rafmacalaba/datause-encoder-data huggingface dataset:
# **5.3** **_Real incomes_**

The SILC survey collects detailed household data on disposable income. Figure 7 and Figure 8 display the evolution of real incomes between 2011 and 2018 for the 1st and 10th deciles as well as the average income and the overall CPI.<sup>13</sup> All the numbers are converted into real values using decile-specific inflation rates and normalized to 100 using baseline values as a reference. The graphs confirm the inclusiveness of economic growth in Turkey during the 2010s. The incomes of households from the bottom of the distribution expanded faster than the incomes of the wealthiest households and also faster than the average income. However, the pace of real income growth slowed down as Turkey entered a new period of high inflation in 2017. Between 2011 and 2016, the annualized real growth rate of 1st decile income was 6.3%. Between 2016 and 2018, the corresponding figure dropped to 1.2%. Nominal income figures hide the slowdown since nominalbased growth was similar across the two period (15.2% for the years 2011-2016 and 14.9% for the period 2016-2018). In other words, incomes have been increasing at a similar rate, but this has not translated into stronger purchasing power for the 1st decile after 2016.

Figure 7: Average and 1st decile real income (2011-2018, 2011 =100) <!-- Start of picture text --> 150 18<br>16<br>140 136.0 135.1<br>139.3 14<br>126.7<br>130 12<br>116.5 10<br>120 114.1<br>8<br>107.6<br>110 6<br>100<br>4<br>100<br>2<br>90 0<br>2011 2012 2013 2014 2015 2016 2017 2018<br>1st Decile Overall CPI<br>Real Income<br>Annual CPI Change (%)<br><!-- End of picture text --> _Source_ : World Bank calculations using data from TUIK and the SILC Given the importance of labor income for the bottom half of the distribution –represents 60% of total income in 2019, we look at cash earnings among full-time only workers. Decile specific inflation adjustments are again used to account for distributional differences in changes of cost of living across income groups. Figure 9 displays changes in the real labor income of the

> 13 As mentioned in the methodology section, the deciles used in the income analysis are constructed using income information from the SILC which are different from the expenditure based deciles used in the previous sections. However, the distributional inflation rates are still calculated using the HBS.

19
|**Table A17: Heritage Foundati**<br>**Data Provider**<br>Heritage Foundation|**on Ind**|**ex of**|**Econo**|**mic Fr**|**eedom**|**(HER**|**)**|||
|---|---|---|---|---|---|---|---|---|---|
|**Description**<br>The Heritage Foundation<br>headquartered in Washington<br>**Website**<br>www.heritage.org<br>**Data Source**<br>Index of Economic Freedom<br>**Type**<br>Expert Assessment<br>**Respondents**<br>Staff of Heritage Foundation<br>**Frequency**<br>Annual since 1995<br>**Coverage**<br>Global sample of countries<br>**Public Access**<br>Yes|is a<br>, Unite|nongov<br>d States|ernmen<br>, advoc|tal res<br>ating co|earch<br>nserva|and e<br>tive pu|ducatio<br>blic poli|nal in<br>cies.|stitute|
|**Description**<br>Heritage constructs an Index<br>were major revisions to the<br>these components that are<br>comparable over time: Inv<br>These indicators are scored o<br>the previous year: we therefo|of Ec<br>method<br>based<br>estmen<br>n a 100<br>re lag t|onomic<br>ology i<br>on sub<br>t Free<br>-point<br>he data|Freed<br>n 2006<br>jective<br>dom, F<br>scale.<br>from t|om con<br>and 200<br>assess<br>inancia<br>Note th<br>his sour|sisting<br>7. We<br>ments<br>l Freed<br>at the i<br>ce by o|of 10<br>use d<br>of Her<br>om, a<br>ndicato<br>ne year|compon<br>ata from<br>itage s<br>nd Prop<br>rs refer<br>.|ents.<br>the th<br>taff an<br>erty R<br>to dat|There<br>ree of<br>d are<br>ights.<br>a from|
|2008<br>**Voice and Accountability**<br>NA<br>..<br>**Political Stability and Absence of Violence**<br>NA<br>..|2007<br>..<br>..|2006<br>..<br>..|2005<br>..<br>..|2004<br>..<br>..|2003<br>..<br>..|2002<br>..<br>..|2000<br>..<br>..|1998<br>..<br>..|1996<br>..<br>..|
|**Government Effectiveness**<br>NA<br>..|..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**||||||||||
|Foreign investment<br>X<br>|X|X|X|X|X|X|X|X|X|
|Banking / Finance<br>X|X|X|X|X|X|X|X|X|X|
|**Rule of Law**||||||||||
|Property Rights<br>X|X|X|X|X|X|X|X|X|X|
|**Control of Corruption**<br>NA<br>..|..|..|..|..|..|..|..|..|..|
|**Country coverage**<br>**179**<br> <br>|**157**<br>|**157**<br>|**157**<br>|**155**<br>|**155**<br>|**156**<br>|**155**<br>|**161**<br>|**150**<br>|
|**Year of Publication**<br>**2009**|**2008**|**2007**|**2006**|**2005**|**2004**|**2003**|**2001**|**1999**|**1997**|

