rafmacalaba/datause-encoder-data huggingface dataset:
that the economy discloses the first COVID-19 case to the day that the government first announces the policy to restrict the international travel. _DelayD_ is delay in implementing domestic closedown policy, equals to the number of days, divided by 100, from the day that the economy discloses the first COVID-19 case to the day that the government first announces their domestic closedown policy, which include school closing, workplace closing, public events cancelling, restrictions on gatherings, public transport closing, stay-at-home requirement and restrictions on internal movement. Smaller _DelayI_ and _DelayD_ indicate prompt government response.

We rely on the World Development Indicator (WDI) dataset from the World Bank to obtain basic country-level characteristics including GDP per capita and the total population.<sup>5</sup> For all timevarying variables, we choose the latest year that the observations are mostly available. In addition, we control basic geographic and weather conditions of a country. Since the first large outbreak happened in China, we control for the distance to China—with two dummy variables of the distance being in the middle and the top tercile of the distance distribution.<sup>6</sup> Since temperature plays a role in pandemic spread, we also control for the average temperature since the inception of the COVID-19 on a country until November 2020. In a sensitivity check, we also control for the longitude and latitude of capital of a country.

We add data on democracy and trust from several sources. A dummy variable indicating the status of democracy is from Freedom House. Indicators of out-group trust and individualistic culture are obtained from the World Value Survey (WVS). Out-group trust (“Trust”) measures the extent of trust in strangers and people of different nationalities and religions; a higher value implies stronger general trust. Individualistic culture (Individualistic Culture) is captured by the individual empowerment index (version 2) in WVS, which measures the extent to which the people in a society are mentally and habitually empowered to make their own choices and pursue them in their actions. Since countries with higher individual empowerment are usually viewed as being more individualistic, this index serves well as a proxy of individualistic culture. For countries having more than one wave of survey covering a period of 1996-2014, we use the data from the latest wave. Trust and Individualistic Culture have significant missing incidence, available in less than half of the number of countries for all other variables. To ensure a reasonable sample size, and to

> 5Data available at World Development Indicators | DataBank (worldbank.org).

> 6 In particular, we measure the distance to Beijing in kilometers. Other functional forms do not make a significant difference.

12
previously, Besley and Burgess (2004) and Marshall and Marshall (2008) are the main source for the data on the flexibility of labor regulations and of measures of social unrest, respectively. A tabular summary of the data is provided in Table 3.

**Table 3: Data Availability**

|**Variable**|**Source(s)**|||**Availab**|**ility**|||
|---|---|---|---|---|---|---|---|
|||_1999_|_2000_|_2001_|_2002_|_2003_|_2004_|
|Productivity|Prowess|||||||
|Age|Prowess|||||||
|Sales|Prowess|||||||
|Type|Prowess|||||||
|Exporter Count|Prowess|||||||
|Localisation|ASI|||||||
|Input-Output|ASI|||||||
|Industrial Diversity|ASI|||||||
|MA|NSSO/ Travel-Time Distance|||||||
|Road Density|ArcGIS|||||||
|Electricity|NSSO|||||||
|Telephone|NSSO|||||||
|Education|NSSO|||||||
|Wages|NSSO|||||||
|Wealth|NSSO|||||||
|Labour Regulations|Besley and Burgess (2004)|||||||
|Riots|Marshall and Marshall (2008)|||||||
|Population|NSSO/Census|||||||

Note: ASI: Annual Survey of Industries; NSSO: National Sample Survey Organisation. The values assigned to different states regarding their labour regulations, and those for road density assigned to districts do not vary over year, and the value does not vary over time.

