rafmacalaba/datause-encoder-data huggingface dataset:
boost short-run growth in the states receiving them, with impact multipliers around 1/3 for one-o transfers as part of stimulus packages ($0.2$0.9, depending on the specication) and 1.5 for permanent Social Security transfers ($0.9$1.9, depending on the specication). The size of the multipliers can be roughly rationalized by a standard NK model with sticky wages and a fraction of hand-to-mouth households.

My estimates of modest temporary cross-region transfer multipliers, in turn, imply that the automatic stabilizers built into the US federal tax-transfer system only have a limited ability to smooth output in regional downturns, perhaps less than in the popular perception. However, this is not to say that other federal policies, such as discretionary federal purchases in regions in recessions, or intergovernmental transfers, might not be more eective (see Chodorow-Reich 2018 for a survey). Moreover, transfers might help to smooth regional consumption, which would enhance welfare even if output smoothing were modest.

# References

- [1] Auerbach, Alan, J., and Daniel R. Feenberg. 2000. The Signicance of Federal Taxes as Automatic Stabilizers. Journal of Economic Perspectives, 14 (3): 37-56.

- [2] Bartik, Timothy J. 1991. Who Benets from State and Local Economic Development Policies? Kalamazoo, MI: W.E. Upjohn Institute for Employment Research.

- [3] Barro, Robert J., and Charles J. Redlick. 2011. Macroeconomic Eects from Government Purchases and Taxes. Quarterly Journal of Economics, 26 (1): 51102.

- [4] Bayoumi, Tatami, and Paul R. Masson. 1995. Fiscal ows in the United States and Canada: Lessons for monetary union in Europe. European Economic Review 39: 253274

- [5] BEA 2009. Regional Quarterly Report (January 2009), Economic Stimulus Payments box https://apps.bea.gov/scb/pdf/2009/01%20January/0109_regqtrlyreport.pdf

- [6] Chodorow-Reich, Gabriel. 2019. "Geographic Cross-Sectional Fiscal Spending Multipliers: What Have We Learned?" American Economic Journal: Economic Policy, 11 (2): 1-34.

- [7] Chodorow-Reich, Gabriel, Laura Feiveson, Zachary Liscow, and William Gui Woolston. 2012. "Does State Fiscal Relief during Recessions Increase Employment? Evidence from the American Recovery and Reinvestment Act." American Economic Journal: Economic Policy, 4 (3): 118-45.

39
**Table 6: Premia by Foreign Investor Characteristics**

|**Variable**|**Thresholds**||**Measure of FDI S**|**pillover Potential**||
|---|---|---|---|---|---|
||foreign investor = 1 if ...<br>and 0 otherwise|**(1)**<br>**_inp_dom_**|**(2)**<br>**_emp_dom_**|**(3)**<br>**_market_**|**(4)**<br>**_assist_**|
|_own_|50 >=_own_< 100%|19.3783*|0.8246|18.4457|0.7381|
|||(0.053)|(0.751)|(0.533)|(0.433)|
||_own_= 100%|20.1105***|0.5891|15.6657|1.0395|
|||(0.006)|(0.769)|(0.575)|(0.185)|
|_age_fdi_|5 >=_age_fdi_< 10|-4.1679|1.1154|-5.3242|-0.4357|
|||(0.518)|(0.730)|(0.707)|(0.638)|
||10 >=_age_fdi_< 20|6.3996|1.9615|-6.8739|1.5076*|
|||(0.176)|(0.403)|(0.487)|(0.080)|
||_age_fdi_>= 20|-13.8976*|6.9023**|-0.8358|0.9591|
|||(0.055)|(0.040)|(0.965)|(0.210)|
|_tech_|_tech_= 2|0.6802|0.7089|20.1645|6.1271***|
|||(0.945)|(0.784)|(0.133)|(0.000)|
||_tech_= 3|-1.2057|0.8178|9.5329|.|
|||(0.924)|(0.705)|(0.487)|.|
|_origin_|_origin_= SSA|2.6070|-1.2141|31.4395***|4.5044***|
|||(0.739)|(0.800)|(0.000)|(0.000)|
||_origin_= Asia|-1.1053|-7.1175|30.3003***|-1.5248*|
|||(0.890)|(0.171)|(0.001)|(0.072)|
|_motive_market_|_motive_market_= 2|0.0312|-4.1798*|16.9894|.|
|||(0.998)|(0.075)|(0.290)|.|
||_motive_market_>= 3|-0.4772|-2.2504|26.7538***|1.1809**|
|||(0.926)|(0.252)|(0.000)|(0.040)|
|_motive_cost_|_motive_cost_= 2|2.3507|-12.0948**|3.0408|-1.6694*|
|||(0.770)|(0.050)|(0.786)|(0.051)|
||_motive_cost_>= 3|-0.9970|-3.6712|8.7955|-0.0534|
|||(0.877)|(0.109)|(0.440)|(0.940)|
|_motive_res_|_motive_res_= 2|-10.0951|-4.0206|3.0942|-5.3253***|
|||(0.223)|(0.292)|(0.810)|(0.000)|
||_motive_res_>= 3|10.3145|-2.0761|-33.1588**|-10.5863***|
|||(0.274)|(0.509)|(0.023)|(0.000)|
|_motive_asset_|_motive_asset_= 2|3.7012|2.9197|4.6732|.|
|||(0.682)|(0.369)|(0.688)|.|
||_motive_asset_>= 3|-5.4219|2.6393|2.5715|-0.6596|
|||(0.669)|(0.485)|(0.814)|(0.458)|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01 (p-values in parentheses). Note: All variables except for _assist_ refer to FY 2012. Each panel in a column is estimated as a separate regression. All regressions control for country-sector fixed effects. Standard errors are robust to heteroscedasticity. No observations for _tech_ =4. Missings indicate variables that were dropped from the regressions.

