rafmacalaba/data-use-mentions-extended huggingface dataset:
83 other variables constant. That is, for a given level of total income, more unequal regions tend to spend a lower share of that income on agricultural goods. The intuition is that redistributing income away from poor farmers into the the hands of richer farmers moves money into the hands of consumers who spend a relatively lower share of this additional income on agriculture, due to non-homothetic preferences. This intuition is reinforced by the fact that the effect of inequality on agricultural expenditure disappears in the homothetic case ( _η_ = 1).

In this section, we test this prediction using 2019 HIES data. Specifically, we use data on landholdings and expenditures on various goods to construct district-level measures of aggregate food expenditures, aggregate total expenditures, and the variance of (the log of) landholding size as proxies for _Xn_<sup>_A_,</sup><sup>_En_,and</sup><sup>_σ_</sup> _Ln_<sup>2,respectively.Wethenregressthelogoffoodexpenditureson</sup> land inequality and the log of total expenditures. Results are displayed in Appendix Table A6. The coefficient on log total expenditures is 0.893 in Column (1). A 10% increase in total expenditure is associated with an increase of only around 9% in our agricultural expenditure proxy, which is itself indicative of non-homothetic preferences as it implies that agriculture’s expenditure _share_ declines with income. More importantly for us, the coefficient on the land inequality proxy is -0.0035, pointing to a negative association between inequality and agricultural expenditure, as predicted by equation (14). Column (2) repeats the estimation strategy of Column (1) but uses an adjusted version of the dependent variable that maps more closely to the theoretical constructs in equation (14), with very similar results.<sup>111</sup> Table A6: District-Level Food Expenditure and Land Inequality

||**Depende**<br>Logof food|**nt variable:**
associated with an island or mainland coast), as described in Hoozemans et al. (1993).<sup>13</sup> Five possible responses to SLR, or categories of wetland migratory potential (WMP), were defined for the DIVA database:

|WMP1:|No, or hardly any change|
|---|---|
|WMP2:|A retreat of the coastline, combined with inland migration of coastal<br>ecosystems|
|WMP3:|A retreat of the coastline without the possibility of inland migration due<br>to topography (e.g., coastlines with relatively high relief)|
|WMP4:|A possible retreat of the coastline but increase of flooding area behind<br>the coastline (“ponding”)|
|WMP5:|Total loss of the coastal ecosystem (Hoozmans et al. 1993).|

In the DIVA database, no mangroves occur in areas with the most extreme responses, WMP 1 or WMP 5 (other wetlands may fall in these categories). If mangroves can migrate, category WMP 2, then they may survive in their current location to the extent that natural migration or sediment accretion keeps pace with sea level rise (Alongi 2008). Mangroves in the category WMP 3 cannot migrate, and the human resources associated with them will lose their protection. Mangroves in category WMP 4 are at great risk, but may survive, depending on the effect of flooding behind the coastline. If the flooding is severe enough and persists long enough to seriously disrupt the flow of freshwater and nutrients to mangroves, the mangroves will be severely degraded and may die, putting at risk the population currently protected by them.

Geographic overlays of mangroves with the WMP characteristics of the coastlines from the DIVA database<sup>14</sup> indicates across our study area, 68 percent of the mangroves fall under WMP 2

> 13 The migratory potential of mangroves also depends on a wide range of additional factors that are site
(e.g. in irrigation), data on the amount, type, and quality of the structures constructed under the program are needed. The only study that, to our knowledge, links outcomes to nature and quality of MNREGS works, finds higher that quality of works under MNREGS led to significant, though quantitatively small (0.5 to 0.8 points) increases in the share of cash crops grown (Shah 2013).

To explore this further, we complement, for a subset of the villages in our sample, household-level data on production and labor supply with information on the quantity of MNREGS employment overall vas that in irrigation which would be expected to yield benefits in the short term. A coefficient on the latter that is at least as large and significant as the former would provide circumstantial evidence for infrastructure constructed by MNREGS having a significant short-term productivity effect whereas an insignificant or small effect would point towards observed productivity effects being driven by resource transfers instead.

# **3. Estimation strategy and descriptive statistics**

> 10 Desai _et al._ (2015) report completion rates of at most 51%. To our knowledge the only study exploring this aspect in more detail is Shah (2013). 11 Ideally, assets (e.g. small irrigation) generated under the program would simultaneously reduce the risk of moving into higher value crops.

