rafmacalaba/datause-encoder-data huggingface dataset:
# **6 Extensions**

## **6.1 External shocks, government expenditure, and debt**

It is frequently claimed among policymakers that an important aspect of the link between external shocks and economic performance in low-income countries results from the effect of these shocks (specially commodity price shocks) on government expenditure, on a country’s ability to serve its debt obligations, and on its level of indebtedness. To explore this possibility we follow Deaton and Miller (1996) and add to the vector of endogenous variables _y_ the following variables: real consumption ( _CONS_ ), investment ( _INV_ ), and government expenditure ( _GOV_ ) (all real per capita in logs). So, the vector _y_ in equation (1), corresponds now to _y_ = ( _GDP, CONS, INV, GOV_ )<sup>_′_</sup> _._ We do not include net exports ( _NX_ ) explicitly in the system because they can take negative values, which would make the estimation in logs unfeasible. Notice that because of the exclusion of net exports and the estimation in logs, there is not a trivial co-integration relationship among the components of _y._ To recover the decomposition of output that we show in the IRF below we use the average shares of each component in the sample, and compute net exports _NX_ as a residual.<sup>13</sup> The results of this exercise are reported in Figure 4, which shows the dynamic responses of output, government expenditure, and the current account (net exports) to each of the exogenous shocks. As, by definition, the change in the current account is equal to the change in the capital account plus the change in reserves, the evolution of net exports give us a sense of what is happening with the net foreign position of a country.

The figure shows that government expenditure usually moves in tandem with total output. So, shocks that tend to increase real activity tend to increase government expenditure. To the extent that government expenditure is used to provide valuable social services, the results suggest that negative external shocks will affect the ability of the government to provide such services. Also, as long as the increase in government expenditure is not purely financed with tax revenue, government debt may increase as a result of a positive shock. Interestingly, the different panels of Figure 4 show that, although government expenditure always moves in the same direction as total output, the response of government expenditure to a shock vis-`a-vis total output is much smaller in the case of natural disasters. This finding provides some evidence that government may try, at least

> 13The shares used in the figure are 0.78 for consumption, 0.12 for investment, and 0.23 for government consumption.

21
# **The Exceptional Persistence of India’s Unorganized Sector**

Ejaz Ghani, William R. Kerr and Stephen D. O’Connell

**Keywords:** Informal, structural transformation, transition, entrepreneurship, employment, agglomeration, urbanization, development, poverty, India.

**JEL Classification:** J16, L10, L26, L60, L80, M13, O10, R00, R10, R12

**Sector Board:** Economic Policy (EPOL)

_Author institutions and contact details:_ <u>Ghani: World Bank, Eghani@worldbank.org; Kerr: Harvard University, Bank of Finland, and NBER, wkerr@hbs.edu; O’Connell: World Bank</u> and CUNY Graduate Center, <u>soconnell@gc.cuny.edu.</u> _Acknowledgments_ : A first draft of this paper was circulated in November 2011. We thank Swaminathan Aiyar, Ahmad Ahsan, Mehtabul Azam, Henry K Bagazonzy, Kaushik Basu, Steven Brakman, Louise Fox, Arti Grover, Himanshu, Lakshmi Iyer, Henry Jewell, Emmanuel Jimenez, Ravi Kanbur, Vinish Kathuria, Tarun Khanna, Homi Kharas, Luke Simon Jordan, Kalpana Kocchar, Amitabh Kundu, Peter Lanjouw, Pratap Bhanu Mehta, Pradeep Mitra, Rakesh Mohan, Truman Packard, Onno Ruhl, Gunjan Sharma, Arvind Virmani, and Hyoung Gun Wang for helpful comments on this work. We are responsible for any errors. We thank the World Bank's South Asia Labor Flagship team for providing the primary datasets used in this paper. We are particularly indebted to Shanthi Nataraj for sharing her wisdom regarding the industrial survey data. Funding for this project was provided by World Bank and Multi-Donor Trade Trust Fund. The views expressed here are those of the authors and not of any institution they may be associated with. The research in this paper was conducted while Kerr was a Special Sworn Status researcher of the US Census Bureau at the Boston Census Research Data Center (BRDC). Support for this research from NSF grant ITR-0427889 [BRDC] is gratefully acknowledged. Research results and conclusions expressed are the authors' and do not necessarily reflect the views of the Census Bureau or NSF. This paper has been screened to ensure that no confidential data are revealed.
The risk management framework for government debt covers interest rate, foreign currency, liquidity and credit risks with indicators such as average term to maturity, (modified) duration, convexity, average term to re‐fixing, share of foreign currency denominated debt, share of indexed / variable debt, debt coming due in the next year, and currency composition of foreign currency debt. In a few cases, stress tests and cost‐at‐risk assessments are also mentioned. A few countries list fiscal indicators for monitoring sovereign liabilities: ratio of government debt‐to‐ GDP, ratio of guaranteed debt to direct debt, short term debt as a percentage of public expenditures, etc.

