rafmacalaba/datause-encoder-data huggingface dataset:
- 67 -

Table 29: Per Capita Public Expenditure (Revenue)al on Health by States

||Per capita<br>at 1980-<br>|expenditure<br>81 prices|Rate of<br>growth|Lif<br>Expect|e<br>ancy|Inf<br>Mort|ant<br>ality|
|---|---|---|---|---|---|---|---|
||1976-77|1986-87||1976|1986|1976|1985|
|Andhra Pradesh|18.1|30.4|5.3|47.9:|53.1|122|83|
|B.ihar|8.7|15.0|5.6|42.3|46.0|NA|105|
|Gujarat|20.2|39.6|7.0|50.2|52.4|146|98|
|Haryana|20.4|37.5|6.3|52.9|54.8|112|85|
|Karnataka|20.0|23.2|1.5|54.5|56.3|89|71|
|Kerala|24.8|29.3|1.7|61.7|65.5|56|32|
|Madhya Pradesh|17.6|18.3|0.4|46.9|49.0|138|122|
|Maharashtra|20.3|44.7|8.2|53.5|56.3|83|68|
|Orissa|15.7|32.2|7.4|44.0|49.1|127|130|
|Punjab|24.1|32.8|3.1<br>.|58.4|60.5|108|71|
|Rajasthan|21.4|32.8|4.4|49.3|51.9|142|108|
|Tamil Nadu|22.9|33.3|3.8|50.3|53.4|110|80|
|Uttar Pradesh|10.0|19.1|6.7|42.8|4tX.2|178|140|
|West Bengal|22.0|25.4|1.4|49.6|52.0|NA|77|

# Source: RBI Bulletins, Various Issues.

a/ Includes expenditure on family welfare and water supply.

and Madhya Pradesh. These states have the highest concentration of poverty, the lowest life expectancy and the highest infant mortality. However, there - appears to be no overall bivariate relationship not even a high rank correlation - between health expenditures and outcomes. This is largely because life expectancy and infant mortality depend less on total health outlay than on its composition, together with other factors such as primary health care, immunization, protected water, female education, etc. Detailed break-up of health expenditures is not available state-wise.

Assuming that the relative share of various segments of health expenditure re..ained unchanged (which is by and large the case in most
Tanzania’s negative deviations from maize production trends in 1980, 1982, and 1983 indicate increases in poverty in the case-study simulations. In the case of Scenario 1980, the cost of living effect served to reduce poverty by 2400 people, but was overwhelmed by the 5200 people impoverished by the earnings effect. In the cases of Scenarios 1982 and 1982, there were sharp increases in food prices arising from the negative production shocks to maize, leading to major increases in poverty through higher costs of living.

**Table 6: Poverty Reduction in Tanzania Due to Global Maize Production Deviations from Trend (1000s of people)**

|**Year**|**Poverty Cha**<br>**Expo**<br>|**nge Under Re**<br>**rt Ban, by Eff**<br>|**gime 1, No**<br>**ect**|**Additional**<br>**Export Ban (Di**<br>**and R**<br>|**Change in Pove**<br>**fference betwe**<br>**egime 1), by Ef**<br>|**rty Due to**<br>**en Regime 2**<br>**fect**|
|---|---|---|---|---|---|---|
||**Cost of**<br>**Living Effect**|**Earnings**<br>**Effect**|**Total**|**Cost of**<br>**Living Effect**|**Earnings**<br>**Effect**|**Total**|
||**I**|**II**|**III**|**IV**|**V**|**VI**|
|1995|-149.60|-5.20|-149.60|2.70|1.70|4.40|
|1971|-124.20|-8.20|-124.20|0.80|1.10|1.90|
|1980|-2.40|5.20|-2.40|0.30|-0.10|0.20|
|1982|135.40|9.00|135.40|0.00|-0.20|-0.20|
|1983|173.70|25.00|173.70|0.10|0.00|0.10|

Source: Authors’ simulations The presence of an export restriction (Regime 2) is found to be generally poverty increasing. In the case of Scenarios 1995 and 1971 it dampens both improvements in the cost of living as well as the effects of greater earnings. For Scenarios 1980, 1982, and 1983, the presence of an export exacerbates the lower factor returns, and leads to higher values for the poverty-increasing earnings effects.