56
# Market Access and Development of the ICT Sector in the West Bank

### Chiara Fratto<sup>_∗_</sup> and Elisa Giannone<sup>_†‡_</sup>

Keywords: ICT, market access, input-output, mobility restrictions, West Bank JEL codes: O18, F1, O25, O33, R1

## **1 Introduction**

Despite the popular view that, with the advent of digital technologies, the world has become flat (Friedman (2005)), geography still plays a key role in the economy. We commute every day, and merchandise gets shipped every day. People and goods travel constantly and costs are not trivial. This is true at a large scale for shipments along the global supply chains as well as at a smaller scale for daily commutes of workers. Geography has become even more important in the COVID-19 era, with teleworking, travel restrictions, lockdowns, and the disruption of the global supply chain (Papanikolaou and Schmidt (2020), Dingel and Neiman (2020)).

Some industries are affected by spatial concerns more than others (Kolko (2010)). Distance can be an impediment to access international markets or a protectionist device to nurture local firms (Krishna and Krueger (1995)). So, this begs the question: in a world with scarce resources, would one dollar be better invested towards shortening distances, such

> _∗_ Chiara Fratto (corresponding author), cfratto@imf.org, International Monetary Fund, 700 19th St NW, Washington, DC 20431.

> _†_ Elisa Giannone, Pennsylvania State University, State College, PA 16801.

> _‡_ The authors acknowledge the generous funding of PEDL-CEPR Exploratory Grant and the World Bank. The authors wish to thank all the participants of the 2019 World Bank’s workshop on the digital economy in MENA for their valuable feedback, and in particular Daniel Lederman, Christina Wood, Bob Rikjers, and Ana Paula Cusolito. We are grateful to conference and seminar participants for their helpful comments and suggestions, including Fernando Alvarez, Faisal Awartani, Ufuk Akcigit, Chris Blattman, Jonathan Dingel, Richard Hornbeck, Erik Hurst, Rula Kher, Eliana La Ferrara, Omar Omran, Alessandra Voena. We thank Elia Pietro Boe for his excellent research assistance, and Alpha International for Research Polling, and Informatics for its technical assistance. The views expressed in the paper are those of the authors and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. The views expressed in the paper do not necessarily presented the views of the Palestinian Central Bureau of Statistics.

1
when i= j, where α is the vector of CDE substitution parameters used in the GTAP model and s is the vector of consumption shares. For each country represented in the GTAP database, we use the estimated parameter values for α in the GTAP database (Dimaranan 2006). In the few cases where country estimates were not available in the GTAP database, we used the relevant regional estimates. The consumption shares s for each household were obtained directly from the household survey data by calculating the observed consumption shares for each commodity relative to total consumption. As an indication of the broad range of the own-price elasticities of demand implied by the CDE, we present own-price elasticities of demand for major commodities in each country in Appendix table 3. These values appear to be reasonable, with average absolute values generally between 0 and 0.3.

Using the elasticities obtained from (5) and (6), we express second-order changes in the cost of utility for each household as where the ∘ operator denotes element-wise multiplication, i.e. a Hadamard product; ε is a matrix of compensated demand elasticities, p∘x is a vector of expenditure values and p� is a vector of percentage changes in prices. The first term on the right side of (7) is a first-order approximation of the change in expenditure needed to achieve a given level of utility, while the second term takes into account the ability of consumers to adjust their consumption patterns in response to changes in prices in the long run.

