rafmacalaba/datause-encoder-data huggingface dataset:
1.3 joint trade-fiscal reform: a case study

19 consumption of good k, raising revenue through the VAT, vk. However, when the good is in the informal sector, there is no increase in revenue from increased consumption. If the VAT base of formal goods is small (that is, the informal sector is large), revenue following a coordinated tariff and tax reform could decrease. This theoretical postulate guides the analysis of the revenue implications of tax policy reform in Nepal, a country with a large informal sector that is hard-to-tax.<sup>22</sup>

# 1.3.2 _Simulation Model_

The empirical analysis in this section draws on simulations conducted using the TRIST developed by the World Bank (Brenton et al. 2011). It uses a partial equilibrium model that quantifies the effect of trade reform scenarios on imports, revenue and production (please refer to the appendix) for the simulation model and an illustration). The model makes the following key assumptions: (1) it is derived from standard consumer theory and elasticities play a central role in determining the magnitude of demand response to price change; (2) there is imperfect substitution between imports from different countries, following Armington (1969), and each product is modeled as a separate market; (3) the economy is small and open such that all changes in tariffs are passed on, but change in demand by consumers in the small country does not affect world prices.

Percentage change in the price of good j from country i (∆pij) when tariff and other domestic taxes are lowered is as follows: the prime indicates post-reform values of tariff (τ), excise duty (e) and the VAT (v).

> 22 Keen (2007) argues that the theoretical result of Emran & Stiglitz (2005) does not fully take into account the efficacy of VAT as a taxation device. It is not just a tax on final consumption, but a charge on all imports and sales at every stage of transaction (with credit or refund given to registered taxpayers of VAT). Thus, while the informal sector can evade income tax, it can only escape from VAT partially, for it acts like a tax on all purchases the informal sector makes from the formal sector. This point is valid, but does not alter the basic thesis that, all else being equal, domestic tax collection is decreasing in the size of the informal sector.
Table 3.1: Accounts in the Yemen 2004 Social Accounting Matrix

|**Sectors (25)**|**Sectors (25) -- cont.**|**Institutions (3)**|
|---|---|---|
|_Private(17)_|_Government(8)_|Hoseholds|
|Agriculture|Water and sanitation|Government|
|Crude oil,gas and other mining|Other infrastructure|Rest of the world|
|Food and beverages|Healthgovernment||
|Textil|Basic educationg1-g6|**Interestpayments (3)**|
|Wood,paper andpress|Basic educationg7-g9|Domestic interestpayments|
|Liquidpetroleumproducts|Secondaryeducation|Foreign interestpayments|
|Chemicalproducts|Tertiaryeducation||
|Non-metal industry|Othergovernment|**Capital accounts (3)**|
|Metal and equipment||Hoseholds|
|Other manufactures|**Factors ofproduction (13)**|Government|
|Construction|Unskilled labor|Rest of the world|
|Other services|Semi-skilled labor||
|Health Private|Skilled labor|**Investment accounts (9)**|
|Basic educationg1-g6|Private capital|_Private(2)_|
|Basic educationg7-g9|Natural resource|Gross capital formation|
|Secondaryeducation|Government capital(8)|Stock changes|
|Tertiaryeducation||_Government(8)_|
||**Tax accounts (4)**|Water and sanitation|
||Commoditytaxes|Other infrastructure|
||Factor taxes|Healthgovernment|
||Direct taxes|Basic educationg1-g6|
||Import taxes|Basic educationg7-g9|
|||Secondaryeducation|
|||Tertiaryeducation|
|||Othergovernment|

Source: Yemen SAM 2004.

The government is disaggregated into eight sectors: four cycles (levels) of education (basic grades 1 to 6, basic grades 7 to 9, secondary, and tertiary cycles), health, water and sanitation, other public infrastructure, and other government services. In the following, the basic grades 1-6 are referred to as primary education (following international standards for the length of primary education). In addition to other private services, the private service sector is also disaggregated into four education sectors (with the same 16
restrictions or reversed previous liberalization. This pattern can be best seen for restrictions on resident outflows, which were tightened after the global financial crisis and for restrictions on nonresident outflows where the cumulative change index is negative, meaning that restrictions have tightened in net terms since 1999.