19
shaping conflicts. For instance [36] finds that positive prices shocks to a bulky commodity leads armed groups to create a monopoly of violence to impose taxation and regulate production in Eastern Congo. Along the same lines, [32] use mineral international price changes and data on historical concessions to show that armed groups tend to reduce violence in areas near the mines. This “protection effect” is consistent with violence reducing economic profitability through higher labor costs. Others find that fighting around diamond mines did not affect civilians in Sierra Leone, but was rather limited to violence among soldiers [3, 22]. This result is echoed by [49], which finds that violence against civilians was lower in diamond areas in Angola. Finally, [10] find that price shocks have heterogeneous effects: in labor-intensive sectors, commodity price drops result in higher incentives to join armed groups, while in the capital-intensive sector the rise in the price elicits predatory behavior from armed groups.

The rest of the paper is structured as follows. Section 1 provides some context and describes our methodology, while section 2 presents and discusses the results of the analysis. Section 3 concludes. Details about the methods are available in Annex A; supplementary materials can be found in Annex B.

# **1 Background and methodology**

## **1.1 Context**

During our period of study, 42 production sites in both Syria and Iraq (34 in Syria and 8 in Iraq; see map on Figure 1) had been or are under Daesh control, out of a total of 75 identified oil sites in Syria and 114 in Iraq (see Annex A section A.3 for details on the identification of oil assets and the delineation of Daesh territorial control).

Early accounts of the group’s oil production and the revenues generated indicated that oil was a significant source of financing for the organization. The 2014 Oil Market Report of the International Energy Agency estimates an output of 70,000 barrels per day (bpd) [25]. Other 4
- Aron, A. (2007). The neural basis of inhibition in cognitive control. _Neuroscientist 13_ (3), 214–28.

- Barkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of adhd. _Psychological Bulletin 121_ (1), 65–94.

- Baruch, G. (1981). Moral tales: parents’ stories of encounters with the health professions. _Sociology of Health & Illness 3_ (3), 275–295.

- Blattman, C. and J. Annan (2015). Can Employment Reduce Lawlessness and Rebellion? A Field Experiment with High-Risk Men in a Fragile State. _forthcoming in American Political Science Review_ .

- Blattman, C., N. Fiala, and S. Martinez (2014). Generating skilled employment in developing countries: Experimental evidence from Uganda. _Quarterly Journal of Economics 129_ (2), 697– 752.

- Bryman, A. (2003). _Quantity and quality in social research_ . New York: Routledge.

- Dohmen, T., A. Falk, D. Huffman, U. Sunde, J. Schupp, and G. Wagner (2011). Individual risk attitudes: Measurement, determinants, and behavioral consequences. _Journal of the European Economic Association 9_ (3), 522–550.

- Draca, M. and S. Machin (2015). Crime and Economic Incentives. _Annual Review of Economics 7_ , 389–408.

- Eigsti, I.-M., V. Zayas, W. Mischel, Y. Shoda, O. Ayduk, M. Dadlani, M. Davidson, A. J. Lawrence, and B. Casey (2006). Predicting cognitive control from preschool to late adolescence and young adulthood. _Psychological Science 17_ (6), 478–84.

- Ersche, K., A. Turston, S. Pradhan, E. Bullmore, and T. Robbins (2010). Drug addiction endophenotypes: impulsive versus sensation-seeking personality traits. _Biological Psychiatry 68_ (8), 770–3.