# **4 Results: The Extensive Margin**

## **4.1 Across Firms**

The results of the econometric specification are provided in Table 4. The dependent variable is ‘start’ i.e. a dummy variable that equals one if the firm starts exporting and zero otherwise. All columns include year and industry (2-digit NIC level) controls. From left to right, the columns present estimations that include an increasing number of variables and then finally also include location-specific effects. Model specification (1) controls for firm-level characteristics only; in addition to these, model (2) includes agglomeration variables; model (3) adds first-nature geography variables; model (4) adds further infrastructure variables, and model (5) adds institutional variables. Model (6), which includes all variables, firm, second and first nature, infrastructure and institutional, also includes state fixed effects. Owing to limited data availability for 14
Table 5.1 Sectoral CDs for IDB Prajects 1979-1986

|-Sector<br>-<br>1979-82|1983-86<br>1979-86|
|---|---|
|Agriculu<br>74.1|48.3<br>64.0|
|Fisheries<br>35.6|59.2<br>43.1|
|Industry<br>.<br>27.2|26.0<br>25.0|
|Toiurism<br>24.0|82.3<br>80.4|
|Mining<br>20.1|47.6<br>45.9|
|Energy<br>10.3|14.6<br>20.1|
|:Transport<br>26.6|27.7<br>27.3|
|Communications<br>54.6|83.4<br>74.4|
|Urban Development<br>83.5|74.5<br>76.3|
|Science and- technology|91.2<br>912|
|-1Education-<br>31.9|77.1<br>5837|
|Kealth-<br>.87.2|87.3<br>872|
|:-Satation<br>.-<br>_MA51_|45-6<br>47.6|
|E: vironment;<br>*-<br>-Rx -:;-45.0|45.0|
|Tota<br>:533|37.-<br>4-47|
|.S-ouce: -Pows (1989: Table 3.4)||

But most studies have used "objective indicators": such as life expectancy (e.g. Anand and Ravallion). Only very few studies have use direct measures of poverty as a dependent variable: Morley (1992) is one of the few to use a poverty measure (the headcount index) as the dependent variable in a regression analysis. One World Bank (1990) report on adjustment lending briefly mentions poverty incidence measures. Ravallion and Huppi (1991) use more sophisticated measures, but their diswssion of the link between these measures and policy is brief.

Most studies use indicators that capture one aspect of 'living standards or are proxies. Some of these measures are broad indicators, that is ones covering the population as a whole, such as nutritional status, literacy, school enrolment rates etc. The assumption is that the brunt or benefit of changes in this indicators will be felt by the poor, which is probably not an unjustified assumption, but one that could perhaps be subject to some critical scrutiny where possible.<sup>3</sup> 93
Effects of Fiscal Consolidation in the OECD _FYgre 1_ <!-- Start of picture text --> CBO Budget Deficit Estimates<br>450<br>400 -<br>3. 35<br>o300.<br>2,W<br>200.<br>150 I I I I I I<br>1993 1A94 1995 1998 1997 1998 199 2000 2001 2002 2003<br>Year<br>-|- March 93 Capped Baseline - Estimate of Administratfon Baselin<br>-|- Uncapped Baseline - New Baseline under OBRA -93<br><!-- End of picture text -->

_Source: CBO (1993)_ to GDP must be approaching the benchmark of 60 % of GDP at a reasonable pace'. Of these reference indicators, the last criteria on fiscal deficits is the focus of this paper.

To give an indication of the current fiscal position in Europe, Figure 2 shows fiscal deficits relat ivc to GDP of EC countries in 1992. This figure also contains a line indicating the Maastricht guideline for fiscal deficits of 3% of GDP. Based on the fiscal deficits in 1992, it is clear that some countries, notably Italy, United Kingdom, Belgium, Greece and Portugal need to undertake a significant fiscal consolidation between now and 1997 if they are to satisfy the Maastricht guidelines for fiscal convergence. In many of these countries the fiscal deficits have worsened in 1993 due to the economic slowdown in Europe. A similar, and indeed more extreme picture emerges from examining the ratio of government debt to GDP for these economies. In this paper we ignore the debt targets and concentrate merely on the deficit targets as these are more likely to be achievable in the time frame considered (see Giovannini and McKibbin (1992)).