**Chart 5: Cumulative changes across regions**

<!-- Start of picture text --> Restrictions on residents inflows Restrictions on residents outflows<br>2000q1 2005q1 2010q1 2015q1 2000q1 2005q1 2010q1 2015q1<br>Asia ECA Asia ECA<br>LAC MEA LAC MEA<br>Restrictions on non-resident inflows Restrictions on non-resident outflows<br>2000q1 2005q1 2010q1 2015q1 2000q1 2005q1 2010q1 2015q1<br>Asia ECA Asia ECA<br>LAC MEA LAC MEA<br>15 15<br>10 10<br>5 5<br>0 0<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>-2 -2<br><!-- End of picture text --> _Note:_ The chart displays the index of cumulative changes to inflows and outflows of CFMs on residents and nonresidents, respectively, over time per region. For comparability, indices are standardized by the number of countries in a region.

# **4. Are CFMs Used as a Cyclical Policy Tool?**

In this section, we ask how the use of CFMs aligns with domestic business or credit cycles, country specific capital flow cycles, or with global financial cycles. We separately analyze the use of CFMs on resident inflows, resident outflows, non-resident inflows, and non-resident outflows. We also ask how other macroeconomic policy tools, such as monetary policy, exchange rate 16
P I . ii ii ii EEI PP PPhEEssiiccP PPeessttmmeeIItt = ββ× I iittiiP ssiizzee + PP

# **_Debt to Physical Investment Ratio_**

To obtain the estimates for the missing financing variables (debt and equity), for projects not awarded through equity sales, we predicted the debt to net physical investment ratio using type, sector, country, and financial closure year dummies: I 0 + E + , ii ii ii ii DDeeDDtt ttff NNeett PPhEEssiiccP PPeessttmmeeIItt RRPPttiiff = ββ CCee + SSeeccttffPP + CCffPPIIttPPEE ii ii where represents each project in the PPI sample. Of the 1,906 observations, we imputed 546 FFiiIIPPIIcciiP CCPPffssP ee YYeeP + PP missing values. We apportioned physical investments, net of direct subsidies if observed, based ii on the predicted ratio between debt and equity. Direct subsidies were assumed to be zero if not input in the dataset.

For projects awarded by equity sales (in Brazil and Argentina), where the debt-to-investment and direct subsidies-to-investment ratios are missing for some years, we used the average ratios for the project for all years available. If no financing information was observed for any of the years, we imputed the debt to net physical investment ratio in the same manner as for projects not awarded through equity sales.

# **_Contract Period_**

We imputed the missing contract period using the following regressions and rounded to the nearest integer.

# _Electricity projects_

We imputed missing values of contract period for electricity projects in Albania, Argentina, Brazil, Bulgaria, Ghana, Indonesia, Kenya, Peru, South Africa, and Ukraine using implementation type, segment in electricity sector, and type of technology, by running a linear ordinary least squares regression separately for each country.

We imputed missing values of the contract period for electricity projects in Ethiopia, Georgia, and Kosovo using the same variables as in the other countries. However, because of sample limitations, we ran regressions using all the projects in the same region: 0 + E . ii ii ii ii ii We used the following number of observations to input missing values: PPeePPiiffdd = ββ CCee + SSeeLLmmeeIItt + EEeecchIIffPPffLLEE + PP

- Albania: 9 observations were used to impute 2 missing values.

- Argentina: 82 observations were used to impute 4 missing values.

- Brazil: 565 observations were used to impute 13 missing values.

