rafmacalaba/data-use-mentions-extended huggingface dataset:
components of productivity growth. They identify the dominant persistent and permanent variations in productivity. These are assumed to reflect lasting structural influences on productivity, such as technological innovation, or organizational changes. This paper uses a new spectral SVAR identification methodology that identifies technology shocks as those that drive the largest proportion of low (long-run) frequency labor productivity variation. The SVAR approach effectively filters through other less persistent changes in productivity or changes that drive only a small proportion of long-run productivity variation.<sup>3</sup> Demand shocks could also have persistent effects on productivity growth, which are not ruled out in our SVAR identification. An example of a persistent demand shock is identified using a modification of the approach for identifying technology shocks. Importantly, the persistence of the effect of the identified demand driver is found to vary across countries according to their capacity to enact counter-cyclical fiscal policy.<sup>4</sup> Finally, the literature has pointed towards evidence that a large proportion of cross-country synchronization of labor productivity growth is driven by business-cycle factors as opposed to technology spillovers (Huo et al., 2020; Imbs, 1999). Accordingly, using the identified technology measure, we assess whether these findings also apply to our expanded dataset covering EMDEs. We address the following questions:

- How can researchers identify technological and non-technological factors driving productivity developments across a wide range of countries? Are the macroeconomic responses to these shocks different across developed and developing economies?

- Can non-technology shocks have a persistent impact on labor productivity, especially in EMDEs? If yes, are there defining features associated with countries having persistent (or less persistent) impulse responses?

- After removing the cyclical and other non-technological components from labor productivity, how synchronized are the technology-driven productivity components across countries?

We make multiple contributions to the literature, which has until now focused on advanced economies. This paper is the first study to separate technology and non-technology drivers of productivity across a broad range of countries using a new spectral SVAR approach introduced by Dieppe
in the use of short panels. In fact, 20.7% of the total number of country-sample years were years with an autonomous regulator and 31% with an electricity or energy regulatory law.

The key data sources used are:

- US Energy Information Agency – for data on generation capacity by country (GW) 1980-2001 (Noted that the EIA series does not distinguish between publicly and privately owned generation capacity.)

- World Bank Development Indicators - for Per capita GDP in $US1995; electric power transmission and distribution losses and other control variables

- The Preetum Domah 2001 survey of electricity regulators for data on electricity regulatory governance, privatisation and competition (supplemented by the authors’ own research).<sup>18</sup>

The Domah survey data (covering 50 developed, transition and developing countries) are the best data currently available to estimate the impact electricity regulators, not least because it allows the _dating_ of regulatory reforms, primarily because it records the year in which key regulatory legislation was enacted.

The Domah data set is very suitable for a preliminary investigation of the impact of regulation but is far from ideal. In particular, it suffers from the following:

- 1) The data on electricity market structure is relatively weak and the data on privatisation very limited;

- 2) There is no data on the informal, practical aspects of regulation (e.g. security of tenure of regulatory agency heads or commissioners, etc);

- 3) The data on regulatory governance, competition and privatisation has no time dimension beyond a simple 0/1 dichotomy set at the year in which key regulatory legislation was enacted;

- 4) The data on the formal aspects of regulation only allows for a 4-element index rather than a larger index.  These data weaknesses should be born in mind when considering the econometric results.

# **3.2.2. Econometric Issues**

Panel data generally allow major opportunities for carrying out investigations that are not possible with single-year cross sections or single-country time series
– Table A.18 Treatment effects on women’s empowerment and well-being

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Women empowerment**||||||||
|Women agency index|-0.000|1.000|-0.059|-0.049|-0.069|0.737|1,824|
||||(0.045)|(0.053)|(0.054)|[0.619]||
||||[0.246]|[0.398]|[0.252]|||
|Partners’ views index|-0.001|0.999|.|.|0.051<br>(0.068)|.|830|
||||||[0.464]|||
|**Women fnancial index**|0.000|1.000|0.413***|0.399***|0.427***|0.852|1,824|
||||(0.084)|(0.134)|(0.091
economic shocks in Iran.