- Fafchamps, M., D. J. McKenzie, S. Quinn, and C. Woodruff (2014). When is capital enough to get female microenterprises growing? Evidence from a randomized experiment in Ghana. _Journal of Development Economics 106_ (1), 211–226.

- Fox, R. C. (2004). Observations and reflections of a perpetual fieldworker. _The ANNALS of the American Academy of Political and Social Science 595_ (1), 309–326.

- Group, N. E. (2003). Suitability of different reference periods for measuring household consumption. results in pilot survey. _Economic and Political Weekly 38_ (4), 25–31.

- Holt, C. and S. Laury (2002). Risk aversion and incentive effects. _American Economic Review 92_ (5), 1644–1655.

- Jamison, J. and D. Karlan (2011). Measuring preferences and predicting outcomes. _Working paper_ .

- Karlan, D., R. Knight, and C. Udry (2015). Consulting and capital experiments with microenterprise tailors in ghana. _Journal of Economic Behavior and Organization 118_ , 281–302.

- LeCompte, M. D. (1987). Bias in the biography: Bias and subjectivity in ethnographic research. _Anthropology & Education Quarterly 18_ (1), 43–52.

lix
WPS4087

# **Migration and Remittances: Causes and Linkages**<sup>**1**</sup>

**Yoko Niimi  and Çağlar Özden**

**DECRG World Bank**

## **_Abstract_**

The paper empirically examines the determinants of remittance flows at the cross-country level. It considers, among other things, the significance of the level of migration, the education level of migrants and financial sector development in determining remittances. Given the potential endogeneity problems, the migration and financial development variables are instrumented in the estimation. The migration level is found to be the main driver of remittance flows even after controlling for the endogeneity bias through instrumental variable estimation. We find that the education level of migrants relative to the population in home countries, the size of the economy, and the level of economic development of recipient countries adversely affect remittance flows. While the effect of financial sector development is found to be positive, its significance was not strongly supported in our analysis.

## World Bank Policy Research Working Paper 4087, December 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 We would like to thank Ileana Cristina Neagu for her help with the data.
||**Table A3**|**.2 First-Differe**|**nces Robustne**|**ss check with C**|**PIA-13**||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||_CPIA_|_CPIA_|_CPIA_|_CPIA_|_CPIA_|_CPIA_|_CPIA_|_CPIA_|
|**GDP per capita (percent change)**|0.3427|0.3144*|0.4248**|0.3527*|0.3627*|0.3120*|0.3145*|0.1347|
||(0.2330)|(0.1756)|(0.1813)|(0.1892)|(0.1846)|(0.1765)|(0.1828)|(0.1896)|
|**Population (percent change)**|-1.0072*|-1.8032***|-1.3101**|-1.7163***|-1.8547***|-1.8047***|-1.8397***|-1.9844***|
||(0.5370)|(0.4321)|(0.5985)|(0.4360)|(0.4802)|(0.4354)|(0.4400)|(0.4249)|
|**Resource (dummy)**|(0.0101)|-0.0200*|-0.0211*|(0.0125)|-0.0235**|-0.0191*|-0.0179*|-0.0165|
||(0.0124)|(0.0105)|(0.0108)|(0.0128)|(0.0106)|(0.0106)|(0.0103)|(0.0104)|
|**SIDS (dummy)**|(0.0125)|-0.0203*|-0.0283**|(0.0184)|-0.0223*|-0.0203*|(0.0078)|-0.0158|
||(0.0126)|(0.0110)|(0.0115)|(0.0120)|(0.0114)|(0.0111)|(0.0119)|(0.0111)|
|**Initial PFM quality (CPIA-13) (level)**||-0.0498***|-0.0562***|-0.0487***|-0.0480***|-0.0499***|-0.0477***|-0.0472***|
|||(0.0068)|(0.0069)|(0.0076)|(0.0074)|(0.0068)|(0.0068)|(0.0069)|
|**Initial GDP per capita (level in log)**|||0.0050||||||
||||(0.0072)||||||
|**Initial regime type**|||0.0034*<br>(0.0020)||||||
|**Tax (percentage point change)**||||0.0149|||||
|||||(0.0093)|||||
|**Aid (ODA) (percentage point change)**|||||0.0008<br>(0.0041)||||
|**Regime (Freedom House, percent change)**||||||0.0179<br>(0.0351)|||
|**Programmatic Parties (percent change)**|||||||0.0271**<br>(0.0133)||
|**Political Stability (percent change)**||||||||0.0555*<br>(0.0285)|
|**Observations**|124|124|124|89|116|124|116|120|
|**R-squared**|0.086|0.390|0.411|0.444|0.373|0.391|0.406|0.415|