Regarding assets, the risk indicators typically include counterparty credit risk / exposure, (conditional) value‐at‐risk, tracking error, interest rate sensitivity measures, default risks and external or internal ratings.

Risk indicators related to counterparty credit risk are also mentioned by a few respondents for direct liabilities, in particular by countries using derivatives to hedge market risks and/or achieve the desired debt portfolio composition, or for contingent assets / liabilities (on‐lending for example). Liquidity risk for cash buffers managed by the DMO and/or the MOF is monitored with a variety of indicators (e.g., percentage of debt redemption for a period of time, share of GDP, fixed amount).

3. Reporting, performance evaluation and accountability The reporting, performance evaluation and accountability on the management of assets and liabilities vary considerable from one country to the other. One‐third of the respondents mention reports on ALM being developed for internal or external audience (see box 5 on the case of Uruguay). In the UK for example, HM Treasury compiles the whole government accounts which consolidates more than 5,500 public sector entities although it does not have direct management responsibility for the assets and liabilities of all component organizations.<sup>19</sup> The budget process is often a key medium to report on management of assets and liabilities, sometimes with a specific parliamentary committee in charge of assessing the policy implemented and the results achieved.

All the countries that responded to this question mentioned formalized reporting processes for government debt and, when mentioned, financial assets managed by SWF and public pension plans, both internally (e.g. quarterly monitoring report to the supervisory board) and externally (e.g. monthly /quarterly debt statistical bulletin, annual report on the debt management strategy).<sup>20</sup> In the case of Chile, the country publishes on a quarterly basis information on the

> 19 HM Treasury’s report for Whole Government Accounts is available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/525617/WEB _whole_of_gov_accounts_2015.pdf.

> 20 Only one respondent mentioned internal reports and no public reports.

18
# **4.1. Guidelines for practice**

1. _Highly autocorrelated outcomes:_ For outcome measures like anthropometric measures or test scores, for which the autocorrelation is high (e.g. ρ=0.6 to 0.8), always include at least one baseline. Difference-in-differences and ANCOVA have much greater power than POST in these cases. Moreover, Bruhn and McKenzie (2009) also show that the power improvements from stratified or matched randomization are highest when ρ is high. However, if the total sample size is fixed, a single baseline and follow-up will offer more power than multiple pre-treatment or post-treatment rounds when ANCOVA is used. If the treatment and control sizes are fixed, the gains from going from one to two post-treatment surveys can still be non-trivial in these cases, but there is little gain from more than say 3 post-treatment surveys.

_2. Outcomes with low autocorrelation:_ For outcome measures like business profits, incomes, or expenditure, for which the autocorrelation is typically low (e.g. ρ=0.20 to 0.40) , it can be optimal to have no baseline at all, and just do a single follow-up survey if the total sample size is limited. If the treatment group size is fixed, researchers can dramatically increase power by doing multiple post-treatment surveys. Even if a baseline is taken, researchers should not use difference-in-differences in such cases, since doing so has much lower power than either POST or ANCOVA analysis.

