rafmacalaba/datause-encoder-data huggingface dataset:
- 28 -

sector only, may have to be widened and strengthened. The credit information in all se_..tors have to be pooled in order to have a good early warning system of problem areas. There are substantial economies of scale to be gained in coordination and pooling of resources in the supervision of financial institutions;

- (7): Traditional banking laws and regulations must also change with the times. As technology evolves, the transmission of shocks within the system accelerates through the payments meclanism, while the scope for insider trading, and white collar bank fraud and theft increases rapidly. The banking laws must therefore change to give scope for greater competition in financial products, while tightening prudenti&l regulation against abuses of the system. At the same time, laws relating to bankruptcy (exit of enterprises) should be reformed to expedite and reduce the costs of bank restructuring. In June 1989, a new Banking and Financial Institutions Bill was tabled before Parliament to address these issues, including powers to the Central Bank to act quickly in future instances of financial distress in all sectors of the financial system;

- (8): With greater internationalization, regulatory supervision over financial institutions not only have to be coordinated among sectors within an economy, but also internationally. The work of the Basle Committee is to be commended in this area;

- (9): In the last analysis, after capital and proper control procedures have been put in place, the key to sound and healthy financial institutions still lies fundamentally in good quality management. Improved internal controls and procedures, penalties against breaches of the law, code of ethics and Chinese walls are still insufficient to deter insider trading, conflict of interests and lending to interested parties, where large sums of money are involved. The temptations in financial institutions are great. Fraud in management must be detected promptly, and must be cut out early so that better procedures and professional competent and trustworthy management can be put into place as soon as possible;

- (10): Finally, the bank restructuring exercise could only be achieved against the background of firm structural adjustment in the macroeconomic environment. The twin beneficial effects of fiscal retrenchment and depreciation of the currency brought a broad-based economic recovery, which assisted the bank restructuring process. Without the recovery of underlying enterprise profitability, a high level of national savings and adequate foreign exchange reserves to cushion the system against shocks, the bank restructuring process would have been much more protracted and difficult. The political will and financial discipline to address the looming twin deficits in the balance of payments and the public sector were vital ingredients of the recovery program.
**Figure 4b: Income per capita (net sales) from millet and sorghum production**

<!-- Start of picture text --> 40000<br>20000<br>0<br>10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5<br>-20000<br>Rural<br>-40000<br>National<br>Urban<br>-60000<br>-80000<br>-100000<br>Log of per eq adult expenditure<br>National Urban Rural<br>Net Sales<br><!-- End of picture text --> Source: Authors’ estimation using ELIM 2006.

**Figure 4c: Income per capita (net sales) from corn production per equivalent adult**

<!-- Start of picture text --> 100000<br>80000<br>60000<br>Rural<br>40000<br>Urban<br>20000<br>National<br>0<br>10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5<br>-20000<br>Log of per eq adult expenditure<br>National Urban Rural<br>Net Sales<br><!-- End of picture text --> Source: Authors’ estimation using ELIM 2006.
glucose, are much easier to be fermented to ethanol than pentoses, e.g. xylose, using the current technologies. Details will be discussed later in this paper.

**Table 1.** Contents of cellulose, hemicellulose, and lignin in common lignocellulosic materials.

|**Lignocellulosic Materials**|**Cellullose, %**|**Hemicellulose, %**|**Lignin, %**|
|---|---|---|---|
|Hardwoods stems|40-55|24-40|18-25|
|Softwood stems|45-50|25-35|25-35|
|Switch grass|45|31.4|12-20|
|_Miscanthus_|40|18|25|
|Coastal Bermuda grass|25|35.7|9-18|
|Corn stover|35-40|17-35|7-18|
|Wheat straw|30|50|15|
|Rice straw|36-47|19-25|10-24|
|Cotton seed hairs|80-95|5-20|0|
|Newspaper|40-55|25-40|18-30|
|White paper|85-99|0|0-15|

Sources: Reshamwala et al., 1995; Cheung and Anderson, 1995; Boopathy, 1998; Dewes and Hünsche, 1998; Sorensen et al., 2008.

