rafmacalaba/datause-encoder-data huggingface dataset:
**–** **<u>Table 3:  Incidence of Questions on Assets, Household Level LSMS questionnaires</u>**

|**Asset**|**Latin America &**<br>**the Caribbean**|**sub-Saharan**<br>**Africa**|**Asia &**<br>**The Pacific**|**E**<br>**Ce**|**urope &**<br>**ntral Asia**|**Middle East &**<br>**North America**||**Total**|
|---|---|---|---|---|---|---|---|---|
||**# % of total**|**# % of total**|**# % of total**|**#**|**% of total**|**# % of total**|**#**|**% of total**|
|Land|26 68%|10100%|7 88%|13|100%|3100%|59|82%|
|Livestock|25 66%|9 90%|8100%|13|100%|3100%|58|81%|
|Housing|36 95%|10100%|8100%|13|100%|267%|69|96%|
|Business|||||||||
|-Farm Equip.|18 47%|7 70%|7 88%|11|85%|3 100%|46|64%|
|-Non-Agric.|11 29%|8 80%|450%|10|77%|3100%|36|50%|
|Financial|||||||||
|-Savings|19 50%|9 90%|5 63%|7|54%|1 33%|41|57%|
|-Stocks|11 29%|2 20%|2 25%|3|23%|0|18|25%|
|-Pension income|36 95%|8 80%|6 75%|10|77%|3 100%|63|88%|
|-Rent, interest &<br>Dividends|38 100%|7 70%|5 63%|11|85%|3 100%|64|89%|
|Other Physical|||||||||
|-Consumer<br>durables|38 100%|9 90%|8 100%|13|100%|3<br>100%|71|99%|
|-Vehicles|35 92%|8 80%|8 100%|13|100%|3<br>100%|67|93%|
|-Other (real<br> estate, jewelry)|24 63%|7 70%|7 88%|12|92%|3 100%|53|74%|
|Total|38 100%|10 100%|8 100%|13|100%|3 100%|72|100%|

Sources: Authors’ calculations based on surveys reported in references for Tables A1-A2. Latin America & Caribbean includes LSMS and quasi-LSMS household surveys.

19
Laursen, Keld and Ammon Salter. 2004. "Searching High and Low: What Types of Firms Use Universities As a Source of Innovation?" _Research Policy_ 33 1201-1215. Lehrer, Mark and Kazuhiro Asakawa. 2004. "Rethinking the Public Sector: Idiosyncrasies of Biotechnology Commercialization As Motors of National R&D Reform in Germany and Japan." _Research Policy_ 33(6-7): 921-938.

Lester, Richard K. and Michael J. Piore 2004. _The Missing Dimension_ . Cambridge, MA: Harvard University Press.

Lorange, Peter and Dominique Turpin. 2004. "How Japan Can Grow." _MIT Sloan Management Review_ Winter 2004.

Maeda, Noboru. 2004. "Japanese Innovation System Restructuring With High-Tech Start-Ups - Creative Destruction of Catch-Up Model, in Micro, Macro and Regional Level." Stanford Japan Center Discussion Papers DP-2004-002-E. Kyoto: Stanford Japan Center.

Mayer, Colin, Koen Schoors, and Yishay Yafeh. 2003. "Sources of Funds and Investment Activities of Venture Capital Funds: Evidence From Germany, Israel, Japan and the UK." NBER Working Paper. Cambridge, MA: National Bureau of Economic Research.

Micklethwait, John and Adrian Wooldridge 2000. _A Future Perfect_ . New York: Crown Publishers.

Migliorato, Paul. 2004. "Toyota Retools Japan." _Business 2.0_ (2), Aug.: 39-41. Miyagawa, Tsutomu, Yukiko Ito, and Nobuyuki Harada. 2004. "The IT Revolution and Productivity Growth in Japan." _Journal of the Japanese and International Economies_ 18(3): 362-389.

