rafmacalaba/datause-encoder-data huggingface dataset:
passes over the consumed items, which allows for better recollection and an increasing amount of data to be collected at each stage. There can also be a verification pass, in which the respondent is asked to confirm the answers recorded. For example, the respondent may start off by giving a broad overview of the food eaten in the past 24 hours, after which there is a more detailed description of each food (including preparation method and ingredients for prepared meals). Attention is given to quantifying the volume or weight of the consumed food with techniques such as pictures, food replicas, weighing, or volumetric estimation using local measures. Consumption by children is sought either from or in the presence of the main adult care giver.

While 24-hour diet recall and weighed food records are trusted by nutritionists, such surveys are few in number and drawn from insufficiently representative samples to provide valid evidence on the prevalence or depth of hunger for entire populations (Fiedler 2013). This is at least in part due to the fact that they are time consuming and therefore expensive to collect. At least three other less resourceintensive alternatives to the FAO approach have been suggested to derive hunger numbers: anthropometric data, self-assessments, and direct use of HCES.

Anthropometric data are an indirect measure, which is highly correlated with energy intake. The obvious criticism is that anthropometrics are also influenced by other factors, such as disease, and it is not clear that anthropometric standards sufficiently reflect either genetic or gender variation. The second alternative is subjective questions where the respondent self-assesses food adequacy. The Gallup World Poll, for example, asks ‘Have there been times in the past 12 months when you did not have enough money to buy the food that you or your family needed?’ (Headey 2013). These one-shot questions are quicker and cheaper to collect than full HCES efforts. However, how well they correlate with other measures (like food consumption) is unclear. Migotto et al. (2007) analyze data from four countries and find that subjective perceptions of food consumption adequacy are, at best, weakly correlated with calorie consumption, dietary diversity, and anthropometric measures.

The third approach, and the one that we concentrate on here, is to use HCES to derive hunger statistics (Svedberg 1999, de Haen et al. 2011, Smith 1998, Smith et al. 2006). The arguments are compelling. HCES are positioned between the single subjective hunger question and the intensive 24-hour recall. They are the respected work-horse of monetary poverty measurement (in regards to global poverty estimates, see Chen and Ravallion 2010) and are now also ubiquitous – with an explosion in availability across developing countries in the last two decades.

6
# **Table 8**

## **Covariates of the global factor loadings**

||||CIF|||||COD|||
|---|---|---|---|---|---|---|---|---|---|---|
|Covariates|1|2|3|4|5|1|2|3|4|5|
|Financial openness|0.251***||||0.139***|0.274***||||0.206***|
||[0.041]||||[0.051]|[0.044]||||[0.050]|
|Trade openness||0.062**|||-0.046||0.0824**|||0.033|
|||[0.024]|||[0.031]||[0.036]|||[0.037]|
|Dti dit (% f GDP)|||0.001***||0.002***|||0.001***||0.001***|
|omesc cre o|||[0.000]||[0.000]|||[0.000]||[0.000]|
|Exchange Rate arrangement||||-0.104***|-0.115***||||-0.042**|-0.042|
|||||[0.0221]|[0.0244]||||[0.0197]|[0.0425]|
|Observations|846|846|846|846|846|846|846|846|846|846|
|Prob > Chi2<br>Note: Panel GLS-AR(1) regres<br>is included. Standard errors i<br>index, trade openness is the<br>of Ghosh, Ostry and Qureshi (|0.000<br>sions. The d<br>n brackets.<br>log of total<br>2015), setti|0.010<br>ependent<br>The explan<br>trade over<br>ng Peg = 1,|0.001<br>variable is t<br>atory variabl<br>GDP, the exc<br>Intermediat|0.000<br>he global fa<br>es are aver<br>hange rate<br>e = 2, and F|0.000<br>ctor loadings<br>ages over the<br>arrangemen<br>loat = 3 . ***|0.000<br>estimates o<br>respective<br>t is measure<br>p<0.01, ** p|0.000<br>ver moving<br>window. Fi<br>d using the<br><0.05, * p<0|0.000<br>20-year wi<br>nancial ope<br>De Facto ag<br>.1|0.000<br>ndows, and<br>nness is the<br>gregate cla|0.000<br>a constant<br>Chinn-Ito<br>ssification|

