rafmacalaba/datause-encoder-data huggingface dataset:
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25
For firm-level controls, all taken from Enterprise Surveys, we try and capture the presence of women networks and mentorship in firms (inverse of men dominated corporate culture) by accounting for the presence of women owners and the proportion of workers at the firm that are women. For women owners, we use a dummy variable equal to 1 if the firm has one or more woman owner and 0 otherwise ( _Woman owner_ ). For women workers, we define this as the proportion of all full-time permanent workers at the firm that are women ( _Women workers_ ). Although we do not have any direct measures of mentorship, we believe that the presence of more women workers could be a proxy for women dominated firms, and women owners are more likely to act as mentors for future women managers.

We also control for the presence of formal training which is identified in the conceptual model as an important determinant of the presence of women in managerial positions, especially when there are men-dominated informal networks in the firm. To this end, we use a dummy variable equal to 1 if the firm offered formal training to its employees (during the last year) and 0 otherwise ( _Formal training_ ).

As mentioned above, segregation of men and women by occupations and sectors is observed and thus we control for dummy variables indicating the industry to which the firm belongs (Industry fixed effects). There are 11 industries – textiles, leather, garments, food, metals & machinery, electronics, chemicals & pharmaceuticals, wood & furniture, non-metallic and plastic materials, auto & auto components, and other manufacturing.

Next, we control for firm-size as measured by the (log of) total number of full-time employees working at the firm at the end of the previous fiscal year ( _Firm size_ ). Apart from the direct effect of firm-size on women managers discussed above, firm-size can serve an important proxy measure for a number of firm attributes such as tendency to innovate, exporting activity, firm-efficiency and growth (Zoltan J. Acs and David Audretsch 1989; Wesley M. Cohen and 15
COVID-19 crisis for El Salvador and Georgia, respectively. Details about the information contained in COV-ES data about government support are given in Section 4.

# **3. Methods**

To estimate the effect of the government support, consider the following specification: ′ oo,ii ii ii ii where is an outcome variable of firm YY = θθSS, is a dummy variable indicating whether the firm + ββXX + εε received any local or national government support, and oo is the set of observed characteristics of YY ii SS ii firm . This specification is not suitable for consistent estimation of for two main reasons. First, XX for most of the support measures, firms need to go through an application process to get it or take ii θθ, some action for this purpose. This creates selection bias. The selection bias will be positive if going through the application process to obtain the support is complex, and only better-performing firms can accomplish it. In this case, the set of firms that obtained support may have inherent features that both made them obtain support and perform better in terms of outcome variable of interest. Consequently, the estimate of would be higher than the true effect of policy (positive selection bias). ii θθ SS In the context of the effectiveness of government support during the crisis, the selection bias can also be negative. It is expected that the firms that were more adversely affected by the pandemic, e.g., lost a larger share of their revenues or experienced deeper drop in liquidity, would try harder to obtain support from the government by working harder to make themselves aware of its existence and trying their best to go through the steps needed to obtain it. While support itself may have eligibility criteria such as firm size, even within the same sized firms, those hit harder are perhaps more likely to obtain it. There is also an element of solidarity contributing to this selection bias. In particular, the firms that are eligible for support may abstain from obtaining it if they do not need it as much, understanding that governments may have limited capacity to reach those that truly need this support. We anticipate a rather strong negative selection bias.

The second main source of our concerns about the above simple specification is omitted variable bias. There may be some unobserved variables that are correlated with and also determine . For example, political connections can help some firms get government support ii SS oo,ii YY 8
fall of the ICU in 2006 was followed by a period of relative stability as a weak TFG left the central and northern regions of Somalia under control of local warlords, essentially creating an enabling environment for piracy to strive in Central Somalia and Puntland.<sup>1</sup> Piracy expanded till 2011 with a total of 246 reported incidents in that year (Figure 1). Within a few months, the Indian Ocean had overtaken other regions as the world’s most dangerous for seafarers.

<!-- Start of picture text --> 2005 2006 2007 2008 2009 2010 2011 2012<br>Year<br>Somalia Indonesia<br>West Africa Caribbean<br>Others<br>250<br>200<br>150<br>100<br>Number of Attacks<br>50<br>0<br><!-- End of picture text --> Figure 1: Pirate attacks off Somalia and in the World Note: The figure plots the number of attacks off Somalia and other regions with piracy problems. Data source: International Maritime Bureau.

