rafmacalaba/datause-encoder-data huggingface dataset:
revenues as an endogenous variable (total expenses minus non-tax revenues). By disregarding the exogenous pressures on revenue and avoiding government accumulation of financial assets, this approach insulates the fiscal forecasts from the 8 cumulative effects of debt service and non-tax revenue. The shortcoming of this approach (treated in section 4.2) is its assumption of instantaneous adjustment of revenue to expenses.

The constant tax rate (or tax smoothing) approach is used to show the dynamics in government revenues, expenses, and financial assets under the assumption that initial tax rates remain constant in the long term. This approach illustrates the fact that demographic pressures affect both revenues and expenses. Future fiscal effects under the different scenarios are shown through the trends in government financial assets.<sup>9</sup>

# **3.3. Baseline Assumptions**

The model's baseline is built on the projections provided by the New Zealand Department of Statistics and the New Zealand Treasury and is based on the following assumptions:

# **_Long-term demographic assumptions_**

Mortality: Medium Fertility: Medium Immigration: 15,000 a year until 1999, 5,000 a year thereafter 8 The self-enforcing fiscal impact of the debt service and the revenue from financial asset management make the modeling results highly sensitive to the initial fiscal stance.

9 The figures in the paper report gross financial assets, which represen **t** he sum of the net financial assets and the gross debt. In New Zealand it is more intuitive to observe assets rather than debt since, according to the baseline under the constant tax rate approach, the government is expected to hold a positive financial asset position in the long term. Gross, rather than net, financial assets illustrate the size of the problem of government financial asset management.

When the government is indebted its official policy target is net debt. Consistent with current government policy, the baseline assumes that the public debt will be retired as quickly as possible. Beginning in 2008 the government finds itself in a positive financial asset position. No official policy target applies in such case.

The model arbitrarily sets 20 percent of GDP as the gross debt target with respect to the smooth functioning of the domestic financial market. It is presumed that government paper serves as the benchmark for interest rates and as an inexpensive and relatively risk-free instrument of domestic monetary policy. In addition, governments may periodically face the need to issue debt. Government borrowing cost are generally assumed to be lower when the government issues debt on a regular rather than an ad hoc basis. The target for the gross debt in the model does not affect the modeling results to any significant extent. It should be noted that comprehensive net worth would represent the most meaningful fiscal indicator if it were incorporated into the model (Bradbury, Brumby, and Skilling 1996).

10
42 investing across borders: do fdi-specific regulations matter?

In all models, the BIT coefficient is highly significant in the selection equation (column 1) and not in the benchmark outcome equation (columns 2), reaffirming its appropriateness as a candidate for exclusion restriction. It passes the prima facie test of a valid exclusion restriction by being shown to affect the propensity to invest, but not the volume of investment. This point is worth emphasizing because as appealing as the technique for controlling truncation bias is, its efficacy can be stymied by the lack of a convincing exclusion restriction. The main exclusion restriction in Helpman et al. (2008) is religion, which Anderson (2011) does not find convincing. Baranga (2009), too, finds problems with the way Helpman et al. (2008) have used the religion variable. Using an alternative but similar index yields a highly significant coefficient in the benchmark OLS regression, weakening the case for the variable’s validity as an exclusion restriction.

_Second_ , how do coefficients change when a dummy for natural resource-rich countries is added to test whether poor countries rich in oil, gas and minerals attract FDI in the extractive industries? To restrict the sample to just developing countries, I drop the FDI-receiving OECD countries. Host countries are assigned a dummy value of one if during 2002 and 2006 their average share of fuel, ores, and metal exports in total exports exceeded 20 percent.<sup>32</sup> In Table 9, the coefficient of the resource dummy is not statistically different from zero indicating that resourcerich developing countries are not likely to attract more FDI after controlling for FDI regulations, the quality of institutions, and other host country characteristics. The statistical significance of coefficients of the FDI Procedures and Arbitrations variables is retained.

