue that it is easier to generate support behind a reform during a crisis, a situation aptly characterizing Jordan. The population in Jordan appeared to sense that the country was in fiscal crisis and petroleum subsidy removals were inevitable. Moreover, from initial episodes of hope, the Arab Spring eventually generated fear of unrest and violence, all the more so given the experiences of neighboring Egypt and the Syrian Arab Republic. The subsidy reforms were likely aided by the strong aversion to the political and social instability that vocal opposition had the potential to generate.

19
lost considerable deposits during the Tequila crisis and, therefore, required liquidity injections from federal sources, provincial politicians were in no position to request many favors. Their banks were adversely affecting the province's fiscal situation, they needed immediate relief, and they would have to accept the terms on which it was offered. Indeed, Fondo Fiduciario employees worked closely with provinces to draft the terms of sale and they determined the amounts to be disbursed to each province.<sup>19</sup> In these ways, Fondo Fiduciario involvement might have actually increased pressure on the provinces to act more quickly.

The crisis itself, and the resulting deposit loss, also must have made the provincial banks less attractive to potential purchasers. The results in Table 4 suggest that the strict provisions imposed by the Fondo or the diminished attractiveness of the provincial banks following the crisis (or both) affected contract terms. Most provisions were better, in the view of provincial policymakers, before the crisis - fewer layoffs were allowed and more assets were transferred to the privatized bank. These results are interesting when combined with the results on the timing of privatization, which indicated that the external shock of the Tequila crisis and poor bank performance increased the likelihood of privatization. Here we find that the Tequila crisis, poor performance, and involvement with the Fondo Fiduciario are correlated with worse contract provisions from the viewpoint of provincial policymakers. This suggests that if policymakers wait until an external crisis or poor performance forces the province to privatize, then the outcome will be less good from their viewpoint. They will be able to guarantee fewer jobs, will be left with larger residual entities, and will be forced to guarantee more of the privatized bank's assets. However, some post-privatization performance data in Clarke and Cull (2000b) shows that the early privatizations were among the least successful. In the end, the best privatization outcomes may only come about when political decision-makers have fewer choices.<sup>2 0</sup>

> 19 The terms of sale and the disbursements were also subject to World Bank approval.

> 20 A more charitable interpretation would be that, in cases where a politician already recognizes the benefits of privatization, a crisis might help her win support for that position.

28
_Table 1.5: Additional Employment Probability for the DTS, General Average HS Grade - Probit_ _Estimate on Current Employment Status_

|VARIABLES|(1)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|
||DTS|DTS|DTS|DTS|DTS|DTS|
|Years of schooling|0.001|−0.028|−0.037**|−0.022|−0.025|−0.046***|
||(0.019)|(0.018)|(0.018)|(0.016)|(0.016)|(0.015)|
|Standardized HS grade|0.040***|0.024|0.017|0.028|0.017|0.017|
||(0.014)|(0.032)|(0.036)|(0.029)|(0.035)|(0.035)|
|Work experience|−0.000|−0.004|−0.005|−0.003|−0.005|−0.005|
||(0.003)|(0.006)|(0.007)|(0.003)|(0.004)|(0.007)|
|Work experience (squared)|−0.000|0.000**|0.000*|0.000***|0.000***|0.000*|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Female|−0.114***|−0.154***|−0.142**|−0.156***|−0.148***|−0.113|
||(0.023)|(0.029)|(0.059)|(0.041)|(0.038)|(0.074)|
|Mother's educational level|−0.002*|−0.002**|−0.002**|−0.001*|−0.003**|−0.003***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|DTS implementation scheme dummy|||||||
|(1=Block release)||0.071||||0.055|
|||(0.138)||||(0.150)|
|Given training plan from TVI (1=yes)||−0.034||||−0.019|
|||(0.067)||||(0.060)|
|Weeks spent for in-school learning|||0.001|||0.001|
||||(0.003)|||(0.002)|
|Weeks spent for in-company training|||0.003***|||0.002***|
||||(0.001)|||(0.001)|
|Number of hours per week for in-company|||||||
|<br>training|||0.032***|||0.029***|
||||(0.011)|||(0.011)|
|Number of hours per week for in-company<br>training (squared)|||−0.000***|||−0.000***|
||||(0.000)|||(0.000)|
|How to learn work process - from|||||||
|supervisors||||0.086||−0.040|
|||||(0.093)||(0.112)|
|How to learn work process - from peers||||−0.137||−0.204|
|||||(0.125)||(0.144)|
|How to learn work process - by yourself||||0.009||−0.010|
|||||(0.158)||(0.118)|
|How to learn work process - from all||||0.073||−0.018|
|||||(0.088)||(0.077)|
|Reported to TVI during in-company training|||||||
|<br>(1=yes)|||||0.021|0.022|
||||||(0039)|(0039)|
|Times visited TVI teacher during in-|||||.|.|
|<br>company training (1=yes)|||||0.006**|0.002|
||||||(0.003)|(0.002)|
|Observations|837|374|374|407|365|418|

_Source: DTS Tracking Survey, 2015, World Bank. World Bank calculations. Note: Standard errors in parentheses. * p<0.1; ** p<0.05; *** p<0.01_ 36