rafmacalaba/datause-encoder-data huggingface dataset:
Very poor households, however, may not acquire many durable goods and spend most of their income on consumption, particularly food consumption. Therefore a more complete assessment of the household’s wealth would need to include a consumption measure as well. Even when available, household food related expenditure or consumption data is an imperfect measure of welfare, as it does not take into account seasonal changes, the distribution of food within the household, and the variability of all individual household members needs. An alternative is to use an outcome based indicator that would reflect how these factors come into play. The use of the body mass index (BMI), defined as the weight in kilograms divided by the square of height in meters, has been suggested as a possible indicator of household food availability (James and others, 1999). It seems reasonable to expect that changes in food availability, such as seasonal food shortages, would be reflected in a low BMI. As women tend to be the most vulnerable household members, their BMI has been chosen as a proxy for household food availability. The cut off point of a BMI of less than 18.5 is generally regarded as an indicator of malnutrition, with a BMI of less than 16 regarded as a sign of severe malnutrition. A dummy variable, which takes a value of one if the mother’s BMI is less than 18.5 is therefore used to proxy for household food insecurity.

Maternal education has been identified as a significant determinant of children’s health in the literature. The analysis incorporates information on the education of the mother and her husband or partner. Eighty two percent of the women in the sample report receiving no formal education. Thirteen percent report at least some primary school. Given the low level of education in the sample, an additional year of education may be a significant factor and therefore the mother’s education variable is defined as the number of years of education. The husband’s or partner’s level of education is also defined as the number of years of education. Nearly two-thirds of the men report no education, twenty percent report some primary education, and five percent report completing primary education.

Household composition may affect how household resources are distributed among household members. Therefore, variables on household size, whether the household is headed by a female, and the number of children under 5 years of age are used in the analysis.

13
pendent from comparative advantage. As in the one-sector model, forces that differ across sector, but are neural to the expect and average profitability ratios in each sector, are immaterial to the sector’s average firm size.

One can continue with the characterization of the equilibrium to show that comparative advantage _does_ determines the fraction of firms in each sector. That is, the ratios _M_ 1 _<u>M</u>_ +1 _M_ 2<sup>and</sup> _M_ 1 _<u>M</u>_ +2 _M_ 2<sup>areafunctionof</sup><sup>_<u>A</u>_</sup> _A_<sup><u>1</u></sup> 2<sup>.Combinedwith</sup> lemma C.3, it follows that cross-country differences in sectoral productivity do not contaminate the sectoral average firm size and only affect the economywide average firm size through the distribution of firm shares across sectors. This means, then, that our strategy of controlling for production structures by fixing the firm shares across two-digit manufacturing sectors in the US. is the theoretically correct approach and does not bias the identification of entry barriers.

# **C.4 Other Considerations**

## **C.4.1 Productivity Distribution of Entrants**

Unlike the stationary distribution, the distribution of productivity at entry is a primitive of the model that is assumed to be constant across countries. Heterogeneity in this distribution would feed into our estimate of the entry barrier.

One could imagine various ways of relaxing this assumption but not many ways of doing so in a disciplined fashion. For instance, one could calibrate a country-specific distribution of productivity at entry from the size distribution of entrants. This information, together with the idiosyncratic distortions of entering firms, allows us to back out a distribution of productivity from firms that are one years old. A more sophisticated approach would be to give entrants a choice of various initial distributions to draw from, at different costs. The biggest hurdle to both approaches is that most databases do not report the firm’s age, making it impossible to separately identify technology adoption at entry from the innovation decisions of firms over the life cycle. Since we do count with some cross-country data on the life cycle dynamics of firms, we 69
<!-- Start of picture text --> (% change wrt Bau) Fossil fuels and energy-intensive industry sectors<br>5.0<br>0.0 =| = se =| i“wa =<br>-5.0<br>-10.0<br>150<br>-20.0<br>-25.0<br>£55 £55 & wage tax £55 & CIT £55 & VAT £55 & EXCISES £55 & Income tax<br>ma-Coal mail ma-Gas<br>@a-Coke-and-pp a-Chemicals Wa-Rubber-and-plastic<br>@a-Non-metallic-mineral-products TM a-Basic-metals Wa-Land-transport<br>Wa-Water-transport We-Air-transport<br><!-- End of picture text -->

