rafmacalaba/datause-encoder-data huggingface dataset:
Figure 32: Access to Credit of Sudanese Agrarian Households, 2014/15

- (a) Credit Access by Region

<!-- Start of picture text --> Family Loans Other Loans*<br>80%<br>60%<br>40%<br>20%<br>0%<br>Nile Eastern Khartoum Blue & White Kordufan Darfur Sudan<br>Nile<br><!-- End of picture text --> _Note_ : * Other loans include those originating from banks and government agencies, nongovernmental organizations (NGOs), microfinance institutions, and employers or landlords.

- (b) Reasons for Not Acquiring Loan

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>No Need Expectation Too Inadequate Fear of Do not Refused Debt Other<br>of Refusal Expensive collateral Debt know any loan<br>lender<br>(c) Purpose of Acquiring Loan<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Agriculture Non-Agriculture Consumption Other<br>Farm inputs Buy animals costs equipment expenses burial On-lending Other<br>Buy heavy equipment Buy other equipment Buy agricultural land Other agricultural Non-Agricultural Business needs Land/building Other business Consumption needs Dwelling costs Religious, wedding, Consumer durables<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

35
- López-Córdova, J. Ernesto and Christopher M. Meissner, 2003, “Exchange-Rate Regimes and International Trade:  Evidence from the Classical Gold Standard Era,” _American Economic Review_ , 93 (1), pp. 344-353.

- Mann, Thomas E. and Norman J. Ornstein, 2012, _It’s Even Worse than It Looks:  How the American Constitutional System Collided with the New Politics of Extremism_ , Perseus Books.

- ---------------, 2006, _The Broken Branch: How Congress is Failing America and How to Get It Back on Track_ , Oxford University Press.

- Maziadi, Smar, P. Farahmand, S. Wang, S. Segal and F. Ahmed, 2011, “Internationalization of Emerging Market Currencies:  A Balance between Risks and Rewards,”  IMF Staff Discussion Note SDN/11/17, October 2011.

- McKinsey Global Institute, 2009, “An exorbitant privilege? Implications of reserve currencies for competitiveness,” A McKinsey Global Institute Discussion Paper.

- Mitchener, Kris James and Marc D. Weidenmier, 2008, “The Baring Crisis and the Great Latin American Meltdown of the 1890s,” _Journal of Economic History_ , Vo. 68, Issue 2, pp. 462-500.

- Mundell, Robert A., 2011, “International Monetary Reform 2011,”  Presentation made at G20 policy forum, Nanjing, China, March 31, 2011.

- ----------, 2005, “The case for a world currency,” _Journal of Policy Modeling_ , Vol. 27, pp. 465-475.

- ----------,2000, “A Reconsideration of the Twentieth Century,” (revised version of the author’s Nobel lecture), _American Economic Review_ , Vol. 90, No. 3, pp. 327-340.

- ----------, 1995, “The International Monetary System:  The Missing Factor,” _Journal of Policy Modeling_ , Vol. 17, No. 5, pp. 479-492.

- Obstfeld, Maurice, 2011a, “The SDR as International Reserve Asset: What Future?.”  Mimeo available at the author’s website.

- -------, 2011b, “International Liquidity: The Fiscal Dimension,” NBER Working Paper Series Number 17379, September 2011.

- Obstfeld, Maurice, J. Shambaugh and A.M. Taylor, 2010, “Financial Stability, the Trilemma, and International Reserves,” _American Economic Journal:  Macroeconomics 2_ , Vol. 2(2).

- -----------, 2005, “The Trilemma in History:  Tradeoffs among Exchange Rates, Monetary Policies, and Capital Mobility,” _The Review of Economics and Statistics_ , Vol. 87, No. 3, pp. 423-438.

- Obstfeld, Maurice and Alan Taylor, 2004, _Global Capital Markets_ , Cambridge University Press.

- Ocampo, Jose Antonio. 2010. “Building an SDR-Based Global Reserve System.” _Journal of Globalization and Development_ 1 (2): Article 14.

