rafmacalaba/datause-encoder-data huggingface dataset:
Table A4: First Stage Regressions: The relationship between the instruments and migration

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Migration|Migration|Migration|Migration|Migration|Migration|
|IV<br>~~h~~igh|136.4***<br>(16.69)|56.80***<br>(13.53)|48.33***<br>(13.13)||||
|IV<br>~~l~~ow|-151.9***<br>(17.20)|-85.19***<br>(14.31)|-72.45***<br>(14.55)||||
|IVR<br>~~h~~igh||||0.132***<br>(0.0164)|0.0581***<br>(0.0137)|0.0473***<br>(0.0130)|
|IVR<br>~~l~~ow||||-0.149***<br>(0.0173)|-0.0854***<br>(0.0146)|-0.0741***<br>(0.0147)|
|perc. of adverse rain events||0.0208|0.0157||0.0212|0.0154|
|||(0.0137)|(0.0149)||(0.0137)|(0.0148)|
|perc. of adverse temp events||-0.0169|-0.0283||-0.0193|-0.0284|
|||(0.0168)|(0.0178)||(0.0167)|(0.0178)|
|bank branches||0.0132***<br>(0.00384)|0.00720**<br>(0.00359)||0.0132***<br>(0.00385)|0.00696*<br>(0.00364)|
|road length (km)||0.000278**<br>(0.000119)|0.000389***<br>(0.000127)||0.000251**<br>(0.000119)|0.000402***<br>(0.000126)|
|population||-0.00230***|-0.00167***||-0.00234***|-0.00165***|
|||(0.000442)|(0.000417)||(0.000444)|(0.000419)|
|population density||-1.061|-1.462*||-0.951|-1.296|
|||(0.878)|(0.868)||(0.878)|(0.868)|
|sex ratio||0.263***|0.151**||0.276***|0.169**|
|||(0.0557)|(0.0677)||(0.0562)|(0.0683)|
|ratio high-skilled males||1.601***|1.676***||1.596***|1.676***|
|||(0.0687)|(0.0738)||(0.0687)|(0.0736)|
|2012 year|3.034***|-0.0101|0.576|3.391***|0.177|0.692|
||(0.759)|(0.613)|(0.620)|(0.755)|(0.609)|(0.614)|
|Constant|63.25***|13.27**|26.14***|62.76***|11.71**|24.23***|
||(0.685)|(5.572)|(7.329)|(0.636)|(5.632)|(7.402)|
|agro-ecological zone|No|No|Yes|No|No|Yes|
|observations|1,135|1,048|843|1,135|1,048|843|
|R-squared|0.094|0.554|0.576|0.089|0.553|0.576|
|IV F-stat|39.41|19.18|12.78|37.90|17.92|13.16|

Note: This table presents the first-stage regressions. The outcome variable is the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The first three columns present the standard shift-share instrument, the second three columns present the residual shift-share instrument. The agro-ecological controls include soils, length of the growing season (in days) and reference ETo (a measure of water stress). Robust standard errors included. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1
Results from Table 3 (columns 2, 4, and 6) and Figure 9 indicate that as water schemes age, their likelihood of failure is best predicted by those factors that cannot be modified, as well as by those that can be controlled during operational stages. Conversely, as water schemes age, the share of failures linked to factors that are relevant at the design and implementation stages decreases slightly. Furthermore, the importance of (1) repair and maintenance and (2) management by a WASHCOM remain high and do not change much over the life of the scheme. In the water schemes’ first year, just over 39% of failures are explained by factors that cannot be modified (14.8% due to political region and 24.3% to hydrogeology), increasing to 49.6% (28.1% due to political region and 21.5% to hydrogeology) during the three-to-sixyear period. After eight years, it increases slightly to 50.8% (28.4% due to political region and 22.4% to hydrogeology). Similarly, the share of the factors that can be changed during the design and implementation stages (scheme size, scheme type, and promoters) decreases from 32.7% in the first year (13.7% attributed to promoters, 9.4% to scheme size, and 9.6% to scheme type) to 21.4% in years three to six (15.2% for promoters, 2.7% for scheme size, and 3.5% for scheme type), and increases slightly to 22.2% after eight years (6.5% for promoters, 7.6% for scheme type, and 8.1% for scheme size). Finally, just over 28.1% of water scheme failures in the first year can be linked to factors that are relevant at the operational stages, such as repair and maintenance and WASHCOM management, increasing to 28.9% for three to six years, and declining slightly to 27% after eight years. Thus, poor repair and maintenance and absence of a WASHCOM, combined, explain between 27% and 30% of failures across all years.