43
Policy Research Working Paper 5293

# **Abstract**

This paper uses 16 waves of panel data from the British Household Panel Survey to evaluate the role of subjective well-being in determining labor market transitions. It confirms a previous finding in the literature: individuals report a fall in their happiness when they lose a job, but they report a smaller fall when they are surrounded by unemployed peers, an effect called the “social norm”. The main results of interest are that job search effort and unemployment duration are affected by the utility differential between having a job and being unemployed. Since this differential is also affected by the social norm, it implies that when unemployment increases, the unemployed are happier and they reduce their search effort. These results indicate that unemployment hysteresis has labor supply causes.

This paper—a product of the Operations & Strategy Unit, Development Economics Vice Presidency—is part of a larger effort in the department to study the determinants of unemployment and labor market transitions. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmavridis@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
and if so, in what ways. Many also presume that the FDI inflow levels to the region are too low. How much is too little? What factors can explain the observed FDI inflows?

The current literature does not answer these questions, partly due to the lack of reliable and comparable micro data. New data is now available. The World Bank’s investment climate surveys in this region offer comparable cross-country, firm-level data with information that allows us to link foreign ownership to firm performance and behavior in thirteen SADC economies.<sup>3</sup> The evidence suggests that FDI has significantly facilitated development in the SADC region. Foreign firms tend to perform better, as measured by both sales growth rates and total factor productivity. They tend to be larger, located in richer and bettergoverned countries with more competitive financial intermediaries. They are also more likely to export than domestic firms. Moreover, domestic firms in the same broad industry tend to benefit from the presence of foreign firms. Since we found positive effects of foreign ownership on local economies, we also examined what determines FDI inflows to this region? To answer this question, we rely on an accepted empirical model to predict the level of FDI inflows per capita (Fan et al. 2009): FDI per capita is a function of market size, infrastructure, education, exchange rates, economic track records (mean growth in the previous 5 years, and the variability of growth in the same period), and institutions. In addition, we look for SADC-specific effects. However, we find that the SADC coefficient is insignificant, which implies that SADC performs as expected, given its average income, human capital, demographic structure, institutions, and economic track record.<sup>4</sup> It seems, therefore, that the usual suspects explain SADC’s FDI performance. We did find some significant differences between SADC and the rest of the world in FDI behavior: in SADC, market size as proxied by the income level is less important and openness more so.

> 3 The fact that we only have comprehensive investment climate survey for SADC explains why we pick SADC rather than the Sub-Saharan region as the focus region for our study.

> 4 Our conclusion may seem to differ from Asiedu (2002), which examines whether Africa differs from other regions in FDI behavior.  Their answer is yes.  However, there is no contradiction here: they focus on Sub-Saharan Africa, while we focus on the southern African countries (SADC), which attracts relatively more FDI than the rest of Sub-Saharan Africa (Basu and Srinivasan, 2002).  Moreover, Asiedu (2002) does not control for country economic performance. So part of the differences may be due to difference in specification.

3
Next, do a line search over some range _{γmin, ..., γmax}_ to find the _γ_ ˆ that minimizes _S_ ( _γ_ ). If we indeed find a nonzero _γ_ ˆ, hypothesis testing proceeds as follows: First we test the symmetry hypothesis _H_ 0 : _βH_ = _−βT ,_ using a conventional Wald test as though _γ_ were known with certainty. As discussed by Hansen (1999), the sampling variance of _γ_ is not of first-order asymptotic importance. If we cannot reject symmetry, we then impose it so that _βH_ = _−βT ≡ β_ 1 _._<sup>27</sup> Finally, we test _H_ 0 : _β_ 1 = 0 _._ In this case the test statistic is non-standard because of the non-identification of _γ_ under _H_ 0 _,_ requiring a special bootstrap procedure.

