rafmacalaba/datause-encoder-data huggingface dataset:
# **Scenarios**

13. As noted above the model was run for the Business as Usual Scenario, plus six scenarios reflecting a menu of instruments that look at the impacts of reducing PM10 and other particles through different tax instruments (see Figure 2). Details of the different scenarios are given in Table 1.

## **Figure 2:  How the CGE Model Works**

<!-- Start of picture text --> CGE Model Instruments Outputs<br>• Simulate Indian economy • Endogenous energy • GDP<br>under various scenarios efficiency and end of pipe • PM10 Emissions<br>covering 57 sectors technology improvements<br>• Environmental taxes<br>• Transition to cleaner and<br>cost effective production<br>technologies and processes<br><!-- End of picture text --> <!-- Start of picture text --> Inputs<br>• Social Accounting Matrix<br>created for India using GTAP<br>data and inouts from<br>various government sources,<br>academic literature<br>• Estimates on potential<br>emission reduction<br>programs<br><!-- End of picture text --> <!-- Start of picture text --> Outputs<br>• GDP<br>• PM10 Emissions<br><!-- End of picture text --> Two types of taxes are modeled: Domestic fuel tax ( _to_ ) is added to the producer price ( _ps_ ) for coal, oil, natural gas and refined oil to obtain the market price ( _pm_ ):

- (1)  eq. 1

The tax rate increases at a decreasing growth rate starting from 2012. Tax rates (to) used in different scenarios are displayed in Table 4.

- (eq. 2)

Imported fuel tax ( _tm_ ) is applied to the import price ( _pms_ ) of coal, oil, natural gas and refined oil.

8
To this end, we create a dummy variable , which is 1 if the prefecture’s ruling warlord was replaced by another in year TTRRDDFFPPTTFFDD _t_ in the post-1916 period, and 0 otherwise. We ii,tt hypothesize that border ruggedness should have negative impact on . Similarly, ii,tt we sum up the frequency of warlord turnover in a given prefecture after 1916, and hypothesize TTRRDDFFPPTTFFDD that this aggregate, F , should also be negatively related to border ii ruggedness. In the warlord years between 1917 and 1926, Chinese prefectures experienced TTRRDDFFPPTTFFDD FFDDF RRF frequent change of rulers, with 270 prefectures having at least one turnover. The average prefecture experienced four changes of controlling warlords in a short span of a decade, totaling 913 such events.

We run the following regressions using the warlord period (1917-1926): 1R R P 1 (6) TTRRDDFFPPTTFFDDFii,tt = ββ 2R R FFRRF P ii + γγ XX2 ii + εεii,tt (7) ii ii ii ii Panel B of Table 6 reports the results. Border ruggedness indeed reduced warlord turnover. TTRRDDFFPPTTFFDD FFDDF RRF = ββ FFRRF + γγ XX + εε In column 1, border ruggedness has a negative and significant coefficient of -0.108, suggesting that a one-SD increase in border ruggedness (i.e., 0.272) is associated with a three percentage points drop in the probability of turnover. In column 2, border ruggedness also has a negative and significant impact on the total number of turnovers in the prefecture, with a coefficient of -0.693, indicating that a one-SD increase in border ruggedness would reduce total number of turnovers in a decade by roughly 0.2 times. These results suggest that warlords’ topographydriven decisions were indeed shaped by ruling horizon.

# **5.2.  Resident Composition Is Unlikely to Be a Mechanism**

Border areas with rugged terrains could attract people who want to escape from the state (Scott 2009). Thus, housing potentially difficult people to govern, rugged borderlines might incubate unrest and rebellion if the ruler did not provide necessary disaster relief. Taking this into account, the warlord might be forced to behave in a more responsible way.

