rafmacalaba/datause-encoder-data huggingface dataset:
- 26 -

pattern. Those countries worth studying in detail because of a substantial potentiality for fuel swLtching in power are relatively few in number.

There ha. been relatively llttle systematlc otudy of fuel switching in developing countries for the power and industrial sectors. Thiu is largely because to capture the major changes it would be necessary to have a lengthy time serLes of data and this in general does not exist. The ten year span of the U.N. data used in tables 2.10, 2.11 and 2.12 is long enough to indicate certain trends, but it is inoufficient for more sophisticated statistical work as will be discusoed below. Data for a ten year perlod have been discussed for a series of reports prepared for the AsLan Development Banks Pakietan (Rhoe (1986)), 8. Korea (KLm 119861), Thailand (Khumsoong at al. (1986]), India (Bhatia (1986]), Bangladesh (al-Husainy (1986]), and some comparative material on industrial and developing countries is given in Leach, Jurass et al. 119861. In all of these there is much less detailed data available than for comparable studies of industrial countries.

Despite the lack of detailed industrial disaggregation and the relatively short runs of data available, the figures that are avaLlable suggest a number of important features of the mixture of fuels used in industry and in power generation:

- (i) Aggregate fuel mix in the industry sector is strongly influenced by the compoostion of the sector. O.B.C.D. experience suggests that any analysio of changing trends in fuel use ln the sector would need to take account of shifts in industrial structure, which in many countries has been strongly affected by the oil shocks as well as by the pace of economic growth. Thio suggests that it would be necessary to have disaggregated data for a successful investigation of inter-fuel substitution.

- _(LLi_ The fuel mix tends to be more evenly spread in the industrial sector than in the power sector, which suggests that there are more possibilitiLe for substitution in industry.

- (iii) In countries where a mixture of fuels was used for power generation in the 1970s there is usually evidence of substantial substitution away from oil

- in the following decade. These countries look to offer the best cases for studying fuel switching.

- (Lv) Many countrLes relied entirely on oil for thermal power generatlon in the 19709 and continued to do so despite the oil shocks. The reasons for thie pattern are probably connected
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda realistic. Therefore, for the long term, the real average covered wage growth rate was assumed to stabilize at a more moderate level of around 3% – slightly higher than in the private sector. This resulted in an increase of the average covered wage relative to the economy wide average wage from the current about 40% and 30% to about 45% and 40% for civil servants and teachers respectively as shown in Figure 38. The assumed wage growth policy for civil servants and teachers was suggested by and discussed with officers in MPS.

Clearly, the assumption that civil servant salaries remain at a level that is less than 50% of private sector average wages for the next 75 years is also a non realistic assumption. Low wages do not allow the government to retain qualified staff and promote corruption. The reform of the PSPF should be accompanied by a review and reform of the civil servant remuneration and hiring policy. While it is important that these two reforms be carried out simultaneously, it is beyond the scope of this study to analyze options for reforming the Ugandan civil service. This analysis is left for future investigation.

Figure 38 reports the projected average wage for civil servants and teachers as a percentage of private sector average wage resulting from the above assumptions.

**Figure 38: Projected civil servants average wage (% of economy wide average wage)**

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2003 2006 2009 2012 2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075<br>Year<br>government teachers<br>Average wage (% economy wide average wage)<br><!-- End of picture text -->

**Source** : WB calculations

# **_Demographic assumptions_**

## **Population Projections**

Population projections for Uganda used in the simulations were provided by the World Bank’s Population Unit. In line with the observed international trends, fertility and mortality rates are assumed to decrease over time. The total fertility rate – which is currently very high by international standards (about 600%) – is projected to gradually

- 81 -
entrepreneurship.<sup>3</sup> However, it is unclear whether these programs reduce migration, or instead promote it by giving youth marketable skills that they can use at destination.

