rafmacalaba/data-use-mentions-extended huggingface dataset:
savings rates; and (b) a less labor-intensive demand pattern under the transfer scheme than under food subsidies, leading to downward pressure on unemployment (and lower wage growth). The difference between low- and high-income household savings rates is a stylized fact of development economics that also is reflected in our archetype LIC.<sup>25</sup> It is also plausible that a transfer scheme of this type, which encourages increased production of commodities with relatively high-income elasticities, is less labor-intensive than a scheme that encourages food demand.<sup>26</sup> On the other hand, the transfer program reaches its objective of benefitting low-income households. Compared to _sub+tax_ , the growth rates for real per-capita consumption are 1.2-1.4 percentage points higher for rural and urban low-income households and 0.5 percentage points lower for high-income households in both regions, reducing aggregate Gini inequality to 0.34 from around 0.38-0.40 for all non-transfer scenarios. Among the HD indicators, the head count poverty rate in 2025 is 9 percentage points lower than for _sub+tax_ , a significant change, while undernourishment increases and other indicators do not change by much.<sup>27</sup> The final simulation, _trn+tax2_ , imposes the tax-financed transfer scheme with the abovementioned administrative costs, reducing the amount of money that can be transferred. Compared to _trn+tax_ , the main difference in the government budget is more government consumption, imposing a reduction in transfers to low-income households that, in 2025 throughout most of the simulation period amounts to around 0.6 percent of GDP. There are virtually no differences in aggregate growth rates except for slightly more rapid consumption growth for the government (to handle program administration) and slightly less rapid growth for low-income households (since the transfers that they receive are lower), leading to a marginally higher Gini coefficient. However, if transactions costs were significantly higher, the distributional consequences of the transfer scheme would also be more significantly weakened
Finally, columns (13) and (14) consider all the variables at once. Although the cross-country association between policy performance and expropriations now becomes statistically insignificant, the remaining findings are broadly similar to those in the bivariate regressions.

> 6 As noted above, sovereign default events last for multiple years in the Tomz and Wright (2010) dataset.  Our focus is on the onset of default episodes.  Accordingly, in the regressions in **Table 2** and **Table 3** , we construct a dummy variable that is equal to one for the first year of each default event, and is zero for all non-default-event years in the dataset (i.e. we drop observations corresponding to the second and subsequent years of the default event).  In **Table 2** , the dependent variable is the average over time of this dummy variable, while in **Table 3** we explore the annual variation in this variable.

11
to 2009. Real GDP _levels_ are expressed in constant local currency units – meaning that they have already been deflated by the GDP deflator – and are shown in Table _3_ . Second, we use GDP and GNI data from the WDIs to estimate different pass-through rates between growth in the national accounts and in household consumption. These data on GDP and GNI growth rates are matched to survey-year estimates of average per-capita household monthly consumption available in PovcalNet (http://iresearch.worldbank.org/PovcalNet/home.aspx). The population data used in the main analysis, and for the purposes of global poverty monitoring, are also taken from the WDIs (available at <u>https://data.worldbank.org/indicator/SP.POP.TOTL?locations=NG).</u> However, we also provide estimates of the absolute number of poor people using NBS’ population estimates.<sup>10</sup>

> 9 Parts of Borno state became inaccessible over the course of the survey. This meant only 530 households were reached, so only 15 Local Government Areas (LGAs) were reached of 27 LGAs that were originally sampled. 10 NBS’ population estimates were used to construct the survey weights for the 2018/19 NLSS. Table 16 Table 16 shows how poverty estimates from this analysis differ when using WDI and NBS population data.

7
# The Energy-Management Nexus in Firms: Which Practices Matter, How Much and for Whom?

Arti Grover<sup>_∗_</sup> Valerie J. Karplus<sup>_†_</sup>

**Acknowledgments:** This paper has been financed by the World Bank’s Climate Innovation Trust Fund supported by the governments of Australia, Denmark, the United Kingdom, the Netherlands, and Norway. We are deeply grateful to Denis Medvedev for providing intellectual guidance for this project, and to Manuela Francisco, Michael Ehst, Gonzalo Varela, Adja Mansora Dahourou, and Rafay Khan for their kind support. The project has benefited from excellent research assistance by Yiran He, Jasdeep Mandia, and Joanna Slusarewicz. We thank Joshua Wimpey for his guidance in using the World Bank Enterprise Survey data, in particular the Green Economy module.<sup>1</sup>

> _∗_ Please address correspondence to: Arti Grover, World Bank, 1818 H Street, Washington DC, USA, E-mail: agrover1@worldbank.org

> _†_ Carnegie Mellon University, Scott Hall 5113, 5000 Forbes Avenue, Pittsburgh, PA 15213 USA, E-mail: vkarplus@andrew.cmu.edu