To check this mechanism, we identify prefectures classified by the Qing government as being rife with violent crimes, and prefectures with a historical record of riots. In the post-1916 sample of prefectures, we further include in the regression these two dummy variables and their 22
Next, linear predictions of child development were calculated at varying levels of depression, anxiety, and parental stress (Table 11). For example, the average predicted level of child development for a child under 3 years old in a household with no maternal anxiety, depression, or parenting stress is 1.74 standard deviations below the mean, compared to the average predicted level of child development for a child in a household with the sample’s median level of maternal anxiety (equivalent to a score of 4), no depression, and no parenting stress of 2.02 standard deviations below the mean. Similarly, for children 3-6-years-old, the average predicted child development in a household with no maternal anxiety, depression, or parenting stress is 33%, compared to the average predicted level of child development for a child in a household with the sample’s median level of maternal anxiety and depression, and no parenting stress of 22%, over 10 percentage points lower.

This can also be visualized by the Margins plot (Figure 7). For children under 3-years-old, as demonstrated by the yellow line, at median levels of depression (depression=6) and parenting stress (parenting stress=6), but minimum levels of maternal anxiety, a child’s predicted child development Z-score is 1.49 SDs below the mean. However, as levels of anxiety increase to median levels of anxiety, the predicted child development Z-score moves to 1.68 SDs below the mean. Figure 8 describes the same phenomenon for children 3-6-years-old. For children 3-6-yearsold, as demonstrated by the yellow line, at median levels of depression and parenting stress, but minimum levels of maternal anxiety, a child’s predicted child development score is 34%. However, as levels of anxiety increase to median levels of anxiety, the predicted child development Z-score moves to 30%.

|**Table 11**<br>Linear predictio|ns of child development by<br>Average ECD for 0-3-year-<br>olds (CREDI Z-Score)<br>(95% CI) (n=1397)|maternal m<br>p-value|ental health<br>Average ECD for 3-6-year-olds (%<br>correct AIM-ECD) (95% CI) Direct<br>Assessment (n=635)|p-value|
|---|---|---|---|---|
|No depression,<br>anxiety or parenting<br>stress|-1.74 (-1.92, -1.55)|<.001|32.99% (25.85, 40.13)|<.001|
|Median depression<br>only|-1.47 (-1.68, -1.26)|<.001|24.91% (19.35, 30.47)|<.001|
|Median anxiety only|-2.02 (-2.24, -1.81)|<.001|21.35% (15.12, 27.59)|<.001|
|Median parenting<br>stress only|-1.78 (-1.92, -1.63)|<.001|38.70% (33.90, 43.50)|<.001|
|Median depression|||||
|& anxiety|-1.76 (-1.89, -1.64)|<.001|21.80% (18.43, 25.18)|<.001|
|Median depression|||||
|& parenting stress|-1.49 (-1.63, -1.36)|<.001|34.32% (30.67, 37.96)|<.001|
|Median anxiety &<br>parenting stress|-1.89 (-2.02, -1.75)|<.001|30.00% (26.22, 33.77)|<.001|
|Median depression,<br>anxiety & parenting<br>stress|-1.68 (-1.75,-1.60)|<.001|30.42% (28.37, 32.48)|<.001|

27
address this issue in a non-classical and non-linear measurement error model, allowing for a general relationship between measurement errors of GDP and nighttime light growth, statistical capacity, and geographic locations.<sup>5</sup> In this paper, we develop a novel approach to estimate the quarterly elasticity between VIIRS nighttime lights and GDP, employing the effective number of nighttime light observations to identify the relationship. The latter provides information to quantify the measurement errors in the lights data, which greatly simplifies the estimation.

In our applications, we construct a light-adjusted measure of economic activity to cross-check official estimates similar to Henderson, Storeygard, and Weil (2012) and Martinez (2021). We check whether measurement errors in the long-term, short-term, and during the COVID-19 pandemic vary systematically with country characteristics. In addition, we study the spatially heterogeneous impact of the pandemic and hence contribute to the large and growing empirical literature on the economic impact of COVID-19.<sup>6</sup>

# **3 Data**

## **3.1 Nighttime Lights**

Nighttime light data have changed over two different generations of satellites. The first generation of nighttime light data, based on the Operational Linescan Sensor (OLS) onboard the DMSP satellites, covers 1992 to 2013 at annual frequency. The second generation, based on the VIIRS Day Night Band (DNB) onboard the Joint Polar Orbiting Satellite System, covers April 2012 to the present at monthly frequency. Compared with DMSP-OLS, VIIRS nighttime lights have higher resolution and benefit from much improved low light imaging (Elvidge, Baugh, Zhizhin, and Hsu, 2013).<sup>7</sup> This paper focuses on VIIRS nighttime lights at quarterly frequency. To obtain quarterly nighttime lights, we use the monthly version of the cloud-free DNB composites, which excludes data impacted by stray light between April 2012 and December