We launched these interventions in April and May 2019. Randomizing the placement of the baseline survey questions, to before, or after, the provision of the informational video and offer of the alternative treatments, enables us to measure the immediate impacts of the treatments on knowledge, beliefs, and intentions. We find all three interventions improved knowledge about backway migration, with those treated being more likely to be aware of how expensive it is, and of the possibility of being forced to work without pay. However, we find no significant impacts on beliefs about the riskiness of the journey or expressed intentions to migrate, although this may reflect our baseline measurement of probabilistic intentions not working as anticipated. The Dakar treatment offered increased knowledge about migrating to Senegal, and reduced interest in learning more about wages in Europe in a behavioral task measure.

We then measured impacts on migration intentions, steps, and actions through an endline survey that took place between September and November 2020, approximately 18 months after the baseline and information intervention. We were able to successfully reinterview 89 percent of the youth, and combined with proxy reporting and WhatsApp location sharing, obtain the current location for all but one of our 3,641 youth. We have three main results. First, the interventions had lasting impacts on migration knowledge, and on intentions to migrate in the next five years. Eighteen months after treatment, those given the information treatment were 5 percentage points less likely to say they are considering taking the backway. The Dakar treatment increased knowledge about migrating to Senegal, reduced the intention to migrate to Europe by 3 to 4 percentage points, and increased intentions to migrate to Senegal by 3 to 7 percentage points. The offer of vocational training reduced intentions to migrate via the backway and to Europe by 5 to 10 percentage points.

> 3 In The Gambia, for example, the EU started The Gambia Youth Empowerment Project (YEP) in 2017 through its Emergency Trust Fund for Africa. https://www.yep.gm/ [Accessed on July 13, 2022.]

4
- Garay, L. J. (2008). Proceso nacional de verificaci ́on de los derechos de la poblaci ́on desplazada. _First Report to the Colombian Constitutional Court_ .

- Guidolin, M. and E. La Ferrara (2007). Diamonds are forever, wars are not: Is conflict bad for private firms? _American Economic Review 97_ (5), 1978–1993.

- Huffington Post (2010). Colombia’s Internally Displaced People.

- Ib ́a ̃nez, A. M. and A. Moya (2006). _C ́omo el desplazamiento forzado deteriora el bienestar de los hogares desplazados? an ́alisis y determinantes del bienestar en los municipios de recepci ́on_ .

- Ib ́a ̃nez, A. M. and A. Moya (2010). Vulnerability of victims of civil conflicts: empirical evidence for the displaced population in Colombia. _World Development 38_ (4), 647–663.

- Ib ́a ̃nez, A. M. and C. E. V ́elez (2008). Civil conflict and forced migration: The micro determinants and welfare losses of displacement in Colombia. _World Development 36_ (4), 659–676.

- Ib ́a ̃nez, A. M., A. Moya, and A. Vel ́asquez (2006). Hacia una pol ́ıtica proactiva para la poblaci ́on desplazada. _Bogot ́a: Universidad de los Andes, Secretariado Nacional de Pastoral Social Caritas Colombia_ .

- IPS (1999, September). Colombia: Desplazados engrosan econom ́ıa informal de Bogot ́a. http://www.ipsnoticias.net/1999/09/repeticion-ciudades-de-america-latina-colombiadesplazados-engrosan-economia-informal-de-bogota/.

- Jaeger, D. A., J. Ruist, and J. Stuhler (2018). Shift-share instruments and the impact of immigration. _NBER Working Paper N. 24285_ .

- Kerr, S. P., W. R. Kerr, and W. F. Lincoln (2015). Skilled immigration and the employment structures of US firms. _Journal of Labor Economics 33_ , S147–S186.

- Kugler, M. and E. Verhoogen (2011). Prices, plant size, and product quality. _The Review of Economic Studies 79_ (1), 307–339.