Morana, Claudio. 2004. "The Japanese Stagnation: An Assessment of the Productivity Slowdown Hypothesis." _Japan and the World Economy_ 16(2): 193-211.

National Science Board 2004. _Science and Engineering Indicators 2004_ . Arlington, VA: National Science Foundation.

Ng, Cecilia S. V. and George S. Yip. 2004. "Japan's Coming Competitive Renaissance." _Strategy + Business_ (34): 47-57.

O'Rourke, Kevin H. and Jeffrey G. Williamson 1999. _Globalization and Its History_ . Cambridge, Mass.: MIT Press.

40
Table 3b: Shares of plants with financial loans by industry and sector

|||||ASI|share|||||NS|S share|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||1989|1994|2000|2005|2010|Average|1989|1994|2000|2005|2010|Average|
|15|Food products and beverages|0.88|0.87|0.89|0.90|0.92|0.89|0.15|0.12|0.10|0.13|0.08|0.11|
|16|Tobacco products|0.22|0.47|0.50|0.51|0.48|0.44|0.01|0.02|0.01|0.00|0.02|0.01|
|17|Textiles|0.95|0.94|0.94|0.94|0.94|0.94|0.10|0.09|0.07|0.07|0.07|0.08|
|18|Wearing apparel; dressing and dyeing of fur|0.98|0.94|0.93|0.93|0.94|0.94|0.13|0.17|0.04|0.07|0.04|0.09|
|19|Leather tanning; luggage, handbags, footwear|0.92|0.90|0.90|0.87|0.93|0.91|0.21|0.16|0.11|0.14|0.06|0.14|
|20|Wood and wood products; straw and plating articles|0.78|0.77|0.74|0.85|0.85|0.80|0.08|0.06|0.04|0.06|0.05|0.06|
|21|Paper and paper products|0.95|0.95|0.96|0.95|0.94|0.95|0.04|0.06|0.05|0.03|0.06|0.05|
|22|Publishing, printing and media reproduction|0.85|0.85|0.90|0.90|0.92|0.89|0.24|0.18|0.19|0.22|0.14|0.19|
|23|Coke, refined petroleum and nuclear fuel|0.78|0.86|0.90|0.83|0.96|0.87|0.31|0.23|0.08|0.21|0.22|0.21|
|24|Chemicals and chemical products|0.92|0.92|0.91|0.89|0.88|0.91|0.05|0.06|0.06|0.04|0.03|0.05|
|25|Rubber and plastic products|0.98|0.95|0.96|0.95|0.92|0.95|0.14|0.25|0.17|0.21|0.10|0.17|
|26|Other non-metallic mineral products|0.82|0.82|0.79|0.77|0.74|0.79|0.10|0.09|0.08|0.11|0.14|0.10|
|27|Basic metals|0.97|0.97|0.96|0.97|0.96|0.97|0.19|0.23|0.10|0.31|0.04|0.18|
|28|Fabricated metal products, except machinery|0.95|0.95|0.94|0.94|0.94|0.94|0.17|0.14|0.11|0.14|0.11|0.13|
|29|Machinery and equipment, n.e.c.|0.95|0.94|0.93|0.94|0.94|0.94|0.13|0.11|0.19|0.16|0.09|0.14|
|30|Office, accounting and computing machinery|0.95|0.96|0.82|0.83|0.91|0.90|0.54|0.10|0.07|0.28|0.18|0.23|
|31|Electrical machinery and apparatus, n.e.c.|0.96|0.96|0.96|0.94|0.92|0.95|0.16|0.18|0.21|0.17|0.10|0.17|
|32|Radio, television, and comm. equipment|0.95|0.92|0.96|0.88|0.90|0.92|0.18|0.07|0.34|0.21|0.04|0.17|
|33|Medical, precision and optical instruments, watches|0.95|0.94|0.91|0.90|0.90|0.92|0.42|0.46|0.22|0.37|0.26|0.35|
|34|Motor vehicles, trailers and semi-trailers|0.96|0.95|0.95|0.94|0.94|0.95|0.15|0.27|0.16|0.43|0.16|0.23|
|35|Other transport equipment|0.95|0.96|0.95|0.95|0.94|0.95|0.18|0.26|0.16|0.12|0.12|0.17|
|36|Furniture, manufacturing n.e.c.|0.88|0.87|0.89|0.92|0.87|0.89|0.09|0.07|0.06|0.12|0.05|0.08|
||Nationwide|0.84|0.87|0.90|0.90|0.89|0.88|0.10|0.09|0.06|0.08|0.06|0.08|