**5.1. The unequal effect of economic shocks on the labor market** Panel A of Table 3 presents the regression results of estimating (1) using outcome variables computed from LFS surveys. The coefficient of the Afghan dummy is consistent with the summary statistics in Table 2: Afghan individuals work longer hours, have lower unemployment and layoff rates, and their duration of unemployment is shorter than Iranians. The estimated coefficients for the economic shock index suggest that following a negative shock, people work longer hours and have higher layoff rates. We also find that negative shock leads to a shorter duration of unemployment which can be due to the hardships that motivate individuals to accept job offers that they would not take in normal times. Our coefficient of interest, which is the interaction of the Afghan dummy with economic shock, is only significant when the dependent variable is working hours. According to column (3) of Table 3, one unit increase in shock measure – corresponding to a year-on-year increase in log(USD to IRR rate) by one unit – raises the weekly working hours of Afghan individuals 1 hour and 27 mins more than Iranians.

In panel B of Table 3, we estimate the heterogeneous impact of economic shock using our matching approach. Although we find similar results for the Afghan dummy and the measure of shock, their interaction term becomes insignificant. This finding shows that the impact of economic shocks on labor market outcomes is not significantly different between refugees and host communities when comparing every Afghan individual with an Iranian with very similar characteristics. Therefore, in turbulent times, Iranian employers do not discriminate against Afghan refugees by laying them off, suggesting economic shocks do not reduce social cohesion between employers and Afghan refugees. However, as we only observe employment and not its features such as expropriation, withholding salaries, and harassment, this evidence is incomplete and social cohesion may be weakened in those dimensions.

Table 4 shows the estimation results using two approaches when the dependent variable is the dummy
100|100|100|100|100|100|100|100|

_Source: Calculations based on the ILFS 2006 data._ 44. Regardless of causal factors, it is worth noting that seasonality sometimes affects the earning capacity of HEs (Table 3.4 in Section 3 finds a very small negative effect, but significant for males only). Among rural HE operators, poor agricultural performance was cited as one of the main reasons why they operate their HEs seasonally (see Box 2.1 in Section 2). Not surprisingly, their business turnover is very small, if any, during the agricultural season. When the rains are erratic and they do not produce enough, their cash flow is reduced to a minimum, which in turn adversely affects the operations of their HEs. Conversely, rural HEs thrive during good agriculture season when farm income augments HEs‟ capitalization and local demand for farm goods is high.

# **4.2 Lack of Credit**

45. Although lack of credit is a common problem across all enterprises in Tanzania, HEs are particularly vulnerable,<sup>12</sup> _mainly because they are left out of the financial sector and policy interventions have been less effective_ **_._** Overall, access to finance is stunted in Tanzania. Data

> 12 In many studies, lack of credit is found to be the most stringent constraint binding HEs from growing and being more productive. For instance, during the FGD survey, participants in every session mentioned lack of capital or credit as the most important constraint they face, at times associating the visit by the study team with assistance that is forthcoming.

22
Child Memory Test 1|56.36|15.53|59.50|15.29|
|Child Memory Test 2|47.19|17.03|50.34|16.52|
|Number of School Age Children|2.21|1.16|2.31|1.16|
||Pane|l C: Permane|nt Income|(Father)|
||Rural (N|OB: 2253 )|Urban (N|OB: 1740)|
||Mean|SD|Mean|SD|
|Permanent Income|199.32|139.53|334.79|258.47|
|Father’s Years of Education|523|362|743|376|
||.<br>Panel|.<br>D: Permanen|.<br>t Income (|.<br>Children)|
||Rural (N|OB: 2419)|Urban (|NOB: 977)|
||Mean|SD|Mean|SD|
|Permanent Income|208.93|149.75|269.39|227.92|
|Child’s Years of Education|8.38|3.49|10.02|3.65|

Notes: Author’s calculation based on the Indonesian Family Life Survey. In Panel A, Rural and Urban are defined based on location of birth. If the respondent was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. The sample in Panel A is based on the 18 to 40 years old in the fifth wave of the IFLS. In panel B, Rural and Urban are defined based on the location of the household in the survey. The summary statistics of Panel B are based on the merged sample of first four waves of the IFLS. Average monthly expense was calculated using the inflation
# Unpaid work burdens and the value of time use data