58
|Large city|0.0158|0.716|
|---|---|---|
||(0.0161)|(0.922)|
|Licenses|0.0284***|0.983**|
||(0.00913)|(0.483)|
|Competitors2|0.0108|1.667*|
||(0.0266)|(0.953)|
|Competitors3|0.0131|0.599|
||(0.0176)|(0.912)|
|Competitors4|-0.00846|0.164|
||(0.0160)|(0.779)|
|International market|-0.000746|0.00731|
||(0.000598)|(0.0328)|
|Employment|0.00856***||
||(0.00205)||
|N|3,136|3,136|

*** p<0.01, ** p<0.05, * p<0.1. Generalized Tobit model estimated by maximum likelihood. Marginal effects are reported, i.e., the coefficients predict (i) the expenditures in R&D and (ii) the likelihood of investing in R&D. Robust standard errors in parentheses. The sample includes data from these surveys: China (2012), Indonesia <u>(2015), Malaysia (2015), Philippines (2015), Thailand (2016), Vietnam (2015).</u>

The decision to invest in R&D appears strongly correlated with using technology licensed by a foreign-owned company: licenses are associated with a 2.8 percentage points higher likelihood to invest in R&D. Also, larger firms appear more likely to invest in R&D. Younger firms or firms led by managers with more experience in the sector are also marginally (statistically) more likely to invest in R&D.

The intensity of R&D investment is associated with being part of a large group and with using technology licensed by a foreign-owned firm. Facing one competitor in the main market is also associated with higher R&D intensity, but the result is noisy. Other factors matter, though to a smaller extent. Having a more educated workforce and more diversified sales, as well as being younger are all associated with higher R&D intensity.

Interestingly, we do not find evidence of agglomeration economies, or that the exposure to international markets or FDI matters. A possible interpretation is that multinationals would not invest in R&D locally if the market size is not sufficiently large or the national academic attractiveness is limited in technologically lagging countries. This would be consistent with the evidence from Latin America discussed in Crespi et al. (2016).

Accounting for this investment in R&D allows us to then estimate the knowledge (technology) production function, which is the second stage of the CDM model. We thus investigate the determinants of (overall) innovation (Table 4; column 1), distinguishing between innovation in product/service (Table 4; column 2) or innovation in process (Table 4; column 3).<sup>9</sup> Having invested in R&D is associated with more innovation, a 10% increase in R&D spending (log constant US$) translates into a 5 percentage points increase in the probability of innovation.

> 9 For brevity, throughout the text we refer to product innovation. The measure is actually broader and encompasses the introduction of new or significantly improved products or services.

8
|**Table 4: Mental Well-Being and Main Str**|**essor fro**<br>**Sample**|**m COVID-19**||**Main Pro**|**blem from**|**COVID-19**|
|---|---|---|---|---|---|---|
||**Size**|**Depression**|**Happy**|**School**|**Finances**|**Social**|
|**Full Sample**|**1550**|**0.16**|**0.68**|**0.50**|**0.09**|**0.21**|
|Males|708|0.13|0.70|0.47|0.09|0.22|
|Females|842|0.19|0.66|0.52|0.09|0.21|
|_test of gender equality_||0.005|0.106|0.053|0.794|0.840|
|Grade 10|545|0.15|0.71|0.47|0.09|0.22|
|Grade 11|593|0.17|0.68|0.48|0.10|0.22|
|Grade 12|412|0.18|0.64|0.57|0.08|0.19|
|_test of grade equality_||0.326|0.072|0.002|0.581|0.380|
|<br>Lowest Wealth Quartile|374|0.18|0.63|0.50|0.12|0.19|
|Highest Wealth Quartile|405|0.15|0.70|0.47|0.07|0.24|
|<br> _test of wealth equality_||0.284|0.054|0.426|0.028|0.153|
|Mother's Education:high school or less|692|0.18|0.64|0.52|0.10|0.19|
|Mother's education:beyond high school|858|0.15|0.70|0.48|0.08|0.23|
|_test of educational equality_||0.240|0.011|0.092|0.108|0.019|
|Mestizo or White|1306|0.16|0.69|0.50|0.09|0.22|
|Indigneous or Other|244|0.21|0.61|0.50|0.09|0.18|
|_test of ethnicity equality_||0.051|0.016|0.921|0.788|0.245|
|Pichincha|1066|0.15|0.69|0.50|0.08|0.22|
|Napo or Orellana|484|0.18|0.65|0.50|0.10|0.19|
|<br> _test of provincial equality_||0.167|0.141|0.904|0.278|0.156|
|<br>Have Computer and Internet|914|0.16|0.70|0.49|0.07|0.23|
|No Internet|404|0.18|0.61|0.52|0.14|0.17|
|_test of technology access equality_||0.321|0.002|0.308|0.000|0.015|
|Notes:<br>**Depression**denotes MHI-5 score below 1|7;**Happy**|denotes happy|most or a|ll of the tim|e;**School, F**|**inances**|

**<mark>Depression</mark>** <mark>denotes MHI-5 score below 17;</mark> **<mark>Happy</mark>** <mark>denotes happy most or all of the time;</mark> **<mark>School, Finances</mark>** <mark>and</mark> **<mark>Social</mark>** <mark>denote student names this as the main problem they personally face from COVID-19.</mark>

22
heterogeneity across countries. Section 6 explores the role of exogeneous growth opportunities in the likelihood of firms to issue securities in capital markets. Section 7 concludes.