# **4.2. Other practical issues**

In practice there are several other factors that should guide researchers in choosing how many survey rounds to conduct. I note some of the key factors here, and their implications for choice of the number of pre-treatment and post-treatment waves:

1. _Over what horizon is a pooled treatment effect relevant and of interest?_ The analysis in this paper has assumed a common treatment effect γ. Of course if the treatment effects vary over individuals, researchers are usually content to estimate an average treatment effect, and the analysis here will still hold. However, in some experiments the effect of the treatment may also vary over time. In this case estimation of γ by pooling multiple post-treatment survey waves will yield an average treatment, where the average is over multiple waves. If the survey waves are relatively close together in time (e.g. monthly or quarterly), then combining several post-treatment waves and getting the average

21
organizations’ role looms large. In addition, experts from other advanced countries can assist the reform efforts. However, it would be best to have local expert groups that have knowledge of reform program and local institutional context.

# **_Conclusion: Leadership and Change_**

Perhaps the most important component of PB success was effective leadership throughout the change. After the demise of the first reform, the Korean government developed a vision and strategy, based on several case studies commissioned to external public research institutes. Top administrators understood that PB reform would require not only administrative capacity but also analytical capacity within leading and line ministries. Thus they assigned these research institutes an important role in supplementing and developing this capacity. Over time, ministries have built up in-house capacity and knowledge regarding PB processes. Because bureaucratic turnover is low, this information is transferable to from administration to administration.

In addition, the Korean government created a sense of urgency among the line ministries and scaled up the reform program as quickly as possible. The Ministry of Planning and Budget stipulated in the budget draft guideline that at least a 10 percent budget cut should be considered for “ineffective” programs. This shocked line ministries into quick action. Another incentive for bureaucrats was public pressure. PB review results were made available to the public, and the media called attention to underperforming organizations. This publicity motivated government officials to honor the new system and, at the same time, helped the reform gain social acceptability among civil society organizations and citizens.

Establishing a dedicated reform team consisting of budget officials and policy researchers from the public institutes allowed reformers to merge expertise on practical and theoretical issues. The team came up with innovative solutions to integrate performance management into the actual budgeting process. The public institutes hosted many workshops and meetings to build consensus on policy direction and coalition among various stakeholders, and at the same time also provided training and consulting services to support line ministries.

Communication with stakeholders is a key element of successful change management. To facilitate additional communication with line ministries, a workshop was held every year to collect their feedback, and their opinion was reflected, as much as possible, in the next year’s PB process.

In part because of effective change management, the second reform program did gain the support of middle managers, often the strongest opponents of change, as well as high-level officials. Soon the legislature wanted a say in the reforms, as well. With the National Finance Act of 2006 and revisions to that act in 2009, PB is in Korea to stay.

25
the model considered in this study is too complex given the interaction of the crop income risk and the cattle holding risk to provide clear theoretical results on consumption and portfolio composition. To deal with this indeterminancy, we control, in the empirical application, for the di¤erences in the degrees of income uncertainty using a vector of household characteristics. The panel data also allow us to further control the e¤ects of precautionary savings.

So far we have theoretically clari...ed the role of livestock in consumption smoothing. We are now set to test the implications of the theoretical model by looking at actual sales and purchases of di¤erent types of livestock. We have not considered slaughtered animals in the analysis. This is mainly because livestock are rarely slaughtered for home consumption in rural Ethiopia (Gryseels and Anderson, 1983). This fact is supported by the data we use in this study. The following sections provide the details of the data and the empirical application to rural Ethiopia.

# 3 Description of the Data

This study is based on a longitudinal survey of 1,477 farm households from 15 villages in rural Ethiopia conducted jointly by the Department of Economics of Addis Ababa University and the Centre for the Study of African Economies of Oxford University. The ...rst three waves were carried out during 1994 and 1995. The survey was designed to produce comprehensive data on various socio-economic characteristics of farm households. Information is thus available on di¤erent variables related to household demographics, assets and income, consumption, health, education and participation in informal as well as formal markets. Besides, it includes information on the incidence of household speci...c random production and livestock shocks.