Developments in the United States and Europe, suggest the following stylization of scenarios for this paper. Fiscal deficit reduction in the United States will be around 1.6 Percent of GDP by 1997.

1 See IMF (1992), Annex II on wThe Maastricht Agreement on Economic and Monetary Union'.

3
Heckman, James and Jeffrey Smith, 1995, “Assessing the Case for Social Experiments,” _Journal of Economic Perspectives_ 9(2): 85-110.

Herbst, Jeffrey, 2000, _States and Power in Africa: Comparative Lessons in Authority and Control_ , Princeton NJ: Princeton University Press.

Hofman, Bert and Jingliang Wu, 2007, “Explaining China’s Development and Reforms,” Paper submitted to the Growth Commission, World Bank Resident Mission, Beijing.

Hyden, Goran, 2007, “Governance and Poverty Reduction in Africa,” _Proceedings of the_ _National Academy of Sciences_ 104(43), pp. 16751-56.

Ianchovichina, Elena, and Will Martin, 2004, “Impacts of China’s Accession to the WTO.” _World Bank Economic Review_ 18(1): 3-28.

Juma, Calestous, 2007, “Lessons Africans Must Learn from Chinese Expansion,” _Business Daily,_ July 13. http://www.ksg.harvard.edu/ksgnews/Features/opeds/071307_juma.htm Klasen, Stephan and Thorsten Nestmann, 2006, “Population, Population Density and Technological Change,” _Journal of Population Economics_ 19:611-626.

Lin, Justin Yifu, 1992, “Rural Reforms and Agricultural Growth in China,” _American Economic Review_ 82: 34-51.

Lin, Justin Yifu and Peilin Liu, 2008, “Economic Development Strategy, Openness and Rural Poverty,” in _Globalization and the Poor in Asia: Can Shared Growth be Sustained_ (edited by Machiko Nissanke and Erik Thorbecke), Palgrave Macmillan.

Luo, Xiaopeng, 2007, “Collective Learning Capacity and the Choice of Reform Path.” Paper presented at the IFPRI/Government of China Conference, _Taking Action for the World’s Poor and Hungry People_ , Beijing.

Mwabu, Germano and Erik Thorbecke, 2004, “Rural Development, Growth and Poverty in Africa,” _Journal of African Economies_ 13: i16-i65.

National Bureau of Statistics, 1996, 2001, 2006, _China Statistical Yearbook ̧_ Beijing: China Statistical Publishing House, Government of China.

Posner, Daniel and Daniel Young, 2007, “The Institutionalization of Political Power in Africa,” _Journal of Democracy_ 18(3): 126-140.

Ravallion, Martin, 2005, “Externalities in Rural Development: Evidence for China,” in Ravi Kanbur and Tony Venables (eds) _Spatial Inequality and Development_ , Oxford University Press.

26
The study begins by developing estimates of electricity consumption for the Indian manufacturing sector in multiple years from 1989 to 2010 using the information collected on the plant-level surveys included for the formal sector through the Annual Survey of Industries (ASI) and for the informal sector through the National Sample Survey Organisation (NSSO). The following survey years are included for both the sectors: 1989-90, 1994-95, 2000-01, and 200506, while the year 2009-10 is included for ASI and the 67<sup>th</sup> round of NSS spanning from 20102011 is included for the unorganized sector.

Our baseline results suggest that that electricity consumption per unit of output in organized and unorganized urban plants has declined during 1989-2010. In the rural areas, by contrast, electricity consumption per unit of output for organized sector plants peaked in 2000 and thereafter it declined. These results broadly confirm the findings of Sengupta (2012) who compares four alternative scenarios of economic growth (8% growth vs. 6% growth) and energy prices (no increase vs. 3% increase) and argues that energy intensity in India will likely continue declining in the next two decades.<sup>3</sup> This result comes out even stronger during periods of high economic growth and energy price increases.<sup>4</sup> Beneath these aggregate figures lies much heterogeneity. We evaluate the differences in usage levels that exist between leading states in India and those that are economically less developed. Our results suggest that the usage of electricity per unit output is remarkably high in states such as Madhya Pradesh and Orissa throughout the five surveys, in some cases twice the level of India as a whole. By contrast, states like Delhi and Haryana display electricity consumption levels that are consistently lower than the national average. We show more broadly a strong link of lower electricity consumption levels per unit of output in more developed states, although we discuss why the sorting of manufacturing plants over locations limits the strength of these conclusions.