**Notes** : Robust standard errors in parentheses; statistical significance is indicated as: *** p<0.01, ** p<0.05, *p<0.1

47
# **1 Introduction**

This paper focuses on the interpretation of the subjective assessment of relative welfare using the Life in Transition Surveys (LiTS). These surveys have been carried out in three waves (2006, 2010, 2016) in about 30 countries in Central and Eastern Europe, Central Asia, as well as Western Europe. Looking at the data from these surveys, a puzzle emerges. In responding to the question of where they place their household on a 10-step ladder (also known as “Cantril’s ladder”) where each step should represent an equal share of people from the poorest to the richest, most surveyed individuals answer that they belong to the middle deciles of the distribution. This ‘bunching in the middle’ implies that there are many rich people who believe they are less (relatively) rich than they actually are when their position is determined by their monetary consumption or assets. Similarly, individuals who are poor according to their objectively measured consumption or assets levels, upgrade their relative positions and place themselves in the steps above those at the bottom. How can this “bunching in the middle” puzzle be resolved? Why is – it that individuals do not distribute uniformly along the ten steps i.e. 10 percent for – each step according to their actual (objectively measured) incomes? Shall one dismiss these subjective assessments as simply mistakes or misperceptions, or do they contain useful information? Can this information be used to guide policy design and assessment, especially when inequality and other distributional issues are emerging as key concerns behind the recent polarization of voting and the emergence of populism? These questions motivate this paper.

The discrepancy between objective and subjective indicators of economic standing has been noted before (Ravallion and Lokshin, 2002). In fact, such discrepancy is one of the most contentious issues dividing those who support adopting indicators of social and economic progress that go beyond the narrow measure of economic production (GDP per capita), and those who dismiss these as “rather silly analyses” (Hamermesh, 2004). The Easterlin paradox originating from his 1974 article – which was asking why well-being – does not seem to go up in line with economic growth is often cited as a catalyst for the attention of the discipline to subjective well-being and for policy makers to devise broad indicators of social and economic progress. By the mid 2010s, the number of papers con– – cerned with happiness economics some published in top journals had increased from less than a few to more than 60 per year (MacKerron, 2012). Across contributors to this literature the consensus is that while monetary income is a strong predictor of subjective well-being, the latter is influenced by many other factors including economic security, access to economic opportunities, health status, education, marriage status, relative income vis-à-vis a reference group, among others. These academic discussions influenced the debate in policy circles, more prominently but not exclusively, in the development policy area. Some clear examples include the Human Development Index introduced by 2
- 29 -

do not know at what boosting rate" they are likely to operate.9 Also, in the simulations reported here we did not consider the responses of those firms which did not have any generators in place and those which either purchased less than 5,000 KWh from PLN or produced less than 1,000 KWh from their own generators. There were 35 such firms in the sample. Removing them reduced the Indonesian simulation sample to 136 firms. This makes our simulation sample more comparable to the Nigerian one.

The Indonesian data on energy sales by PLN show a rapidly increasing trend in the late eighties (of about 16% per annum) with slightly lower projections into the 1990s of about (1315% per annum) [World Bank Report, February 1989]. Because of the rapid growth of the Indonesian economy, it was deemed important to use figures which would be accurate for the survey year 1992. However, in the absence of actual figures for that year, we had to rely on the projections made in the'late eighties. Based on these projections, PLN produced and sold 38,850 GWh of power in 1992/93. This would have cost 3,851 billion rupiyah giving a unit cost (or average tariff) of .99.12 rupiyah/Kwh. Projected PLN revenues would have been 5,386 billion rupiyah, giving a cost recovery rate of 1.398 times the operating cost.'<sup>0</sup> The 136 firms in our sample bought 78,404,000 KWh of power from PLN in 1992 and collectively paid 11,373,340 rupiyah to PLN. At an average cost of 99.12 rupiyah per KWh, the power delivered to these firms cost 7,771,404 rupiyah to produce. Dividing payments to PLN by the cost we get x, = 1.456. Hence, 145.6%% of the cost of serving these manufacturers was recovered by PLN. According to projections, manufacturing in 1992/93

> 9 Similarly, the eossntial substitutes model can only be estimated with a sample of firms each of which is using both power sources.

