rafmacalaba/datause-encoder-data huggingface dataset:
ule comprising of over 345 items, compared to 114 unique items captured in the CPHS. Expenditures on household appliances, personal transport equipment, other durables are notably not covered in the CPHS consumption survey. Both surveys contain information on household asset ownership. Additionally, the NSS’ expenditure based on uniform recall period captures household consumption over the past thirty days, whereas the CPHS collects consumption based on the past four calendar months. Differences in recall periods across surveys can have large impacts on estimates of poverty (Deaton, 2003; Deaton and Dreze, 2002; Tarozzi, 2007).

Seventh, the CPHS household consumption aggregate includes expenditures on insurance premiums and loan repayments, which are excluded in NSS’ consumption expenditure aggregate.

# **2.4 Addressing differences in instrument design**

In this section we document the necessary adjustments we applied to the CPHS datasets in order to address the differences in instrument design between the two surveys. First, we pool the CPHS interviews conducted during the second and third wave of a calendar year and the first wave of the following year to match (as closely as we can) the NSS-2011 reference period of July 2011 to June 2012. The second wave of CPHS starts in May and the first wave ends in April, with households reporting consumption for the past calendar month. Accordingly, CPHS consumption reference period will correspond to April (the month prior to May, when interviews begin) through March of the following year(the month prior to April, the last month of interview). The 2019-20 round of the CPHS consumption overlaps with the first week of the covid induced lockdowns (as the lockdowns in India were imposed on March 24<sup>th</sup> , 2020), and as such may provide limited evidence on how household consumption, poverty and inequality were impacted at the start of the lockdowns<sup>12</sup> Second, we exclude districts that are covered by the NSS consumption survey but not by the CPHS to obtain geographical consistency in our analysis. The excluded non-overlapping districts represent about 4.8 percent of the country’s population in 2011. Third, in an effort to approximate the NSS’ 30-day uniform recall period, we retain item-wise household expenditures for the month preceding the CPHS survey and ignore values that are reported with a lag of two to four months. Fourth, we construct a harmonized basket of items across the two surveys. Expenditures on loan repayments, insurance premiums and household’s private transfers to emigrated members are discarded from the CPHS -- while expenditures on durables, household appliances, etc.

> 12All references CPHS consumption years in this paper refer to the financial year starting April to March. That is, CPHS 2015 refers to the corresponding months in 2015-16.

10
quantitative restrictions on<sup>productsfor whichthey receive concessionsand eliminate</sup> other NTBs on products within<sup>fiveyearsafter receivingconcessionson them.</sup> Another problem with the<sup>earlier PTAswas the practice of "padding inclusion</sup> lists." To prevent this, the<sup>CEPT productsare defined on a sectoral basis but exclusions</sup> from the CEPT are defined<sup>at a moredisaggregatedlevel.176The question remains,</sup> however, whether AFTA will<sup>actually resultin benefits accruingto the ASEAN member</sup> countries.

The potential benefits of AFTA<sup>have been assessed in several studies using a</sup> Computable General Equilibrium<sup>model(CGE).177A study by DeRosa (1995) usedthe</sup> model to deterrnine the effects<sup>of AFTA on the original fiveASEAN countries; i.e.,</sup> Indonesia, Malaysia, the Philippines,<sup>Singapore,and Thailand.(Bruneiwas a member of</sup> ASEAN at the time of the<sup>study but DeRosa did not includeit becauseof its small size</sup> and poor data records.) The<sup>simulationresults for trade areshown in Figure3.1.</sup> Figure 3.1 <!-- Start of picture text --> Effects of AFTA and MFN Liberalization on ASEAN<br>Trade<br>10 -9.<br>28- - -_<br>0 6 Q~~~~~~~~~EAFTA<br>4 2 -__ 2.42.9 . MFN liberalizaton<br>2<br>0<br>ASEAN trade with the Trade within ASEAN<br>world<br><!-- End of picture text --> Source: DeRosa (1995: Figure<sup>2, 3)</sup> The figure shows that trade<sup>creation does occur under AFTA -at about the same</sup> level for both "trade within<sup>ASEAN"(by $2.9 billion,or 19 percent) and "ASEAN trade</sup> with the world" (by $2.4 billion).<sup>However,underthe MFNliberalizationscenario,trade</sup> creation within ASEAN is<sup>somewhat smaller ($1.7billion) and trade with the world is</sup> much greater ($9.1 billion)<sup>than under the AFTA scenario.The enormousdifference</sup> between the two scenarios<sup>in creation of trade with the world is attributed to the</sup> exploitation of the "wider<sup>differences in the sourcesof international comparative</sup> advantage between the ASEAN<sup>countriesand their internationaltrading partners,</sup> especially the major industrial<sup>countries." 178Asfor agriculture,Indonesia, Malaysia,</sup> 53
Table 2. Variance decomposition: VAR with global factors (fraction of variance explained by respective shock, in percent)