Notes: See Table 3a.
economic well-being. They particularly find that access to the pension program and associated transfers have improved food security (reduced hunger) and psychological well-being (reduced mental health problems).

Besides these direct evaluations, there are other indirect pieces of evidence showing the potential of social protection programs to mitigate some of the adverse impacts of the pandemic. For example, Abay et al. (2021b) evaluate whether COVID-19 induced disruptions in school feeding programs affect beneficiary households in Nigeria. They find that disruptions in school feeding services, because of nationwide suspension of schools, have disproportionally affected communities who used to benefit from school feeding services, evidence that highlights the counterfactual role of these social protection services. Other evaluations from the rest of the world also show some encouraging evidence. For instance, LondoñoVélez and Querubin (2020) evalaute an unconditional cash transfer program in Colombia and show positive impacts on food access while Bottan, et al. (2021) show that a large-scale noncontributory pension program in Bolivia has significant impacts in protecting households’ food security during the pandemic.

# **5. Limitations and challenges in delivering social protection programs in Africa**

Despite the instrumental role of social protection programs in Africa, delivering these programs during a pandemic entails several important challenges that can limit the effectiveness of these programs. Most of these challenges relate to targeting, coverage, timeliness, and financing of social protection programs.

# **5.1 Targeting of social protection programs during a pandemic**

Ideally, the social protection responses to the COVID-19 shock should be targeted to those households most likely to be tipped into poverty as a result of the lockdowns and global disruptions in food systems. As discussed in Section 4, short-term safety net transfers can protect vulnerable households from falling into extreme poverty or prevent those households from engaging in costly risk coping mechanisms such as selling-off assets or migrating away from employment that could result in these households joining the ranks of the chronically poor.

Before the pandemic, social safety net targeting in Africa was mostly oriented towards identifying chronically poor households rather than vulnerability to shocks. Most social safety net programs developed in Africa in the first decade of the twenty-first century employed Proxy Means Testing (PMT) for targeting beneficiaries (del Ninno and Mills, 2015). PMT targeting provides a standardized and objective way to identify chronically poor households at relatively low cost. The targeting accuracy of the PMT approach is moderately effective in general (Coady et al., 2004; Brown et al., 2018), but because it is based on household assets at some point in the past, it is least effective at identifying households that are most vulnerable to shocks such as the COVID-19 pandemic. Furthermore, collecting household surveys during 24
56 Table 19: Murder and Robbery