Aside from generally being poverty-increasing (or dampening poverty reduction), the export restriction also has distributional implications. In the four scenarios where the export restriction increased poverty (1995, 1971, 1980 and 1983), the poverty increases were among households that depended on agriculture as the primary source of income, relied on transfers, or had diverse sources of income. In contrast, the export ban actually had the marginal effect of reducing poverty among households that were not involved in agriculture, or relied on wages as their main source of income. The export restriction generally depresses returns to factor incomes, especially in agriculture. At the same time, households that rely less on agriculture are less detrimentally affected by lower factor incomes. So, even though all households benefit from the lower prices that an export ban might induce, wage20
# **Figure 2. Evolution of the Sensitivity of Consumption to Income**

- **a. Cross-sectional -** **_All Countries_**

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>1965 1980 1995 2010<br><!-- End of picture text -->

## **b. Panel -** **_All Countries_**

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>1965 1980 1995 2010<br><!-- End of picture text -->

- **c. Cross-sectional -** **_Advanced Economies_**

## **d. Panel -** **_Advanced Economies_**

<!-- Start of picture text --> 1 1<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>1965 1980 1995 2010 1965 1980 1995 2010<br>Cross-sectional - Developing Economies f. Panel - Developing Economies<br>1 1<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>1965 1980 1995 2010 1965 1980 1995 2010<br><!-- End of picture text -->

- **e. Cross-sectional -** **_Developing Economies_**

Notes: The figure shows the evolution of cross-section (left panel) and panel estimates (right panel) of regressing consumption growth on output growth. Sensitivity of consumption to income refers to the coefficient derived from a regression of consumption growth on output growth. The dots in the left panel show averages over a 12year rolling window. The dots in the right panel show coefficient estimates of 12-year rolling panels. The lines show the 9-year moving average trendlines.

17
But beginning in the Soviet era and extending over nearly three decades following independence, policy has strongly resisted both migration and urbanization in Uzbekistan. As a Soviet Republic, Uzbekistan experienced a rapid fall in the urban population share, declining from 34 percent in 1959 to less than 26 percent in 1989. This eventually left the country as or less urbanized than Soviet Tajikistan or Kyrgyzia (at 25.7 and 37.5 percent in 1989, respectively).<sup>4</sup> No population census has been completed in the intervening years, and the current urban population share is imprecisely measured.<sup>5</sup> However, administrative records suggest that the pace of urbanization stagnated in 2009, leaving the country more than 50 percent rural (using new rural/urban classifications), and substantially less urbanized than otherwise comparable countries.

This paper focuses on two key impediments to achieving the urbanization and growth targets set out by the Government of Uzbekistan: legal limits on internal migration, and the high cost of housing in the most dynamic urban centers. Detailed information on the domestic registration system (commonly referred to as the _propiska_ system), domestic migration, and housing costs are drawn from the Listening to the Citizens of Uzbekistan (L2CU) study, which began in 2018 and remains ongoing as of this writing. The discussion then moves to the views of citizens on how to resolve these challenges. Reform preferences of a nationally representative sample of households are described, and an embedded survey experiment provides additional validation.

This analysis comes in the context of comprehensive national economic reforms. Under new presidential leadership since September 2016, the government set out to transform the prevailing stateled growth model and rapidly become a high-income country. A central objective of these reforms is ensuring that the labor market opportunities generated under the new economic model are widely shared among the population. This paper describes key bottlenecks to these goals. Without action to address the forces slowing domestic migration, theory and international experience suggest that output and productivity growth will be muted, while incomes and employment will remain below potential.

The government has set about rolling back some of the impediments to urban growth. A presidential decree in January 2019 laid out a high-level vision for urban development over the coming years, including:

- Creating conditions for free movement of people from rural to urban areas

- Providing special measures to support investment in residential construction

> 4 Soviet Demographic Yearbook 1991. 5 No national population census has been conducted in Uzbekistan since independence from the Soviet Union in 1991.

6
Figure 5. GVC participation of China and the other developing East Asia relative to the other regions of the world (percent of gross exports).

<!-- Start of picture text --> a. Backward participation in GVCs b. Forward participation in GVCs<br><!-- End of picture text --> <!-- Start of picture text --> China World China World<br>Other East Asia* Developing East Asia (excl. China) Other East Asia Developing East Asia (excl. China)<br>Other ECS LCN (excl. Mex) Other ECS LCN (excl. Mex)<br>MEA North America (incl. Mex.) MEA North America (incl. Mex.)<br>Other EAS SAS Other EAS SAS<br>40 SSF Western Europe 40 SSF Western Europe<br>35 35<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: Authors’ calculations based on OECD Trade in Value Added (TiVA) Notes: Backward participation is measured as the share of foreign value added in the total gross exports of a country. Forward participation is a country’s domestic value added that is reexported by other countries, as a share of the country’s exports.