# **6. Conclusions and Policy Implications**

The NWSS 2015 survey data enable us to identify the failure rates of water schemes across their lifetimes as well as the causes of these failures. Currently, around 46% of all schemes in Nigeria are non-

> nature of the data, and the use of a binary variable as the outcome indicator, it was decided to fit the logit model instead of a log-linear model to the regression presented here. This was done after the WASH Poverty Diagnostic report had already been published.

17
<mark>economies in the 1990s, firms continued to build up their cash balances through the crisis periods. They question the notion of “phoenix miracles” or credit-less recoveries from financial system collapses by showing that firms in these crisis episodes used their positive cash flow shocks to add to their cash balances and repay short-term debt and even the firms facing negative cash flow shocks still had access to long-term debt markets and equity markets. They also find no evidence of a credit-less recovery at the firm-level in the 2008 U.S. financial crisis.</mark>

# **_5.4 Issuance Activities_**

In this section, we first review the evidence on a firm‟s decision to go public, the motivations for international capital issuances, and the prevalence of private equity and venture capital in developing countries.

# _5.4.1 IPOs_

While there have been several studies regarding a firm‟s decision to go public (e.g. Zingales, 1995; Pagano, Panetta, and Zingales, 1998; Chemmanur, He, and Nandy, 2010), most of the evidence is based on the U.S. and other developed financial markets. Few papers have examined the role of public equity markets in developing countries. More recently however, Doidge, Karolyi, and Stulz (2011) show that there has been a large increase in global IPO activity while the US IPO activity has declined over the same period. They study 30,000 IPOs from 90 countries between 1990 and 2007. They find that <mark>global IPOs defined to include both IPOs in which some of the shares are sold outside the home country of the firm going public, and also foreign IPOs, in which of all the shares are sold outside the home country, enable firms to overcome poor institutions in their country of origin. This in turn suggests that with globalization, home institutions and laws are becoming less important in influencing IPO activity.</mark> Investigating why firms go public, Kim and Weisbach (2008) study IPOs and seasoned equity offerings from 38 countries between 1990 and 2003 and find that firms issue public equity to raise capital for investment. On average, firms use 28.4 cents of every dollar raised in an initial public offering (IPO) on capital expenditures and R&D. Gopalan and Gormley (2010) analyze the going-public decision using the collapse of the Indian equity market in 1997 as a 43
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# **Table 1. Sex ratios of children aged below 5 years in developing regions, 2005**

_(Individual countries in each region are shown only if their child sex ratios are above the average of 105)_

|More developed regions|105.4|
|---|---|
|Less developed regions, excludingChina|104.9|
|**_LATIN AMERICA AND THE CARIBBEAN_**|**104.1**|
|**_Sub-Saharan Africa_**|**102.0**|
|_Mauritania_|106.4|
|**_Northern Africa_**|**104.5**|
|_Tunisia_|106.6|
|**_ASIA_**|110|
|**Western Asia**|**104.8**|
|_Armenia_|116.7|
|_Azerbaijan_|116.4|
|_Cyprus_|106.8|
|_Georgia_|115.3|
|Iraq|105.9|
|Israel|105.9|
|Qatar|105.2|
|United Arab Emirates|105.1|
|**South-Eastern Asia**|**104.1**|
|Malaysia|105.7|
|Singapore|107.3|
|Brunei Darussalam|106.6|
|**Eastern Asia**|**120.6**|
|China|122.4|
|Republic of Korea|108.1|
|Dem. People's Republic of Korea|105.2|
|**South-Central Asia**|**107.3**|
|Afghanistan|105.9|
|India|108.6|
|Nepal|105.3|