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||**Tunisia -**<br>**Market**|**Tunisia -**<br>**Extended**|**Egypt,**<br>**Arab Rep.**<br>**- Market**|**Egypt,**<br>**Arab Rep.**<br>**-**<br>**Extended**|
|---|---|---|---|---|
||(0.006)|(0.009)|(0.005)|(0.005)|
|60-64|0.003|0.004|0.012*|0.029**|
||(0.003)|(0.009)|(0.005)|(0.010)|
|**Education (higher ed. omit.)**|||||
|Illiterate|0.018***|0.037***|0.025***|0.014***|
||(0.004)|(0.008)|(0.004)|(0.003)|
|Read & Write|0.013**|0.019***|0.012*|0.010|
||(0.004)|(0.005)|(0.006)|(0.010)|
|Basic Education|0.007**|0.019***|0.019***|0.017***|
||(0.003)|(0.004)|(0.005)|(0.005)|
|Secondary Education|0.007|0.008|0.011***|0.008**|
||(0.004)|(0.005)|(0.003)|(0.003)|
|**Marital status (single omit.)**|||||
|Ever married|0.003|0.001|0.003|-0.011|
||(0.004)|(0.007)|(0.005)|(0.006)|
|**Baseline probability (reference**<br>**group)**|0.000|0.001|0.001*|0.003**|
||(0.000)|(0.001)|(0.000)|(0.001)|
|**N (Obs.)**|4903|4860|17910|17912|

Source: Authors’ calculations based on TLMPS 2014, ELMPS 2018 Notes: *p<0.05; **p<0.01; ***p<0.001. Standard errors in parentheses. “Market” denotes an outcome of being reclassified from not 3-month market employed to added market employed (compared to either remaining non-employed or already being detected as 3-month market employed). “Extended” denotes an outcome of being reclassified from not 3-month extended work to added extended work (compared to either remaining non-working or already being detected as 3-month extended working).

# **6 Discussion and conclusions**

Women’s employment in LMICs is often under-detected, and disproportionately so, compared to measures for men (Anker 1983; Anker and Anker 1989; Desiere and Costa 2019; Discenza et al. 2021; Durazo et al. 2021; Franck and Olsson 2014; Langsten and Salem 2008; Muller and Sousa 2020). Past innovations in improving the measurement of women’s employment include using activities lists or recovery questions, which can substantially increase estimates of women’s employment (Langsten and Salem 2008; Anker and Anker 1989; Discenza et al. 2021; Durazo et al. 2021). In Egypt, perceptions of women’s employment are certainly higher than standard statistics. For instance, in Egypt, 63% of women and 46% of men expect most or all of the women in their community are employed (National Council for Women, Baseera, and World Bank 2023).

21
<!-- Start of picture text --> POLICY DECISIONS:<br>(each year)e<br>- Government's absorption s O Domes;tic |on<br>- Fiscal Degree Deficit of strlaToMonetization Public Deb _ _<br>- Interest rates Obligations ~ ~ ~ ~ ~ ~ ~ . Needs Dmeti r s neNet money bas e I NLATO Real Domestic Short runOutput<br>- CBE's Exchange rate __ Bas a Grot Growth Interest Growth<br>- Proportion of Fore,ign Sector ndtAccouninees _R._t7_e<br>Result to be surrendered o<br>- Degree of sterlization Real<br>of Foreign Sector Results .,(ofCBEl"<br>- Arrears on Foreign Debt Exchange rate<br>Obligations<br>Actual Bloclked --<br>. ~~~Interest and Account Free |<br>Payments market Balance|<br>Deb World's 1<br>1, 1 16~~~~~~~~~~~~~Iflto<br>. L: r~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Government |[<br>|Absorption<br>I .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I. 0<br><!-- End of picture text -->
Several other studies exploit obstacles to mobility as a source of identifying variation in the West Bank and Gaza. Abrahams (2018a), in a closely related study to ours, focuses on how Israeli obstacles disrupted Palestinian commuting, causing employment rates to decline in labor-supplying locations but to increase in labor-demanding locations. Cal`ı and Miaari (2018) document a negative association between local employment rates and mobility restrictions in the short-run. Amodio and Di Maio (2017) study the impact of trade restrictions between the West Bank and the outside world on the efficiency of input allocation among Palestinian firms. Finally, Etkes and Zimring (2015) use the blockade of Gaza as a natural experiment to quantify the gains from trade by using the West Bank as the counterfactual.