> 3 Increased electricity costs in many developing countries are driving the declining trend in energy usage. See, for example, Fischer-Vanden et al. (2013) for the case of China and Prayas (2009) for the case of India. Abeberese (2013) contends that this may not necessarily be good for overall productivity of the sector because high electricity prices may force firms to switch to less technologically intensive products/production processes, which require less electricity for production but lowers their productivity.

> 4 For an analysis of the impact of electricity price on the efficiency of using electricity in a developed country like the United States, see Davis et al. (2008). Other papers that examine the determinants of electricity prices in developed country markets include Davis et al. (2013) and Joskow and Wolfram (2012).

5
pre-reform mean would have been lower, the increment would have been fewer than 10 households per 100, but still substantial.

The value for distribution losses has to be interpreted with care. The estimate of a 19% reduction in losses has to be compared to the overall average value of 15.3% losses. This reduction is equivalent to 2.9 percentage points (taking the total loss to 12.4%), but the prereform mean would be larger than the overall mean, so that the actual reduction would be larger and the post-reform value lower. In this case the transition value showed very little change (down to 15%), but the total reduction is clearly substantial. It should be noted that QFD studies suggested 10% as a target value for efficient firms.

Both the duration of interruptions and the frequency of interruptions fell to about half of the pre-reform values. With a mean duration of around 20 hours per year, the reform resulted in a reduction of around 10 hours a year. This could be valued by using estimates for the cost of shortages (Foster and Steinbuks, 2009) but even without doing so it is clear that privatization leads to a considerable and valuable increase in the quality of supply.

- (ii) **Gassner et al. (2008)** followed a similar approach to the above. Private sector participation was related to performance indicators for distribution utilities using a semi-log function. The results for significant indicators are shown in table 10. Mean values refer to the whole period rather than the pre-reform period.

Table 10: Impact of privatization on performance (Gassner at al. 2008)

|Performance measure|Mean|Post-transit|ion period|
|---|---|---|---|
|||Coefficient|% change|
|Connections per employee|370|0.252|29|
|Energy sales per employee (MWh)|1787|0.274|32|
|Distribution losses %|17|-0.135|−11|
|Bill collection (%)|78|0.374|45|

The results for connections per employee and sales per employee indicate substantial improvements in performance, although not as large as those found by Andrés et al. The reduction in distribution losses is also smaller (equivalent to a fall of 2 percentage points from the mean of 17%). The improvement in bill collection is large, and the overall mean of 78% clearly overstates the pre-reform value. The post-reform value of bill collection is likely to be above 90%.

- (iii) **Vagliasindi (2012)** used a linear model and found significant links between private sector participation and (i) energy sold per worker; and (ii) access.

For the distribution sector privatization was measured by retail sales of the private sector relative to total retail sales. The regression of energy sold per worker on the privatization share gave a value of 9.6, with an overall mean value of energy sold per worker of approximately 25 MWh. A one percentage point increase in the share of private sales is then estimated to increase sales per employee by 0.1 MWh.

43
details some key steps that could strengthen research protocols in financial literacy studies going forward.

Finally, a surprising and yet common omission among virtually all studies is the analysis of costs and benefits of financial literacy interventions. Likewise, we did not find any discussion of potential alternative methods to achieve the desired outcomes across the 188 articles in this literature, which could help benchmark both costs and benefits. Such analyses could be simple to perform in individual studies, and certainly worthwhile to assess returns to development investments.