> 5In the working paper version, Hu and Yao (2021) also provide an elasticity for VIIRS data. However, different from this paper, they aggregate the data to annual frequency, which leaves very few observations for their estimation and requires estimating the model in levels, which weakens the

> 6Miguel and Mobarak (2021) provide a good overview of this literature with a focus on low- and middle-income countries.

> 7For more details on different versions of VIIRS nighttime light data, please visit `https: //eogdata.mines.edu/products/vnl/` .

5
# RESOURCE ENDOWMENTS 1988

<!-- Start of picture text --> LABOR/LAND D64 LAND/CAPITAL<br>0.'1 ' f<br>NrC<br>0.'2 0.1<br>HON PAN<br>cpl mtx<br>USA<br>0 1<br>ES:<br>tO0's 0.'2 01 0 5 0.62<br><!-- End of picture text --> LABOR/CAPITAL FIGURE E.2
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA

|Country|Utility|Connection<br>charge|<br>Fixed<br>charge|**Industrial**<br>Number<br>of<br>blocks|Price<br>of 1st<br>block|**C**<br>Fixed<br>charge|**ommercial**<br>Number<br>of<br>blocks|<br>Price<br>of 1st<br>block|**Gove**<br>**i**<br>Fixed<br>charge|**rnment/p**<br>**nstitutions**<br>Number<br>of<br>blocks|**ublic**<br> <br>Price<br>of 1st<br>block|
|---|---|---|---|---|---|---|---|---|---|---|---|
|TZA3|MWSA||n|1|0.47|n|1|0.40|n|1|0.28|
|UGA|NWSC||n|1|1.05|n|1|1.05|n|3|0.80|
|ZAF1|Drakenstein||—|—|—|—|—|—|—|—|—|
|ZAF2|Tygerberg||y|1|0.82|y|1|0.82|y|1|0.82|
|ZAF3|eThekwini||y|1|0.88|y|1|0.88|y|1|0.88|
|ZAF4|Johannesburg||—|—|—|—|—|—|—|—|—|
|ZMB1|SWSC||—|—|—|—|—|—|—|—|—|
|ZMB2|LWSC||y||||3|0.37||||

_Source:_ AICD WSS Survey Database, 2007. — = data not available.

35
Figure 3.1 Incidence of Conflict in Côte d‟Ivoire: 2001 to 2006 <!-- Start of picture text --> Conflict events<br>250<br>194<br>200<br>145<br>150 120<br>100<br>54<br>32<br>50<br>11<br>0<br>2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> Source: Authors‟ calculation based on the ACLED database In this study we use two main data sources. The data on local incidences of conflict are taken from the Armed Conflict Location and Event Database (ACLED). The latter<sup>1</sup> as described by Raleigh, Hegre, and Carlson (2009) compiles exact locations, dates, and additional characteristics of individual battle events in states affected by civil war. The conflict data for Côte d‟Ivoire are available for the period from 1997 to 2010. The ACLED database on Côte d‟Ivoire reports a total number of 965 conflict events between 1998 and 2008. It tracks rebel activity and distinguishes between territorial transfers of military control from governments to rebel groups and vice versa. The conflict events are disaggregated into six categories: (i) Battle - government regains territory, (ii) Battle - no change of territory, (iii) Battles - rebels overtake territory, (iv) Non-violent activity by a conflict actor, (v) riots/protests, and (vi) Violence against civilians. In Figure 3.1, we show the total number of reported conflicts per year for the period starting from 2001 to 2006. The conflict intensity reached its peak between 2002 and 2004 with a total of 459 conflict events.