# **2. Data**

# _2.1 Data construction_

The data on firm capital raising activity cover the period 1991-2016 and come from the Refinitiv’s Security Data Corporation (SDC) Platinum database. This database provides transaction-level information on new capital raising issuances of common and preferred equity and publicly and privately placed bonds in capital markets around the world.<sup>5</sup> Given that the SDC Platinum database does not collect data on debt issuances with maturities shorter than one year, the dataset does not cover commercial paper.

To examine the comparative performance of issuing and non-issuing firms, we combine the data on equity and bond issuances with data on income and balance sheets for publicly listed companies from the Worldscope database. We match equity and corporate bond issuance data with firm income and balance sheet data using the CUSIP identifier. If the matching proves unsuccessful, we use the following identifiers: SEDOL, ISIN, and Thomson Reuters Permanent Identifier.<sup>6</sup> For the majority of countries in our matched sample, we have both capital market issuance and income and balance sheet data on at least 70 percent of the listed firms in each country, where data on the total number of listed firms per country come from the World Federation of Exchanges database.

Worldscope provides data at the consolidated level for listed companies with subsidiaries. For those parent companies, we aggregate the issuance activity of the parent and the related (listed and

> 5 SDC Platinum collects data on capital raising issuance mostly from filings with local regulatory agencies and stock exchanges, complementing this information with data from other sources such as offering circulars, prospectuses, surveys of investment banks, brokers, and other financial advisors, news sources, trade publications, and wires.

> 6 This matching algorithm allows us to match 87 percent of the equity issuances and 70 percent of the corporate bond issuances reported in SDC Platinum.

8
Thus, a competition policy enforcement in key sectors, such as finance and telecom, should be at the top of the agenda for authorities in MENA if they commit to allowing fintech to transform finance through more inclusivity and break away from stagnant growth.<sup>10</sup> V. Needed: A Digital and Finance Moonshot for the Middle East and North Africa<sup>11</sup> The digital economy holds the promise of a new way forward, but it is still in its infancy, and young people face obstacles in putting technology to productive use. Although the internet and hand-held devices are ubiquitous throughout the MENA region, they are currently used for accessing social media, rather than for launching new enterprises. There are success stories to be sure (Arezki, Belhaj and Shah, 2019). One is Careem, the ride-sharing company that operates in more than 90 cities across 13 countries, and it was purchased by the U.S. ride-sharing giant Uber. Another is Souq, the e-commerce platform that Amazon bought for more than $500 million. These achievements demonstrate the enormous potential for digital commerce in the region. But it is small—too small in a region where 66 percent of the population is younger than 35.

To achieve such an ambitious agenda, countries must mobilize political support and stir the public imagination. They cannot do that if they continue the traditional incremental approach to change. Without a full-blown effort, a generation of MENA youth will be condemned to unemployment or low-productivity, low-wage employment. And the technological gap between the MENA region and the rest of the world will continue to widen.

> 10 It is worth mentioning here that, as MENA countries commit to the development of well-functioning and inclusive financial systems, they should also invest in the development of commensurate capacity for riskmanagement and governance. Finance and financial innovation have become increasingly complex, and they will continue to do so in the future, requiring talented financial manpower, as well as financial regulatory capacity.

> 11 This section draws from Arezki and Ghanem (2017).