36
The data collected by the WBGES 2008 suggest a relation between the implementation of electronic registration and an increase in the number of new businesses registered. Countries like Slovenia, Guatemala, Azerbaijan Jordan, Oman, and Sri Lanka had increases of more than 30 percent in new density rates after the full implementation of electronic registries (Figure 6). These increases cannot be attributed solely to the improvements in the countries’ business registries, but it can be stated that the modernization of their business registries was the culmination of a successful implementation of regulatory reforms when taken together, produced a significant and positive impact in the ease of doing business in these countries.

**Figure 6:  Impact of e-registration implementation**

<!-- Start of picture text --> Slovenia Guatemala Azerbaijan Jordan Oman Sri Lanka<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>t‐3 t‐2 t‐1 e‐registration t+1 t+2 t+3<br>implementation<br><!-- End of picture text --> Source: 2008 World Bank Group Entrepreneurship Survey.

The data also show that the percentage of countries beginning the automation process (electronic data storage) is similar regardless of their stage of economic development (Figure 7). The differences begin to rise gradually in the next steps of the automatization process, resulting in a wide gap between industialized and developing countries in the latter steps; for example, none of the low-income countries in the survey have implemented remote or internet registration, in comparison to 50 percent of highincome countries.

16
52.

- Keyzer, M., Merbis, M., Nubé, M., and van Wesenbeeck, C., 2008. “Food, Feed, and Fuel: When Competition Starts to Bite”. SOW-VU Brief, Center for Food Studies, VU University, Amsterdam.

- Mevel, S. 2008. “Inserting Biofuels into the GTAP Database”. Washington, D.C.: World Bank.

- Mevel, S. 2008. “Splitting the GTAP „other cereal grains‟ (GRO) sector into Corn and Other Cereal Grains”. Washington, D.C.: World Bank.

- Mitchell, D. 2008. “A Note on Rising Food Prices”. Development Prospects Group. World Bank: Washington, D.C. June.

- Runge, C., and B. Senauer. 2007. “How Biofuels Could Starve the Poor”. _Foreign Affairs._ 86 (3): 41-53.

- Taheripour F., Birur D., Hertel T., and Tyner W. 2008. “Introducing Liquid Biofuels into the GTAP Data Base”. GTAP Research Memorandum No.11.

- Taylor, J., and P. Van Doren. 2007. “The Ethanol Boondoggle”. _The Milken Institute Review_ , First Quarter.

- Timilsina,  G.R., Beghin, J.C., van der Mensbrugghe, D. and Mevel, S. 2010. “The Impacts of Biofuel Targets on Land-Use Change and Food Supply: A Global CGE Assessment.” Policy Research Working Paper 5513. The World Bank. Washington, DC.

- Timilsina,  G.R., Beghin, J.C., van der Mensbrugghe, D. and Mevel, S. (2012), The Impacts of Biofuels Targets on Land-Use Change and Food Supply: A Global CGE Assessment, Agriculture Economics, Vol. 43, pp. 313-330.