7
100.0|100.0|100.0|100.0|100.0|
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31
inputs and because they are typically applied roughly in proportion to the amount of land cultivated. Farmers therefore have strong motives to know and use the true plot size in deciding how much of those inputs to apply. We define log-transformed input use ( and objectively measured plot size ( ) and estimate the following input demand function for each input: YY) XX<sup>∗</sup> 0 1 2 3 p 4 5 56 (2) pp where the dependent variable is the input intensity (i.e., input use per unit of land), with YY−XX<sup>∗</sup> = ββ + ββ TT+ ββ XX<sup>∗</sup> + ββ RR + ββ + ββ + ββ θθ + εε standing for log-transformed input use in the post-planting round. All other terms are as defined in equation YY (1). The most important parameters in equation (2) are 1, 4, and 5. The parameter 1 estimates the impact of the information treatment on farmers' input self-reporting at the average plot size and ββ ββ ββ ββ measurement error in our sample. Rejecting the null hypothesis that 1 = 0 indicates information spillovers across self-reported variables. 4 ( 5) captures differential spillover effects associated ββ with plot size (pre-treatment measurement error in plot size). To allow for asymmetric and ββ ββ heterogenous responses, we employ continuous over/underestimation rates in the pre-treatment survey.

One important assumption in the estimation of equation (2) is that recall length does not affect reporting differently for treatment and control groups. As discussed previously and shown in Appendix Table A1, the control group households were subject to a single post-harvest visit, while the treatment group households were visited once during the post-planting period (at the time of the information treatment) and once during the post-harvest period (together with the control group households in the same EAs). This assumption seems reasonable, for at least two reasons. First, Wollburg et al.
2021) 'Covid-19 vaccines: Why some African states can't use their vaccines'. <u>https://www.bbc.com/news/56940657. (Accessed 09/09/2021).</u>

- National Control Centre for COVID-19. (2021) 'COVID-19 Vaccination Sites in Papua New Guinea'. <u>https://covid19.info.gov.pg/covid-19-vaccination-sites-in-papua-new-guinea/. (Accessed</u> 06/10/21).

- National Statistical Office of PNG & ICF. (2019) 'Papua New Guinea Demographic and Health Survey 2016-18'. Port Moresby.

- Nyhan, B. & J. Reifler. (2015) 'Does correcting myths about the flu vaccine work? An experimental evaluation of the effects of corrective information', _Vaccine,_ 33(3): 459-464.

- Nyhan, B., J. Reifler, S. Richey & G. L. Freed. (2014) 'Effective Messages in Vaccine Promotion: A Randomized Trial', _Pediatrics,_ 133(4): e835-e842.

- Palm, R., T. Bolsen & J. T. Kingsland. (2021) 'The Effect of Frames on COVID-19 Vaccine Resistance', _Frontiers in Political Science,_ 3(41).

- Pogo, M., B. Ropa, B. Smaghi, S. Sumun, D. Lapun, J. Landime, M. Williams, A. Pukienei, T. Housen, J. Flint, et al. (2021) 'COVID-19  vaccine  hesitancy  in  essential  workers  and  the community  in Papua  New  Guinea:  an  exploratory  mixed-methods study'. Port Moresby: National Department of Health.

Radio New Zealand. (2021) 'PNG opens vaccine rollout to 18-yr-olds'.

<u>https://www.rnz.co.nz/international/pacific-news/446270/png-opens-vaccine-rollout-to-18-yrolds. (Accessed 06/10/21).</u>
_Figure 12. Incidence, progressivity and marginal contributions in-kind transfers a. Concentration shares for in-kind transfers b. Progressivity of in-kind transfers (Kakwani Index)_ <!-- Start of picture text -->
1.0<br>All net in-kind transfers 0.17<br>0.8<br>Net health transfers 0.38<br>0.6 Hospital healthcare 0.26<br>Primary healthcare 0.55<br>0.4<br>0.2 Net education transfers 0.16<br>Technical educ. -0.07<br>0.0 Tertiary educ.<br>-0.53<br>Secondary educ. 0.13<br>Primary educ. 0.54<br>Pre-school educ. 0.36<br>-0.6 -0.3 0.0 0.3 0.6 0.9<br>Kakwani Index<br>1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text -->