_Source:_ United Nations, Department of Economic and Social Affairs, Population Division, _World Population Prospects: The 2008 Revision_ , New York, 2009 (advanced Excel tables).

22
# **References**

Acemuglu, D., F. Zilibotti and P. Aghion (2006) “Distance to Frontier, Selection and Economic Growth”, _Journal of the European Economic Association_ , Vol. 5, Issue 1, pages 37‐74.

Ackerberg, D., K. Caves and G. Frazer (2006) “Structural Identification of Production Functions”, MPRA Paper 38349, University Library of Munich, Germany.

Ackerberg, D., K. Caves and G. Frazer (2015) “Identification Properties of Recent Production Function Estimators”, _Econometrica_ , Vol. 83, Issue 6, pages 2411‐2451.

Aghion, P., N. Bloom, R. Blundell, R. Griffith and P. Howitt (2005) “Competition and Innovation: An Inverted‐U Relationship”, _The Quarterly Journal of Economics_ , Vol. 120, Issue 2, pages 701‐728.

Aghion, P. and P. Howitt (2009) _The Economics of Growth_ , Cambridge, Massachusetts: MIT Press.

Arnold, J., B. Javorcik and A. Mattoo (2011) “The Productivity Effects of Services Liberalization: Evidence from the Czech Republic”, _Journal of International Economics_ , Vol. 85, No. 1, pages 136‐146.

Arnold, J., B. Javorcik, M. Lipscomb and A. Mattoo (2015) “Services Reform and Manufacturing Performance: Evidence from India”, _The Economic Journal_ , Vol. 126, Issue 590, pages Aw, Y.‐B., X. Chen, and M. Roberts (2001) “Firm‐level evidence on productivity differentials and turnover in Taiwanese manufacturing”, _Journal of Development Economics_ , Vol. 66, No.1, pages 51‐86 Barone, G. and F. Cingano (2011) “Services Regulation and Growth: Evidence from OECD Countries”, Economic Journal, Vol. 121. No. 555, pages 931‐957.

Basu, S., L. Pascali, F. Schiantarelli and L. Serven (2006) “Productivity, Welfare and Reallocation: Theory and Firm‐level Evidence”, NBER Working Paper Series No. 15579, Cambridge MA: NBER.

Beverelli, C., M. Fiorini and B. Hoekman (2015) “Services Trade Restrictiveness and Manufacturing Productivity: The Role of Institutions”, CEPR Discussion Paper No. 10834, London: CEPR.

Borchert, I., B. Gootiiz and A. Mattoo (2012) “Policy Barriers to International Trade in Services: Evidence from a New Database”, Policy Research Working Paper Series No. 6109, Washington DC: World Bank.

Bourlès, R., G. Cette, J. Lopez, J. Mairesse and N. Nicoletti (2013) “Do Product Market Regulations in Upstream Sectors Curb Productivity Growth? Panel Data Evidence for OECD Countries”, _The Review of Economics and Statistics_ , Vol. 95, No. 5, pages 1750‐1768.

Cummins, J. and M. Rubio‐Misas (2006) “Deregulation, Consolidation and Efficiency: Evidence from the Spanish Insurance Industry”, _Journal of Money, Credit and Banking_ , Vol. 38, No. 2, pages 323‐355.

Duggan, V., S. Rahardja and G. Varela (2013) “Services Reform and Manufacturing Productivity: Evidence from Indonesia”, Policy Research Working Paper Series No. 6349, Washington DC: World Bank.