16
Table 6: Impact of Heat Exposure on Standardized Scores

||St|andardized Score||
|---|---|---|---|
||Total|English|Math|
||(1)|(2)|(3)|
|T_>_33C|_−_0_._010<sup>_∗∗∗_</sup>|_−_0_._003<sup>_∗_</sup>|_−_0_._001|
||(0_._002)|(0_._001)|(0_._001)|
|T 30-33C|_−_0_._005<sup>_∗∗∗_</sup>|_−_0_._0003|0_._0003|
||(0_._001)|(0_._001)|(0_._001)|
|T 27-30C|_−_0_._001|0_._001<sup>_∗∗_</sup>|0_._002<sup>_∗∗_</sup>|
||(0_._001)|(0_._001)|(0_._001)|
|T 24-27C|_−_0_._002<sup>_∗∗∗_</sup>|0_._0001|0_._001|
||(0_._001)|(0_._0005)|(0_._001)|
|T 21-24C|_−_0_._002<sup>_∗∗∗_</sup>|_−_0_._0004|0_._0003|
||(0_._001)|(0_._0004)|(0_._001)|
|T_<_18C|_−_0_._003<sup>_∗∗_</sup>|_−_0_._002<sup>_∗∗_</sup>|_−_0_._003<sup>_∗∗_</sup>|
||(0_._001)|(0_._001)|(0_._001)|
|Female * T_>_33C|0_._001<sup>_∗∗∗_</sup>|0_._0002|0_._001<sup>_∗∗∗_</sup>|
||(0_._0002)|(0_._0002)|(0_._0002)|
|Female * T 30-33C|0_._001<sup>_∗∗∗_</sup>|0_._001<sup>_∗∗∗_</sup>|0_._001<sup>_∗∗∗_</sup>|
||(0_._0004)|(0_._0003)|(0_._0003)|
|Female * T 27-30C|0_._0004|0_._0001|0_._0004<sup>_∗_</sup>|
||(0_._0003)|(0_._0002)|(0_._0002)|
|Female * T 24-27C|0_._0001|_−_0_._0003<sup>_∗_</sup>|0_._001<sup>_∗∗∗_</sup>|
||(0_._0002)|(0_._0002)|(0_._0002)|
|Female * T 21-24C|0_._0004|0_._0001|0_._001<sup>_∗∗_</sup>|
||(0_._0003)|(0_._0002)|(0_._0002)|
|Female * T_<_18C|0_._00005|_−_0_._0005<sup>_∗_</sup>|0_._001<sup>_∗∗_</sup>|
||(0_._0004)|(0_._0003)|(0_._0004)|
|Female|_−_0_._598<sup>_∗∗∗_</sup>|_−_0_._359<sup>_∗∗∗_</sup>|_−_0_._441<sup>_∗∗∗_</sup>|
||(0_._058)|(0_._039)|(0_._052)|
|**Fixed Effects**||||
|Year|X|X|X|
|School|X|X|X|
|Stream|X|X|X|
|Observations|2,132,635|2,132,635|2,132,635|
|R<sup>2</sup>|0.248|0.271|0.141|

Notes. Estimates are from linear regressions using panel fixed effects. The dependent variable is the standardized test scores of students taking the 12th grade exams and is constructed to have a mean of (or close to) zero; we consider the total score in all exams, and then the separate scores by subject. All regressors include the gender of the student and the number of days in the temperature bin in the school-year cycle: June-May. All regressions control for the number of days in the precipitation bins for the school year. Errors are clustered at the school level. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

25
|**Table 6.1:**<br>**Sector**|**Employment**<br>**Total**<br>**1992**|**in Bahia, 1992**<br>**Total**<br>**2001**|**and 2001**<br>**Annual**<br>**Growth**<br>**(percent)**|
|---|---|---|---|
|**Industry**|**659,116**|**754,809**|**1.52 **|
|Formal|432,777|490,871|1.41|
|Informal|23,007|18,133|-2.61|
|Self-employed|174,994|220,279|2.59|
|Employer|28,338|25,526|-1.15|
|**Services**|**1,839,542**|**2,427,415**|**3.13**|
|Formal|845,716|1,190,824|3.88|
|Informal|111,185|103,986|-0.74|
|Self-employed|827,510|1,034,397|2.51|
|Employer|55,131|98,208|6.63|
|**Agriculture**|**2,457,009**|**2,172,201**|**-1.36**|
|Formal|664,786|648,338|-0.28|
|Informal*|835,935|630,093|-3.09|
|Self-employed|921,849|845,377|-0.96|
|Employer|34,439|48,393|3.85|
|**Other**|**209,662**|**313,113**|**4.56**|
|Public sector|145,647|243,343|5.87|
|Other formal|46,347|40,240|-1.56|
|Other informal|-|1,386|-|
|Self-employed|16,740|27,933|5.85|
|Employer|928|211|-15.17|
|**TOTAL**|**5,165,329**|**5,667,538**|**1.04 **|

_Source_ : SEI, based on PNAD 1992/2001. People age 10 or older, occupied in the reference week, by position in the primary job, by gender, and by fields of activity of the main job - Bahia Informal jobs pay lower salaries than formal jobs during 1995-2001. Informal sector workers in Bahia receive an average monthly wage income of R$256 and no social benefits compared to formal sector workers that earn R$595 and social benefits. Moreover, the informal sector earnings fell nearly 10 percent during 199501. Increased informality could be one of the main reasons why the income of the lowest 20 percent fell in the 1990s (see Table 6.2).