|Shocks|Forecast<br>horizon<br>(quarters)|GDP|Productivity|Inflation|Interest<br>Rates|Credit|Credit<br>Spread|Default<br>Rates|
|---|---|---|---|---|---|---|---|---|
|Credit|1|8.9|6.5|6.8|9.9|14.6|9.2|15.7|
||4|9.7|8.8|9.2|10.1|13.9|9.5|14.9|
||8|10.6|10.3|10.6|10.5|12.5|10.9|14.2|
||12|10.8|10.4|10.9|10.8|12.1|11.1|13.9|
|Productivity|1|9.3|7.1|23.5|9.1|9.1|8.5|10.7|
||4|10.5|9.4|19.6|10.3|11.4|9.9|12.2|
||8|12.1|11.0|16.6|11.8|13.3|11.4|12.5|
||12|12.3|11.4|16.3|12.3|14.5|11.8|12.5|

Source: Helbling et al (2011).

Note: The roles of credit and productivity shocks in explaining global business cycles are shown using a VAR model that includes the estimated global factor of each variable and US credit spreads and default rates. The table reports the fraction of the forecast error variance of these variables that is explained by global credit and productivity shocks for different forecast horizons. Though both shocks are identified simultaneously, the variance decompositions need not add up to 100% because other potentially unidentified shocks make up the rest of the variance. Credit is measured by the aggregate claims on the private sector of deposit money banks and is obtained from the International Financial Statistics (IFS) of the IMF. The default rate series correspond to the monthly rates for US speculative-grade corporate bonds rated by Moody's Investor Service. GDP data are chained volume series from the OECD. The interest rates correspond to nominal short-term government bill rates and are taken from IFS. Labour productivity is defined as real GDP per hours worked and is obtained from the OECD. Inflation corresponds to the change in each country's CPI. The sample includes G7 countries for the period Q1 1988 to Q4 2009.

57
905In addition to Improving the accessibility of data, there Is a need for a more conslstent applicatlon of the OMS definitlons of financial ratios as well as more attention given to quality control. In many cases, figures presented were ambiguous;y defined In documents and a full understanding of the situation was not possible.

The need for quality control of SARs also remains high. SARs are the principal document and used constantly throughout project Implementatlon and provide significant Information for the Borrowers and their consultants as well.

Specific recommendations for Improving project and sector performance are summarized as follows:

1. systematically examine optlons for sector restructuring, In order to strengthen market forces, Improve the environment In which the utility functions, and Increase Incentives for enhanced utility efficiency;

2. place greater emphasis on Improving productive efficiency, with special reference to malntenance, rehabilitation, and distributlon network Investments, In order to Improve losses and the quality of service; measuring productive efficiency can only be achieved by developing more systematic collection and analysis of performance indlcators; 3. strengthen the analysis of power-energy-macroeconomic linkages, and pay more attentlon to project evaluation In the sectoral and national economic context. In particular, assess the feasibility of the sector Investment program and the ability of the sector and government to finance the program; 4. In determining Investment and pricing policy, adopt less deterministic tnalytical approaches that can better account for the greater uncertainties in the current environment. Also carry out a more Indepth rlsk and sensitivity analysis of the Impact of poor project and
Table 5: LPM estimates for regressions as correlations for Malaysia

||**(1)**<br>**R&D**|**(2)**<br>**Intg. Assets**<br>**Purch.**|**(3)**<br>**Intg. Assets**<br>**Used**|**(4)**<br>**Intg. Assets**<br>**Prod.**|
|---|---|---|---|---|
|Index * (D/T)|1.296**|‐0.754|‐0.090**|‐0.017|
||(0.043)|(0.177)|(0.045)|(0.572)|
|Constant|‐0.006|0.126***|0.011***|0.005**|
||(0.897)|(0.009)|(0.004)|(0.043)|
|Observations|39206|39206|39206|39206|
|R2A|0.476|0.245|0.067|0.064|
|R2W|0.005|0.002|0.000|0.000|
|RMSE|0.216|0.214|0.059|0.060|