> 1 **Disclaimer:** 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.
Our baseline sample period focuses on the effects of the earlier years of the AIA from 2007 to 2013 for total investment and cloud. The year 2013 is chosen as this is the first time for which questions on cloud use were included in the E-commerce survey. We assume zero adoption for all firms in 2007, consistent with the assumption of DeStefano et al. (2023), as this is before cloud computing arrived in the UK. In the robustness section we test for the use of the other time periods for which information on cloud use by firms is available, namely 2015, 2017 and 2019. Information on the use of big data analytics are collected for the years 2015 and 2019 and for AI in the year 2019. Again we assume zero adoption in 2007 for both. Worker-level estimation of the demand for data workers and total investment at the firm level takes advantage of annual data for the years 2007 to 2013 and 2007 to 2019 (inclusive).

Table 2 below provides summary statistics of the main variables and time periods we use in the main body of the paper. For the period up to 2013 our data show that 28% of firms in our sample use cloud, but that this varies across types of cloud service. For example, only 6% of firms use cloud for finance software, whereas 16% use cloud for storage of files. For big data analytics, 21% of firms use big data by 2015 (26% in 2019), while only 1.7% of firms use AI by 2019. Additional summary statistics for all firm variables for all time periods are available in Table A1, and worker summary statistics are in Table A2.

# **4 Empirical strategy**

To identify the effect of the AIA capital allowances on capital investment, cloud technologies, big data and AI we use a difference-in-differences (DID) specification with some minor distinctions discussed below depending on whether we model IT investment (available annually) or the technology variables (available in specific years).
(Walzer and Chicoine, 1989:79). Furthermore, unlike the theoretical expectations that preference costs increase at extreme levels of decentralization, we see that the USA has been able to keep these costs lower than the other case study countries. This can be understood by looking at recent trends in road financing structures in the United States. Traditionally, intergovernmental aid (federal aid) has constituted a major source of revenue for state and local governments. Beginning in 1987, the federal government stopped the federal General Revenue Sharing (GRS), which was a transfer used to finance road construction and improvements. Since then, funds for road works are raised locally from general appropriations. These funds are not earmarked. This has put state and local governments in a tight budgetary position, perhaps increasing their productivity. It was predicted that the cost of bringing roads to good condition are well beyond the current fiscal capacity of most local govermnents. A survey conducted in 1987 indicated that bringing all roads in the system to an acceptable<sup>2 0</sup> performance level would cost over six times the average spending per mile by governments (Walzer et al, 1987). The actual effect has been to increase efficiency, as preference costs have not declined since 1987. A caveat is necessary at this point: some evidence suggests that 20 An acceptable level in the eye of local governments is below the desired engineering performance standards but it is consistent with local demand for the service.

21
A. Sanghi and D. Johnson

|12|Kenya’s apparel exports to the US (2000-2014)||
|---|---|---|
||UN Comtrade 2015||
|13|Kenya’s services exports overall are strong<br>UN Service Trade 2015||
|14|Kenya’s FDI is low. Trend in red (1980-2014)<br>World Development Indictors World Bank 2015 . . . . . . . . . . . . . . . . . . . . . . . .|19|
|15|Kenya underperforms in attracting FDI relative to potential. Trend in red (1980-<br>2014)||
||World Development Indictors World Bank 2015 . . . . . . . . . . . . . . . . . . . . . . . .|19|
|16|Kenya’s Gross domestic savings has fallen sharply since 1993. Trend in red (1980-<br>2014)||
||World Development Indictors World Bank 2015 . . . . . . . . . . . . . . . . . . . . . . . .|20|
|17|Chinese FDI in Kenya is growing quickly since 2009 (2003-2012)<br>UNCTAD FDI/TNC database 2015<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|21|
|18|China’s FDI represents large share of total FDI (2003-2012)<br>Authors’ own calculation based on World Development Indictors World Bank 2015 . . . . .|21|
|19|Investment from China rising; investment from UK and US falling<br>KenInvest 2015||
|20|China and France top sources of FDI infows for Kenya (2012)<br>UNCTAD FDI/TNC database 2015<
The equally-weighted aggregate poverty measures for each of these three main headings are then weighted equally (one-third each) to form the composite index, also echoing the HDI. A household is identified as being poor if it is deprived across at least 30% of the weighted indicators. While the HDI uses aggregate country-level data, the Alkire-Santos MIP uses household-level data, which are then aggregated to the country level. Alkire and Santos construct their index for more than 100 countries.