The remainder of this paper is organized as follows. The next section elaborates on the context. Section 3 discusses the construction of the accessibility index, which is our key independent variable. Lights and fatalities data are presented in section 4. Section 5 presents our empirical strategy. The results are presented in section 6. A final section concludes.

# **2 Context**

The West Bank is an elevated plateau bordered on its north, west, and south by coastal Israel, and to its east by Jordan. It is small: about 56km (34.8 miles) at its widest and about 133km (82.6 miles) at its lengthiest, roughly 1/4 the area of New Jersey. Approximately 2.7 million Palestinians live there, while approximately 330,000 Israeli civilians live in over 160 separate, segregated settlements and outposts throughout the West Bank.<sup>4</sup> Since 1967, a major part of the West Bank has been controlled by the Israeli army. The army maintains a constant troop presence of bases, garrisons, patrols, and watchtowers throughout the West Bank’s interior, while all borders of the West Bank (including its border with Jordan) are controlled by Israeli security fencing, walls, and staffed by Israeli civil administrators and border police at every port of entry and exit. The Israeli army responds to ongoing Israeli-Palestinian conflict by both offensive retaliations (Jaeger and Paserman, 2008) and defensive efforts aimed at intercepting militants before they reach Israel or its settlements. This latter policy intensified under the auspices of ‘Operation Defensive Shield’, and involved the deployment of hundreds of physical obstacles (Cal`ı and Miaari, 2018) inside the West Bank, along the internal road network, and the construction of a 500km wall that separates Israel from the West

> 4See Niksic et al. (2014). Since our study does not concern East Jerusalem, we count Palestinians and Israelis inside the West Bank only.

5
the average EBRD columns reveal a difference between groups (a) and (b) and groups (c) and (d). In the first two groups there is convergence in that the difference between the highest and lowest ranked countries has declined over time. For groups (c) and (d) the opposite holds – there is divergence, largely reflecting the weak ‘performance’ of Macedonia, FYR and Kyrgyzstan.

**<u>Table 6. Timing of Reform Measures and GATS Commitments</u>**

|Country|EBRD<br>Score<br>2004|Rank|Average<br>EBRD<br>Score|Rank|GATS<br>schedule<br>submitted in|Strongest<br>Reform<br>Period|Strongest<br>change in<br>EBRD<br>Index|
|---|---|---|---|---|---|---|---|
|**Group (a)**||||||||
|Hungary|3.8|(1)|3.1|(1)|1994|1991|0.61|
|Slovak Republic|3.1|(3)|2.1|(4)|1994|1995|0.39|
|Czech Republic|3.4|(2)|2.6|(3)|1994|1993|0.50|
|Poland|3.4|(2)|2.9|(2)|1994|1995|0.45|
|**Group (b)**||||||||
|Latvia|3.11|(3)|2.30|(3)|1999|1994|0.95|
|Lithuania|3.05|(4)|2.19|(4)|2001|1995|0.67|
|Estonia|3.44|(1)|2.68|(1)|1999|1993|0.45|
|Slovenia|3.17|(2)|2.47|(2)|1995|1998|0.44|
|**Group (c)**||||||||
|Croatia|3.17|(1)|2.21|(1)|2000|1994|0.56|
|Macedonia, FYR|2.22|(4)|1.82|(4)|2004|2000|0.22|
|Romania|2.94|(3)|1.96|(3)|1994|1998|0.39|
|Bulgaria|3.06|(2)|2.08|(2)|1997|1999|0.33|
|**Group (d)**||||||||
|Moldova|2.33|(2)|1.80|(2)|2001|1997|0.44|
|Kyrgyz Rep.|1.94|(4)|1.56|(4)|1999|1994|0.33|
|Georgia|2.28|(3)|1.62|(3)|2000|1997|0.50|
|Armenia|2.39|(1)|1.82|(1)|2004|1995|0.33|