On the farm profit side, we evaluate each segment of the second order matrix π π pp pw �πwp πww<sup>� calculated separately with medium- and long-run assumptions, in each case</sup> following closely the production structure used in the GTAP model. For the medium-run elasticities, we assume that all factors but labor are immobile and the production structure is as in Figure 1. For the long-run elasticities, we follow the production structure in Figure 2, with all factors mobile except land and natural resources, which can be transformed between commodityproducing activities with an elasticity of transformation equal to one as in the GTAP model. While our calculation of the elasticities is based on the same parameters as used in the standard GTAP model, each household is considered to have its own specific set of endowments which are imputed from its observed outputs using the factor intensities provided by the GTAP 10
Policy ReseaRch WoRking PaPeR 4740

# **Abstract**

Concerns have been raised about the impact of rising food prices worldwide on the poor. To assess the (short term) impact of rising food prices in any particular country it is necessary to look at both the impact on food producers (who benefit from an increase in prices) and food consumers (who loose out when the price increases), with a focus on poor producers and consumers. In Ghana, the impact of a change in the price of rice is not ambiguous because a large share of the rice consumed is imported, so that the negative impact for consumers is much larger than the positive impact for producers. For maize by contrast, the impact is ambiguous since much of the consumption is locally produced. Using a recent and comprehensive household survey, this paper provides an assessment of the potential impact of higher food prices on the poor in Ghana using both simple statistical analysis and non-parametric methods. The paper finds that rising food prices for rice, maize, and other cereals would together lead to an increase in poverty, but that by contrast to a number of other countries, this increase, while not negligible, may not be as large as feared.

This paper—a product of the Development Dialogue on Values and Ethics, Human Development Network—is part of a larger study by the Africa Chief Economist Office and the Development Dialogue on Values and Ethics on the impact of the food price crisis in Africa and the policy responses available to governments. This research was started in the Africa PREM department and benefits from funding from the Africa Region Regional Studies Program as well as the Belgium and Luxemburg Poverty Reduction Partnerships. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at qwodon@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
-40-

inflation. These numbers are complemented with others from a previous study on financial liberalization in South East Asia<sup>21</sup> .

The values of the efficiency cost depend on the assumption about the inflation rate (in the first column). They are higher than those that are obtained by estimation of the demand for money (transaction) in industrialized economies (Cagan [1956]). This properties was discussed already in Section III.

Table 4 The Efficiency Cost of TAxatiQn

|Country|Inflation<br>Rate|MEC|
|---|---|---|
|Somalia|60%|.43|
|Zaire|60%|.43|
||100%|1.0|
||200%|Z|
|Thailand|5% a/|.58|
|Indonesia|13% a/|1.19|

Notes: All values are in percentage points.

a/ This value is the difference between the average real rats before and after the liberalizations. (For more discussion see Chamley and Qaizar, [1989]).

Comparison with the Cross-Section Evidence 21Chamley and Hussain [1989].
_Low-Income Countries’ Access to Private Debt Markets_

# **Figure 8 GNI per capita when developing county first issued sovereign bond**

<!-- Start of picture text --> 8000<br>6000<br>4000<br>2000<br>0<br>Low-income countries<br>-2000<br>cut-off (in $2000)<br>Deviation of GDP per capital from low-income<br><!-- End of picture text --> Private debt issuance by developing countries has become dominated by the corporate sector. Private corporations’ share of total private debt issuance has risen from one-half at the beginning of the decade to two-thirds in 2007, while governments’ (sovereign debt issued to private creditors) share declined from one-third to a low of only 7 percent (figure 9).

**Figure 9 Private debt issuance by developing countries, 1990-2008**

_Source:_ Dealogic DCM Analytics and Loan Analytics. _Note:_ P = projection.

The conventional wisdom is that countries must first gain access to the international bond market by issuing sovereign bonds, in order to set a benchmark for pricing subsequent corporate issues. Although this has been the case for the majority of countries, there are many examples where corporations based in developing countries have issued bonds prior to sovereign issues. For instance, two commercial banks in 12
# **Table A.4.2 Chile: Innovation Output**