> 5 In certain emergency situations (such as on the battle field), treatment decisions often require a prioritization of patients (“triage”) and it appears that this is typically based on the probability of survival, which is a single index. But then one is not creating a “maship index” since the variables and weights are entirely determined by their ability to predict that probability. There is nothing analogous to this probability in a MIP. As Mead Over points out in a blog comment: “In the physical examination example, where the situation is not life threatening, both the doctor and the patient presume that the patient’s valuation of the information deserves priority, since presuming otherwise would be unnecessarily paternalistic.”

4
village, highlighting the increased importance of migration. Most sample parcels (77%) are in the village in flat terrain (73%) with 15% mountainous and 8% in valley bottoms. Few producers adopted fertility-improving practices such as fallowing (15%), crop rotation (13%), or terracing (7%) and planting of fruit (18%) or other trees (8%).

Most of smallholders’ land area is used for maize (46%) followed by wheat (24%), sorghum/millet (10%) and various crops including legumes, tubers, and cash crops including coffee or tobacco that together account for some 20% of area. With an output value of 305 US$/ha at the 2006 exchange rate, organic manure is used on 36% of plots, improved seed on 11%, and chemical fertilizer on 7%. Data on labor use allows inferences only on which household members participated in specific tasks, suggesting that land preparation and management tasks such as ridging and weeding are, with very few exceptions, performed manually with females being more involved than men.

Village level variables highlight Malawi’s diversity in terms of inheritance practices (Berge _et al._ 2014) and the extent to which land scarcity has increased market activity or given rise to intensification (table 3). 67% of villages follow matrilineal inheritance patterns (61%, 33%, and 6% of which matri-, patri-, or neolocal), 28% a patrilineal one (93% and 7% of which patri- and neo-local), and 5% others. With 84% and 79%, respectively, matrilineal inheritance prevails in the South -where matrilocality dominates- and the Center -where villages are almost equally split between matri- and patri-locality. It is virtually absent in the North where only 2% of villages report to follow a matrilineal and patrilocal inheritance pattern.

7
made_** by some relevant reference group. Poverty is then measured by comparing actual expenditures to the CBN. A person is not deemed poor who consumes less food (say) than the stipulated basic needs but could consume it on rearranging her budget allocation. This helps in avoiding any possible disagreements that might arise when choosing the basket more normatively (Ravallion 1998).

Rwanda uses a semi-normative approach to compute the food component of the poverty line. For the poverty line based on EICV4 data, the estimation is done in three steps. In the first step, focusing on the consumption patterns of the reference group (bottom 40 percent), quantities consumed for all items are backed out using survey reported prices and total expenditure. These quantities are then converted to a common unit, quantities in kilograms—consumed per adult equivalent per day. Using the standard FAO calorie table, these quantities are then converted to calories consumed per adult equivalent per day to obtain the average calorie consumption in the reference group. This equates to 1,335 calories per adult equivalent per day, which corresponds to an average total quantity of 1 kilogram of uncooked food per adult equivalent per day (NISR 2015).

In the second step, NISR studies the resulting food basket, as determined by the actual consumption patterns of the reference group and finds that the basket is dominated by low calorie food items (sweet potatoes, Irish potatoes and cassava root), very little cereals, and some items that are not necessarily of relevance to the poor. This is where the normative part of the approach comes in. In a ‘revealed preference’ approach, the reference group’s food basket is taken as given and is converted to calories and then scaled up to arrive at the final food poverty line. In this case, any changes to the food poverty line can only come from changing the choice of the reference group or the caloric threshold. Once the reference group is fixed, the quantities of each item in the food basket are taken as
(which is imposed only on the “formal”good, and not on the “informal”one) generates, in and of itself, an initial distortion in which workers are incentivized to work in the informal sector more than under the absence of such a tax. Therefore, the marginal productivity of formal workers is, in equilibrium, larger than that of informal ones. Under this situation, an increase in the VAT rate simply increases the initial distortion, raising the share of informality, and reducing total output. Moreover, larger increases in the share of informality and more profound falls in output are observed when the tax increase is implemented in a context of higher initial VAT rates. Naturally, while we exempli...ed the mechanism using an increase in the VAT rate, the same results hold for a reduction in the VAT rate.<sup>6;7</sup> In line with theoretical distortionary and disincentive-based arguments, we ...nd, using our novel worldwide narrative, that the e¤ect of tax changes on output is indeed highly non-linear. Our empirical

> 5 As we will discuss later in Sections 5.6 and 6, macroeconomic-based non-linear e¤ects are not behind the non-linear e¤ects associated with distortionary and disincentive-based type of arguments.

> 6 See Appendix 2 for details on the model. If the production functions of the formal and informal sectors allowed larger productivity in the formal sector than in the informal one (as it is frequently assumed based on ...rm level data evidence) and/or if there existed a segmentation in the labor market where formal workers were paid more than informal ones (as empirical evidence strongly suggests), the proposed mechanism as well as the output and informality implications would be further ampli...ed.