Note: * p<0.10; ** p<0.05; *** p<0.01, representing p‐values not standard errors. The term (D/T) is computed as the proportion of ICT‐services as input in total input expenditure for each sector. R&D is Research and Development activities in thousands RM. Assets Purch. is other assets (such as patent, goodwill, work in progress) new purchases including imports of both new and used assets in thousands RM. Assets used is other assets (such as patent, goodwill, work in progress) purchases of assets previously used in Malaysia including those reconditioned or modified before acquisition in thousands RM. Assets prod. is other assets (such as patent, goodwill, work in progress) assets produced by the establishment for its own use, the costs of all works done during the year should be recorded in thousands RM. All dependent variables are transformed into a binary mode varying between 0‐1 with a value of 1 assigned for any value > 0. Robust standard errors clustered by sector. Fixed effects by firm, sector and year are applied. A lag of 1 year is also applied.

26
# TAILE I CfTfD ENAIm AL REIATIOU8UIP

Central CovenwMt Ffnwices cont$d Recurrent expaiitsure:

> * V,. wugr

k-I Gds, * . U, 3

**k-1**

where: V waes aN salwies war Gd9S - goods nd services wages and salaries: nominal growth rate target k - 197, 198. 1t89 Int 9 DCD6g#. r I OthREX.. k-I OthiEX, u _ _ _ _ __ _ _ DPIc, 3 I SPfc 5 k-I , where: Into - Interest on debt outstanding and disbursed (foreign DoDg - or domestic Governrent debt outstanding depending on specification of k) and disbursed othREX, - Other recurrent empenditure k - 187, 1988, 1989
|||||134.82||||90.1||||100.17|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|R Adj sq.|0.309|0.316|0.32||0.447|0.448|0.459||0.083|0.098|0.107||
|N|10906|10906|10906|10906|4922|4922|4922|4922|5984|5984|5984|5984|

The regression also includes oblast dummies, dummies for different types of schools and evening schools. The full table can be found in excel tables which can be made available upon request. *** means significant at the 1% level, ** at the 5 % level and * at the 10 percent level.T statistics are based on robust standard errors for the OLS specifications.

28
less empirical evidence in favor of a causal effect of environmental shocks on migration (Beine and Jeusette, 2021). The use of migration flows as the dependent variable in the regressions increases the probability of finding an impact, and data on direct measures of migration should be collected accordingly. _Ex post_ counterfactual policy evaluation of development and social policy interventions aimed at either favoring or reducing weather-induced migration flows is also scant. There is a need for data observed on such programs to improve our understanding about the role of policy in mediating the causal relationship between climatic shocks and human mobility. As a complement to that, the collection of detailed community-level data to supplement household survey data on the roll-out and implementation of such programs is also a key element to be considered.

Last but not least, the growing availability of big data and citizen-generated information has spurred a debate on their potential integration or complementarity with more traditional data collection methods, such as census and surveys, to fill migration data gaps. In principle, these new data sources could massively improve the quantity of data available to study climate change and migration. Entities such as the European Commission and the International Organization for Migration have already started to assess the potential of sources of big data. Among the most prominent examples of these non-traditional data sources: mobile phone call detail records (CDR); Internet activity such as Google searches; online media content; geo-referenced social media activity, which can be obtained via advertising platforms offered by social media (IOM, 2018). There are also some first recent scientific works based directly on these sources. Lai et al. (2019) employed a massive data set of 72 billion anonymized CDRs in Namibia from October 2010 to April 2014, to explore how internal migration estimates can be derived and modeled from CDRs at subnational and annual scales. As for social networks, Spyratos et al. (2019) used anonymized and publicly available data provided by Facebook’s advertising platform to estimate the number of Facebook Network (FN) “migrants” in 119 countries of residence and concluded that these estimates could be used for trend analysis and early-warning purposes. Specifically concerning climate change, some have highlighted that in combination with field-level data derived from household surveys and key-informant networks, big data could be used to detect how sudden-onset natural disasters and progressive environmental change impact migration patterns (Franklinos et al., 2020). Along these lines, for example, Lu et al. (2016) used anonymized CDR from a mobile network provider (Grameenphone) to retrieve the geographical position and movements of users, so as to be able to examine the human mobility effects of the 2013 Mahasen cyclone in Bangladesh. Finally, for an interesting comparison between mobile phone and census data, see Wesolowski et al. (2013) and Kirchberger (2022) for a 21
A 22