Eschenbach, E. and B. Hoekman (2006) “Services Policy Reform and Economic Growth in Transition Economies”, _Review of World Economics_ , Vol. 142, No. 4, pages 746‐764.

Fernandes, A. and C. Paunov (2012) “Foreign Direct Investment in Services and Manufacturing Productivity: Evidence for Chile”, _Journal of Development Economics_ , Vol. 97, Issue 2, pages 305‐321.

25
# **Executive Summary**

**_Public service delivery has drawn government attention in China._** China has a long tradition of providing access to basic public services and achieving human development indicators that are higher than expected for a country at its level of economic development. Over the past 30 years, China’s economic development has been spectacular, lifting hundreds of millions out of poverty and raising average per capita income to the level of lower-middleincome countries.  Analyses suggest, however, that the improvements in human development outcomes during this time period have been somewhat slower compared to the preceding 30 years and compared to the rates of improvement that would be expected to accompany such high levels of economic growth.  Furthermore, China has experienced rising disparities in human development outcomes as well as income.  Slower progress in human development and rising disparities have partly reflected the expected challenges of transition, including rapid structural shifts in the economy, massive urbanization and vast transformation in the public service delivery systems.  In recent years, the Chinese government has reemphasized the importance of public services and launched large-scale policies to enhance their coverage, quality and equity in utilization.  Importantly, China has set for itself an overarching development objective to create a _Harmonious Society_ , which puts people at the center of development.

The Government of China has recognized that in order to achieve the development objective to create a _Harmonious Society_ , it is necessary to make public services work for all citizens. China’s leadership has accelerated efforts to develop “service-oriented” government. In this perspective, top policymakers have emphasized the importance of increasing transparency, expanding public participation and improving accountability in government and public service delivery.

**_This study concentrates on urban services and employs an innovative approach to gauge citizens’ experience and address governance challenges in public service delivery._** To gather citizens’ feedback, a citizen report card was developed and provides a scientific basis for a large-scale survey conducted in 2006, covering 5,000 households in five cities (Chengdu, Dalian, Shanghai-Pudong, Shenzhen and Xi’an).  Several case studies complemented the survey to explore the related governance issues, emphasizing financing mechanisms and accountability relationships for public service delivery.

## **_The study mainly illustrates the usefulness of citizens’ feedback for policy development and_**

**_implementation._** Specifically, the study shows that the citizen scorecard survey – using a carefully designed questionnaire to gauge citizens’ satisfaction with public services – can help policymakers reveal weaknesses and monitor progress in public service delivery.  From the time of the citizen scorecard survey quoted in this study, China has continued enhancing the institutional and policy frameworks for public service delivery.  Hence, many issues

2
Policy Research Working Paper 9830

# **Abstract**

This paper introduces a micro-founded methodological framework to estimate the housing deficit across and within emerging economies. It introduces the Adequate Housing Index, which provides a comparable assessment of adequate housing based on seven adequacy dimensions that are held constant across all countries. The adequacy dimensions were obtained and harmonized from countries’ most recent household expenditure and consumption surveys. The paper documents large differences in housing adequacy across a sample of 64 emerging economies, as well as wide within-country disparities such as across income groups, locations, and occupations. Estimates of the Adequate Housing Index show that across the sample of 64 emerging economies, there is a current housing deficit of 268 million housing units affecting 1.26 billion people. About 26 percent of the current housing stock in these economies is inadequate. The paper further estimates that at least 40 million additional housing units will have to be added by 2030 to provide adequate housing to all and accommodate the growing population and urbanization patterns.