_Third_ , do the main results stand when alternative time periods, namely the individual years of 2006, 2007, and 2008, are considered? I do not consider years prior to 2006 because the explanatory indicators of FDI regulation cover prevailing regimes during or before 2006. Table 10 reports results obtained by estimating the model using data for 2006. GDP, GDP per capita, and tariffs averaged over the 32 Fuel products are SITC Section 3; metals and ores are SITC Divisions 27, 28, and 68.
There have been a number of studies trying to isolate which country specific factors are important in explaining capital structure choices. Booth, Aivazian, Demirguc-Kunt, and Maksimovic (2001) find that while similar factors such as profitability and asset tangibility affect debt ratios in both developed and developing countries, there are also systematic differences in the way these ratios are affected by institutional factors such as capital market development, GDP growth, and inflation.<sup>23</sup> Giannetti (2003) uses data on unlisted companies from 26 European countries and finds that good creditor rights protection is important in obtaining financing for firms investing in intangible assets that cannot be provided as collateral (e.g. R&D) and also those in sectors with high volatile returns. Fan, Titman, and Twite (2010) find that a country‟s taxation and inflation policies as well as its legal and financial institutions have an important effect on capital structure and debt maturity choices. Firms use more debt when dividends are more highly taxed, use less debt and of shorter maturity when inflation is lower, and use more short-term debt in countries that are viewed as more corrupt. Controlling for corruption, the legal tradition, i.e. common law versus civil law only influences the debt maturity decision and not the leverage ratio.<sup>24</sup> While the above cross-country studies of capital structure are focused on domestic firms in different countries, Desai, Foley, and Hines (2005) analyze the capital structures of foreign affiliates of U.S. corporations. They find that multinational affiliates use less external debt in countries with underdeveloped capital markets and weak creditor rights and make greater use of internal capital markets (borrowings from parent companies) to overcome capital market imperfections. They also find the overall level and composition of debt to be very sensitive to tax incentives.

Overall the body of evidence suggests that institutional factors such as legal institutions, the level of banking and stock market development are important determinants of firms‟ leverage choices and choices short-term versus long-term debt.

> 23 Rajan and Zingales (1995) use balance sheet data of large listed companies from G7 countries and find similar results - factors identified in previous studies as correlated with firm leverage in the US are similarly correlated in other countries.

> 24 There is a large literature in international economics on how globalization (both financial liberalization and financial crises events) might affect debt maturity structures (e.g. Rodrik and Velasco (1999), Schmukler and Vesperoni (2006), Caballero and Krishnamurthy (1998), Calvo (1998), Calvo and Mendoza (1996), Calvo and Reinhart (2000), Chang and Velasco (1999, 2001a), Furman and Stiglitz (1998), and Sachs, Tornell, and Velasco (1999)

39
product of the Beirut region would increase by 1.8%. The introduction of the BRT and the expansion of associated road network would have similar effects on congestion and on congestion costs and would increase the Beirut region’s gross product by 2.3%. Social welfare, measured in terms of consumer utility, would increase by about 4%. On the other hand, demand side instruments such as increased gasoline taxes and parking fees, if implemented on their own, would have negative economic consequences including slight drops in gross national product and in welfare.

Considering that most past studies on transport congestion management focused mostly on demand side instruments, this study has brought additional insights comparing various policy instruments on both the demand and the supply sides. Some limitations should be kept in mind while interpreting the results and policy findings. Although we found that the BRT is a most promising option for addressing congestion in Beirut, it may, in the future, cause problems of overcrowding and other unintended negative social impacts.<sup>11</sup> In the future, congestion might occur again due to increased volume of vehicles and transport service demand as population of the city, income level and vehicle ownership would increase. Therefore, demand side options have an important role as complements to supply-sided measures in the longer-run. Another limitation is that we had to work on a high level of aggregation dividing Beirut into just two zones. The results would have been more precise had we divided the city into additional zones. However, detailed data needed to divide the city more regions for a more detailed study are not currently available.

# **References**

Abou Zeid, M., Hassan, L. A-H. 2016. Beirut Mobility Study: Data Items Report (Fourth Revision, September 4), American University of Beirut.

Anas, A. and R. J. Arnott, 1991. “Dynamic Housing Market Equilibrium with Taste Heterogeneity, Idiosyncratic Perfect Foresight and Stock Conversions,” _Journal of Housing Economics_ , 1, 2-32.

Anas, A. and Chu, C., 1984. “Discrete Choice Models and The Housing Price and Travel to Work Elasticities of Location Demand,” _Journal of Urban Economics_ , Vol. 15, pp. 107-123.

Anas, A. and Y. Liu, 2007. “A Regional Economy, Land Use, and Transportation Model (RELUTRAN©): Formulation, Algorithm Design, and Testing,” _Journal of Regional Science_ , Vol. 47, No. 3, pp. 415-455.

> 11 For more on the limitation of BRT refer to Gilbert (2008) and Suzuki et. al. (2013).

46
traffic per freeway lane – leads to a decrease in productivity growth per worker of up to 0.033%. With a panel of similar geographical scope, Jin and Rafferty (2017) show that an increase in congestion growth by one percent – measured by an index of traffic delays – furthermore causes a decrease in employment growth of 0.08%.