*”Other East Asia” ‐ includes BRN, HKG, KOR, JPN, MAC, SGP, TWN; Other EAS ‐ East Asia and Pacific (all income levels) except East Asia; Other ECS ‐ Europe and Central Asia (all income levels) except Western Europe; LCN ‐ Latin America & the Caribbean (all income levels); MEA ‐ Middle East and North Africa (all income levels); SAS ‐ South Asia; SSF ‐ Sub‐Saharan Africa (all income levels).

There are important differences in GVC participation between developing East Asian countries (Figure 6). Within developing East Asia, the share of foreign value added in gross exports ranges from high for Malaysia to low for Indonesia (as well as Lao PDR and Myanmar, based on the EORA MRIO data). In terms of dynamism, interesting patterns emerge for three countries. Vietnam’s participation in GVCs has increased steadily overtime and especially in recent years, reflecting its deeper engagement into GVCs. In contrast, the Philippines saw a decline in the backward participation – possibly reflecting a retreat from GVCs, rather than an upgrade to more value added‐intensive activities. Finally, for China, the share of foreign value added in gross exports has been declining since the mid‐2000s, reflecting the substitution of imported inputs for domestically‐produced ones. At the same time, China has gradually increased its forward participation in global value chains with increasing exports of intermediate inputs.

12
liquidity constraints more binding. Even though the pandemic crisis did not affect income at home as much as expected income at destination, the pandemic generated a negative shock by lowering remittances received by most households at home. This reduction may prevent potential migrants from affording the costs of moving – which seem to have increased with border closing and higher smuggling fees.

Despite this reduction in intentions to migrate, the desire to migrate to Europe is still incredibly high amongst these young men, with 65 percent saying they are likely or very likely to try to migrate, and 58 percent saying they would consider the backway. This suggests the need for exploring policy options to open up legal migration alternatives, given the risks of backway migration.

Finally, from a methodological viewpoint, this paper also illustrated how priming and salience experiments can be used in migration studies to investigate the impact of different factors on migration intentions. In our case, the COVID-19 pandemic was already so well-known and salient that our salience experiment did not change intentions. But we believe this tool could be useful for examining impacts of other important trends and factors that may not be so top-of-mind, and which are hard to themselves randomize, on migration intentions. For example, respondents could be randomly primed to think about climate change by recalling recent droughts, floods, and hurricanes, or by being given a passage to read or translate about this issue before being asked migration intentions. A second example would be to investigate how important being close to family members is for migration intentions by randomly priming some respondents to talk about family traditions, how parents help with childcare, or other activities that make feelings of closeness to family more salient before asking migration intentions. We look forward to seeing more applications in future work.

# **References**

Avdiu, Besart and Moritz Meyer (2021) “How COVID19 changed the path of remittances in The Gambia”, _Africa Can End Poverty Blog_ <u>https://blogs.worldbank.org/africacan/how-covid19changed-path-remittances-gambia</u> Bah, Tijan, Moritz Meyer and Sering Touray (2021) “The Impact of COVID-19 on Labor Market Outcomes: Evidence from High Frequency Phone Survey from The Gambia”, Mimeo. World Bank.

19
**Table 5. Food-price inflation and experiential food insecurity by area, controlling for fixed effects**

||Worried a<br>last 7 days<br>|bout food,<br> (se)<br>|Food inse<br>last 12 mo<br>|cure in the<br>nths(se)<br>|Months fo<br>last 12 mo<br>|od insecure,<br>nths(se)<br>|
|---|---|---|---|---|---|---|
||Urban|Rural|Urban|Rural|Urban|Rural|
|Maize price 12 months ago (month 1)|0.004*|0.004***|0.003|0.004***|0.000|0.021***|
||(0.002)|(0.001)|(0.002)|(0.001)|(0.007)|(0.004)|
|Change in inflation rate of maize (past 12 months)|0.115***|0.032***|0.139***|0.068***|0.617***|0.320***|
||(0.035)|(0.011)|(0.039)|(0.011)|(0.130)|(0.049)|
|FCS|-0.002|-0.004***|-0.000|-0.003***|-0.008|-0.019***|
||(0.002)|(0.001)|(0.002)|(0.001)|(0.006)|(0.004)|
|Daily per capita calorie consumption (thousands)|0.007|-0.017|-0.043*|-0.012|-0.000|0.032|
||(0.022)|(0.013)|(0.024)|(0.014)|(0.066)|(0.057)|
|Annual per capita expenditure (Kwacha)|-0.000|-0.000|0.000|-0.000*|0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Number of observations|5,358|16,480|5,358|16,480|5,358|16,480|
|Adjusted R2<br>*** p<0.01, ** p<0.05, * p<0.1.|0.098|0.041|0.103|0.067|0.133|0.076|

Notes: All models are estimated in Stata using xtreg and control for fixed effects. Observations are weighted to be representative of individuals who lived in urban areas in both waves, and those who lived in rural areas in both waves.