The villages were selected using a cluster sampling technique that incorporated the main agro-ecological zones as its basic element so as to capture the various farming systems of the country. Households from each village were chosen randomly proportional to the population size of their respective region with respect to the national population. However, we do not claim that these villages would represent rural Ethiopia at large. The whole idea is rather to get an insight into the behavior of farm households particularly on their investment decisions by exploiting the relatively large size of the data set.

For this study we need data on livestock transactions, production, livestock and health related shocks, individual and household level characteristics. Di¤erences in the timing of the survey rounds and lack of comparable data on all variables, however, do not allow us to use the information from all three rounds. The empirical application is thus based on data solely from the ...rst and third rounds which had exactly the same set of questions on the variables of interest.

12
Table 3: Connection types (Share of firms by country, number of connections, and whether they have connections only with other firms, or with firms and other entities)

|||Each|frm’|s num|ber of|connec|tions:|
|---|---|---|---|---|---|---|---|
||Connection with:|1|2|3|4|_>_=5|tot|
|Russian Federation|Diverse entities|60|57|83|90|98|75|
||Exclusively other frms|40|43|17|10|2|25|
|Spain|Diverse entities|43|85|100|75|98|71|
||Exclusively other frms|57|15|0|25|2|29|
|Slovak Republic|Diverse entities|96|100|100|100|100|99|
||Exclusively other frms|4|0|0|0|0|1|
|Serbia|Diverse entities|94|100|77|100|100|97|
||Exclusively other frms|6|0|23|0|0|3|
|Bulgaria|Diverse entities|63|100|100|100|100|91|
||Exclusively other frms|37|0|0|0|0|9|
|Romania|Diverse entities|89|97|92|100|99|95|
||Exclusively other frms|11|3|8|0|1|5|
|Hungary|Diverse entities|88|98|100|100|99|97|
||Exclusively other frms|12|2|0|0|1|3|
|All countries|Diverse entities|65|60|84|91|98|78|
|All countries|Exclusively other frms|35|40|16|9|2|22|
|All countries|All frms|100|100|100|100|100|100|

Source: Firm name connected firms’ database. Note: In the dataset, firms can be connected with diverse entities, which include other firms, individuals, political party, etc., or exclusively with other firms, the second line for each country in the table above.

29
**_Table 3: Tertiary Educated Workers across Manufacturing Sub-Sectors_**

||Mozambique<br>Average Share of<br>Workers with<br>University Degree|South Africa<br>Average Share of<br>Highly Skilled<br>Workers|
|---|---|---|
|Food and Beverages|1.7%|5.2%|
|Textiles|1.8%|3.8%|
|Wearing Apparel|0.4%|3.3%|
|Wood & Wood Products|0.2%|2.5%|
|Furniture|1.4%|6.0%|
|Metal Products and Machinery|2.5%|10.2%|

Source: Investment Climate Surveys and 1989 South Africa input-output table.

The Investment Climate Surveys also provide some indication of the perceptions by manufacturing firms on the affordability and quality of specific professional services (Table 4). Affordability seems to vary across professional services: the percentage of firms that found accounting services to be affordable is high in Mozambique, as in the rest of the region, but legal, engineering, and IT services seem to be less widely affordable. But the perception of quality is much lower in Mozambique than other countries across the whole spectrum of services. We should note that firms that find services affordable tend to be medium-sized or large firms.

**_Table 4: Affordability and Quality of Professional Services_**

||Percentage of<br>Firms that<br>Find<br>Accounting<br>Services<br>Affordable|Average<br>Quality of<br>Accounting<br>Services|Percentage of<br>Firms that<br>Find Legal<br>Services<br>Affordable|Average<br>Quality of<br>Legal<br>Services|Percentage of<br>Firms that<br>Find<br>Engineering<br>Services<br>Affordable|Average<br>Quality of<br>Engineering<br>Services|Percentage of<br>Firms that<br>Find IT<br>Services<br>Affordable|<br>Average<br>Quality of IT<br>Services|
|---|---|---|---|---|---|---|---|---|
|**Mozambique**|**89%**|**1.5**|**44%**|**1.0**|**38%**|**1.0**|**52%**|**1.0**|
|Eritrea|93%|3.3|97%|3.0|60%|3.0|82%|2.6|
|Ethiopia|74%|3.2|75%|2.9|62%|3.0|51%|2.5|
|Mali|74%|2.7|33%|1.9|57%|2.5|64%|2.8|
|Senegal|91%|3.0|71%|2.6|63%|2.8|69%|2.9|
|Tanzania|81%|3.4|75%|3.0|67%|3.1|70%|3.0|
|Zambia|79%|3.3|66%|3.0|75%|2.9|83%|2.8|