This paper is a product of the Sector Economics and Development Impact Department, International Finance Corporation. It is part of a larger effort by the World Bank Group to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www. worldbank.org/prwp. T he authors may be contacted at dbehr@ifc.org; khaider@ifc.org; ssingh13@ifc.org; azaman@ifc.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
||Table2.1<br>|:Popu<br>1991|lation|in Argent<br>Grouped|ina andits|Regions,1|991a|nd200<br>200|1<br>1<br>Grouped||
|---|---|---|---|---|---|---|---|---|---|---|
||Total|Urban<br>as a<br>share<br>of|Rural<br>as a<br>share<br>of|<br>rural<br>as a<br>share<br>of|Dispersed<br>rural<br>as a<br>share|<br>Total|Urban<br>as a<br>share<br>of|Rural<br>as a<br>share<br>of|<br>rural<br>as a<br>share<br>of|<br>Dispersed<br>rural<br>as a<br>share|
|||total|total|total|of||total|total|total|of|
||||||l l|||||l l|
|||||rural<br>Pampe|tota rura<br>anaregion||||rural|tota rura|
|Buenos Aires|12,594,974|95.2|5.1|29.8|70.2|13,827,203|96.4|3.8|40.3|59.7|
|Entre Ríos|<br>1,020,257|<br>77.6|28.9|21.7|78.3|1,158,147|82.5|21.2|28.8|71.2|
|La Pampa|259,996|74.2|34.8|55.3|44.7|299,294|81.3|23.0|61.8|38.2|
|<br>Córdoba|2,766,683|86.0|16.2|38.2|61.8|3,066,801|88.7|12.7|45.9|54.1|
|Cdad Bs. Aires|2,965,403|100.0|0.0|0.0|0.0|2,776,138|100.0|0.0|0.0|0.0|
|Santa Fe|2,798,422|86.8|15.2|40.7|59.3|3,000,701|89.2|12.2|47.1|52.9|
|Total Pampeana|19,440,332|91.5|9.3|34.1|65.9|24,128,284|94.1|6.3|42.4|57.6|
|||||Cuy|oregion||||||
|Mendoza|1,412,481|77.8|28.5|13.1|86.9|1,579,651|79.3|26.1|16.6|83.4|
|San Juan|528,715|80.3|24.6|35.0|65.0|620,023|86.0|16.3|35.2|64.8|
|San Luis|286,458|81.1|23.3|42.3|57.7|367,933|87.1|14.8|51.9|48.1|
|TotalCuyo|2,227,654|78.8|26.8|21.3|78.7|2,567,607|82.0|21.9|23.7|76.3|
|||||North|westregion||||||
|Catamarca|264,234|69.8|43.2|66.0|34.0|334,568|74.0|35.0|68.9|31.1|
|Jujuy|512,329|81.6|22.5|32.7|67.3|611,888|85.0|17.7|40.3|59.7|
|<br>La Rioja|220,729|75.7|32.1|63.9|36.1|289,983|83.1|20.3|62.0|38.0|
|<br>Salta|<br>866,153|79.0|26.6|25.6|74.4|<br>1,079,051|83.4|19.9|34.3|65.7|
|Santiago del Estero|671,988|60.7|64.8|22.7|77.3|804,457|66.1|51.3|24.0|76.0|
|Tucumán|1,142,105|76.6|30.5|13.9|86.1|1,338,523|79.5|25.8|15.9|84.1|
|TotalNorthWest|<br>3,677,538|<br>74.4|<br>34.4|27.8|72.2|4,458,470|78.6|27.2|31.2|68.8|
|||||North|eastregion||||||
|Corrientes|795594|74.1|34.9|15.3|84.7|930991|79.4|26.0|16.3|83.7|
|<br>Chaco|,<br>839,677|<br>68.6|<br>45.8|<br>11.9|<br>88.1|,<br>984,446|79.7|<br>25.5|<br>17.8|<br>82.2|
|Formosa|<br>398,413|67.8|47.5|14.4|85.6|<br>486,559|77.7|28.7|15.4|84.6|
|Misiones|788,915|62.5|59.9|15.0|85.0|965,522|70.4|42.0|15.0|85.0|
|TotalNorth East|2,822,599|68.3|46.3|14.1|85.9|3,367,518|76.7|30.4|16.1|83.9|
|||||Patago|niaregion||||||
|Chubut|357189|878|139|488|512|413237|895|117|549|451|
|<br>Neuquén|,<br>388,833|.<br>86.3|.<br>15.9|.<br>30.0|.<br>70.0|,<br>474,155|.<br>88.6|.<br>12.9|.<br>33.4|.<br>66.6|
|<br>Río Negro|<br>506772|79.9|25.1|35.4|64.6|<br>552822|84.4|18.5|42.0|58.0|
|<br>Santa Cruz|,<br>159,839|<br>91.4|<br>9.4|<br>49.9|<br>50.1|,<br>196,958|96.1|<br>4.0|<br>38.7|<br>61.3|
|Tierra del Fuego|<br>69,369|97.0|3.1|23.8|76.2|<br>101,079|97.1|3.0|42.9|57.1|
|<br>Total Patagonia|1,482,002|85.5|16.9|37.6|62.4|1,738,251|88.8|12.6|42.4|57.6|
|Total Argentina<br>Source: INDEC,Nati|32,615,528 <br>onal Populati|87.2<br>on Cens|14.7<br>us 1991|27.1<br>and 2001.|72.9<br>|36,260,130|89.4|11.8|32.0|68.0|