- van der Mensbrugghe, D. 2009. “The ENVironmental Impact and Sustainability Applied General <u>Equilibrium (ENVISAGE) Model”. Washington, D.C.: World Bank.</u>

33
# Figure 8.A. Economic activity during global recessions

## A. Output

## B. Output per capita

<!-- Start of picture text --> Index, t-1 = 100 Index, t-1 = 100<br>114 105<br>107<br>100<br>100<br>1975 1982<br>95<br>93 1975 1982 1991 2009<br>1991 2009 Average<br>Average<br>86 90<br>t-4 t-3 t-2 t-1 t t+1 t+2 t-4 t-3 t-2 t-1 t t+1 t+2<br>C. Industrial production D. Trade<br>Index, t-1 = 100 Index, t-1 = 100<br>110 120<br>1975 1982<br>1991 2009<br>110<br>Average<br>100<br>100<br>90<br>90<br>1975 1982<br>1991 2009 80<br>Average<br>80 70<br>t-4 t-3 t-2 t-1 t t+1 t+2 t-4 t-3 t-2 t-1 t t+1 t+2<br>E. Unemployment rate F. Oil consumption<br>Percent Index, t-1 = 100<br>7 110<br>105<br>6<br>100<br>5<br>1975 95<br>1975 1982<br>1982<br>4 1991 90 1991 2009<br>2009 Average<br>3 Average 85<br>t-4 t-3 t-2 t-1 t t+1 t+2 t-4 t-3 t-2 t-1 t t+1 t+2<br><!-- End of picture text --> Sources: British Petroleum; Feenstra, Inklaar, and Timmer (2015); Haver Analytics; International Monetary Fund; Kose and Terrones (2015); Organisation for Economic Co-operation and Development; World Bank. Note: Year “t” denotes the year of the respective global recessions (shaded in gray). Average refers to an average of four global recessions with available data. Output, output per capita, industrial production, trade, and oil consumption are index numbers equal to 100 one year before year “t” (i.e., t-1 = 100). Aggregates for output, output per capita, and industrial production are market-weighted. For details, see Appendix 2.

37
It is around this historical wage increase that the elasticities of substitution between Mode 1 services and skilled labor ( σm ) in India and the Rest of the World are calibrated<sup>14</sup> . In countries that are major demanders of Indian Mode 1 services, the Indian services are conceptualized as competing directly with the activities of skilled workers in the Rest of the World due to their lower costs. Mode 1 service imports are thus substitutable with labor in the importing countries. Examples of this would be activities such as data processing or telemarketing, where the imported data is used by a firm for activities that could be done by workers employed within the firm or industry. Consistent with this assumption, an arbitrarily high elasticity of substitution (five) is chosen for the Rest of the World.

The differences in the cost of Mode 1 services and labor prices in the importing country thus provide some intuition into how Mode 1 services from India and labor in the Rest of the World relate to each other. I can apply this intuitive approach to now determine the relationship between labor and Mode 1 services in Indian production. Indian labor costs are lower than labor costs in the countries that import its services (e.g. the USA, UK, Germany, inter alia<sup>15</sup> ), and so the Mode 1 services from those countries must cost more than the Indian workers that are involved in the same activities. A relationship of substitutes between Mode 1 services and Indian labor is thus counter‐intuitive. Since the relatively lower factor prices in India means that there is no factor price advantage that Indian firms can exploit through outsourcing, it is reasonable to conceptualize the imported services as being complementary to the skilled Indian workers. For example, management consulting services or personnel development services provided from more developed countries can be considered complementary to skilled indigenous personnel. m The elasticity of substitution ( σ ) in India must thus be low.

m Figure 3 shows how the skilled wage changes for different σ values assuming the same increases in Indian exports and imports of Mode 1 services. Note that for values above 0.15, skilled wages fall in India. This contradicts the empirical evidence over this period and so larger values must be ruled out. Taking the elasticity of substitution in the Rest of the World as given, and targeting the historical skilled labor wages rise, The elasticity of substitution between Mode

> 14 Due to data constraints, robust estimates of this elasticity through statistical methods are challenging to determine. A calibration approach that is internally consistent is taken here instead.

> 15 More than 53 percent of Mode 1 services imported into India are from the USA, UK, Germany, France, Italy, Japan, Singapore, Spain, and the Netherlands, according to data from the GTAP Database V.6 (Dimaranan, 2006).