The collapse of piracy after 2011 was as sudden as its rise. IMB attributes 15 piracy attacks to Somali pirates in 2013, 11 in 2014, and none in the first two quarters of 2015 (International Maritime Bureau 2015). As of June 2015, a total of 1,099 attacks on the high seas have been attributed to Somali pirates. Over the 2005-2012 period, pirates of Somalia attacked vessels as far south as the Mozambique Channel and went north all the way to the strait of Hormuz; their catchment area moreover extended west to the south of the

> 1The capital region of Mogadishu and southern Somalia were however the subject of attacks from the insurgent group Al-Shabaab, originally the armed wing of the then-defunct ICU. Few pirate attacks overall originated from the pirate stronghold of Kismayo in Southern Somalia.

6
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34
## **Box 7: Snapshot of Public Enterprise sector in Sri Lanka**

The state-owned or public enterprise sector in Sri Lanka has been an important part of the economic policy debate in the country for the last thirty years. Before the first wave of economic liberalization reforms in 1977, over 60 percent of manufacturing was in the public sector and accounted for half of all manufacturing employment, while public entities accounted for nearly 90 percent of all imports and 30 percent of exports.<sup>14</sup> Post-1977 reforms greatly reduced these numbers, but public enterprises (PEs) are still an important part of the economic landscape.

The government had a controlling interest in 107 public enterprises in 2009. These PEs are involved in many commercial activities, including finance (26), infrastructure (11), agriculture (9), plantations (11), construction and manufacturing (16) services (16) and healthcare (6). Taken together, the turnover in PEs amounted to almost 17 percent of GDP in 2009. However, this may underestimate the true extent of the influence on economic activity as, for example, the two state-owned banks account for over 50 percent of the assets of the banking system. Moreover, government has invested directly or indirectly in 52 PE subsidiary companies and has golden share holding rights in 23 regional plantation companies. The largest SOEs are the CEB and CPC.

In addition to hardening budgets, an important item on the PE agenda (Box 7 provides a snapshot of the public enterprise sector) is to develop a strategic vision regarding their role vis-àvis the private sector, which as noted above, is something private investors are seeking. The Government has recently taken over management of Sri Lankan Airlines and Sri Lanka telecom, taken ownership of Sri Lanka Insurance Corporation and Shell Gas, and land ownership of Lanka Marines Services and Waters Edge Ltd. It is important clarify the state of play regarding these specific companies and related future plans.

# **_d. Infrastructure and Human Capital_**

Public investments in infrastructure and human capital need to be linked to Sri Lanka‘s anticipated future sources of growth. At the macroeconomic level (and as captured in the results of the simulation exercise), long run per capita growth is driven by TFP growth, growth in the labor force and human capital and capital accumulation as influenced by savings, domestic and foreign. At a more micro level, growth will be driven by the investment decisions of private actors in agriculture, manufacturing and services. One view expressed during interviews is that Sri Lanka is too small to permit manufacturing to become a significant driver of long-run growth. Another view commonly expressed was that there is a shortage of low-skilled workers; on the

> 14 World Bank (2004).

30
as a VAT payer (SARS,2003). Some services provided within the financial sector are also exempt. (International VAT Monitor,1995:376).

In this paper, we take the economic and tax structure of the SAM, including the VAT and its exceptions, as the basis for the CGE model and we examine possible tax reforms that will improve welfare impact.

# **2.2 Specification of a VAT with Rebates on Intermediate Inputs**

Rather than treat consumption tax payments as a retail sales tax, we specify a European-style value added tax with rebates for intermediate input and investment purchases. With this treatment, there is no cascading effect on prices of taxes on intermediate goods.

We assume the VAT is administered using the “invoice method.” All transactions are taxed at a fixed proportional rate regardless of whether they are final or intermediate transactions. Firms can deduct taxes paid on intermediate inputs and that tax amount is reported on the invoices for intermediates. Import sales are subject to a VAT, export sales are not.

To calibrate the model, we assume the observed VAT tax revenue in the SAM is the gross payment by sector.<sup>8</sup> Producers must pay the VAT on retail sales and can collect a rebate on taxes paid by intermediate input use. For each production activity, we compute the rebate on intermediate expenditure using data on input-output relationships. We assume a Leontief production structure with fixed input-output shares defining intermediate demand.