# **2.2.2. Grasses**

Herbaceous lignocellulosic materials are among the high yield biomass in the world. The promising candidates for biofuel production include switchgrass, Bermudagrass, and _Miscanthus_ . Switchgrass is a warm-season perennial grass native to North and Central Americas (Subbendieck et al., 1997). It can also be found in Africa and South America. It can grow to a height of more than 3 m and have a root depth of more than 3.5 m (Weaver, 1968). Switchgrass can grow on a wide variety of soils including marginal land areas with limited fertilizer requirement. Its yield varies with the growing conditions, but the annual biomass production is generally 6 – 15 dry tons per acre. _Miscanthus_ is originally a native grass in Southeast Asia. It was introduced to Europe as an ornamental garden grass. It is now considered as a promising energy crop in Asia and Europe. _Miscanthus_ can reach a height of over 2 m during the establishment year and up to 4 m in subsequent years in Europe and as tall as 7 to 10 m in China, with root penetration of over 1 m into the soil (El Bassam, 1998). Similar to switchgrass, the annual production rate of _Miscanthus_ can be 6 – 15 dry tons per acre. It grows well in marginal 12
estimate the average treatment effect. In contrast, 2SLS and MLE will estimate local average treatment effects (Imbens and Angrist, 1994). Specifically, they will estimate the impact of registering for taxes on firms for which distance to the tax office influences their formality status. These are firms for which distance makes a difference to whether they know how to register, or which are enough at the margin of registering that a small change in the travel costs of registering will influence their registration decision.

There are good reasons to believe that the reasons for being informal vary with firm size, since many of the costs and benefits of becoming formal will vary with firm size. To explore the possibility of different effects of a NIT, we use propensity-score matching to estimate the impact of having a NIT for different firm size groupings.<sup>9</sup> The same set of variables as were used in the OLS regressions are used for matching purposes. Nearest neighbor matching was used to estimate average treatment effects matching firms to their three nearest neighbors<sup>10</sup> .

Table 6 reports the results of propensity score matching. The first two columns report the results of matching, with and without controls for firm size. After controlling for firm size, the average treatment effect of a NIT is a 20.9 percent increase in profits – larger than the OLS coefficient, but smaller than the MLE/2SLS coefficients. Again, with heterogeneous treatment effects the propensity-score matching treatment effect will differ from the average treatment effect estimated by OLS and the local average treatment effect estimated by MLE and 2SLS. We then split firms into three size groupings, based on terciles of capital stock and groupings of 0 to 1, 2 to 5, and 6 and above workers. Although the smaller sample sizes when we split the sample this way lead to some statistically insignificant results, the point estimates are suggestive of heterogeneity of treatment effects. Having a NIT is estimated to _lower_ profits for very small firms by 61.6

> 9 One could also estimate the treatment effects regressions for subsamples. However, with our rich set of controls and smaller samples, convergence of the MLE was a problem for these restricted samples. The propensity-score matching estimators enable the large number of covariates to be employed more efficiently, and thus we use this method to investigate heterogeneity of effects. 10 Matching was carried out using the nnmatch command in STATA. See Abadie, Drukker, Herr and Imbens (2004).

- 17 -
implying that richer states tend to have higher levels of social infrastructure. Higher GSDP raises the money available for social infrastructure spending, leading to greater infrastructure development in these states (Randolph et al.,1999; Arimah, 2005; Vinogradova et al., 2015). However, the GSDP coefficients under the social infrastructure case are smaller than those in the physical infrastructure case thereby implying that economic development has more pronounced impacts on physical infrastructure development than social infrastructure. This may be due to the weak demand factor and priorities of the state government on providing basic physical infrastructure facilities. When GSDP was replaced by per capita GSDP in our model we obtain similar results. Thus, states with higher GSDP or per capita GSDP will have higher social infrastructure development.