15
activity. Given that the history of rapid urbanization in Korea mirrors the process of rural to urban migration taking place in China, one might be concerned that incidence of delayed retirement of China’s rural farmers, who are less affluent and have lacked pension support, may follow a similar pattern to Korea.

**Phased or Gradual Retirement?** The literature on retirement in developed countries has emphasized that retirement is often a gradual process: workers may reduce work hours at present employers, move to work arrangements requiring fewer hours per week or even transition in and out of retirement.<sup>18</sup> Using the US Health and Retirement Study (HRS), Gustman and Steinmeier (2000) find that twenty-two percent of the population reported being partially retired at some point, and that a fifth of the population had partially retired by age 65. Exits from employment, or the labor force, are also not always permanent: Gustman and Steinmeier’s study finds that over four waves, 17 percent of the sample experienced a reversal in which they moved from lessintensive work to more work.

The particular set of incentives influencing the labor supply decision of older workers in urban China, both before and after reaching mandatory retirement age, are likely to lead to similar patterns of gradual retirement as observed in other countries. In urban areas, low unemployment rates and shortages of skilled labor in a rapidly growing economy offer opportunity for older skilled workers. On the other hand, older long-term urban residents who own residences have experienced a sharp increase in their wealth over the last decade (Park and Porter, 2011), which may raise reservation wages and reduce the likelihood of returning to work.

In order to gain a sense of the importance of gradual, or phased retirement, we show average hours of work per week across the age distribution in Figure 3 below. The first panel shows average work hours unconditional on work status, with the labor hours of respondents who are not working equal to zero. In panel B, average hours are reported only for those respondents who

> 18Gustman and Steinmeier (1984) found that self-reports of partial retirement among US men aged 58 to 69 was quite common, particularly partial retirement into a job different than the one held at age 55. Blau (1994) emphasizes that older workers transition in and out the labor force (“retirement”) with considerable frequency and that these transitions are often not picked up in annual data.

11
Table 8 reports regressions for roads. Again, most of the results fail to be significant. However, in column 4, the interaction with the MENA dummy indicates that the length of the road network has a positive effect on the MENA region (effect of 0.568 = -0.930 + 1.498*MENA). On the other hand, the quality dummy is negative and significant, indicating that the low proportion of paved roads has been a drag on growth.

**Table 8: Roads**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||OLS|OLS|OLS|OLS|OLS|2SLS|2SLS|
||pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|
|Constant|-0.023|-0.024|-0.122|-0.025|-0.118|-0.012|-0.457|
||(0.012)**|(0.012)**|(0.085)|(0.012)**|(0.086)|(0.011)|(0.314)|
|pcgdp90|-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)|
|school_enrol_secondary|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)|
|school_enrol_primary|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)|
|inv_gdp|0.144|0.140|0.154|0.135|0.132|0.141|0.135|
||(0.026)***|(0.027)***|(0.037)***|(0.028)***|(0.038)***|(0.030)***|(0.043)***|
|Pcroad|-0.570|-0.518||-0.930||-1.547||
||(0.568)|(0.561)||(0.659)||(0.936)||
|Roadqual||0.000||0.000|0.000|-0.000|0.000|
|||(0.000)||(0.000)|(0.000)|(0.000)|(0.000)|
|pcroad_mena||||1.498||||
|||||(0.830)*||||
|roadqual_mena||||-0.000||||
|||||(0.000)**||||
|pcroad_gr|||0.104||0.103||0.446|
||||(0.085)||(0.086)||(0.320)|
|pcroad_grmena|||||-0.007|||
||||||(0.007)|||
|Observations|79|79|59|79|59|58|51|
|R-squared|0.47|0.47|0.45|0.49|0.48|0.47|0.24|
|Wu-Hausman F<br>test, p-value||||||0.27|0.36|

Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Instruments are beginning of the period indicators for: infrastructure, agri/gdp, population density, population. Variables are in per capita terms except where variable name does not begin with ‘pc’.