The rebate to activity, A, is based on total intermediate input use: _REBATE_ ( _A_ ) = ∑ _tvat_ ( _C_ ) ⋅ _PQ_ ( _C_ ) ⋅ _QINT_ ( _C_ , _A_ ) _c_ where: tvat(C) is the value added tax by commodity sales PQ(C) is price of commodity c QINT(C, A) is the quantity of intermediate commodity, c, purchased by activity, a The price of the aggregate intermediate input now includes the rebate per unit of aggregate intermediate input: _PINTA_ ( _A_ ) = ∑ _PQ_ ( _C_ ) ⋅ _ica_ ( _C_ , _A_ ) − _REBATE_ ( _A_ /) _QINTA_ ( _A_ ) _c_ where: PINTA(A) is the price of the intermediate aggregate input PQ(C) is the price of the composite commodity ica(C,A) is the intermediate input c per unit of aggregate intermediate QINTA(A) is the quantity of aggregate intermediate input 8 We use the effective tax rates based on tax collection data. The rates differ by sector and differ from the reported statutory rate of 14% due to sector aggregation, the treatment of the informal sector and/or measurement error.

7
countries. Their student population consists of a fairly narrow age group. The next indicator, education attainment rate, captures such differences in access to education.

<u>Education Attainment Rate</u> gauges the educational attainment of the population aged 2534 (Figure 11).

**Figure 11 Education Attainments Rates**

<!-- Start of picture text --> United States<br>Canada<br>Netherlands<br>Australia<br>United<br>Ireland<br>Sweden<br>Finland<br>High Income<br>France<br>Belgium<br>Average<br>Germany<br>Italy<br>Colombia<br>LAC Average<br>Mexico<br>Brazil<br>Peru<br>Austria<br>0% 5% 10% 15% 20% 25% 30% 35%<br>Education Attainment Rates (%)<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries As expected, attainment rate of high-income countries exceed those of the four LAC countries. The four LAC countries and Austria have the lowest attainment rates. Generally speaking, the predominantly-Anglophone countries have a higher educational attainment (26 percent) compared to LAC countries (8 percent) and Continental Europe (16 percent). Educational attainment differ only marginally among the four LAC countries, Colombia has the highest attainment rate in this study with 11 percent among students aged 25-34 at tertiary education. Mexico and Brazil follow with eight percent of attainment. The last country is Peru with 7 percent.

Although the participation rates of the four countries differ by a considerable distance, the attainment rates are found to be in the same range. The difference between participation and attainment could be explained by three factors: (i) a difference in participation in tertiary education of persons outside the age-range captured by the participation rate in this study; (ii) higher internal efficiency of a tertiary education system leading to lower drop out and higher attainment rates through remedial education, student financing and the like; and (iii) a recent increase in participation rates will take at least 4 years to start impacting the attainment rate.

23
_Annex 2: Deriving village-level program saturation and unit values from national data and robustness checks on unit value results_ We explore whether the association between prices and program saturation is detectable beyond our experimental sample by combining a number of data sources to derive estimates of villagelevel program saturation and of unit values (which we use as proxies for prices) for various food categories. The following describes the steps we take to generate these variables.

# <u>Beneficiary and Census data</u>

Program administrative data, reported by provincial offices of the Department of Social Welfare, provide village-level information on the number of beneficiary households enrolled in _Pantawid_ in each year from 2009 to 2015 (Bvy). In order-to estimate village-level program saturation, we scale these by an estimate of the total number of households in each village. To generate this estimate we combine data from the Census for 2015 and from the Family Income and Expenditure Survey (FIES) for 2009, 2012, and 2015. The census provides an estimate of the number of individuals in each village for 2015 (Pv2015); the FIES provides an estimate of the average number of individuals per household in each village in each year (m̅vy); and the ratio of these is our estimate of households per village (Pv2015 / m̅vy). Dividing the program data on beneficiary households per village by the estimated total number of households per village yields our estimate of village- and year-specific program saturation:

# <u>Unit-values (or price proxies)</u>

We do not observe prices at the level of each village. Instead, we use unit values of food items derived from the 2009, 2012, and 2015 rounds of the Philippine national household budget survey (the FIES). There are 93 food items common across all three rounds of FIES. We derive the household-level unit value for each food item by dividing household expenditure on that item by the quantity consumed. The village-level unit value is defined as the median unit value observed for the village. We aggregate the 93 items into 15 broad food categories provided by FIES: regular rice; other rice; roots and tubers; fresh eggs; processed eggs; fresh fish; processed 70
# **-** **<u>Table A.5 :  Using Distance to Least Cost GQ Network as Instrument</u>**

||(1)|(3)|(5)|(7)|
|---|---|---|---|---|
|||||ln(Land Sale,|
|Dep. var.|ln(Landgini, 2012)|ln(Landless, 2012)|ln(Tech use, 2012)|2012)|
|**ln(MA1996)**|**0.188*****|**0.653*****|**0.334*****|**1.983**|
||**(0.06)**|**(0.17)**|**(0.10)**|**(2.05)**|
|ln(Slope)|-0.041|-0.078|-0.074|-0.607|
||(0.03)|(0.10)|(0.05)|(1.12)|
|Elevation|-0.169|0.026|0.264|3.094|
||(0.14)|(0.42)|(0.25)|(5.19)|
|Crop Suitability|-0.003**|-0.008**|-0.001|-0.010|
||(0.00)|(0.00)|(0.00)|(0.05)|
|ln(Rain)|0.047|0.137|0.008|-1.237|
||(0.05)|(0.16)|(0.09)|(2.15)|
|ln(Temperature)|-0.326|0.069|0.672|-0.343|
||(0.37)|(1.03)|(0.64)|(14.30)|
|ln(Rain CV)|0.117|0.409|0.414*|10.793*|
||(0.13)|(0.40)|(0.23)|(5.60)|
|ln(Temp. seasonality)|-0.037|-0.083|-0.098|-4.600|
||(0.07)|(0.21)|(0.12)|(3.21)|
|Pop. density, 1961|0.019***|0.019|-0.009|-0.165|
||(0.01)|(0.02)|(0.01)|(0.33)|
|Constant|-1.426|-12.200*|-10.039**|-27.137|
||(2.18)|(6.32)|(3.95)|(87.85)|
|First Stage|||||
|Weak Identification|24.9|28.46|28.46|28.46|
||0.0000|0.0000|0.0000|0.0000|
|Observations|200|212|212|212|
|R-squared|0.333|0.365|0.524|0.162|

Estimated by 2SLS using predict market access as the instrumental variable. Predicted market access is estimated from a zero stage including railway length, distance to least cost network, and the IVs chosen by Lasso (distance to GQ x Pop. density, 1961; and railway length x Crop suitability). All columns are estimated by 2SLS and include state fixed effects. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
# Financial Development and Innovation in Small Firms

Siddharth Sharma<sup>∗†</sup> JEL Classification: G21; O16, O30

> ∗International Finance Corporation. E-mail: SSharma1@ifc.org. All views expressed are my own.

> †I am grateful to Mohammad Amin, Simeon Djankov, Ernesto Lopez-Cordova, Enrique Seira and participants at World Bank/IFC seminars for helpful suggestions.

1
<!-- Start of picture text --> Figure 2. Population Growth and Population Share of<br>Taiping Regime versus All Sample Chinese Prefectures<br>1,200 40%<br>Taiping Rebellion<br>1,100 35%<br>1,000<br>30%<br>900<br>25%<br>800<br>20%<br>700<br>15%<br>600<br>10%<br>500<br>400 5%<br>300 0%<br>1820 1851 1880 1910 1953 1982 2000<br>Total Population (million) Population Share of TPR (%)<br>Notes: The graph is based on the population panel (1820-2000), which consist of 266 out of the 317 prefectures<br>in the Qing dynasty in 1820 in China. We exclude prefectures in minority areas of China that were underdeveloped<br>and less populated before 1953, that is, Xinjiang, Tibet, Qinghai, and the north of the Great Wall (Inner Mongolia,<br><!-- End of picture text --> Notes: The graph is based on the population panel (1820-2000), which consist of 266 out of the 317 prefectures in the Qing dynasty in 1820 in China. We exclude prefectures in minority areas of China that were underdeveloped and less populated before 1953, that is, Xinjiang, Tibet, Qinghai, and the north of the Great Wall (Inner Mongolia, Outer Mongolia); we similarly exclude three provinces in Northeast of China (Fengtian, Jilin, and Heilongjiang).

45