In the same vein, investment is found to have a positive and significant effect on social infrastructure development. Greater investment in social infrastructure, such as education, health, housing, and sanitation, leads to a higher supply of such infrastructure services. Additionally, investment in physical and social infrastructure provisions could promote demand for these provisions through the multiplier effect (Vinogradova et al., 2015). The results are in line with the findings in previous studies such as Dao (2008), Cerra et al. (2017), and Vinogradova et al. (2015). More importantly, the services sector GSDP (SSER) also positively affects social infrastructure. An increase in the services sector GDP increases demand for them and induces the supply of social infrastructure. States such as Kerala, Maharashtra, Himachal Pradesh, and Karnataka have higher services GDP. These states have also performed better in social infrastructure development, indicating the positive relationship between the two.

**Table 6: Determinants of Social Infrastructure**

|**_Variables_**|**_Model 1_**|**_Model_**<br>**_2_**|**_Model 3_**|**_Model 4_**|**_Model 5_**|**_Model 6_**|**_Model 7_**|**_Model 8_**|
|---|---|---|---|---|---|---|---|---|
|**_Constant_**|-2.65*<br>(-2.53)|-2.57*<br>(-2.70)|-0.54<br>(-0.57)|-0.01<br>(-0.05)|-2.53*<br>(-2.32)|-1.11<br>(-0.42)|-1.77*<br>(-2.47)|-0.24<br>(-1.08)|
|**_SINFRAt-1_**|<br>0.57**<br>(6.87)|<br>0.43**<br>(3.74)|<br>0.36**<br>(5.97)|<br>0.56**<br>(5.88)|<br>0.38**<br>(7.53)|<br>0.26**<br>(4.88)|<br>0.28**<br>(4.88)|<br>0.34**<br>(5.08)|
|**_GSDP_**|0.45*<br>(2.88)||0.40**<br>(2.97)|||0.11**<br>(2.07)|-|0.06*<br>(2.02)|
|**_PGSD_**|<br>-|0.72**<br>(3.06)||0.44*<br>(2.44)|0.62**<br>(4.88)|<br>-|0.41*<br>(2.34)|<br>-|
|**_INV_**|0.005**<br>(4.54)|<br>0.004**<br>(3.52)|0.002*<br>(2.12)|<br>0.002*<br>(2.16)|<br>-|-|<br>0.003*<br>(2.66)|0.004**<br>(4.84)|
|**_CEXP_**|<br>-|<br>-||<br>-|0.002*<br>(2.53)|0.003**<br>(2.18)|||
|**_SSER_**|0.005*<br>(2.36)|0.007*<br>(2.57)|0.006**<br>(3.61)|0.01*<br>(2.91)|0.004*<br>(2.53)|0.005<br>(1.10)|0.012**<br>(5.23)|0.01**<br>(4.28)|

24
# **_Choosing sectoral price deflators_**

An official price deflator for sectoral gross fixed capital formation is not available for the whole of the period since 1952. Even at the aggregate level, a deflator is only available for the period 1952 to 2004. Since then the NBS has only published a deflator for the sum of fixed capital and inventory investment. On the other hand, it has published a national price index for fixed asset investment continuously since 1989. This series has sub-indices for three separate assets: construction, equipment and other assets. Given the difficultly in interpreting whether such investment should be included in GFCF, the price index for _other assets_ has been ignored.

The price trends for equipment and construction have differed markedly. Between 1995 and 2016, equipment prices fell by 1.1% annually, while construction prices rose by 2.6%. Such large differences in the movement of prices mean that a price index using the base-period expenditure shares for equipment and construction will gradually resemble an index based only on the price of construction expenditure. It will thus understate the extent of differences in the movement of prices between the four sectors of capital formation that should reflect the different shares of construction and equipment in each sector.