|||**Murder**||**A**|**rmed Robber**|**y**|
|---|---|---|---|---|---|---|
|**Inequality**In**PoliceStation**|(008)5|(0.09)||0.16|(0.12)6**||
|**MeanExpenditure**||0.25<br>(0.05)**|0.24<br>(0.06)**||1.20<br>(0.07)**|1.15<br>(0.07)**|
|**UnemploymentinCriminal**||**0.11**|0.12||0.49|0.43|
|**CatchmentArea**||(0.07)|(0.07)||(0.09)**|(0.10)**|
|**RichestPolice Stationamong**<br>||0.05|0.05||-0.16|-0.17|
|**Neighbors**||(0.08)|(0.08)||(0.11)|(0.11)|
|**Within-GroupInequality**|||0.29<br>(018)|||0.14<br>(024)|
|**Between-GroupInequality**|||.<br>0.03<br>(0.01)*.|||.<br>-0.06<br>(0.02)**|
|**Population**|0.92<br>(0.02)**|0.90<br>(0.02)"*|<br>0.88<br>(0.02)**|0.89<br>(0.03)**|0.88<br>(0.03)**|0.87<br>(0.03)**|
|**ProvinceDummies**|||||||
||-0.10<br>|-0.06<br>|-0.09<br>|0.16<br>|0.64<br>|0.43<br>|
|**EastemCape**|(0.08)|(0.11)|(0.12)|(0.14)|(0.15)**|(0.17)*|
||-0.31|-0.21|-0.20|-0.92|-0.13|-0.28|
|**NorthernCape**|(0.12)**|(0.13)|(0.13)|(0.21)**|(0.20)|(0.20)|
|**FreeState**|-0.62<br>(0.11)**|-0.46<br>(0.12)**|-0.43<br>(0.12)**|0.08<br>(0.17)|1.13<br>(0.17)**|0.90<br>(0.17)"*|
|**Kwazulu-Natal**|0.25<br>(0.08)**|0.26<br>(0.i0)*|0.21<br>(0.11)|1.52<br>(0.14)**|1.83<br>(0.14)**|1.73<br>(0.15)**|
|**NorthwestProvince**|-0.42<br>(0.10)**|-0.34<br>(0.12)**|-0.35<br>(0.13)**|0.50<br>(0.17)**|1.28<br>(0.17)**|1.05<br>(0.18)**|
|**Gauteng**|033|016|013|256|204|209|
||.<br>(0.09)**|.<br>(0.**10)**|.<br>(0.10)|.<br>(0.15)**|.<br>(0.13)**|.<br>(0.13)**|
||-0.27<br> ******|-0.27<br>11*|-0.29<br>012*|1.12<br>1**|1.66<br>16**|1.53<br>016**|
|**Mpumalan,qa**|(0.**10)**|(0.)|(.)|(0.7)|(0.)|(.)|
|NorthernProvince|**-1.01**<br>(0.11)**|-0.97<br>(0.13)**|-0.97<br>(0.15)**|0.53<br>(0.18)**|1.02<br>(0.18)*|0.67<br>(0.21)**|
|**Constant**|-5.84<br>(0.22)**|-7.25<br>(0.36)**|-6.82<br>(0.54)**|-6.42<br>(0.32)**|-14.36<br>(0.54)**|-13.51<br>(0.76)**|
|**PseudoR**<sup>**2**</sup>|0.148|0.149|0.149|0.148|0.151|0.150|
|Observations|1066|1063|1061|1066|1063|1061|

Standard errors in parentheses. * significant at 5%; ** significant at 1%. Within-Group and Between-Group Inequality refer to inequality within and between racial groups in each police station.
|(2011)|(2008)|
|---|---|
|South Africa|Peru*|
|(2011)|(2008)|
|Tanzania*|Uruguay*|
|(2011)|(2008)|
|Uganda|Venezuela, RB*|
|(2011)|(2008)|

Note: This table shows the countries used in this study. The year in which NTM data was collected is in parentheses. An asterisk indicates those countries for which the NTM data are publically available in WITS as of June 2013. Source: Authors’ computation For the purposes of our analysis, a standardized methodology was developed in order to harmonize each country’s NTM data file. Firstly, we only used in the analysis those NTMs that applied to each of a country’s trading partners. In those cases where the database did not specify the group of countries to which an NTM applied, it was assumed that it applied to all trading partners.

The second step in the harmonization was to standardize the product codes to the six digit level. In some cases only a two digit chapter heading was provided in the database. In these cases, it was assumed that the NTM applied to all products at the six digit level under that chapter. When a NTM was stated as affecting a product at an eight or ten digit level, the product code was trimmed to six digits to allow a consistent cross-country comparison exercise. In all cases, it was assumed that the NTM affected all goods under the six digit classification. Thirdly, we noted that four different NTM nomenclatures were used in the collection of the NTM data; each of the databases was converted to the 2012 version using a concordance across nomenclatures.