||**Patents**|**Innovation**<br>**sales**|**Product**<br>**innovation**|**Process**<br>**innovation**|
|---|---|---|---|---|
|Constant|-3.21|N/A|-0.33|-1.28|
|**Firm Characteristics**|||||
|Export active|**_0.40_**|0.11|0.04|**0.46**|
|Firmage (lnage)|**0.49**|0.04|**_0.13_**|0.01|
|Firmsize (lnsize)|-0.04|-0.06|-0.09|**_0.13_**|
|**Collaboration on innovation with**|||||
|Universities|**1.48**|**0.53**|**1.49**|0.62|
|Consultants|-0.26|-0.10|0.39|0.50|
|Suppliers|**0.64**|0.28|**1.22**|**1.34**|
|Other firms|0.10|-0.09|-0.04|0.26|
|**Absorptive capacity**|||||
|R&Ddepartment|**0.53**|0.16|**0.65**|0.35|
|Innovation intensity|0.02|0.02|**0.08**|**0.19**|
|Investmentin innovationeducation|**0.62**|**0.54**|**0.98**|**0.72**|
|Permanentinnovationactivities|-0.12|**0.66**|**1.03**|**1.15**|
|**Human capital**|||||
|Knowledge of managersratedhigh|-0.35|**_-0.22_**|-0.14|-0.02|
|Knowledge ofsupervisorsratedhigh|**_0.41_**|**0.30**|**0.39**|**0.44**|
|Knowledge ofworkersratedhigh|**0.52**|**-0.49**|**-0.50**|**-0.58**|
|Managers education and training rated<br>high|-0.36|**-0.24**|**0.59**|-0.11|
|Supervisors education and training rated<br>high|0.10|**0.31**|-0.24|0.23|
|Workereducationand trainingratedhigh|-0.41|0.10|0.28|0.22|
|**Sector– Manufacturing**|||||
|Food,Beverages andTobacco|Ref|Ref|Ref|Ref|
|Textile, WearingApparelandLeather|-0.01|**_0.25_**|0.12|0.00|
|Wood and wood products,furniture|**0.64**|**0.38**|**_-0.37_**|0.18|
|Paper, printing and publishing|0.30|**0.40**|-0.14|0.05|
|Chemicals, petroleum, coal, rubber and<br>plastics|0.14|0.09|0.00|0.05|
|Non metallic mineral products, except<br>products ofpetroleumand coal|-0.61|**0.68**|**1.16**|0.07|
|Basicmetal industry|0.38|-0.25|**_-0.77_**|-0.11|
|Fabricated metal products, machinery and<br>equipment|-0.20|0.03|0.03|0.12|
|Other manufacturingindustries|-0.16|**_0.25_**|-0.32|-0.64|
|Observations<br>|4570|4570|4570|4570|
|R<sup>2</sup>|0.25|0.08|0.33|0.37|
|LogLikelihood|-164|-855|-311|-295|

Note: **Bold** is denoting significance in a 5% level. **_Bold + Italic_** denotes significance in a 10% level.

30
targeted policies and interventions and the evidence-based approaches required to address the deprivations faced by men and women and boys and girls living in forcibly displaced households.

The comparison with monetary poverty shows the differences and complementarity of the measures, can provide a first step to better understand the different conditions and challenges faced by forcibly displaced communities, and suggests potential policy responses for addressing them. The findings demonstrate the value added of the multidimensional monetary poverty measure to analyze the overlapping deprivations, particularly in contexts of forced displacement. The MPI’s indicators allow policy makers to identify the arenas for intervention, and further analysis decomposing each country’s MPI in greater detail and disaggregating by geographic and administrative areas, as well as age groups, would benefit targeting measures.

In general, the results offer compelling evidence in support of a more disaggregated multidimensional poverty analysis. Our findings indicate that differentiation by gender of the household head, displacement status and subgroups of headship have implications for policy and targeting. This is important with the existing limitation of data (particularly in situations of displacement) to capture gender differences in multidimensional poverty. Due to data limitations, our analysis of differences is limited to the gender of the household head and type of headship. Although the analysis at the household level remains relevant given the high prevalence of female-headed households in displacement situations, individual level analysis is invited as individual level data becomes available.

# **References**

Agbodji, A. E., Batana, Y., M., and Ouedraogo, D. (2015). Gender Inequality in Multidimensional Welfare Deprivation in West Africa. _International Journal of Social Economics_ , 42(11): 980-1004.

Alkire, S. and Foster, J. (2011). Counting and Multidimensional Poverty Measurement. _Journal of Public Economics_ , 95(7): 476-487.

<mark>Alkire, S., Foster, J. E., Seth, S., Santos, M. E., Roche, J. M., and Ballon, P. (2015).</mark> _<mark>Multidimensional Poverty Measurement and Analysis</mark>_ <mark>. Oxford: Oxford University Press.</mark> Alkire, S. and Jahan, S. (2018). ‘The new global MPI 2018: Aligning with the Sustainable Development Goals’, OPHI Working Paper 121, University of Oxford.

Alkire, S., Kanagaratnam, U., and Suppa, N. (2020). ‘The global Multidimensional Poverty Index (MPI) 2020’, OPHI MPI Methodological Note 49, Oxford Poverty and Human Development Initiative, University of Oxford.

<mark>Appleton, S. (1996). Women-headed households and household welfare: An empirical deconstruction for Uganda.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>24</mark>_ <mark>(12), 1811-1827.</mark> Assad, R., Krafft, C., & Pastoor, I. (2021). Gender Role Attitudes and Gendered Outcomes: Syrian Refugee Adolescents in Jordan. World Bank and Policy Research Working Paper, forthcoming. World Bank Group.