There are several methods available to overcome this so-called substitution bias. One method is to change the weights annually, another is to use a Törnqvist price index, which is calculated as the weighted geometric average of the price relatives using arithmetic averages of the value shares in adjacent years. Data for the share of construction and equipment in fixed asset investment exist since 1981. They show a stable trend in the share of construction in national total fixed asset investment over the period 1981 to 2007. After that date, the share of construction reflects the increase in the share of construction intensive sectors in total FAI, as the shares by sector have remained stable. It is only possible to calculate the asset structure of investment by sector in urban areas (which account for almost all FAI) since 2004.

The split the two types of asset (construction and equipment) varies considerably between the four sectors of investment. At the two extremes, construction assets account for almost all real estate investment while equipment accounts for nearly 40% of business sector investment in 2010. Moreover, the value share of different types of assets in total fixed asset investment has remained quite stable over time. Consequently, the price index for the fixed asset investment of the four sectors has been calculated by assuming the shares of different asset types in investment by sector have remained constant since 1981 at their 2010 levels (Table 7).

**Table 7 Share of type of fixed asset by economic sector**

2010

||Construction|Equipment|
|---|---|---|
|Business|0.602|0.398|
|Infrastructure|0.938|0.062|
|Government|0.873|0.127|
|Real Estate|0.981|0.019|
|Overall|0.742|0.258|

_Source_ : NBS 14
managers to make choices that improve the firm's performance (Jensen (1986), (1989) and Wruck (1990)). However, a high leverage may also weaken the incentive to pursue efficiency, since borrowers' relative stake in the firm is smaller. 15 Finally, to test whether the maturity structure of debt has an impact on firms' investment policies, we estimate a simple augmented accelerator model, with the investment rate as dependent variable and the rate of growth in sales, the ratio of cash flow to total assets, leverage, and the maturity structure of debt as regressors. We allow the maturity variable to enter both contemporaneusly and lagged once. A significant cash flow variable is likely to capture the importance of financing constraints and may also reflect the fact that contemporary cash flow acts as a proxy for expected profitability.'<sup>6</sup> Also, higher leverage should lead to a greater premium on debt and then have a negative impact on investment, if the rate of interest is an increasing function of firm's leverage because of the existence of agency and/or financial distress costs.1<sup>7</sup> As mentioned in Section 2, the prediction on the relation between investment and debt maturity is instead ambiguous. However, the common wisdom is that the availability of long term finance should stimulate investment in fixed capital by reducing the fear of liquidation and by allowing firms to make long term plans. Moreover, if the interest

**15** The **adverse consequences ofthe conflict between borrowers and lenders is emphasized in Jensen and Meckling (1976), Myers (1977),** Stulz **(1990).**

16 **See Fazzari, Hubbard and Petersen** (1988) **for** an **empirical investigation of cash flow and financing constraints.** 17 **See Lang, Ofek and Stulz (1995) for an empirical analysis using US firms panel data.** 23
# **Figure 7. Differences in Investment Behavior: Connected versus Unconnected Domestic Listed Firms**

This figure shows differences in investment behavior (for capital expenditures, acquisitions, research & development, and cash & short-term investments) between connected and unconnected firms in the propensity score matched (PSM) sample. The figure plots, for each variable, difference-in-differences (DiD) coefficients (and their 90% confidence intervals) obtained by estimating Equation (1). The DiD coefficients show, for each year, the average differences for each dependent variable between connected and unconnected firms (relative to the 2012 difference). The 2012 coefficients show the differences between connected and unconnected firms in 2012. The shaded areas capture the implementation of the Stock Connect program and the incorporation of domestic listed stocks into the MSCI Emerging Markets Index. Table 9 reports the coefficients shown in this figure.