Table 9 reports regressions for water, showing no significant link between the number of connections and growth. Although some studies (WHO, 2006) have found that countries with a well functioning water sector also experiment strong growth, the findings of this study are not conclusive in this area. Investment in water may have indirect impact to growth through external effects such better health and better productivity of workers (Galiani et al, 2005).

# **Table 9: Water**

20
|Population|POP|Population ages 15-64 (% of total)|World Bank (WDI)|
|---|---|---|---|
|Governance|VA_PER_R<br>NK|Voice and accountability|World Bank (WGI)|
|Public spend|PSPEND|Public spending on education (% of GDP)|World Bank (WDI)|
|Infrastructure 1|IS_ROD_P<br>AVED|Roads paved (% of total roads)|World Bank (WDI)|
|Infrastructure 2|EG_ELC_H<br>YRO_ZS|Electricity production from hydroelectric (% of total)|World Bank (WDI)|
|Wealthdummuy_|WEALTH|Dummy variable based on the decomposition of<br>(lower=0, lower middle=1, upper=2) wealth level|Author compute|
|Conflictdummy_|CONFLICT|Dummy variable based on the decomposition of<br>(Conflict=1 versus absence of conflict=0) countries|Author compute|
|Saheldummy_|SAHEL|Dummy variable based on the decomposition of<br>(Sahel=0 versus Forest=1) countries|Author compute|
|Legaldummy_|LEGAL|Dummy variable based on the decomposition of (French<br>law=0 versus Common law=1)countries|Author compute|

WDI: World Bank Development Indicators. FDSD: Financial Development and Structure Database.

_Source_ : Authors’ construction.

**Table A.4: Mortgage Depth Data Sources**

|Country|Region|No. of Obs|Period|Source|
|---|---|---|---|---|
|**Botswana**|Sub-Saharan Africa|1|2009-2009|Other|
|**Burkina Faso**|Sub-Saharan Africa|5|2006-2010|Central Bank|
|**Burundi**|Sub-Saharan Africa|5|2007-2011|Central Bank of Burundi|
|**Cameroon**|Sub-Saharan Africa|1|2005-2005|Other|
|**Central African Republic**|Sub-Saharan Africa|1|2005-2005|Other|
|**Chad**|Sub-Saharan Africa|1|2005-2005|Other|
|**Congo, Rep.**|Sub-Saharan Africa|1|2005-2005|Other|
|**Gabon**|Sub-Saharan Africa|1|2005-2005|Other|
|**Ghana**|Sub-Saharan Africa|2|2006-2008|Other|
|**Guinea**|Sub-Saharan Africa|1|2005-2005|Other|
|**Kenya**|Sub-Saharan Africa|5|2006-2010|Other|
|**Malawi**|Sub-Saharan Africa|2|2006-2007|Central Bank|
|**Namibia**|Sub-Saharan Africa|1|2011-2011|Hofinet|
|**Nigeria**|Sub-Saharan Africa|2|2006-2008|Other|
|**Senegal**|Sub-Saharan Africa|1|2004-2004|Other|
|**South Africa**|Sub-Saharan Africa|12|2000-2011|South African Reserve Bank|
|**Tanzania**|Sub-Saharan Africa|1|2009-2009|World Bank Mission April 2009|
|**Togo**|Sub-Saharan Africa|5|2006-2010|Other|
|**Uganda**|Sub-Saharan Africa|2|2010-2011|Central Bank|

_Source_ : Authors’ construction based on Badev et al. (2014) database on housing finance.