27
The panel component of the AWMS R3 further reinforces that the trends in the assets and consumption variables between the IE-LFS and AWMS R3 reflect actual changes in living conditions. An advantage of the AWMS R3 compared to other phone surveys is that about 82 percent of the sample comprises the same households that participated in the original household surveys.<sup>10</sup> Using the pure panel component, it is possible to validate the observed changes in the household’s living conditions and isolate any changes as stemming from the reweighting process. Table 6 shows the living conditions for the same households in the IE-LFS and AWMS R3 with and without weights. Changes in the variables between the IE-LFS and the AWMS R3 hold even in the absence of weights, further supporting that the imputed poverty rates are the results of actual changes in living conditions and are not driven by sampling bias toward households with better-off initial conditions.

**Table 6: Unweighted trends on key household model variables between IELFS and AWMS R3**

|||Panel household<br>AWMS R3|s|
|---|---|---|---|
||IELFS (1)|<br>_panel_ (2)|Ratio (2)/(1)|
|**Household Characteristics**||||
|Household Size|7.8|8.4|1.1|
|**Asset Ownership**||||
|Refrigerator|18.3|18.2|1.0|
|TV|48.3|42.3|0.9|
|Bicycle|11.2|23.1|2.1|
|Motorcycle|19.1|20.9|1.1|
|Car|12.2|7.1|0.6|
|Washing Machine|15.1|16.8|1.1|
|Vacuum Cleaner|7.7|7.7|1.0|
|Small livestock|36.3|48.6|1.3|
|Large livestock|45.8|45.6|1.0|
|**Dwelling Characteristics**||||
|Roof material: Concrete|23.4|23.4|1.0|
|Wall material: Concrete|22.6|18.0|0.8|
|No Toilet|6.8|2.9|0.4|
|**Consumption in the last 7 days**||||
|Beans/Pulses|78.7|80.8|1.0|
|Meat|57.5|60.2|1.0|
|Milk|41.1|48.3|1.2|
|Apples|22.6|11.4|0.5|
|Eggs|42.6|65.3|1.5|
|Chocolate|58.9|75.1|1.3|
|Fuel for Car|24.7|26.0|1.1|

**_Source_** _: World Bank estimations using IE-LFS households surveys and AMWS rounds. Note: Shows characteristic of households that participated in both the IE-LFS 2019/20 and IE-LFS 2021, and those AWMS R3 participants whose identity match original IE-LFS respondents._

> 10 For the sample from the IE-ELFS 2019/20, the identity of the original respondents was done directly as the name of the household head was available in the information the NSIA shared. In the case of the IE-LFS 2021, no information on the names of previous respondents was available. Instead, we used information on the household head— including age, gender, location, and number of members—to assess if the respondent household was the same that participated in the IE-LFS 2021.

12
# Table of Contents

|I.<br>Introduction ........................................................................................................................................... 3|
|---|
|II.<br>Overview of the fiscal system in Albania ............................................................................................. 5|
|III.<br>Methodology ..................................................................................................................................... 9|
|IV.<br>Applying the Method to Albania: Data and Empirical Strategy ..................................................... 12|
|Household Survey Data .......................................................................................................................... 12|
|Fiscal Interventions Included .................................................................................................................. 12|
|Empirical Strategy .................................................................................................................................. 13|
|Model performance ................................................................................................................................. 17|
|V.<br>Results: Distributional Impact of the Net Fiscal System in Albania................................................... 18|
|VI.<br>Incidence, Progressivity and Marginal Contributions of Taxes and Social Spending .................... 21|
|Taxes and Contributions ......................................................................................................................... 23|
|Direct and In-Kind Transfers .................................................................................................................. 26|
|VII.<br>Additional applications for policymaking ....................................................................................... 28|
|VIII.<br>Conclusions and Policy Insights ..................................................................................................... 31|
|IX.<br>References ....................................................................................................................................... 32|
|X.<br>Annexes............................................................................................................................................... 37|

2