28
_ceteris paribus_ . Further, the estimated elasticity of output to changes in the real effective exchange rate, δ 2 , is about two times higher in the Slovak Republic than Macedonia, where the estimates are 0.06 and 0.12, respectively. The relative magnitudes of δ 1 and δ 2 in both economies imply that stabilization of output could be more effective through the exchange rate transmission channel rather than the interest rate transmission channel of monetary policy. Under the exchange rate targeting regime, however, this channel is much less effective because most of the changes in the real effective exchange rate are bound to happen through the adjustment in relative prices (i.e. domestic relative to foreign). Since prices are usually less flexible than exchange rates, macroeconomic stabilization of output using the exchange rate channel would result in longer-lasting deviations of output from its potential, other things equal. The estimates of δ 3 for Macedonia and the Slovak Republic are 0.23 and 0.14, respectively, implying that a larger portion of consumers in Macedonia relative to the Slovak Republic are rule-ofthumb consumers, i.e. non-Ricardian households (see Gali et al., 2007). Overall, the IS equation fits somewhat better the data of the Slovak Republic than Macedonia where the adjusted R squares are 0.54 and 0.52, respectively. Nevertheless, the estimates of σ _IS_ for the Slovak Republic and Macedonia, of 1.85 and 1.53, imply that it is the Slovak Republic who faces marginally higher aggregate demand shocks.

Consider now the estimated AS equations. In order to achieve a reasonable fit to the CPI inflation data we had to use long-differencing, namely year-to-year changes in CPI as the dependent variable, and the annual cumulative output gap and year-to-year change in the nominal effective exchange rate as the explanatory variables.<sup>9</sup> The estimates of ρπ for the two countries suggest that the inflation dynamics is driven more by inflation expectations rather than past inflation. It appears that the estimate of ρπ = 0.57 applies to both Macedonia and the Slovak Republic. The elasticity of inflation to an increasing output gap,<sup>λ</sup> 1<sup>, is estimated to be almost three times higher in Macedonia,</sup> 0.039 versus 0.015 in Slovakia. The relative magnitudes of<sup>λ</sup> 1<sup>thus imply about two</sup> times higher output rigidity in Macedonia. Hence, a given increase in capacity utilization

> 9 Note that the year-to-year long differencing on quarterly data induces by definition third-order autocorrelation in the AS shocks that is handled according in estimation of the HAC covariance matrix.

11
**Figure 4a: Regional Growth Rates**

<!-- Start of picture text --> Advanced Economies<br>4%<br>2%<br>0%<br>2005 2006 2007 2008 2009 2010 2011<br>-2%<br>-4%<br>Actual Projected<br><!-- End of picture text -->

# **Developing and Emerging Economies**

<!-- Start of picture text --> 10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>2005 2006 2007 2008 2009 2010 2011<br>Actual Projected<br><!-- End of picture text -->

# **United States**

<!-- Start of picture text --> 6%<br>4%<br>2%<br>0%<br>2005 2006 2007 2008 2009 2010 2011<br>-2%<br>-4%<br>Actual Projected<br><!-- End of picture text -->

# **China**

<!-- Start of picture text --> 15%<br>10%<br>5%<br>0%<br>2005 2006 2007 2008 2009 2010 2011<br>Actual Projected<br><!-- End of picture text -->

# **Euro Area**

<!-- Start of picture text --> 4%<br>2%<br>0%<br>2005 2006 2007 2008 2009 2010 2011<br>-2%<br>-4%<br>-6%<br>Actual Projected<br><!-- End of picture text -->

**Developing Asia**

<!-- Start of picture text --> 15%<br>10%<br>5%<br>0%<br>2005 2006 2007 2008 2009 2010 2011<br>Actual Projected<br><!-- End of picture text --> Source: International Monetary Fund, World Economic Outlook.
|3 - General Taxes on Goods and Services:|3,185|46|11|
|---|---|---|---|
|**Sales Tax on Imported Goods**|**972**|**14**|**3**|
|**Sales Tax on Domestic Goods**|**805**|**12**|**3**|
|**Sales Tax on Services**|**522**|**8**|**2**|
|Sales Tax on Commercial Sector|885|13|3|
|4 - Taxes on International Trade & Transactions|293|4|1|
|Customs Duties and Fees|266|4|1|
|Customs Fines|27|0|0|
|_Non-tax Revenues_|2,409|35|8|