> 10 While in Nigeria, NEPA barely covered operating cost in mid-1989, in Indonesia PLN more than covered operating cost because of the need to pay for capital expansion in a rapidly growing economy. By contrast, in Nigeria there was comparatively little capital expansion or the central government subsidized such expansion rather than requiring NEPA to pay for it.
-2-

- (Li) Given the difficulty of obtaLning such data an attractive alternative would be to use elasticLties or fuel switching prLces estimated from those countriLe where data is more readlly available. However from the lsues surveyed in thLs paper it appearo that the opportunity to "import" reliable informatlon of thic type may be very limlted.

- (LiL) Engineering stuiies on fuel .witchlng in industry are rarely avallable. Teihnology Ls so specific to the process studied that the lack of homogeneity in output makes this option of very little interest for extrapolation.

- (iv) EngineoerLng studies of fuel switchlng in power generatlon do exLit ln large numbers. The simpler type of study compares the total costs of alternative methods of generating a given amount of power on a greenfield aLt.. The availability of standard cost and operatlng data as well as local fuel costs and associated infrastructure costs allow a series of alternatlve scenarios to be evaluated and the price at which (ex ante) fuel switching would just be worthwhlle to be identified. This approach is clearly of value in that lt can be readilly extrapolated to any country or aituatLon by alterlng a few parameters (e.g. fuel prices at the burner tip). Variations in local conditions may imply that there is no general answer, but the implied spread sheet methodology would be easy to use and some of the key parameters (capital costs and technlcal performance of alternative technologies) would be universal. The limitation of this type of analysis is that it is really applicable only in a very special aituation. A program to reduce pollution could well be

- economy wide, which would involve all plants in the power generatLon system. The need to consider the system raises two crucLal points for the power sector. First, much of the capital is already installed so that reducing pollution would involve conversion or scrapping decisions - both of these require information on the system in a particular country. Secondly, the fact that the power industry is in practice operated interdependently because of p;ak loads and uncertain demand, means that in considering whether to alter one plant (or even how to add capacity) the total system requirements need to be taken into account. Such a procedure is well established in power system planning, but the implication is that there is no simple or general guide to the prices at which varLous amounts of fuel substLtution take place - the answers are system speciflc.

- (v) Econometric studies of fuel switching in both the power and industrial sectors have been carried out for the industrialized countries. An attractive feature of the econometric approach is that it is not (typically) plant level, but rather is for the whole economy. It gives an average response which in principle could be used for economy wide policy analysis. There are two key issues which must be resolved satisfactorily before such eatimates can be used:

- (a) are the eatinated values an accurate guide to what is the actual situation in the country of study?

- (b) can estimates from one country or industry be used as reliable guides for another country or lndustry?
development strategy favored the rural economy, and investment in physical and social infrastructure. The implementation of the adjustment program led to gains in economic efficiency and to a more labor-intensive pattern of growth that ensured gains for most workers in lower income groups. In addition, budgetary transfers to local governments were protected while development expenditures were reallocated to sectors, such as agriculture and human resource development, with greatest potential benefit for the poor.

Table 3.1. Distribution of Household Expenditure in Indonesia 1993-2002

|Year|Mean|Lowest<br>Decile|2nd|3rd|4th|5th|6th|7h|8th|9th|10th|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1993|68.54|3.88|4.80|5.68|6.59|7.59|8.70|10.09|11.97|15.13|25.57|
|1996|86.62|3.57|4.39|5.20|6.05|6.99|8.08|9.44|11.34|14.63|30.31|
|1999|66.17|4.01|4.95|5.83|6.69|7.62|8.63|9.87|11.55|14.40|26.45|
|2000|74.21|3.98|5.24|6.08|6.88|7.72|8.70|9.89|11.56|14.35|25.60|
|2002|81.84|3.64|4.77|5.57|6.35|7.20|8.20|9.53|11.45|14.78|28.51|

Source: World Bank Global Poverty Monitoring Database.

Despite the remarkable progress achieved in the 1980s, poverty reduction and balanced regional development were identified as key challenges facing Indonesia in the 1990s (World Bank 1990). We now review achievements over that decade with reference to the analytical framework discussed above. Table 3.1 contains the data upon which we base our analysis. Each row of the table gives the mean of monthly household expenditures in 1993 Purchasing Power Parity<sup>11</sup> (PPP) dollars, along with expenditure share of each decile. The year 1993 (our base year) is also the year when Indonesia became a middle-income country according to World Bank’s classification.

> 11 The World Bank 1993 PPP conversion factor is equal to 635.655 rupiahs to a dollar.  This is what one would use to convert dollars in local currency in 1993.  The local Consumer Price Index (CPI) can then be used to translate these values into values for a year other than 1993.

15