# **5.2 Baseline results (** _γ_ = 0 **)**

Before turning to estimation of the office-holding influence parameter _γ_ , we consider the role of economic stake – irrigated landholdings on the channel – in isolation, focusing on the form of aggregator function _G_ . In Table 2, we present results for equation (14) using the delivery performance ratio, _Ypc_ = log _DPRpc,_ and land values per acre, _Ypc_ = log _Vpc,_ along with various _G_ functions (mean, 80th, 90th, and 98th percentile) and _γ_ set to zero. As mentioned, in the case of DPR (columns 1-4), we stack the 4 years (2011-2014) of _kharif_ season data and cluster our standard errors on channel-position.<sup>28</sup> Thus, the estimates of _βH_ and _βT_ reflect the average impact of (log) influence at channel positions head and tail, respectively, on the _percentage difference_ in DPR between head and tail of that channel. Land value estimates in columns 5-8 have the analogous interpretation. However, in this case, we stack outlets and, within outlets, the two watercourse-level observations (see subsection 4.1.2), with standard errors again clustered on channel-position.

As indicated in equation (14), all regressions include a set of channel-level controls ( _Zc_ )

> 27Under the null, we may first-difference equation (14) to obtain

> 28We drop 74 channel-position-year observations with missing or unusable discharge data.

26
|**Table**|**Lo Real Value of**<br>**A2. National results u**|**sing the OLS estima**|**tor**|
|---|---|---|---|
||**g**<br>|**Log Daily Calorie**|**Log Food**|
||**Monthly Food**<br>**Consumption**|<br>**Intake Per Capita**|<br>**Consumption Score**|
|LogWheat Flour Price|-0.425***|-0.184***|-0.189***|
||[0.0364]|[0.0244]|[0.0270]|
|Agricultural Household|0.0351***|0.0302***|0.0310***|
||[0.0100]|[0.00713]|[0.00727]|
|Log Real Value Monthly|0.0366***|-0.00489|0.0381***|
|HousingPer Capita|[0.00684]|[0.00444]|[0.00479]|
|Log Real Value Monthly|0.0996***|0.0375***|0.0571***|
|Durables Per Capita|[0.00444]|[0.00332]|[0.00303]|
|Log Real Value Livestock|0.0133***|0.00670***|0.0166***|
|Per Capita|[0.00128]|[0.000932]|[0.000999]|
|LogKerosene Price|0.233***|0.179***|-0.0286|
||[0.0644]|[0.0461]|[0.0477]|
|LogVegetable Oil Price|-0.140**|0.0136|-0.0329|
||[0.0598]|[0.0443]|[0.0442]|
|LogLocal Rice Price|-0.0059|-0.109***|0.0734***|
||[0.0398]|[0.0264]|[0.0263]|
|LogLamb Price|-0.128|-0.0488|-0.0199|
||[0.0903]|[0.0629]|[0.0644]|
|LogMilk Price|-0.140***|-0.143***|-0.103***|
||[0.0376]|[0.0280]|[0.0268]|
|Head Age|0.00173|0.0284|0.024|
||[0.0264]|[0.0213]|[0.0200]|
|Head Married|0.00754|-0.0323***|0.0621***|
||[0.0144]|[0.0106]|[0.0101]|
|Head Literate|-0.0371***|-0.000189|-0.0453***|
||[0.00775]|[0.00610]|[0.00572]|
|Number Men|0.00194|-0.004|0.0198***|
||[0.00382]|[0.00303]|[0.00294]|
|Number Women|-0.00192|-0.00502*|0.0167***|
||[0.00353]|[0.00275]|[0.00256]|
|Number Children|-0.0208***|-0.0212***|0.0128***|
||[0.00177]|[0.00144]|[0.00127]|
|Plateau|0.00367|5.49E-06|-0.00466|
||[0.0210]|[0.0141]|[0.0153]|
|Mountainous|-0.00373|0.00126|-0.0115|
||[0.0196]|[0.0141]|[0.0139]|
|Observations|20,483|20,483|20,483|
|R-squared|0.353|0.25|0.392|

Note: Each column represents a separate regression with population weighted estimates. Robust standard errors -in brackets- are clustered by stratum and adjusted for survey design. Real values reflect adjustments for spatial and temporal price differences. Plains is excluded category. *, **, and *** denote significance at 10%, 5%, and 1%, respectively.