As seen above, desire and availability for paid work, and hours worked, are not independent of unpaid responsibilities. Recent evidence from available time use surveys across countries shows that women’s total (paid and unpaid) work burdens are substantially greater than men’s (United Nations, 2015). Figure 5, using the 2013-14 Uganda LSMS-ISA, shows that time spent across paid and unpaid work is also higher for rural women, stemming from their engagement across paid and contributing family employment, own use production, and domestic activity. Figure 5 also indirectly highlights the role that unpaid care work plays in rural women’s work burdens; women’s time spent in unpaid work rises steeply in their childbearing years, whereas for men it falls, and women’s unpaid work burdens also fall much more slowly than time spent in paid work as men and women age.

## Figure 5.  Uganda National Panel Survey, 2013-14: hours spent across paid and unpaid non-market activities in the last week

<!-- Start of picture text --> Rural (own-reports)<br><!-- End of picture text --> <!-- Start of picture text --> Urban (own-reports)<br><!-- End of picture text --> Few socioeconomic surveys have detailed data on time spent across different areas of unpaid work (among the LSMS surveys, for example, the Uganda survey has the most detailed breakdown of time spent across multiple unpaid activities; see Figure 6). Well-designed time use surveys are critical in highlighting total work burdens, but are not without significant costs and measurement issues (Buvinic and King, 2018), and may be best as standalone surveys. Among socioeconomic surveys, narrowing the set of activity categories to those that are most contextually and policy-relevant is a priority measurement initiative under the EDGE2 project with UNSD and ILO (ILO, 2018d).

Figure 6 shows, for
### **2.2.2 Censoring and Endogeneity Concerns**

The censoring concerns in this specification mirror those outlined in Section 2.1.2, and so we implement the same Tobit correction. In contrast, new endogeneity concerns arise in this empirical specification. In addition to domestic value added, the levels of domestic production, imports, and foreign value added may be correlated with the residual variation in tariffs. Most importantly, foreign value added may increase with tariffs. In our model, the price of foreign value-added inputs rises mechanically with the tariff. Outside the model, one might (also) be concerned that foreign firms engage in “tariff jumping,” shifting to local final production (using imported inputs) in high tariff sectors/countries.<sup>34</sup> If so, the coefficient estimate on the FVA-Ratio will be biased upwards, which could lead us to find a zero/positive coefficient erroneously. We discuss this issue further below.

# **3 Data**

This section describes how we construct our data on the value-added content of production and bilateral trade policy. It also offers a first peek at the data.

## **3.1 Value-Added Content of Final Goods Production**

To calculate our measures of the value-added content embodied in final goods production (DVA and FVA), we use data from the World Input-Output Database (WIOD).<sup>35</sup> It contains an annual sequence of global input-output tables for the 1995-2009 period covering 35 industries across 27 EU countries and 13 other major countries.

Following Los, Timmer and de Vries (2015), we use these data to compute the national origin of value added contained in the final goods that each country produces. Intuitively, the global input-output table enables one to trace backwards through the production process to assess the value and identify the national origin of the intermediate inputs used (both directly and indirectly) to produce each country’s final goods. With
The global system ensures improved horizontal equity because individuals with one or multiple income sources are treated equally. However, it presents challenges in administration, particularly in developing countries with limited third-party reporting. On the other hand, a schedular system treats each income source individually, making it easier to implement and administer. Nevertheless, ensuring horizontal equity can be challenging in this system, as taxpayers with multiple income sources may be taxed differently for the same level of income compared to those with a single income source.

The impact of global and schedular tax systems on tax fairness (unequal distribuion of tax burdens) hinges on the number of income sources a taxpayer has. However, it is essential to note that we did not encounter conclusive evidence pointing definitively in one direction or the other in reviewing the literature with regard to the number of income sources and gender. Furthermore, delving into this issue would extend beyond the scope of our current study, which centers exclusively on business income taxes (and does not extend to other income sources and how they are taxed).