For empirical purposes, we disaggregate the conflict events into 50 departments, which are nested into 19 regions in Côte d‟Ivoire. To decipher the causes and consequences of conflict at the local level, many studies have used smaller geographical regions or artificial geographic grid-cells (without pertaining to any meaningful sub-national border) as the unit of analysis. Some researchers prefer to follow the grid-cell approach because the unit of analysis does not

> 1For more information go to the ACLED website at  http://www.prio.no/CSCW/Datasets/Armed-Conflict/Armed- <u>Conflict-Location-and-Event-Data/</u>

7
These findings are largely consistent with results recently published by the World Bank (2011). In particular, partial correlation analyses of the World Bank confirm that employment growth over a three year period is positively correlated with both the size of the firm and the location in a capital city or cities with over a million people. Furthermore, business environment constraints are perceived significantly higher in armed conflict areas when compared to low-conflict areas, which may affect firms’ growth performance. More generally, the report confirms that the entire region is struggling with instability and conflict: the business environment shows that political instability is one of the top three constraints in countries where the question was included in the underlying enterprise survey instruments of the data considered. Also, the report finds a positive relationship between employment expansion and innovation in a broad sense, which is typically linked to physical investment.

# **Summary**

South Asia Interim Enterprise Survey data of 2009/10 confirms that South Asia has performed very well – a substantial share of establishments expanded their employment, and expanding firms outweigh the share of establishments that shed employment. Despite the positive aggregate dynamics, this growth was far from uniform. There has been great variance across industries, size classes of firms, countries and regions.

At the sector and industry level, especially real Estate Renting & Business Activities, the transport industry, fabricated metal products, machinery and equipment, chemicals, rubber and plastics and construction performed well. At the country level, especially establishments in the Bhutan sample have increased employment at a significantly faster pace than other countries.

Employment generation is primarily driven by large companies with over 100 employees. This particularly holds for Sri Lanka, Bhutan, Afghanistan, Pakistan and Bangladesh. Similarly, firm age matters for employment generation. Firms older than 26 years represent 21% of the sample; however, they create approximately half of all jobs, since they are significantly larger than companies of other age classes. Young firms that are not older than five years account for 17% of all observations, and generated 16% of the net employment. The age of an establishment does not significantly affect employment growth – neither for shrinking, nor for fast expanding companies.

There is strong evidence that the severe conflicts have a strong impact on firm growth patterns. Especially urban areas in conflict countries could not participate in the growth of the region as a whole. Urban growth poles were the true bearers of employment expansion in South Asia.

## _Policy conclusions_

These results should be not be interpreted separately, and implications of the results of this paper should consider other findings. Hence, the implications on economic policies should be embedded in a broader analysis of the region, and get fine tuned in the country and sector environment.

20
to smaller transaction sizes requires human resources and capital, it appears that the lowest tercile in Benin are less involved in this activity than the other terciles.

**Table 27. Change in Transaction Sizea by Firm Assets**

||B|enin|||M|alawi||
|---|---|---|---|---|---|---|---|
|Terciles|Mean|S.d.|N|Terciles|Mean|S.d..|N|
|**Working capital**||||||||
|<$167|8.03|9.51|72|< $78|25.62|34.90|245|
|$167-$667|4.10|7.63|113|$78 - $244|34.70|50.71|245|
|>= $667|2.80|5.28|125|>= $244|15.82|47.33|244|
|**Human resources**||||||||
|<1 employee|1.93|2.66|36|0- 1 employee|28.03|43.52|523|
|I - 2 employees|6.32|9.02|114|> I employee|19.24|50.29|202|
|> 2 employees|3.90|6.92|162|Total|25.58|45.64|725|
|**Social Capital**||||||||
|<10|2.91|3.61|75|_< 15_|18.32|33.12|219|
|10-33|6.01|8.85|116|_15-36_|_25.51_|41.30|268|
|>= 33|4.18|7.75|124|>= 36|31.54|56.94|247|
|Gender||||||||
|Male|4.02|8.77|70|Male|23.31|43.49|466|
|Female|4.67|7.16|246|Female|29.02|48.47|268|
|Total|4.53|7.53|316|Total|25.39|45.43|734|

**a** Ratio of number of clients to number of suppliers.

This can be explained in that the smallest trading firms may typically buy in retail quantities and sell in retail quantities. Finally, contrary to expectations, there appears to be relatively little distinction between male and female traders in this activity, suggesting that retail activity is not exclusive to women.