34
Benefits in Russia 12 of employing different types of workers to their marginal products, so that, even if equally productive, 'high risk' workers would get paid less. If firms were not allowed, or not prepared, to discriminate in wages in this way, the outcome would be unemployment of high risk groups. If public policy is to shield individuals from the costs of pre-school education or health care, these services cannot be left to the enterprise but would have to be supported from public funds. Even though, amongst large firms, the incidence of such costs per worker may be approximately equal ex post by virtue of the law of large numbers, firms will still have an incentive to hire only low risk workers in order to save on such expenses.

In principle, user fees could achieve much the same result as wage flexibility and hence mitigate some of the adverse incentive problems that differential risk categories might impose. However, the current practice in Russia whereby generally 20% of the cost of pre-school education is borne directly by the parents and the remaining 80% by the parents' employer or employers, though alleviating the financial imbalance between enterprises which provide pre-schools and those that do not, continues to impose a potential financial burden on the eLnployers of workers with young children.

(3) trvices provided for the local community, whether or not employees of the enterprise (for example, provision of public infrastructure, hospitals, or transport subsidies). If access to these services is open to al1 citizens of a locality, they cannot form part of the wage package, unlike cases (1) and (2), and hence, in a mul.i-firm labour market, their cost will be incident on the enterprise itself and cannot implicitly be shifted to workers in the form of a lower cash wage. (In a company town, on the other hand, it is possible for the enterprise to 'tax' its workers to pay for public services, see section 7 below.) Whether the costs of these services be transferred to consumers, to the local government or to central government depends ultimately whether the services are private goods, local public goods or have an income redistribution objective. For example, in current conditions in Russia there is probably not much ;ustification in terms either of economic efficiencv or income cistribution for public transport subsidies, while hospitals might reasonably be made a responsibility of local government. The survey evidence, reported in Table 1,
- 73 -

incentives and disincentives bears no obvious relation to social economic goals that might in principle guide the construction of an incentive system. The unintended consequences of these distortions, therefore, almost certainly include significant waste of investment resources. As well, some incentives can have the opposite effect to what is intended. The example of tax holidays is very instructive since many developing countries resort to it. Our calculations show that tax holidays can actually impose a net penalty on investors in some cases. It is of possible value orly to firms with sufficient taxable profits against which to use the benefits of the tax holiday; i.e. it is highly unlikely to be of value to the weak or infant investors or to the industries which usually are the intended beneficiaries of the measures. Similarly, tax rate reductions can reduce the incentive to invest, especially for finns which already have negative marginal effective tax rates. Finally, many measures have very differing effects according to the type of firm and investment under consideration. This is especially true of comparisons between tax loss and taxpaying firms.

V. The **Impact of** Tax Incentives on Investment: A Brief Survey of the Empirical Approaches and Applications in Developing Countries The empirical analysis of tax incentives for developing countries is of recent origin, and only a few published studies are available as of this date (see e.g. Agell, 1985, Ebrill, 1987). In industrialized nations, empirical approaches to evaluation of tax incentives have varied from opinion surveys to rigorously derived testable models; from partial equilibriurn to general equilibrium; and from macro- to microeconomic analysis. This Section provides an overview of the principal approaches, notes their key assumptions and caveats and discusses the findings of recent developing country applications.

# _A. Surveys of Firms_

Opinion surveys of company executives have been frequently used to evaluate the effectiveness of tax incentives (see Guisinger and Associates, 1985). An objective assessment of the impact of tax measures is not possible through opinion surveys. Opinion surveys do not provide data on observed behaviour both before and after a policy change, and, hence, the validity of their results are doubtful. In spite of these limitations, opinion surveys can serve as a useful complement to more rigorous empirical analysis of these issues.

Two recent opinion surveys have explored the effectiveness of investment incentives in developing countries. Guisinger and Associates (1985) sought to examine the policies of governments and companies towards foreign direct investment in both developed and developing countries. In an attempt to be