Finally, the data was checked to ensure no duplications at the country-product-NTM code level, and we dropped from the analysis SPS and TBT measures that were applied to all products as these were assumed to be data entry mistakes. For each six digit code, two indicators were developed; a dummy indicating that at least one NTM applied to that good, and a count variable showing the total number of NTMs applying to that product. The same indictors are also created at the NTM chapter level; a dummy indicating the application of at least one regulation from a particular NTM chapter to a product, the second indicator showing the actual number of NTMs from each chapter applying to each good. In this analysis we investigate only SPS and TBT measures.

# **d. The Incidence of NTMs in Central America**

In this section, two simple inventory indicators are used to measure the prevalence of NTMs: the frequency ratio (percentage of product lines exposed to NTMs) and the coverage ratio (share of total imports exposed to NTMs).

The frequency ratio accounts only for the presence or absence of an NTM, and indicates the percentage of imported products to which one or more NTMs are applied. In more formal terms, the frequency ratio of NTMs imposed by country j (Fj) is calculated as: 9
equation (23): We also construct a range of price indices at the quarterly level using alternative methods. The first index weights each product group using the expenditure shares of only products chosen to construct the price index, that is, products that satisfy the consistency criterion illustrated above, as opposed to all products in the data. The second index uses the Tornqvist index mentioned above. The third index constructs the price index in one stage instead of two stages. The fourth index uses a weighted geometric average in the first stage instead of a weighted arithmetic average. The fifth index uses weights that are fixed over time at the base period to ensure that the results are not driven by shifts in the consumption bundle over time as opposed to actual price changes. To construct such an index, only products that appeared consistently over the entire sample period can be used. The sixth index again uses fixed weights but also base observations that are sampled from the last observable posted price for each store-product-quarter.<sup>63</sup> The seventh index uses weights constructed from households using the Nielsen Consumer Panel instead of the Nielsen Retail Scanner, which allows us to construct weights by household income brackets. We present results focusing on SNAP-eligible households as classified above. All indices are highly correlated and results are robust to using any of the above methods. We show these indices for New York City in Appendix Figure O9, which are nearly identical across construction methods.

# **E Plausible Exogeneity**

We relax the identifying assumption by allowing the IV to be plausibly exogenous ala Conley et al. (2012) with the IV having a direct impact on outcomes even after controls.

We obtain a back-of-the-envelope estimate of how large this impact would be quantitatively. Since SUA depends on both energy costs and idiosyncratic state policies, we allow the IV to reflect energy costs, which may have an impact on the outcomes even after energy controls if the controls are not perfect. Pass-through formulas imply that the effect of energy costs on retail prices is just the cost share of energy costs. The 2009 economic census estimates that the share of electricity, purchased fuels, and other utility payments is 5.5% of operating expenses. Given a gross margin of around 30% in the grocery sector based on the Annual Retail Trade Survey, this implies an upper bound of about 0 _._ 055 _×_ 0 _._ 3 = 0 _._ 0165 for the cost share of energy costs. Therefore, we use 0 _._ 02 as our back-of-the-envelope estimate of the direct effect of the IV on retail prices, which is denoted as _δ_ . When _δ_ = 0 _._ 02, our estimated coefficient is 0 _._ 066 and remains statistically significant at the 1% level.

> 63The posted price is actually recorded as a weekly unit value for Saturday-ending weeks. DVG highlight that this feature creates a slight aggregation bias but the bias is relatively small for state-level shocks in their calibrations.

54
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NEW ZEALAND: Both the IMR and life expectancy at birth are based on average vital registration data for the period 1985 to 1989 as published in _PVSR._ PAPUA NEW GUINEA: The IMR is from ESCAP 1988 and is based on analysis of the 1980 census. Life expectancy at birth estimates are from _World Population Prospects 1990_ and are based on indirect estimates from the 1980 census, projected with the U.N. slow mortality decline pattern.

PHILIPPINES: The IMR is from _World Population Prospects 1990._ Life expectancy at birth estimates are based on Coale-Demeny "West" model life tables selected on the basis of the IMR.