Lastly, when a country adopts joint filing for married couples, women can face a higher marginal tax rate than if they filed their tax returns separately. This bias towards women is due to their lower income in most cases (see Capraro, 2014). Joint filing is an aspect that previous research has identified as particularly susceptible to explicit gender bias (see Barnett and Grown, 2004; OECD, 2022; Stotsky, 1997). The Ethiopian system does not provide for joint filing; therefore, it is not expected to be affected by such bias.

# **3. Data**

The data for this paper comes from the Ministry of Revenue (formerly the Ethiopian Revenue and Customs Authority). This administrative database offers a wealth of information, encompassing declared sales, costs of goods sold, expenses, profit, tax payable, deductions for losses, and profit tax due. A business’s total sales and other income contribute to its overall business 9
**Table 4: Self-reported reasons for not using a SEBRAE service**

|Fraction of firms mentioning this reason|Control<br>at follow-up (mu|Treatment<br>ltiple mention)|
|---|---|---|
|Lack of time|0.571|0.624|
|I am happy with my business' performance|0.286|0.158***|
|Other|0.194|0.198|
|Lack of interest|0.102|0.131|
|High cost|0.031|0.027|
|Received services from other provider|0.020|0.037|
|N|98|298|

Notes: This table shows reasons that firms reported on the follow-up survey for not using a SEBRAE service after the information intervention. These reasons were only collected for firms who had not used a SEBRAE service between the time of the intervention and the follow-up survey, according to SEBRAE-PR administrative data. Stars refer to differences in fractions of the treatment group and the control group. ***, **, and * indicate significance at the 1, 5, and 10 percent level.

20
<br>LVAX share LVAX share<br>LVAX (million USD) LVAX (million USD)<br>1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011<br><!-- End of picture text --> Note: Based on export-weighted data for 31 countries that are present in the database every year since 1995. Income group is defined according to the World Bank country group 2011 classification. High-income countries are represented by Australia, Canada, Denmark, Finland, Germany, Hong Kong SAR, China, Japan, the Republic of Korea, New Zealand, Singapore, Sweden, Taiwan, China, United Kingdom, and United States.

16
br>Household assets & house characteristics||Y|Y|Y|Y|Y|Y|Y|||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|Y||

**Note:** Empirical distribution of the error terms model with bootstrapped SEs is used. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. All estimates are obtained with population weights. The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. Imputed consumption per capita for the second round uses the estimated parameters based on the data from the first round. 100 simulations are implemented. True consumption per capita is the estimate directly obtained from the survey data. Estimates shown in boldface or with a “*” respectively fall within the 95% confidence interval or one standard error of the true rate.

54
World Bank (2017). Doing Business 2017: Kenya.

http://www.doingbusiness.org/~/media/wbg/doingbusiness/documents/profiles/country/ken.pdf, accessed June 4, 2017.

World Bank Group (June 2016). Breaking Down Barriers: Unlocking Africa's Potential through Vigorous Competition Policy. Washington, DC.

Retirement Benefits Authority (RBA). Annual Report & Financial Statements 2015. Nairobi, Kenya.

Reuters (October 12, 2012). Uganda power firm Umeme prices IPO at 275 shillings per share. http://www.reuters.com/article/uganda-umeme-ipo-idUSL5E8LCF5V20121012, accessed June 11, 2017.

Nairobi Securities Exchange (2016). Annual Report & Financial Statements 2016. https://www.nse.co.ke, accessed June 11, 2017.

Anyanzwa, James (March 2, 2017). Kenya: NSE Falters As New Products Fail to Bring in Investors. http://allafrica.com/stories/201703300621.html, accessed June 10, 2017.

Alushula, Patrick (February 14, 2017). <mark>Most new faces in Nairobi bourse drop below IPO price Read more.</mark> https://www.standardmedia.co.ke/business/article/2001229353/most-new-faces-in-nairobibourse-drop-below-ipo-price, accessed June 2, 2017.

Teche, Enos (June 29, 2016). Firms reject IPOs, seek investors in divesture – study. http://www.thestar.co.ke/news/2016/06/29/firms-reject-ipos-seek-investors-in-divesture-study_c1377090, accessed June 10, 2017.