> 7 While di¤erent in the nature of the object under analysis (output versus tax burden), JR and our simple model based on the role played by the informal sector, also relate to
# **1. Introduction**

Collective actions in the commons are complex (for example, see Wade 1987a: Wade 1987b; Ostrom, Walker and Gardner 1992; Baland and Platteu 1993; Bardhan 1993; Ostrom et al. 1999; Ostrom 2003). The role of interest group homogeneity/ heterogeneity in determining successes and failures of collective actions in commons has been studied over a long period of time, as heterogeneity makes it harder for communities to reach an agreement about the sharing of benefits and costs of collective action (examples include Kanbur 1992; Baland and Platteu 1995. Alesina, Baqir and Easterly 1999; Poteete and Ostrom 2004). However, research on the determinants of collective action in the commons generally focuses on interest-group heterogeneity in endowments, wealth, economic interests, culture, political convictions, gender, etc., implicitly assuming that groups perceive the same problems but have different priorities (Baland and Platteu 1999; Bardhan and Dayton-Johnson 2000; Poteete and Ostrom 2004; Somanathan, Prabhakar and Mehta 2007; Ruttan 2008; Araral 2009; Marchiori 2014; Kölle 2015). This paper changes the focus to the role played by perceptions themselves. Within localities, collective action may be easier if elite and non-elite households have similar perceptions of relevant problems. Regionally, collective action may be aided by common perceptions among local elites who communicate across village lines. The case study on environmental perceptions in Indian Sundarbans presented in this paper is expected to shed light on patterns of perceptions differing with income, education and location of households, using new survey data.

The Sundarbans is home to some of the poorest and the most vulnerable communities of India. The region is currently threatened by several environmental factors related to climate change, including increased frequency and intensity of cyclonic storms (Bandyopadhyay et al. 2018), fluctuations in temperature and rainfall, and rising salinity as sea level rise continues 2
and adjust their purchasing behavior. We thus conclude that the size and timing of the eect is hard to reconcile with a pure growth eect, and must be at least partly driven by a compliance response to information reporting.

# 6 Impact of Withholding

Having show that information reporting increases reported taxable income, this section studies whether withholding has an impact on compliance beyond the impact generated by information reporting. We use the August 2011 reform of the sales tax withholding rate schedule to isolate the impact of withholding, holding the information environment constant. We start by analyzing the rst stage impact of the reform on realized witholding rates and card use, then conduct a

> 42Note also that the increase in reported taxable income (reported tax liability for the sales tax) is associated with an increase in the reported prot rate (reported value-added rate for the sales tax).

> 43For the event of credit/debit card reporting (last two gures) we use the reported tax liability (rather than reported taxable income) as outcome variable, and sales tax collected and input tax credits deducted (rather than sales and costs) for the decomposition, as these variables are available on the monthly sales tax declarations. The results are similar when conducting the analysis at the annual level with reported taxable income for the income tax as outcome. However, the event is less sharp in this case, as the rst credit card report may be led in the middle of a tax year rather than at the beginning of the year.

> 44This is true except in the case of corporations receiving a rst sales report from other rms (fourth panel), whose trend diverges from the control group at _k_ = _−_ 1 rather than _k_ = 0 .

25
br>0.824|0.840|0.584|86.776|
|Observations<br>244|244|244|244|
|_Information experiment_||||
|Treatment (1 year)<br>0.150***|0.129**|0.037|3.300|
|(0.055)|(0.054)|(0.075)|(2.078)|
|WY _p_-value<br>0.020|0.080|0.560|0.280|
|Control mean<br>0.730|0.750|0.550|87.770|
|Observations<br>174|174|174|174|
|_Single grant observation_||||
|Treatment (1 year)<br>-0.056|-0.084|0.013|-1.839|
|(0.055)|(0.053)|(0.085)|(2.123)|
|WY _p_-value<br>0.740|0.340|0.900|0.740|
|Control mean<br>0.910|0.910|0.615|91.231|
|Observations<br>151|151|151|151|

Notes: Robust standard errors in parentheses. Westfall and Young (WY) p-values reported. Dependent variables in columns (1)-(3) are binary (where 1=Yes). Dependent variable in column (4) is continuous. Data source: Parent association surveys. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01
# **Table A16: Plausibly exogenous instrument regressions**

|Dependent variable:|Employment<br>status|Weekly labor income<br>($1,000 AUD)|
|---|---|---|
||(1)|(2)|
|Standardized mental health|||
|scores|-0.064***|-0.192***|
||(0.009)|(0.055)|
|Lower bound|-0.076|-0.271|
|Upperbound|-0.022|-0.093|

_Notes:_ The lower bound and upper bound are estimated using the _plausexog_ command in Stata developed by Clarke and Matta (2018). Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one.

48