18
Survey for 2000-01.<sup>xvi</sup> The CGE model has in most respects a standard structure, the novelty coming from the explicit modeling of the health sector, comprising public (NHS) and private health care, and its interaction with the rest of the economy through its differential impact across sectors, factors and household types (specified in Table 2).<sup>xvii</sup>

# INSERT Table 2

# _Setting Up the Model Experiments_

We employ the model in two types of experiments, both targeted at alleviating rationing in UK health care, and both observed in reality. Firstly we examine the impact of importing medical services, i.e. skilled health personnel, consisting of doctors and nurses (experiment 1). On entering the UK, foreign doctors and nurses are assumed to become part of the existing domestic household structure, i.e. they are perfect substitutes for their domestic equivalents. This assumption takes into account that many of them plan to stay and will thus become permanent UK households in the long-term. Furthermore, their wages are maintained at pre-immigration levels so that domestic workers are not worse off as a consequence of the policy. This assumption is representative of the UK situation, given that wages of health workers in the UK are essentially fixed in bilateral bargaining rounds between the Department of Health (constrained by the Treasury) and the medical profession (represented by, among others, the British Medical Association). However, in order to illustrate the welfare implications of wage protection of the medical profession, we subsequently consider the impact of allowing the wage of skilled health workers to fall. The experiment uses three alternative assumptions regarding the share of foreign worker income remitted abroad, adopting illustrative values of 0%, 50% and 100% respectively.<sup>xviii</sup> Varying the share of migrant income remitted will have differential welfare implications since
<u>Table 3. Determinants of crop productivity and earnings</u>

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Sample|Full|Full|Full|Full|Full|Full|Full|Male|Female|Full|Male|Female|
|Dependent variable|Log of<br>productivity|Log of<br>productivity|Log of<br>productivity|Log of<br>productivity|Log of<br>productivity|Log of<br>productivity|Log of<br>productivity|Log of<br>productivity|Log of<br>productivity|Log of crop<br>income|Log of crop<br>income|Log of crop<br>income|
|Gender (=1 if female)|0.254***|0.151***|0.129***|0.013|-0.065*|-0.155***|-0.072***|||-0.084***|||
||(0.036)|(0.034)|(0.036)|(0.034)|(0.035)|(0.033)|(0.025)|||(0.025)|||
|Age|||0.013*|0.014**|0.015**|0.016**|0.009**|0.009*|0.012|0.008*|0.008|0.010|
||||(0.007)|(0.006)|(0.007)|(0.006)|(0.005)|(0.005)|(0.011)|(0.005)|(0.005)|(0.011)|
|Age squared|||-0.000**|-0.000***|-0.000***|-0.000***|-0.000**|-0.000**|-0.000|-0.000**|-0.000**|-0.000|
||||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Years of education|||0.013***|0.012***|0.026***|0.024***|0.006**|0.007**|0.004|0.007**|0.007**|0.004|
||||(0.004)|(0.004)|(0.004)|(0.003)|(0.003)|(0.003)|(0.007)|(0.003)|(0.003)|(0.007)|
|Household head|||0.052|0.052|0.152***|0.143***|0.068**|0.074**|0.042|0.071**|0.079**|0.047|
||||(0.041)|(0.038)|(0.038)|(0.035)|(0.027)|(0.034)|(0.050)|(0.028)|(0.035)|(0.050)|
|Share of nonfarm labor income in|||-0.607***|-0.500***|-0.934***|-0.808***|-0.509***|-0.519***|-0.409***|-0.537***|-0.533***|-0.470***|
|total income (%)|||(0.040)|(0.037)|(0.041)|(0.039)|(0.031)|(0.034)|(0.093)|(0.032)|(0.035)|(0.095)|
|Share of nonlabor income in total|||-0.723***|-0.544***|-1.043***|-0.853***|-0.579***|-0.573***|-0.554***|-0.588***|-0.572***|-0.554***|
|income (%)|||(0.054)|(0.052)|(0.051)|(0.049)|(0.041)|(0.048)|(0.081)|(0.042)|(0.049)|(0.083)|
|Incidence of livestock activity|||-0.137**|-0.065|-0.181***|-0.111*|-0.036|-0.052|0.005|-0.044|-0.049|-0.034|
||||(0.068)|(0.062)|(0.064)|(0.058)|(0.045)|(0.047)|(0.147)|(0.045)|(0.046)|(0.153)|
|Household size|||0.009|0.012|0.018**|0.020**|0.003|0.007|-0.001|0.002|0.005|-0.003|