## Capex over 2012 Assets

Acquisitions over 2012 Assets <!-- Start of picture text --> 0.12 0.08<br>Stock Stock<br>Connect Connect<br>0.10<br>0.06<br>0.08 MSCI<br>Incorporation<br>0.04<br>0.06<br>MSCI<br>0.04<br>Incorporation 0.02<br>0.02<br>0.00<br>0.00<br>-0.02 -0.02<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>DiD Coefficient DiD Coefficient<br><!-- End of picture text --> Research & Development over 2012 Assets Cash & Short-term Investments over 2012 Assets <!-- Start of picture text --> 0.08 0.50<br>0.40<br>0.06<br>0.30<br>0.04<br>0.20<br>0.02<br>0.10<br>0.00<br>0.00<br>-0.02 -0.10<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>DiD Coefficient DiD Coefficient<br><!-- End of picture text --> <!-- Start of picture text --> 0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>-0.10<br>2018 2019 2020 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>DiD Coefficient<br><!-- End of picture text --> DiD Connected vs Unconnecte ~~d~~ Confidence Interval (90% CI)
# **1. Introduction**

The COVID-19 pandemic is testing global food and social protection systems at an unprecedented scale. The spread of the pandemic is disrupting food systems and undermining the food and nutrition security of households (Amjath-Babu et al., 2020; Barrett, 2020; Béné, 2020; Devereux et al., 2020; GAIN, 2020; Reardon et al., 2020b; Swinnen, 2020; Torero, 2020). There is concern that developing countries with poor health care and more limited social protection systems may be especially badly affected. Recent projections show that globally the pandemic is likely to push 88 million to 115 million people into extreme poverty in 2020 (World Bank, 2020), a third of these being from Sub-Saharan Africa. Globally, the pandemic is projected to double the number of people facing acute food insecurity by the end of 2020 (about 135 million people before the crisis) (WFP, 2020).

There are at least three ways in which the pandemic may affect household food security. First, individuals in some households may contract the virus and this will have both direct economic impacts such as loss of earnings and indirect effects through the need to meet medical costs. Even in the absence of direct contraction, fear of contracting the virus could reduce incomegenerating activities. Second, government restrictions on movement and gatherings aimed at slowing the spread of COVID-19 have disrupted livelihood activities, thereby reducing household incomes (Abay et al., 2020; Amare et al., 2020; Arndt et al., 2020; World Bank, 2020). Third, access to food has been affected by disruptions to markets and food value chains (e.g., Aggarwal et al., 2020; Hirvonen et al., 2020; Mahajan and Tomar, 2020). However, empirical evidence on the magnitude of the impact of the pandemic on household food security remains scant, partly because the pandemic is still unfolding and detailed household survey data are not available yet.

Within this context, this paper makes three contributions. First, we add to the small but growing literature on the impact of the pandemic on household food security in Sub-Saharan Africa, specifically rural Ethiopia. Second, we assess the effectiveness of a social protection intervention in mitigating these malign impacts. Gentilini et al. (2020) note that since the outbreak of the pandemic, more than 150 countries and territories have implemented or announced plans to implement social protection measures, yet little is known about the effectiveness of these interventions. Similarly, in the context of the COVID-19 pandemic, the mechanisms through 2
be relatively high, around 50 percent. Finally, contrary to the empirical literature for the United States described above we found very low values for the estimates of the weight granted to social welfare in the government’s objective function (around two times the weight granted to foreign lobbying contributions), which underscores the importance of foreign (and domestic) lobbying in determining US trade policies.

The paper is organized as follows. Section 2 discusses the pattern of foreign contributions by Latin American producers in the United States, as well as the structure of tariff preferences granted by the United States. Section 3 presents the analytical setup. Section 4 describes the empirical strategy and section 5 presents the results. Section 6 concludes.

# 2 Foreign lobbying and tariff preferences

This section discusses the data sources and variable construction and provides a description of the pattern of Latin American lobbying and tariff preferences by country and sector.

## 2.1 Foreign Lobbying

The data set on foreign lobbying used in this paper was provided by the US Department of Justice. Foreign lobbying activities have to be reported following the legislation known as the Foreign Agent Registration Act (FARA) from 1938.<sup>7</sup> The US Department of Justice organizes annually a report on

> 7Note that since 1995 foreign commercial lobbyists may actually also report under the Lobbying Disclosure Act.

4