- _c. Redistributive effects of in-kind transfers_

<!-- Start of picture text -->
1.5 1.24<br>1.05<br>0.95<br>1.0<br>0.5<br>0.04 0.13 0.05 0.03 0.09<br>0.0<br>-0.5<br>-0.55<br>-1.0<br>Pre-school educ. Primary educ. Secondary educ. Tertiary educ. Technical educ. Net education transfers Primary healthcare Hospital healthcare Net health transfers All net in-kind transfers<br><!-- End of picture text -->

_Source: Authors’ calculation based on UNHS 2016/17._ _Note for c: A positive redistributive effect means inequality declines, while a negative redistributive effect means
within each cross-section collected (Patalen et al., 2020).

The NNS in 2013 is a unique data set, due to the availability and richness of the individual-level consumption data. Despite the value of food consumption and/or dietary data, there are very few examples of nationally representative individual-level dietary surveys outside North America and Europe. There are significant challenges to collecting such data (Coates et al., 2017). And, as a result, there are relatively few individual-level data sets which analyze consumption for all those 7
(2016). Impact of cash transfer programs on food security and nutrition in sub-Saharan Africa: A cross-country analysis. _Global Food Security_ , _11_ , 72–83. https://doi.org/10.1016/j.gfs.2016.07.009

- UNCTAD. (2023). _A World of Debt: a growing burden to global prosperity_ . Washington DC. Retrieved from file:///C:/Users/user/Downloads/osgmisc_2023d4_en.pdf

- UNICEF. (2022). _Social Protection Budget Brief 2022/23_ (Vol. 2022/23). Lilongwe, Malawi.

- Vaitla, B., Coates, J., Glaeser, L., Hillbruner, C., Biswal, P., & Maxwell, D. (2017). The measurement of household food security: Correlation and latent variable analysis of alternative indicators in a large multi-country dataset. _Food Policy_ , _68_ , 193–205. https://doi.org/10.1016/j.foodpol.2017.02.006

- Verduzco-Gallo, Í., Ecker, O., & Pauw, K. (2014). _Changes in food and nutrition security in Malawi: Analysis of recent survey evidence_ (Working Paper No. 06). Washington, DC. Retrieved from

http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/128205/filename/128416.pdf

- Waldman, K. B., Ortega, D. L., Richardson, R. B., & Snapp, S. S. (2017). Estimating demand for perennial pigeon pea in Malawi using choice experiments. _Ecological Economics_ , _131_ , 222–230. https://doi.org/10.1016/j.ecolecon.2016.09.006

- Walls, H. L., Johnston, D., Tak, M., Dixon, J., Hanefeld, J.,
.

- Linnerud, Kristin, Torben K. Mideksa, and Gunnar S. Eskeland (2011). “The Impact of Climate Change on Nuclear Power Supply”. _The Energy Journal_ 32.

- LoPalo, Melissa (2023). “Temperature, Worker Productivity, and Adaptation: Evidence from Survey Data Production”. _American Economic Journal: Applied Economics_ 15, 192–229.

46
countries, are either based on relatively small samples (typically covering a few hundred firms or even fewer), or use a sampling approach that is not well suited to draw inferences on a target population (Schneider, 2020). Therefore, caution must be exercised in generalizing the results from these case studies. Liu et al. (2021) offer more robust evidence drawing on World Bank Enterprise Surveys data for 24 countries to explore gender gaps in business performance. They find that women-led businesses were more likely to be closed, and closed for longer periods of time, than men-led businesses and that women entrepreneurs expressed more pessimistic views about the future. While related, our paper expands this analysis in several directions. First, by leveraging two different data sources, i.e. World 5
or perceptions of quality and corruption – then public opinion surveys can be helpful. For example, Afrobarometer is an example of a large-scale household survey that measures governance performance in many political dimensions as well as with regard to service delivery. Afrobarometer tracks trends in public attitudes towards the institutions that serve them on a regular basis, asking questions about whether public health care services are affordable, whether medicines are available, whether individuals feel they are treated respectfully, whether doctors are absent, whether waiting times were long, and whether illegal payments were demanded (Afrobarometer 2009). The answers to these questions are comparable across countries in terms of being representative of the national sample; however, they do not account for differences in the structures of health care services across countries and ways in which the units of analysis – the facility or health care organization – may differ across contexts. That is, the questions are often too general to link respondents‟ views to specific health care service providers. Such population perception surveys can be useful complements to facility surveys but are decidedly second best options.