Note: Lines in bold are included in the analysis Source: Ministry of finance/ General Government Bulletin: <u>https://mof.gov.jo/enus/datacenter/financialbulletins/generalgovernmentfinancebulletins/generalgovernmentbulletins2018.aspx</u>

# Expenditures

Jordan’s total government spending in 2018 was JOD 8.9 billion (around 30 percent of GDP). Of this, 8.7 percent of GDP was spent on the various categories of social and non-social spending included in this study. The largest budgets are in the education and indirect subsidies (electricity and water), followed by health and lastly direct cash transfer programmes. Table 2 further breaks out the spending included in the analysis. The water and electricity subsidies are not on the central government budget but are implicitly public spending as the state-owned utility companies finance the gap between tariffs and the cost of delivery; see table notes for details of how they are estimated.

**_Table 2 Jordan central government expenditures, 2018_**

|||**2018**|**Percent of total**|
|---|---|---|---|
||**JOD Million**|**Percent of GDP**|**analysed**<br>**spending**|
|**_Total government spending_**<sup>a</sup>|8,912|29.7||
|**_Primary government spending_**<sup>b</sup>|7,564|25.2||
|**_Social and non-social spending analysed_**|2,609|8.7|100.0|
|**_Direct Transfers_**|277|0.9|9.2|
|NAF|104|0.3|3.5|
|Bread compensation scheme|171|0.6|5.7|
|Zakat fund cash transfer|2|0.0|0.1|
|**_Education_**|1,015|3.4|33.7|
|Preschool|4|0.0|0.1|
|Basic|717|2.4|23.8|
|Secondary|102|0.3|3.4|
|Vocational|25|0.1|0.8|
|Tertiary|116|0.4|3.9|
|Administrative|50|0.2|1.7|
|**_Health_**|763|2.5|25.3|
|Inpatient|711|2.4|23.6|
|Outpatient|38|0.1|1.3|
19 _Table 6: Results for log per capita earned income_

|VARIABLE||_log per ca_|_pita earned_|_income_||
|---|---|---|---|---|---|
||(A)|(B)|(C)|(D)|(E)|
|SAH_CHANGE|**-0.100**||**-0.084**|**-0.107**|**-0.107**|
||**-3.190**||**-1.910**|**-2.950**|**-2.950**|
|SAH_POS_CH_LGE||-0.147||||
|||-0.770||||
|SAH_POS_CH_SML||0.059||||
|||0.710||||
|SAH_NEG_CH_SML||-0.137||||
|||-1.760||||
|SAH_NEG_CH_LGE||-0.482||||
|||-2.950||||
|POOR X SAH_CHANGE|||-0.036|||
||||-0.520|||
|HI X SAH_CHANGE||||0.033||
|||||0.410||
|URB_HI X SAH_CHANGE|||||0.031|
||||||0.340|
|RUR_HI X SAH_CHANGE|||||0.036|
||||||0.280|
|**_LINEAR COMBINATIONS_**||||||
|_SAH_NEGCHANGE - SAH_POSCHANGE_|-0.175|||||
||-1.607|||||
|_SAH_POS_LGE - 2 * SAH_NEG_SML_||-0.265||||
|||-1.137||||
|_SAH_NEG_LGE - 2 * SAH_NEG_SML_||-0.207||||
|||-1.011||||
|_SAH_CHANGE + POOR X SAH_CHANGE_|||**-0.119**|||
||||**-2.418**|||
|_SAH_CHANGE + HI X SAH_CHANGE_||||-0.075||
|||||-1.089||
|_SAH_CHANGE + URB_HI X SAH_CHANGE_|||||-0.076|
||||||-0.905|
|_SAH_CHANGE + RUR_HI X SAH_CHANGE_|||||-0.072|
||||||-0.600|
|N|7,486|7,486|7,486|7,486|7,486|

_Note: Results estimated with household-level fixed effects. Coefficients on controls (household mean age (level, squared, cubed), household size (level, squared, cubed), and wave dummies are not reported. t- statistics are reported under each coefficient. Coefficients in bold are significant at the 10% level (twosided). The test for symmetry between positive and negative shocks is based on a regression where positive (SAH_POSCHANGE) and negative (SAH_NEGCHANGE) health change are included as separate variables, ranging from one to three. Results are not reported_