15
6. Trade Linkages between China and other B&R Economies

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

# **_6.2. China’s Position in GVCs_**

We use the vertical specialization index discussed earlier (VS) to map the foreign content of China’s gross exports. We notice in Figure 40 that the share of foreign content of exports has increased up to 29 % in 2005 and started decreasing to reach 24 % in 2010. This is an indication that total exports are using more domestic inputs and rely a bit less in foreign intermediate goods for production. Figure 41 further shows that the pattern is similar for both exports to B&R and non-B&R economies.

FIGURE 40: SHARE OF FOREIGN CONTENT OF CHINESE EXPORTS, 1995-2010 <!-- Start of picture text --> 29<br>25<br>20<br>15<br>1995 2000 2005 2010<br>30<br>20<br>10<br>0<br><!-- End of picture text -->

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

FIGURE 41: SHARE OF FOREIGN CONTENT OF CHINESE EXPORTS TO B&R AND OTHER ECONOMIES, 1995-2010 <!-- Start of picture text --> 30<br>28<br>26<br>25 25<br>23<br>20<br>17 17<br>16<br>14<br>13<br>1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010<br>non-B&R B&R ROW<br>30<br>20<br>10<br>Foreign Content of Exports (Backward, VS)<br>0<br><!-- End of picture text --> 33
# **I. Introduction and Summary of Conclusions**

In recent years, the performance of Argentine exports has been particularly strong. Argentina has benefited from positive terms of trade shocks in some sectors but higher export volumes have been the most important source of export growth (Figure 1). In 2005, merchandise exports reached about US$40 billion, which is more than 50 percent above the peak prior to 2002. This trend continued in 2006: as of September exports of goods had grown by 12.3 percent.

<!-- Start of picture text --> Figure 1<br><!-- End of picture text --> <!-- Start of picture text --> Total Exports Total Exports Price and Quantity Real Exchange Rate<br>50 35 300 (Average 1965 - 2005 = 100)<br>200<br>45 30<br>Price<br>40 25 250 180<br>35 20 Quantity 160<br>30 15 200<br>25 10 140<br>20 5 150<br>120<br>15 0<br>10 -5 100 100<br>5 -10 80<br>0 -15 50<br>60<br>0 40<br>Percentage growth, right axis US$ billion, left axis<br>2006 export values are based on January-September annual exports growth.<br>Source: Ministry of Economy and production, Argentina, and International Financial Statistics.<br>199319941995199619971998199920002001200220032004Jan-Sep 2006 2005<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005<br><!-- End of picture text --> The improvement in export performance was accompanied by a drastic change in relative prices following upon the currency devaluation in 2002 which has significantly improved the competitiveness of Argentine exports overall. Aggregate export statistics show that the momentum of export performance has been sustained by manufactured goods which led exports growth in 2006 (Figure 2).

**Figure 2 Evolution of Exports by Sector**

<!-- Start of picture text --> Exports of Primary Products, Fuels and Energy Exports of Industrial and Agricultural Manufactures<br>18 35 35 40<br>16 30<br>30 30<br>14 25<br>20 25<br>12 20<br>15<br>10 20<br>10 10<br>8 5 15<br>6 0<br>0 10<br>4 -5<br>2 -10 5 -10<br>0 -15 0 -20<br>Percentage growth, right axis US$ billion, left axis Percentage growth, right axis US$ billion, left axis<br>2006 export values are based on January-September annual exports growth.<br>Source: Ministry of Economy and production, Argentina.<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Jan-Sep 2006 2005 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Jan-Sep 2006 2005<br><!-- End of picture text --> Looking beyond aggregate trends, this paper examines changes in the structure of Argentine exports, and their potential impact on future growth. To that end, we apply the conceptual 2
<!-- Start of picture text --> 60,000<br>50,000<br>40,000 —AL calibrated from Aggregate Illegal<br>Income (billion pesos of 2005)<br>30,000 Bs 5 a Sa tes ve, +++» ALpesos calibratedof 2005) from Cocaine (billion<br>oy = -AL calibrated from Common Crime<br>20,000 . =~o 7 == --—. (biilion of pesos 2005)<br>“ ee<br>Bid a cm<br>= Pe<br>LOjOOO «fae<br>- wee<br>()<br>1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br><!-- End of picture text -->