# SrrC Grgous Included in the UJNIDOQ Derinition of Manufacures

# SrrCmCrup Dduption

|01|Meat and meat preparation|
|---|---|
|02|Dairy products and eggs|
|032|Fish in airtight containers, n.e.s. and fish preparations|
|042.2|Rice, glazed or polished|
|046|Meal andflourof wheat or of meslin|
|047|Meal and flour of cereals except of wheat or of meslin|
|048|Cereal preparations and starch|
|052|Dried fruit|
|043|Fruit, preserved and fruit preparations|
|_055_|Vegetables, roots and tuber, prepared or preserved|
|06|Sugar, sugar preparations and honey|
|071.3|Coffee extracts, essences, concentrates and similar preparations|
|072.2|Cocoa powder, unsweetened|
|072.3|Cocoa butter and cocoa past|
|073|Chocolate and other food preparations containing cocoa or chocolate|
|074|Tea and mat6|
|081|Feeding-stuf**f**or animals|
|_09_|Miscellaneous food preparations|
|11|Beverages|
|122|Tobacco manufactures|
|221.9|Flour and meal of oil-seeds, oil nuts and oil kemels|
|231'|Crude rubber including synthetic and reclaimed|
|243|Wood, shaped or simply worked|
|251|Pulp and waste paper|
|262.6|Wool shoddy|
|262.7|Wool or other animal hair, carded or combed (excluding tops)|
|262.8|Wool tops|
|262.9|Waste of wool an other animal hair, n.e.s.|
|263|Cotton|
|266|Synthetic and regenerated (artificial) fibers|
|267|<br>Waste materials from textile fabrics (including rags)|
|232|Petroleum products|
|4|<br>Animal and vegetable oils and fats|
|_5_|<br>Chemicals|
|62|Manufactured goods classified chiefly by material|
|7|Machinery and transport equipment|
|83|<br>Miscellaneous manufactured articles|

1 less 231.1 natural rubber and similar natural gums

- 2 less 667 pearls and semi-precious stones

3 less 863 developed and cinematographic film and 896 works of art, collectores pieces and antiques.
**Table 7: Exploring Heterogeneity by Region Subgroups**

||Dependen<br>|t Variable: Log [Self-Re<br>|ported Productivity (B<br>|irr/HA)]<br>|
|---|---|---|---|---|
||(1)|(2) ‡|(3)|(4) ‡|
|**_Female and Region Interactions_**|||||
|Female x Tigray †|0.3312**|-0.0858|0.0505|-0.0222|
||(0.1304)|(0.1289)|(0.1580)|(0.1439)|
|Female x Amhara †|-0.1715|-0.4098**|-0.4148**|-0.4890**|
||(0.1685)|(0.1729)|(0.1797)|(0.1903)|
|Female x Oromiya †|-0.0764|-0.0683|0.1006|0.1220|
||(0.2077)|(0.1895)|(0.1249)|(0.1299)|
|Female x SNNP †|-0.6145***|-0.2828*|-0.0519|0.0119|
||(0.1684)|(0.1567)|(0.1378)|(0.1390)|
|Female x Other Region †|-0.7693|-0.2656|-0.5208*|-0.2466|
||(0.4801)|(0.3403)|(0.2926)|(0.3186)|
|**_Region Dummies_**|||||
|Tigray †||0.9207***||0.4946*|
|||(0.2858)||(0.2625)|
|Amhara †||0.7421***||0.4544**|
|||(0.2475)||(0.2002)|
|Oromiya †||0.4957*||0.2871|
|||(0.2627)||(0.1955)|
|SNNP †||0.1721||0.2056|
|||(0.2924)||(0.2137)|
|**Baseline Covariates**|No|No|Yes|Yes|
|**Observations**|1,518|1,518|1,518|1,518|
|**R-Squared**|0.018|0.069|0.346|0.352|

Note: Clustered Standard Errors at EA (Enumeration Area) Level in Parentheses. *** p<0.01, ** p<0.05, * p<0.1 † Denotes a Dummy Variable ‡ In regressions 2 and 4, dummy variable for Other Region is omitted because of collinearity.