29
|_σ_|Risk aversion|2|
|---|---|---|
|_γ_|Consumption component (utility)|.36|
|_β_|Discount factor|0.98|
|_α_|Capital share|0.34|
|_δ_|Depreciation rate|0.04|
|_λ_|Convergence rate|0.001|
|_φ_|Elasticity of the discount factor|0.001|
|_τ_|Foreign investment cost|1_._397_×_10<sup>_−_4</sup>|
|_~~g~~_|Long run growth rate|1.0055|

Table 1: Calibrated parameters data are from the OECD’s statistics. G.6’s average PPP GDP is calculated by taking the total G.6’s PPP GDP divided by total G.6’s population. The output ratio is the ratio of G.6’s average PPP GDP and the U.S.’s PPP GDP obtained also from the OCED database. Data is quarterly from 1970,QI -2000,QI<sup>8</sup> .

|||Estimated values|Standard errors|
|---|---|---|---|
|_ρz_|Persistence of transitory shocks|0.8404|(0.0002)|
|_ρg_|Persistence of growth shocks|0.5598|(0.0001)|
|_σ_<sup>_H_</sup><br>_z_|Standard deviation of Home transitory shocks|0.0070|(0.0000)|
|_σ_<sup>_F_</sup><br>_z_|Standard deviation of Foreign transitory shocks|0.0040|(0.0000)|
|_σg_|Standard deviation of growth shocks|0.0042|(0.0000)|
|_φ_|Adjustment cost|1.5086|(0.0005)|

Table 2: Estimated parameters

# **4.2 Impulse Responses and Business Cycle Moments**

To gain insights into how valuation effects influence the NFA position, I study the impulse responses to the two kinds of productivity shocks. Figure 6 in Appendix D contrasts the impulse responses of consumption, investment, current account, valuation effects and changes in net foreign assets to a 1% (i.e. _εz_ = 0 _._ 01) positive _transitory_ productivity shock and a 0.1% (i.e. _εg_ = 0 _._ 001) _trend_ (growth) productivity shocks.

The ratio of current account to income has a positive response (of about 0.5%) to

> 8I restrict the data until 2000 because after 2000, a significant part of U.S.’s consumption results from trading with China.

15
**Table 1: Greenfield FDI by Destination and Sector in MENA ($US Billion), 2003-2012**

**<mark>The Distribution of Greenfield FDI into MENA countries by Destination and Origin 2003-2012</mark>** _<mark>$US Billion</mark>_

||**Resources and**<br>**Energy**|**Non-resource**<br>**Manufacturing**|**Tradable**<br>**Services**|**Non-Tradables**|
|---|---|---|---|---|
|Algeria|21.1(33)|19.2(30)|12.7(20)|11.9(18)|
|Bahrain|4.1(15)|3.7(13)|13.7(49)|6.4(23)|
|Egypt|31.9(31)|12.1(12)|18.9(18)|41.6(40)|
|Iran|23.2(67)|10.0(29)|0.7(2)|1.0(3)|
|Iraq|24.6(36)|1.3(2)|18.8(28)|23.3(34)|
|Jordan|2.3(8)|5.8(19)|15.4(50)|7.1(23)|
|Kuwait|1.6(18)|0.2(2)|4.4(52)|2.4(28)|
|Lebanon|0.4(4)|1.2(11)|5.6(54)|3.2(31)|
|Libya|9.7(25)|1.7(4)|3.1(8)|24.0(62)|
|Morocco|9.9(21)|8.1(17)|15.4(33)|13.8(29)|
|Oman|7.5(18)|16.4(40)|5.0(12)|11.9(29)|
|Qatar|46.5(45)|11.9(12)|14.7(14)|29.2(29)|
|Saudi Arabia|69.1(52)|30.1(23)|23.1(17)|11.4(9)|
|Syria|11.7(35)|2.5(7)|14.7(44)|4.5(13)|
|Tunisia|5.2(12)|3.8(8)|16.3(37)|19.3(43)|
|United Arab Emirates|7.8(6)|24.8(19)|54.0(41)|44.9(34)|
|Yemen|6.2(62)|0.7(7)|1.6(16)|1.4(14)|
|Total|282.2(30)|153.5(16)|238.1(26)|257.3(28)|

Source: Authors’ calculations based on fDi Markets. GCC=Gulf Cooperation Council. Row percentages in parentheses.

26