______________________________________________________________________________________

-32-
066|-0.054|-0.128|-0.618|-0.626|
||(0.081)|(0.081)|(0.080)|(0.088)|(0.089)|(0.083)|(0.514)|(0.535)|
|Women can work the same night|-9.626***|-9.150***|-8.745***|-6.629**|-6.797**|0.503|0.482|-0.971|
|<br>hours as men Y:1 N:0|(2.700)|(2.767)|(2.760)|(2.872)|(2.889)|(2.945)|(3.945)|(3.954)|
|Exports (%, cell average)*GDP per|||||||0.056|0.065|
|<br>capita (logs)|||||||(0.059)|(0.078)|
|Exports (%, cell average)*WBL<br>Index||||||||-0.112<br>(0.392)|
|No. of Workers (logs)*Women can|||||||0.055|0.445|
|<br>work the same night hours as men|||||||(0.715)|(0.726)|
|<br>Y:1 N:0|||||||||
|No. of Workers (logs)*WBL Index||||||||-3.368*<br>(
raising interest rates and results in lower investment by crowding out private investment and hence lowering infrastructure development (Aschauer, 2000; Aizenman et al., 2007; Winter, 2017).

Among other variables affecting physical infrastructure, urbanization seems to have a positive and significant effect on infrastructure growth. This implies that rising urbanization increases infrastructure facilities, possibly due to increased demand for the latter (Démurger, 2001 and Li, 2017; Maparu et al. 2021). As per the 2011 census, around 31% of the population lives in urban areas, and is expected to rise to more than 50% by 2050 (UN, 2014). The demographic shift will require huge infrastructure expenditures to increase and upgrade the existing stock of infrastructure facilities such as transport, telecom, water, and energy. On the other hand. Population density has a negative impact indicating the dominance of scale impact over the demand impact (Randolph et al.,1999). Financial development proxies - measured as bank credit to GSDP (CRE) are found to impact infrastructure development positively. The development of the financial sector facilitates long-term project financing with a reasonable cost which is vital for infrastructure development (Agrawal, 2000). Similarly, the social infrastructure index (SINFRA) also positively affects physical infrastructure development supporting the complementarity hypothesis among different types of infrastructure facilities (Cerra et al., 2017).

We also find the coefficient of the dummy variable positive and statistically significant. This indicates that if the center and state governments belong to the same political party or in a coalition government, it would have a positive impact on the state’s infrastructure development. State governments with the same political party or coalition in the central government tend to get higher support in terms of financial resources and relevant policies (e.g., the central government’s support on national highways that pass through the state, building electric power plants and transmission lines, central government’s incentives to promote renewable energy) from the central government for infrastructure projects
is present in Germany, Poland and up to a certain degree also in Spain. In these three countries de‐ routinization seems to be a common trend.

<u>Figure 6: Change in employment shares, by country and occupation</u> <!-- Start of picture text --> Germany: 1994‐2013 Poland: 1992‐2013 Spain: 1990‐2013<br>15.6% 14.7%<br>12.0%<br>6.9%<br>1.6%<br>‐4.2%<br>‐8.5%<br>‐10.5%<br>‐27.6%<br>Non Routine Routine Non Routine Non Routine Routine Non Routine Non Routine Routine Non Routine<br>Man Cog Man Cog Man Cog<br>percent of wage employees<br><!-- End of picture text --> Source: Authors calculations using LIS harmonized household surveys. Note: This figure shows the change, in percentage points, of the share of total employment (wage employees, excluding self‐employed) over a period of about twenty years of three occupations categories: non‐routine manual task intensive occupations, routine ones, and non‐routine cognitive tasks intensive ones. The initial and final years depends on data availability. For more details on the construction of the occupation categories please see the appendix.