Table 6 also provides information on the correlation between timing of WTO commitments and actual policy (reform) measures. In group (a), for instance, Hungary saw a large increase in its reform index in 1991, much before its GATS schedule was submitted. For the other three countries there is greater coincidence of GATS and policy implementation, regardless of how much they committed to in the GATS. The Baltic countries are similar to Hungary in taking major reform steps before their GATS schedules took effect. The opposite is true for Slovenia, where the policy stance improved after the GATS commitments were made. Even though the magnitude of changes in policy differs between Croatia and Macedonia, both pursued reforms before their GATS schedules were submitted, whereas the opposite holds for 15
FAO. 2011. _State of Food and Agriculture 2010-11: Women in Agriculture-Closing the Gender Gap for Development._ Food and Agriculture Organization of the United Nations.

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Gautam, M. 2015. “Agricultural subsidies: Resurging interest in a perennial debate.” _Indian Journal of Agricultural Economics_ 70 (1): 83–105.

Gautam, M, Laborde, D, Mamun, A, Martin, W, Pineiro, V and Vos, R. 2022. _Repurposing Agricultural Policies and Support._ Gulati, A., and P. Banerjee. 2015. “Rationalizing Fertilizer Subsidy in India: Key Issues and Policy Options.” Working Paper 307, Indian Council for Research on International Economic Relations. - <u>https://www.academia.edu/14970315/Rationalizing_Fertilizer_Subsidy_in_India Key_Issues_and_Policy_Options.</u> Guo, S and Kontou, E. 2021. “Disparities and equity issues in electric vehicles rebate allocation.” _Energy Policy_ 154: 112291.

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14
Figure la plots the predicted<sup>and observedlevelsof R&Das ashareofGDP as</sup> function of the log GDP per<sup>capita.Thepredictedvalueisgeneratedfrom a regression of</sup> the log of the ratio of total<sup>R&DexpenditurestoGDPonlogGDPpercapitaandits</sup> squared term. Due to concerns<sup>about theinfluenceof outliers,thismodelwasestimated</sup> as a median regression. The<sup>estimatedcoefficientsarepresentedin thefirstcolumnof</sup> Table 1, and Figure la illustrates<sup>theresultingpositive relationshipbetweenR&Deffort</sup> and log GDP per capita.<sup>Itisclearfromthisevidence thatR&Dexpenditures/GDPrise</sup> with development and that<sup>therateof increasealso riseswith GDP percapita.Though</sup> the apparent curvature is partly<sup>afunctionof thelog transformationof GDP per capita</sup> employed to more clearly<sup>displaythedifferencesamongpoorcountries, theelasticities</sup> estimated in Table 1 eventually<sup>exceedunitysuggestingthatthesecondderivativeis, in</sup> fact increasing. The fixed effects estimates<sup>incolumn2indicatethatthenon-linear</sup> between<sup>R&Dandthelevelof developmentisaphenomenon that</sup> positive relationship occurs within countries, and<sup>it is not an exclusive feature of thecross-country variations.</sup> Figure lb presents a first<sup>cutat lookingat howa fewselect countriesfrom several</sup> regions compare to the<sup>predictedvalue.Whatis immediatelystrikingisthatKorea,</sup> Finland, and Israel show<sup>substantial "takeoffs"relativetothemediantrajectory.Two</sup> Latin American countries,<sup>Argentinaand Mexico, whichhadsimilar levelsof incomeas</sup> Korea and Israel prior to<sup>theirtakeoff hoveronor belowthepredictedvaluefor their</sup> level of development. Both<sup>China andIndiaappear to be followingmorein thefootsteps</sup> of the "take off" countries<sup>than the LatinAmericans.</sup> Figures 2a-d present the residuals<sup>from a more generalandflexible specification</sup> that includes log GDP, log<sup>GDPsquared,loglaborforce,and log laborforcesquared,</sup> and year dummies as explanatory<sup>variables.Thisallowsforindependenteffectsrelated</sup> to the size of the economy<sup>andsizeof thelaborforceratherthan percapitaincomeor</sup> development per se. In this<sup>case,the predictedvalueiscaptured bythehorizontalaxis</sup> and we observe the evolution<sup>of countryR&Deffortacrosstimeasopposedtoacross</sup> income levels. The selection of countries<sup>reflectstheavailabilityof datainthecaseof</sup> the developing countries<sup>andanattempt to presentabroadcrosssectionoftypesof</sup> countries. For the developing<sup>world, we focuson Latin Americaand Asia.Africa'sdata</sup> 6
Policy Research Working Paper 8274