# **6. Market Performance**

# **_6.1 Margins_**

**Gross Margins.** The gross margin-the difference between the value of sales and purchasesvaries significantly between the two countries: it is 2.4 times higher in Malawi than in Benin (t-value of 4.83). Median margins differ by the same order of magnitude. Since total sales do not differ markedly between the two countries, higher gross margins in Malawi must come from a

larger difference between buying and selling price.

46
female-owned informal businesses are more likely to be operated from within the household. The concentration of females into sectors with limited technology and economies of scale, such as retail, likely partly contributes to the differences in the performance of female vs. male-owned businesses (World Bank 2012; Essers et al., 2021).

Informal businesses constitute an important source of livelihood for their owners. In the existing literature, these businesses are often viewed as side activities run to supplement income from employment elsewhere. However, this does not appear to be the case in the Informal Sector Enterprise Survey data. Only about 12% of informal business owners have employment with a contract elsewhere, and about a third of them were unemployed before starting the business. In formal micro-enterprises, by contrast, 33% of owners currently work elsewhere while 13% were unemployed prior to starting or acquiring the business. About 66% of informal owners note that they started their businesses primarily because they could not find a better source of livelihood.<sup>12</sup> Aga et al. (2020) analyze the income of households and main owners that comes from informal business activities and shed light on the reliance on such sources in an economic crisis, such as the one induced by the coronavirus. Importantly, for about 70% of the informal businesses, the owners are the main income earners for their household, ranging from as high as 86% in Zimbabwe to 53% in Lao PDR. Female owners are much less likely to be the primary earners: 50% compared with 85% for male informal business owners. By comparison, 83% of formal micro-enterprises are owned by the household’s primary income earner. The average informal business owner lives in a household with five members, ranging from an average of 4.6 in Zimbabwe to 6 in Zambia.

Informal Sector Enterprise Surveys also enable the study of the link between the informal sector and migration that has been extensively investigated (e.g., Harris and Todaro, 1970; Mazumdar, 1976; Banerjee, 1983; Elgina and Oyvatb, 2013). Informal businesses are often viewed in the literature as temporary means of subsistence for new rural migrants to the city as they seek employment in a modern sector of the economy. However, this view of the informal sector as a mere waypoint on the path to formal sector employment has conflicted with decades of persistently low rates of formalization despite rising urbanization (Kanbur, 2017). Informal Sector Enterprise

> 12 Interestingly, about 72% of micro-enterprises were started by owners primarily due to the lack of adequate income from elsewhere. On the other hand, micro-enterprise owners are more likely than informal owners to have paid employment elsewhere. This contrasts sharply with the image of small businesses owners in developed countries who voluntarily quit their salaried jobs in the search of more independence and outlets for entrepreneurial self-expression (Watson et al, 1998).

13
Policy Research Working Paper 8242

# **Abstract**

This paper analyzes data on female and male entrepreneurship that were collected by the World Bank Group’s Entrepreneurship Database. Recognizing the importance of a differentiated approach to entrepreneurship in terms of legal entities, the data on female and male business owners are collected at the level of limited liability companies and sole proprietorships. Forty-four of the 143 economies that participated in the Entrepreneurship project provided some sex-disaggregated data for 2016. The paper finds that the gender gap in business ownership remains high in many economies around the world. In the majority of the analyzed economies, less than one-third of new limited liability company owners are women. Although sole proprietorships are more frequently used by female entrepreneurs, only three economies have similar or equal number of women business owners relative to men. The gap in female entrepreneurship is especially apparent in low-income economies, where women are much less likely than men to start a new business. The paper also provides new insights into the relationship between female entrepreneurship and various institutional factors, including women’s financial inclusion, the gender gap in education, and legal rights disparities. The analysis suggests a need to expand the collection of sex-disaggregated data, to trace the economies’ progress in narrowing the existing gender gap in entrepreneurship.

This paper is a product of the Global Indicators Group, Development Economics. 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 authors may be contacted at fmeunier@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