7
where is the debt-elasticity of the interest-rate spread, and is the long-run external debt to GDI ratio.<sup>21</sup> If , the government can issue debt at the ψψ dd world interest rate, . If tt−1, an increase in debt of 1 percent of GDI leads dd ≤dd to percentage points increase in the country spread. WW tt−1 ff dd > dd **Fiscal rules.** As in the Default submodel, a fiscal rule determines both the ψψ

**Fiscal rules.** As in the Default submodel, a fiscal rule determines both the timing and composition of government expenditure. The LTGM-NR External Balance allows for four types of rules: BBR, BBR-HR, SSR, and SSR-HR (see Table 1), up from three types of rules in the Default submodel (where there is no SSR-HR). The External Balance submodel defines the timing aspect of fiscal rules in terms of how the primary balance evolves. The BBR fixes the headline primary balance as a share of GDI at a target (usually fixed but could vary over time exogenously) (see top line of equation (29)). tt bb<sup>�</sup> This policy leads to pro-cyclical spending: a one-dollar increase (fall) in resource revenues leads to (almost) exactly one dollar increase (fall) in expenditure.

The SSR mitigates pro-cyclicality by fixing the _structural_ primary balance at target the primary balance tends to increase (decrease) in periods of high (low) tt<sup>_._The structural primary balance adjusts for the commodity cycle so that</sup> bb<sup>��</sup> commodity prices. More specifically, the structural primary balance is computed based on structural resource revenues, −e , where the structural revenue, , is defined as in the default model (equation (15)). The following tt tt tt bb<sup>�</sup> = zz̆ pp equation summarizes the fiscal target under BBRs and SSRs: tt zz̆ [Fiscal Rule] (29) �<sup>B</sup> S<sup>RR: bbtt= bb�tt+ eett</sup> where � adjusts the target by a fraction tt tt<sup>+ ee</sup> tt of the deviation of the gg RR: bb<sup>�</sup> = bb<sup>��</sup> public debt-to-GDI ratio from its long-run level, tt tt−1 gg , thus ensuring debt ee = φφ�dd −dd φφ sustainability. The parameter ensures the stability of the public debt and controls gg dd its volatility.<sup>22</sup> φφ

> 21 For simplicity, the parameter is usually set to zero or 0 but the user can choose other values.

> 22 Condition )⁄ ) ensures that the debt-to-GDI ratio fluctuates within bounds around the long-run level. In the limit dd YY the fiscal rule collapses to a debt-rule: dd . WW φφ> (1 + ff (1 + gg gg tt gg

> φφ→∞ 16 dd = dd
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