The Heritage Foundation (2015). 2015 Index of Economic Freedom. Kenya Country Page. Washington, DC.

Deloitte (2016). 2016 Africa Private Equity Confidence Survey. EAVCA; SAVCA.

Central Bank of Kenya (August 2016). The Kenya Financial Sector Stability Report, 2015. Issue No. 7. Nairobi, Kenya.

Central Bank of Kenya (August 2015). The
G. Roland. 2011. "The Long-Run Impact of Bombing Vietnam." _Journal of Development Economics_ 96 (1): 1-15. Munteanu, C., et al. 2017. "Nineteenth-Century Land-Use Legacies Affect Contemporary Land Abandonment in the Carpathians." _Regional Environmental Change_ 17 (8): 2209-2222. Muriuki, J., et al. 2023. "Spillover Effect of Violent Conflicts on Food Insecurity in Sub-Saharan Africa." _Food Policy_ 115. Neyter, R., S. Zorya and O. Muliar. 2024. "Agricultural War Damages, Losses, and Needs Review ", Kyiv School of Economics, Kyiv. Nivievskyi, O. 2020. "Where Is the State Agricultural Land Disappearing?", Economic Pravda, Kyiv. Nivievskyi, O., O. Donchenko and P. Iavorskyi. 2021. "Assessing the Role of Small Farmers and Households in Agriculture and the Rural Economy and Measures to Support Their Sustainable Development." Kyiv School of Economics, Kyiv. Potapov, P., et al. 2022. "Global Maps of Cropland Extent and Change Show Accelerated Cropland Expansion in the TwentyFirst Century." _Nature Food_ 3 (1): 19-28. Raleigh, C., A. Linke, H. Hegre and J. Karlsen. 2010. "Introducing Acled: An Armed Conflict Location and Event Dataset: Special Data Feature." _Journal of Peace Research_ 47 (5): 651-660. Rockmore, M. 2020. "Conflict-Risk and Agricultural Portfolios: Evidence from Northern Uganda." _Journal of Development Studies_ 56 (10): 1856-1876. Rose, A., Z. Chen and D. Wei. 2023. "The Economic Impacts of Russia–Ukraine War Export Disruptions of Grain Commodities." _Applied Economic Perspectives and Policy_ 45 (2): 645-665. Sessou, F. E. and C. H. C. A. Henning. 2024. "Conflict and Farm Inputs Investment : Can Social Safety Nets Have Any Mitigation Effect?" _Applied Economics_ 56
_procedures)?_ The answer “Yes” is coded with one and “No” with zero (panel B). 8) _Can the deposit insurance agency/fund take legal action against bank directors or other bank officials?_ The answer “Yes” is coded with one and “No” with zero (panel C). 9) _Has the deposit insurance agency/fund ever taken legal action against bank directors or other bank officials?_ The answer “Yes” is coded with one and “No” with zero (panel C). 10) _Are non-residents treated differently than residents with respect to deposit insurance scheme coverage?_ The answer “Yes” is coded with one and “No” with zero (panel C).

# **3.5 Time series: Coverage limits, co-insurance, and coverage ratios**

The database includes time series data for co-insurance and coverage limits. The limits and the co-insurance requirements since the year of EDIS adoption and the revisions to them over time are presented in Table A.1.7. The amount of coverage is seen to vary across different schemes. They are also adjusted through time to account for inflation as well as changing economic conditions. Table A.1.7 provides the coverage limits, the currency they are measured in and the co-insurance percentages.

Finally, the database provides ratios of coverage amounts to GDP per capita and deposits per capita, where all are expressed in local currency units. The sample years span 1960 to 2003 in the main database online.<sup>11</sup> The underlying data, that is GDP per capita, total deposits, population, and coverage amounts, are also reported there. In Table A.1.8 we present the two coverage ratios for 1999-2003. Figure 6 provides the ratio of deposit coverage to GDP per capita in 2002 for selected countries. We see that the ratio is quite high for some developing countries. For example, the coverage amount is about ten times