28
# **8. Concluding Remarks**

The global population of forcibly displaced people reached 70.8 million in 2018, the highest since after World War II. About one-third of these people are refugees, of whom more than threequarters are hosted in developing countries, mainly in Africa (UNHCR, 2018). This triggered increased interest in understanding the economic implications of hosting refugees in developing countries. Changes in household welfare outcomes are powerful in reflecting the net such implications. In light of this, I examine the impact of hosting refugees on household welfare in Ethiopia, a developing country hosting one of the largest numbers of refugees worldwide and the third largest in Africa.

To identify the impact, I exploit a large spatial difference in within-village temporal variations in refugee intensity, which followed the recent uptick in the flow of refugees into the country, conditional on household- and round-specific fixed effects and a rich set of time-varying covariates. However, I also confirm the main findings by extending the empirical strategy to an instrumental variables approach, instrumenting refugee intensity with a weighted sum of the number of refugees hosted in Ethiopia by country of origin, whereby the weights are the inverse geographic distances between a survey village and each of refugees’ countries of origin.

I find evidence that hosting refugees has different implications on household welfare depending on the type of welfare metric. While reducing consumption expenditure per capita with an estimated elasticity of about 0.19, it has no statistically significant effect on wealth. Based on a recent survey in Verme and Schuettler (2021), the finding on consumption is not in line with 80%90% of the existing evidence if the broader literature that examines the short- or long-term welfare impacts of hosting refugees, expellees, or internally displaced persons is considered. Similarly, while increasing households’ probability of falling into consumption poverty, it has no effect on wealth poverty status. In particular, I estimate that a 1% increase in refugee intensity increases the probability of falling into consumption poverty by about 18 percentage points. Decomposing household consumption expenditure per capita into food, education, and other non-food components, the results further reveal that hosting refugees alters the composition of consumption, as it solely affects food consumption expenditure. The consumption effects prevail in rural areas with no effects in urban centers, while no heterogeneity is found between the two areas concerning wealth and the status of wealth poverty results.

32
INTRODUCT1O N hvii Table 14 Annual Population Increase and Growth Rate<sup>by World Bank Region and Country Department,</sup> 1985-2100