30
# **Table 6. The effect of victimization on health and education expenditures by event type and perpetrator**

|VARIABLES|Health (ln)|Education (ln|)||Health (l|n)|||Edu|cation (l|n)||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||Event|type|<br>|Perpetrat<br>|or|Event|type||Perpetrat|or|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Conflict events|-0.018<br>(0.098)|-0.173*<br>(0.090)|||||||||||
|Property|||0.049<br>(0.243)|||||-0.550***<br>(0.199)|||||
|Violence||||0.082|||||-0.322||||
|||||(0.239)|||||(0.336)||||
|Insurgents|||||-0.080<br>(0.125)|||||-0.137<br>(0.175)|<br>||
|Bandits/criminals||||||-0.378|||||-0.471||
|||||||(0.508)|<br>||||(0.327)|<br>|
|Pastoralists/nomads|||||||0.215**|||||-0.126*|
||||||||(0.092)|||||(0.073)|
|Observations|2,075|2,075|2,075|2,075|2,075|2,075|2,075|2,075|2,075|2,075|2,075|2,075|
|R-squared|0.053|0.115|0.053|0.053|0.053|0.053|0.054|0.118|0.113|0.113|0.113|0.113|
|Number of households|717|717|717|717|717|717|717|717|717|717|717|717|
|Controls|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-roundFE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|

_Note:_ Dependent variables are household (log) health and education expenditures. Data used are from the three waves of the GHS and telephone survey for conflict. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level.

Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 38
that nearly one-third of the firms report zero score, while the average for all firms is 0.28 (standard deviation of 0.29).

_Energy Intensity_ . Energy intensity is the amount of energy used (expenditure or physical quantity) normalized by output, measured using last fiscal-year sales. Studies suggest large heterogeneity in energy intensity within narrowly defined sectors, and these variations are higher than that in other inputs (Lyubich et al., 2018). This is larger than the productivity spread for relatively homogeneous industries, such as cement (Syverson, 2004).

Energy intensity in expenditure (and physical quantity) is calculated by summing energy expenditure (quantity) for firms that report electricity use and at least one out of three possible primary fuel types. More than half of the firms in the ( _>_ 20,000 firm) sample report non-missing values for all fuel types. Summary statistics for both energy intensity and management measures are reported in Appendix Table A3. For a description of data preparation and cleaning, as well as the components of the generic and energy-centric management practices, see Appendix Section A1.

# **3.2 Stylized Facts about Management Practices and Energy Use**

After cleaning data from the World Bank Enterprise Surveys for 31 countries, our sample includes 14,713 firms reporting energy consumption in value terms and 8,272 firms reporting energy consumption in physical quantity terms. For 8,476 firms, the generic management score can be computed, contingent on firms responding to at least six of the eight aggregated questions from the management practices module. The energy-centric management score can be computed for 18,621 firms that answer at least eight of the nine energy management practice questions from the Green Economy module. Appendix Table A3 presents descriptive statistics for key variables included in our regression analysis. In this section, we highlight the main stylized facts on management practices and energy use that emerge in our data.<sup>6</sup>

> 6For our regression, we use data that are winsorized at the top and bottom 1 percent. The composition of firms across countries and two-digit sectors before and after winsorization is similar. Notably, services firms do not generally report capital stock and share of high-skilled labor, and hence we omit these variables

11
<!-- Start of picture text --> 14%<br>12%<br>10%<br>8%<br>fo<br>6%<br>4%<br>2%<br>0% = = | =<br>-2% 2007. 2008 +=2009 2010 2011 2012 2013 2014 # 2015<br>Agriculture Manufacturing Other Industries Services<br><!-- End of picture text -->

<!-- Start of picture text --> 7,000<br>6,000<br>5,000<br>4,000<br>3,000<br>2,000 ee<br>1,000<br>0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>Agriculture =Industry (incl.manufacturing) =—=Services<br><!-- End of picture text --> <!-- Start of picture text --> a | a a od<br><!-- End of picture text -->