Source: Investment Climate Surveys. Notes: The surveys were conducted for different countries in different years. A higher value of the quality index indicates better quality.

This evidence from the investment climate surveys matches the anecdotal evidence that emerged from interviews in Mozambique. The shortage of professionals is particularly felt in terms of graduates of good or sufficient quality. Multinational auditing and accounting firms face shortages of entry-level professionals not because of a lack of applications but rather because many of the candidates applying do not have the requisite quality of training. For example, in 2005, several auditor positions in one of the multinational auditing and accounting firms went unfilled since candidates with sufficiently good CVs were not found. Moreover, multinational auditing and accounting firms face severe shortages of senior-level professionals, due to the relatively high 16
|**Panel (c): Tamil Nadu**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Employed|Durables|Inp<br>Tuition|ut:<br>Livelihood|Politics|Bank|Mobility:<br>Taluk Office|Police Station|
|ST|0.0867|-0.0609|-0.111*|-0.0884|-0.0986|-0.0304|0.0165|-0.00567|
||(1.78)|(-1.16)|(-2.07)|(-1.35)|(-1.73)|(-0.44)|(0.24)|(-0.16)|
|MBC|-0.00889|-0.0305|-0.0437*|-0.0201|-0.0454**|0.00678|-0.0584**|-0.00910|
||(-0.52)|(-1.73)|(-2.55)|(-1.00)|(-2.67)|(0.32)|(-2.64)|(-0.78)|
|BC|-0.0486***|0.0101|-0.0143|0.00286|-0.0107|0.0411*|-0.0334|-0.0259**|
||(-3.31)|(0.72)|(-0.93)<br>|(0.16)|(-0.76)|(2.27)|(-1.76)|(-2.62)|
|Some schooling|0.0395**|0.00402|0.000037<br>1|0.0347*|0.0118|0.0478**|0.0454*|0.00979|
||(2.99)|(0.29)|(0.00)|(2.08)|(0.89)|(2.92)|(2.46)|(1.04)|
|Female-headed household|0.0601***|0.0703***|0.0842***|0.0151|0.0710***|0.0711**|0.0208|0.006|
||(3.37)|(3.60)|(4.17)|(0.61)|(3.89)|(2.94)|(0.81)|(0.40)|
|Per-capita expenditure|-0.0196***<br>(-3.53)|0.00143<br>(0.28)|0.00436<br>(0.85)|0.0110*<br>(1.97)|0.00678<br>(1.46)|0.0351***<br>(5.95)|0.0470***<br>(5.25)|0.0107**<br>(2.60)|
|Per-capita expenditure|||||||||
|squared|0.0001***|-0.00001|-0.0003|-0.0001*|-0.0000|-0.0002***|-0.0003***|-0.0001**|
||(3.94)|(-0.25)|(-0.87)|(-2.20)|(-1.25)|(-5.74)|(-5.61)|(-2.80)|
|Land|0.0001|-0.0011**|0.0003|-0.0012**|0.0004|0.0002|0.0004|-0.0002|
||(0.25)|(-2.66)|(0.80)|(-2.59)|(1.95)|(0.43)|(0.66)|(-1.05)|
|Observations|5190|3284|3280|3283|3279|3299|3299|3299|
|Adjusted R-squared|0.276|0.119|0.119|0.150|0.077|0.135|0.149|0.051|

Notes: (1) Source: Author's calculations based on data collected by Social Observatory, World Bank and Government of Tamil Nadu. . Notes (2) - (5) of Table 3(a) apply.

29