||Annual po|pulation|increase|(millions|)|Ann|ual growt|h rate (|percent)||
|---|---|---|---|---|---|---|---|---|---|---|
|Regionand<br>coumtry<br>department|1985-90 2|000-05 2|025-30 2|050-75 21|00-25|1985-90 20|00-05 2|025-30 2|050-75 2|100-25|
|World|88.2|91.7|78.5|37.2|4.9|1.74|1.43|0.91|0.35|0.04|
|Borrowers and non-borrowers|||||||||||
|Sub-SaharanAfrica|15.4|22.6|27.2|18.2|2.2|3.11|2.92|1.91|0.82|0.08|
|Asia and Oceania|46.7|42.4|28.7|8.5|1.5|1.74|1.26.|0.68|0.18|0.03|
|Europe, Middle East,<br>and North Africa|15.2|17.3|16.6|9.1|1.0|<br>1.29|1.21|G.88|0.40|0.04|
|America|10.9|9.4|6.0|1.4|0.2|1.57|1.10|0.57|0.12|0.02|
|Borrowers only|||||||||||
|Africa Region|14.2|21.2|25.8|17.4|2.1|3.17|2.98|1.94|0.84|0.08|
|<br>1 Occidental<br>and Central|1.4|2.1|2.6|1.8|0.2|3.32|3.03|2.03|0.89|0.08|
|2Eastern|3.6|5.5|6.9|4.7|0.5|3.18|3.00|2.01|0.86|0.08|
|3South-Central<br>and<br>Indian Ocean|1.8|2.8|3.5|2.4|0.3|3.13|3.06|2.04|0.89|0.08|
|4 Western|4.1|5.8|5.8|3.5|0.4|3.15|2.86|1.63|0.69|0.07|
|5Sahellan|1.1|1.8|2.7|2.0|0.3|2.79|3.00|2.27|1.02|0.12|
|6Southern|2.2|3.3|4.3|2.9|0.3|3.33|3.05|2.06|0.8S|0.08|
|Asia Region|45.0|41.1|28.6|8.6|1.5|1.80|1.30|0.71|0.19|0.03|
|1 Other South|3.5|4.0|3.1|1.5|0.3|2.44|2.01|1.07|0.41|0.06|
|2 Southeast|6.0|5.5|4.1|1.3|0.2|2.08|1.46|0.83|0.22|0.03|
|3China|15.4|12.5|7.1|1.3|0.2|1.43|0.96|0.46|0.08|0.01|
|4 India|16.5|16.1|12.0|3.9|0.7|<br>2.05|1.53|0.86|0.24|0.04|
|5 IndonesiaandPacific|3.5|3.0|2.3|0.6|0.1|<br>1.98|1.32|0.76|0.18|<br>0.03|
|EMN Region|12.3|15.6|16.2|9.3|0.9|<br>2.44|2.17|1.43|<br>0.59|<br>0.05|
|<br>IPakistan and Turkey|*4.8|5.7|5.8|3.2|0.3|3.01|2.40|1.50|0.59|0.05|
|2 North Africa|1.6|2.0|1.8|0.8|0.1|<br>2.76|2.29|1.31|<br>0.46|<br>0.04|
|3 Middle East|5.4|7.4|<br>8.4|5.2|0.5|<br>2.99|2.68|1.74|<br>0.73|<br>0.06|
|4 Europe|0.5|0.4|0.2|0.0|0.0|0.44|0.37|0.14|0.03|0.01|
|LAC Region|8.4|7.5|5.7|1.6|0.2|<br>2.02|1.40|<br>0.80|<br>0.19|<br>0.02|
|<br>1 Bra2il|2.9|2.4|<br>1.7|<br>0.4|0.1|<br>2.05|1.28|<br>0.71|<br>0.14|<br>0.02|
|2 Central America|2.5|2.4|<br>1.9|<br>0.6|0.1|<br>2.25|1.61|<br>0.96|<br>0.23|<br>0.02|
|3 Caribbean and Other|1.5|<br>1.5|<br>1.1|<br>0.4|0.1|<br>2.08|1.49|<br>0.87|<br>0.23|<br>0.03|
|4 Temperate and Tropical|<br>1.4|<br>1.3|<br>1.0|<br>0.3|0.0|<br>1.77|1.28|<br>0.73|<br>0.17|<br>0.02|

Note: World Bank regions and country departments are defined in Exhibit 2.
firms. If multinationals use mostly Kazakhstan labor, their expansion is likely to increase the demand for Kazakhstan labor in these sectors. Here we estimate the impact of employing the upper or lower bound estimates of this share in all business services sectors.

We find that the impact on the welfare estimates of lower or higher share of imported inputs in the business services sectors is only one-tenth of a percentage point of consumption. But the impact on labor demand in the business services sector is more significant. For example, skilled labor demand in telecommunications increases by 5.8% with our central estimates of labor demand change, but would increase by 7.2% with the lower shares of imported inputs by multinationals and by 4.5% with higher shares of labor demand by multinationals. There is a similar range of results for labor demand in most of the business services sectors. With sufficiently high share of expatriate labor use by the multinationals, the demand for labor in the business services sectors would decline. But we expect to see an increase in the demand for labor in telecommunications, financial services and truck transportation, and a decline in air transportation services and science servicing. Except for air transport, the shift in employment in all sectors is less than ten percent of initial employment.