# **Abstract**

This paper examines the role of multinational enterprises in sustainability reporting. The study assesses how multinational enterprise status correlates with a company’s average disclosure rate and probability of reporting on economic, labor and social, environmental, and governance indicators. It uses a unique data set that offers company-level information on sustainability reporting from the Global Reporting Initiative, which covers 2,020 companies in 81 countries and 54 sustainability indicators. The summary statistics show that multinational enterprises and large domestic companies have higher average disclosure rates than small and medium-size enterprises. However, the econometric analysis suggests that multinational enterprise status does not matter for the average disclosure rate, but company size shows a strongly positive correlation. Differentiating by type of multinational enterprise reveals that the relationship becomes positive and significant for private companies. By contrast, the correlation between multinational enterprise status and the average disclosure rate does not vary by listing status, sector, region, or income level. Focusing on the relationship by development category also shows no significant correlation. Finally, accounting for the heterogeneity of the sustainability indicators, the study analyzes the relationship between multinational enterprise status and the probability of disclosure at the detailed indicator level, and confirms a significant correlation for 12 indicators.

This paper is a product of the Macro Trade and Investment Global Practice Group. It is part of a larger effort by the World Bank 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://econ.worldbank.org. The author may be contacted at dwinkler2@worldbank.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
Table 4: Economic activities and educational investment

||E|ngaged in a|ny activities (0/|1):<br>|Educa<br>|tional investment<br>|
|---|---|---|---|---|---|---|
|||||Non-Farm|In|Log (Educational|
||Any work|Wage|Agriculture|Enterprise|school|<br>Expenditure)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|||||||Expenditure > 0|
|Over CES-D cutoff<br>(>10) for|||||||
|depressive<br>symptoms (0/1)|-0.080***|-0.025*|-0.049***|-0.035*|-0.017|-0.200**|
||(0.020)|(0.013)|(0.018)|(0.021)|(0.017)|(0.092)|
|Covariate shock<br>2014-2016|0.044***|-0.022|0.029|0.095***|0.009|-0.106|
||(0.016)|(0.015)|(0.023)|(0.024)|(0.016)|(0.092)|
|Idiosyncratic shock<br>2014-2016|-0.024|0.010|-0.024|-0.050**|0.005|-0.025|
||(0.020)|(0.021)|(0.021)|(0.023)|(0.021)|(0.079)|
|Experienced HH|||||||
|death 2014-16|-0.025|0.026|-0.035|0.007|-0.025|0.017|
||(0.021)|(0.018)|(0.023)|(0.027)|(0.027)|(0.108)|
|Conflicts between|||||||
|2014-2016|0.042|0.005|-0.034|0.033|0.013|0.198|
||(0.027)|(0.030)|(0.041)|(0.046)|(0.044)|(0.337)|
|Log per capita<br>consumption in|||||||
|2013|-0.003|0.085***|-0.094***|0.020|0.012|0.562***|
||(0.012)|(0.012)|(0.014)|(0.013)|(0.009)|(0.082)|
|N|4316|4316|4316|4316|4051|2925|
|r2|0.233|0.110|0.337|0.183|0.150|0.523|

Notes: Columns 1-5: Ordered logit with clustered standard error (EA), Column 6: Probit with clustered standard error (EA), *** p<0.01, ** p<0.05, * p<0.1. Covariates include urban, household head, female, age, age squared, years of education, marital status, Muslim, “other or unknown religion" and "recently moved or missing EA".

23