**Table A.5: Housing Loan Penetration by Country** **<u>(2011)</u>**

|Country|%|Country|%|Country|%|
|---|---|---|---|---|---|
|**Angola**|4.3|Rwanda|2.1|Kenya|1.2|
|**Burundi**|0.1|Sudan|6.4|Liberia|3.6|
|**Benin**|0.6|Senegal|0.1|Lesotho|0.8|
|**Burkina Faso**|0.7|Sierra Leone|0.5|Madagascar|0.6|
|**Botswana**|1.8|Somalia|6.9|Mali|0.8|
|**Central African Republic**|1.1|Chad|6.3|Mozambique|1.1|
|**Cameroon**|1.7|Togo|2.3|Mauritania|6.1|
|**Congo, Rep.**|0.3|Tanzania|5.4|Mauritius|5.7|
|**Comoros**|0.7|Uganda|1.2|Malawi|6.5|
|**Djibouti**|6.3|South Africa|5.4|Niger|1.0|
|**Gabon**|0.6|Congo, Dem. Rep.|0.7|Nigeria|0.3|
|**Ghana**|2.8|Zambia|1.8|||
|**Guinea**|0.2|Zimbabwe|1.3|||

_Source_ : Authors’ construction based on Badev et al. (2014) database on housing finance.

22
**Table 4: Explaining the output losses in recessions**

|_Dependent variable: Average output loss_|[1]||[2]||[3]||[4]||
|---|---|---|---|---|---|---|---|---|
|Constant|0.026<br>(0.008)|**|0.028<br>(0.007)|**|0.029<br>(0.007)|**|0.027<br>(0.007)|**|
|_External Shocks_|||||||||
|TOT*Open|‐0.127|**|‐0.120|**|‐0.115|**|‐0.116|**|
||(0.057)||(0.050)||(0.051)||(0.051)||
|TOT*private credit|‐0.122|**|‐0.118|**|‐0.112|**|‐0.120|**|
||(0.053)||(0.051)||(0.053)||(0.053)||
|TOT*output Herfindhal|0.571|**|0.552|**|0.555|**|0.591|**|
||(0.265)||(0.236)||(0.231)||(0.229)||
|Interest US*Dummy IND|‐0.008<br>||||||||
||(0.049)||||||||
|Interest US*Dummy EAP|0.259|**|0.271|**|0.271|**|0.295|**|
||(0.091)||(0.093)||(0.093)||(0.090)||
|Interest US*Dummy LAC|‐0.005<br>(0.080)||||||||
|Sudden Stops|0.015|**|0.016|**|0.016|**|0.016|**|
||(0.006)||(0.006)||(0.006)||(0.006)||
|Sudden Stops*Private credit|‐0.010||‐0.010||‐0.010||‐0.012|*|
||(0.007)||(0.007)||(0.007)||(0.007)||
|_Macroeconomic Stability_|||||||||
|Domestic currency overvaluation|0.027|*|0.028|*|0.029|*|0.032|**|
||(0.016)||(0.015)||(0.015)||(0.015)||
|log (1+inflation rate)|0.006||0.006||0.006||0.006|*|
||(0.004)||(0.004)||(0.004)||(0.004)||
|_Exchange Rate Regime_|||||||||
|Floating ER|‐0.006|**|‐0.007|**|‐0.007|**|‐0.005|**|
||(0.002)||(0.002)||(0.002)||(0.002)||
|Floating ER*TOT|0.017||0.018||||||
||(0.024)||(0.023)||||||
|Fix ER|‐0.003||‐0.003||‐0.003||||
||(0.003)||(0.003)||(0.003)||||
|Fix ER*TOT|0.010||0.004||||||
||(0.054)||(0.053)||||||
|_Other variables_|||||||||
|Institutional quality|‐0.019|*|‐0.023|**|‐0.024|**|‐0.024|**|
||(0.011)||(0.009)||(0.009)||(0.009)||
|Dummy IND|‐0.002||||||||
||(0.002)||||||||
|Dummy LAC*Dummy 80s|0.000<br>(0.004)||||||||
|Number of episodes|120||120||120||120||
|Adjusted R squared|0.379||0.399||0.409||0.407||

Robust standard errors in parenthesis. *, ** the coefficient is significant at 10% and 5% level, respectively.

27