# **V. Conclusions**

In this paper we have developed an innovative small open economy computable general equilibrium model of the Kazakhstan economy that is capable of assessing the impact of the liberalization of barriers against foreign direct investment and the impact of local content and VAT policies in the oil and gas sectors. Estimates of the ad valorem equivalence of the barriers against foreign direct investment were prepared for this analysis based on surveys conducted in Kazakhstan. We find that the source of the largest gains to Kazakhstan from WTO accession is that additional multinational service providers will reduce the quality-adjusted cost of purchasing business services in Kazakhstan and that these gains are rather substantial when compared with the typical gains from constant returns to scale models of tariff liberalization. We believe that these results are consistent with the economic geography literature and the earlier urban economics literature that suggest that access to a diverse set of service providers with a domestic presence is crucial for growth.<sup>21</sup>

> 21 See Vernon (1960), McKee (1988), Marshall (1988), Holmes (1995), Hummels (1995), Chinitz (1961) and Fujita, Krugman and Venables (1999).

16
# I1I.2 **The** Fiscal Deficit and **The Inflation Tax: Statistical Evidence**

In the previous section we saw that the revenue from money creation appears to be closely associated to the deficit of the Consolidated Public Sector. This should not surprise us since the printing of money is one of the only two ways to finance the deficit of the CPS, the other being issuing new interest earning debt. Irn this section we will try to identify more closely the nature of this relationship using regression analysis.

Our methodology consists in considering the revenue from money creation as an endogenous variable that is explained, among other things by the CPS need to finance its deficit. We therefore consider the deficit of the CPS as the exogenous variable that gives raise to the need of resorting to the inflation tax. Because of the possibility of financing the deficit with internal or external debt one should not expect a one-to-one stable relation between the inflation tax and the deficit. In fact, as our discussion above indicates, there were periods in which it was apparent that debt financing was much more used than in others. Nevertheless, one should consider that debt generation in excess of real growth will eventually have to be canceled or financed through inflation as the market for placing new debt eventually dries out Since it was also obvioub from the inspection of the data that a change in the financing modality took place from 1976 onwards (towards using more debt financing), we have included a dummy (D76 equal zero up to 1975 and one afterwards). Regression III.1 reports the results concerning the relation between the inflation tax (ITAX) and the total deficit of the Consolidated Non-Financial Public Sector(DEFT).

REGRESSION III.1 ITAX 1.248 - 2.138 )76 + 0.564 DEFT (1.82) (-2.85) (6.19) R2 Adj.:0.6 D-W.Stat.:2.31 Sample: 1963-1987 The regression was also tried with correction for first order autocorrelation of residuals but the **AR(1)** coefficient did not turn out as significant.

20
Table 2: Characteristics of Women in Sample Who Work Outside the Home (n=441)

|variable|mean<br>Garment Indus|std dev<br>try (n=338)|mean<br>Non-Garme|std dev<br>nt (n=97)|P-value<br>diff in means|
|---|---|---|---|---|---|
|typical hours per day|11.780|2.448|8.209|1.850|0.000|
|wage (taka)|2946.413|1384.954|2842.243|1364.929|0.643|
|experience (years)|3.964|3.693|7.422|5.719|0.000|
|commute time (minutes)|19.160|13.208|n/a|n/a|n/a|

_Commuting data available for garment industry workers only_
regulations. According to surveys, the coastal cities continue to benefit from more satisfactory investment climate than the rest of the country. The capital city of Tunis, followed by the 2 other economic poles Sousse and Sfax are the top 3 ranked regions in terms of business climate index where business climate is considered "very satisfactory" mainly due to the quality of infrastructures, a better living environment, the existence of a dynamic local industrial fabric and the availability of skilled labor force. In contrast, the interior regions (North-West, Center-West and South-West) are ranked at the bottom of this index well below the average, with business climate considered as "not at all satisfactory."