SINGAPORE: The IMR is an official estimate for 1987 from vital registration, as published in _PVSR_ (1/89). Life expectancy at birth is from _World Population Prospects 1° .._ d is based on vital registration data through 1988.

SOLOMON ISLANDS: Life expectancy at birth estimates are Bank projections from official estimates from the 1986 census for 1980-84. The IMR is derived from a corresponding Coale-Demeny "West" model life table.

SRI LANKA: The IMR is projected from the 1987 Demographic and Health Survey estimate for 1983-87 and is consistent with vitai registration estimates of 1986-88 as published in _PVSR._ Life expectancy at birth estimates are from _World Population Prospects 1990_ and are based on vital registration estimates projected with the U.N. medium decline model.

TAIWAN, CHINA: Life expectancy at birth is est'mated from the official CDR and age-sex distribution as published in the Tbiwan Statistical Data Book 1987. The IMR is from U.S. Bureau of the Census (1989) and corresponds to the life expectancy at birth estimates in Coale-Demeny "East" model life tables.

THAILAND: The IMR is projected from the 1987 Demographic and Health Survey estimate for 1983-87. Life expectancy at birth estimates are government projections and are consistent with model life table estimates based on the IMR.

TONGA: The IMR is from the U.S. Bureau of the Census (1989). Life expectancy at birth estimates are corresponding Coale-Demeny "West" model life table estimates.

VANUATU: The IMR is a Bank projection from an official estimate derived from the 1979 census. Life expectancy at birth estimates are corresponding Coale-Demeny "West" model life table estimates.

VIETNAM: The IMR is an internolation of an estimate derived from the 1976 census (King and Vu 1979) and a 1989 census indirect estimate. The 1988 Vietnam Demographic Survey estimate was also considered. Life expectancy at birth are Coale-Demeny "West" model life table estimates corresponding to the estimated IMR.

WESTERN SAMOA: Both the IMR and life expectancy at birth are Bank projections from official estimates for 1986 as published by ESCAP, 1988.

# **Southwest Asia**

19
structural and distributional implications of economic shocks and policies. In particular, _the framework should account for interdependence between policy issues and instruments, and for the distribution of economic welfare among socioeconomic groups, households and individuals_ . A general equilibrium framework offers an attractive point of departure because it has both macro and micro elements upon which to base the analysis of some macroeconomic shocks and policies.

A general equilibrium analysis of the distributional implications of macroeconomic shocks and policies may follow three basic approaches: (1) the standard representative household (RH) approach, (2) the extended representative household approach (ERH), and (3) the microsimulation approach. The standard RH approach limits the analysis of the distributional impact of shocks and policies to their effects on the mean welfare within a few representative socioeconomic groups. In this framework, one can predict the impact of shocks and policies on the functional distribution and on between-group inequality but not on poverty. Poverty analysis requires the specification of the size distribution within groups. One way of extending the RH is to model withingroup size distribution with a density function associated with a well-known distribution such as the lognormal or the beta (Dervis, de Melo and Robinson 1982; Decaluwé, Patry, Savard and Thorbecke 1999). Another way is to use unit record data drawn directly from a household survey to represent the size distribution of economic welfare. This is consistent with the microsimulation approach (Ravallion and Lokshin 2004, Bourguignon, Robilliard and Robinson 2003).

This paper focuses on the ERH approach. However, instead of relying on a parametric distribution, it demonstrates how the Lorenz curve can help link an extended functional distribution to a size distribution of economic welfare to analyze the poverty impact of macroeconomic events. Indeed, the Lorenz curve provides an integrative framework for both the simulation and ethical evaluation of inequality and poverty. Most, if not all, inequality and poverty measures of interest can be computed from the level of income or expenditure **x** , the associated density function **f(x)** , and a poverty line (for poverty measures). We can recover both the level of income and the density function from a parameterized Lorenz function and the mean of the distribution. This information is all we need for the computation of the relevant indicators. There are examples from 1