The two approaches presented above do not produce the exact same ranking of occupations, however the two rankings are not far apart. This is because, as illustrated in Figure 5, there is a correlation between the intensity of task contents of occupations and wages paid in these occupations. Using the normalized task index of occupations, Figure 7 shows that, for Germany and Spain, the intensity of non‐routine cognitive tasks is higher for jobs at the top of the wage distribution while routine task intensity is higher in the middle of the distribution.
constraints.** Absent stabilizing fiscal arrangements, heavy reliance on food and agricultural trade can contribute to volatility in public finances and erode fiscal sustainability: rising food prices may increase tax revenues from the agricultural sector and encourage governments to spend. Conversely, when food prices fall, evenue losses in the agricultural sector are exacerbated by political pressures to subsidize food production. During the sharp rise in food prices in 2007-08, LICs’ fiscal balances deteriorated, on average, by close to 1 percentage point of GDP, in part due to higher food import bills. Food price spikes may also cause sociopolitical instability, including political unrest and food riots (Barrett 2013).

**Monetary policy constraints.** In countries where inflation expectations are not wellanchored and monetary policy frameworks are weak, the increase in inflation caused by 5Severe terms of trade shocks are considerably more common in LICs than in advanced economies and, of all possible external shocks, tend to have the most severe output cost in LICs (IMF 2011; Becker and Mauro 2006).

> 6Based on a sample of 54 EMDEs.

7
**|122 516|130 387|129 860|126 484|108 150|
|**Mean number of workers per firm**|33.15|39.15|41.42|43.61|47.47|
|**Mean firm age**|12.02|12.70|13.38|14.06|14.92|
|**Mean sales per worker (R‘000)**|10.42|9.87|9.86|9.87|10.21|
|**Mean value added per worker (R’000)**|3.46|3.49|3.53|3.52|3.58|

_Source: SARS-NT Panel (own calculations)_ .

Table 1 above highlights the key summary statistics – variable definitions are provided in the data appendix. The SARS-NT panel covers formal workers employed by registered firms in the private sector.<sup>9</sup> From 2010 to 2014, the total number of workers ranges from 4 million to 5.5 million. The number of firms varies over the period, peaking at 130,387 firms in 2011. The average number of workers per firm increased from 33.15 in 2010 to 47.47 in 2014. Half the firms in our panel employ 8 or fewer employees, emphasizing the large number of small formal firms in terms of employment size present in the panel. The average firm age increased from

> 7 We use the same panel as Pieterse et al. (2018), Kreuser and Newman (2018) and Edwards et al. (2018). Differences across key variables are due to differences in how these variables are defined. Refer to the data appendix for further detail.

> 8 See Pieterse et al. (2018) for a detailed explanation of the key sources of tax data used to create the panel, questions that arise as a result
common type of tobacco product consumed, and only a small percentage of the population consumes unfiltered cigarettes or both (Little et al. 2019).<sup>3</sup> Evidence also suggests that smoking frequency (the number of cigarettes smoked daily) tends to be higher for individuals with low educational attainment and those unemployed (Little et al. 2019).<sup>4</sup> Table 1. Adult smoking prevalence in Georgia _<u>(Percent of adults)</u>_

||Male|Female|All|
|---|---|---|---|
|Current tobacco smokers|57.0|7.0|31.0|
|Dailytobacco smokers|51.5|6.2|28.0|

_Source:_ Taken from WHO 2017b. Data from the STEPS survey 2016, for adults aged 18 to 69.

> 2 <mark>Georgia was an important producer of tobacco leaves and manufactured cigarettes for the Soviet Union. The tobacco industry collapsed in the early 1990s after independence, leading to the dominance of domestic markets by international brands (Little et al. 2019).</mark>

> 3 According to a household survey on tobacco consumption patterns conducted in 2017, 87 percent of smokers consumed filtered cigarettes, 9 percent consumed unfiltered cigarettes, and 3 percent consumed both. Only 1 percent preferred other products, including cigars and e-cigarettes. The survey interviewed 2,997 smokers in the cities of Tbilisi and Kutaisi, and the municipalities of Zugdidi, Gori and Akhaltsikhe. It was not designed to be representative of the national population (Little et al 2019).

> 4 Individuals with less than secondary school education smoked 23 cigarettes per day, and those unemployed smoked 22 cigarettes per day (Little et al. 2019).

3