**Table 4.** _Regional Ranking based on Business Climate Index_

|**Regions**|**Ranking**<br>**2015**|**Index**<br>**2015**|**Ranking**<br>**2016**|**Index**<br>**2016**|**Ranking**<br>**2017**|**Index**<br>**2017**|**Ranking**<br>**2018**|**Index**<br>**2018**|
|---|---|---|---|---|---|---|---|---|
|**Grand Tunis**_(Tunis/ Ariana/_<br>_Manouba/ Ben Arous)_|**1**|4,45|**1**|2,65|**1**|3,90|**1**|4,30|
|**North East**_(Bizerte/ Nabeul/_<br>_Zaghouan)_|**3**|3,70|**4**|1,96|**3**|2,95|**3**|3,40|
|**North West**_(Beja/ Jendouba/_<br>_Le Kef/ Siliana)_|**5**|2,90|**7**|1,75|**7**|2,30|**5**|2,81|
|**Center**<br>**East**<br>_(Sousse/_<br>_Monastir/_<br>_Mahdia/ Sfax)_|**2**|3,98|**2**|2,60|**2**|3,35|**2**|3,82|
|**Center West**_(Kairouan/_<br>_Sidi Bouzid/ Kasserine)_|**7**|2,60|**5**|1,92|**5**|2,40|**6**|2,72|
|**South East**_(Gabes/ Medenine/_<br>_Tataouine)_|**4**|3,10|**3**|2,16|**4**|2,70|**4**|3,25|
|**South West**_(Gafsa/ Tozeur/_<br>_Kebili)_|**6**|2,65|**6**|1,80|**6**|2,35|**7**|1,96|
|**_Average Index_**||**3,34**||**2,12**||**2,85**||**3,18**|

_Source : IACE reports on regional attractiveness, 2016, 2017, 2018, 2019_

# **4. Variables and Methodology**

The objective of this section is to identify the factors that influence the regional attractiveness of FDI inflows in Tunisia. We will attempt to provide a comprehensive analytical approach that looks at a set of economic, institutional and geographical determinants. The goal of this analysis is two-fold. First, we will ask how each governorate’s own economic and business environment contributes to the attractiveness of this type of export-oriented FDI. Second, we will empirically measure to what extent the inflows of FDI to the country tend to follow an agglomerate approach within certain regions. It is not the purpose nor scope of this paper to unpack the underlying reasons of this approach either from home or host country perspectives. Instead, we focus on investigating the determining factors that have influenced the longstanding geographical distribution of FDI primarily directed toward coastal regions. Using a spatial analysis, we aim to demonstrate empirically how the geographical location of certain regions and their
# **Other factors, such as the decline of the dollar contributed to food commodity price**

**increases.** The U.S. dollar depreciated about 35 percent against the euro from January 2002 to June 2008, and the depreciation of the dollar has been shown to increase dollar commodity prices with an elasticity between 0.5 and 1.0 (Gilbert 1989, Baffes 1997). However the dollar depreciated much less against most Asian currencies and a tradeweighted real exchange rate for U.S. bulk agricultural exports computed by the USDA (USDA 2008h) depreciated only 26 percent during that period. The elasticity should be less than 1.0, because the exchange rate does not pass-through completely in many countries due to policies (Shane and Liefert 2007). A comparison of the real tradeweighted exchange rate and the index of food prices (Figure 12) shows a general correspondence between dollar depreciation and food price increases. If the elasticity is taken as the mid-point of the range from 0.5 to 1.0, the increase in food prices due to the decline of the dollar would have been about 20 percent (26% x 0.75) between January 2002 and June 2008.

<!-- Start of picture text --> Figure 12. Food Prices vs. Exchange Rate<br>250<br>Food<br>Real Exchange Rate<br>200<br>150<br>100<br>50<br>0<br>Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08<br>Source: DECPG calculations based on USDA data.<br><!-- End of picture text -->

**Speculative and investor activity has also increased and could have contributed to food price increases.** A reflection of this increased activity was the quadrupling of the number of wheat futures contacts traded on the Chicago Board of Trade from 2002 to 2006 as shown in Figure 13. However, the increase in futures contracts does not coincide closely with the increase in wheat prices, which raises doubts about the impact on prices. The impact on prices is hard to quantify and most studies do not find that such activity changes prices from the levels which would have prevailed without such activity (Gilbert 2007), however, they may change the rate of adjustment to a new equilibrium when fundamental factors change.

15