Traffic disruptions and congestion have been shown to have negative productivity effects in developing countries too. Based on a survey with commuters in Kumasi, Ghana, congestion has been estimated to result in an average loss of daily productive hours of 9% per worker (Harriet et al. 2013). In the Greater Cairo Metropolitan Area in Egypt, traffic congestions cause direct costs of US$ 5.1 billion annually as of 2010, an amount that is only expected to increase (World Bank, 2013). The upside to these numbers is that addressing congestion does not have to be costly. A recent modeling study in Grand Casablanca, Morocco, finds that a combination of demand‐side instruments – a fuel tax, a parking fee, and a bus fare – increases income by 0.8% through decreasing travel times and thus freeing up travelers’ time for other activities. These gains can be further increased by adding bus services or constructing new roads (Anas, De Sarkar and Timilsina, 2018).

4. Firms adapt to unreliable infrastructure – often at considerable cost As infrastructure directly affects firms’ productivity, firms take infrastructure quality into account when making business decisions. Through adaptation measures, firms can influence to what degree they will be affected by disruptions.

This can be illustrated when considering the effects of a large shock, such as an earthquake. Figure 4 plots the recovery of a directly affected firm’s production capacity. The recovery from damages to the firm’s productive assets is slowed down by the loss of electricity, water, and gas supply (subsumed under ‘lifelines’). Here, two factors determine the impact of the infrastructure disruptions; it is influenced by both speedier service restoration (i.e. when the upward steps appear in the fat black line) and a firm’s resilience to infrastructure disruptions (i.e. the size of the vertical difference between the fat black line and the thin black line). Through adaptation measures, firms can increase their resilience and decrease the negative impact of the loss of infrastructure services (Kajitani and Tatano, 2014).

As such adaptation measures protect firms from losses, the real impact of infrastructure disruptions on firms is difficult to estimate. They require consideration of the costs of adaptation investments along with the cost of the disruption. This section discusses various measures that firms implement to cope with unreliable infrastructure. It includes the costly location choices by firms in response to infrastructure constraints, the increase in inventory to cope with unreliable transportation systems, the direct cost of backup self‐generation incurred by firms, and the dynamic consequences of unreliable electricity supply on industry composition.

13
- Second, workers of different age cohorts in a cross-section implicitly form a synthetic panel: workers of different ages today represent the effects of early-life health at different points in history.

We exploit district-by-time variation to investigate the association between adult wages in 2005 and the disease environment in districts throughout India in the 1970s and 1980s. In particular, we match historical, district-level census data on the disease environment with cross-sectional survey data on adult wages. Figure 1 presents our identification strategy graphically. The graph plots average wages, net of district fixed effects, as a linear function of age. The most visible feature of the graph is the upward slope: within essentially all Indian districts, older workers are paid more than younger workers, on average.

Our identification is found in the _difference_ between the two slopes. The average disease environment was better in 1990 than in 1970 in essentially all districts. However, these improvements were not uniform across districts. In districts where the disease environment improved more quickly over this period, younger workers would be expected to have relatively better early-life human capital than older workers in the same district, in comparison with the difference between older and younger workers in other districts where the disease environment improved more slowly. Therefore, our identification strategy asks whether the positive gradient between ages and wages is _less steep_ in districts where the disease environment improved _more quickly_ . An initial answer is visible in the difference in two slopes in Figure 1: the age profile of wages is less steep in the half of villages with above-average declines in infant mortality.

To quantify this effect, we estimate the following district-by-year differences-in-differences regression: where _i_ indexes individual adult workers, _d_ denotes districts, and _t_ represents years of birth. We use district fixed effects _α_ to control for any average differences in labor markets across

> to geographic differences in average human capital. For example, if an improving disease environment in the 1980s caused more factories to be built in a district in the 1990s, this change in labor demand would be absorbed by the district fixed effect in our 2005 cross-section, insofar as it impacted workers of different ages in our sample equally. If such endogenous demand responses are quantitatively important, then the true positive effect of the early-life health environment on wages may be greater than our estimates.

5
<!-- Start of picture text --> Figure S4: Percentage of Firms Using E-filing<br>by the Number of Taxes Paid<br>(2)<br>‘0 45.99<br>So<br>vt<br>32.41<br>_~2<br>co<br>®o<br>oO<br>®<br>ao<br>oO<br>N<br>o 10.49 9.259<br>1.852<br>oO<br>“1 0 1 2 3 4<br>Number of Taxes<br>Source: USAID 2008 [South Africa]<br><!-- End of picture text --> <!-- Start of picture text --> Table 2: E-filers by Industr<br>Industry | Number| % _|<br>Grand Total 324 100%<br>*Source: USAID, 2008<br><!-- End of picture text -->
**<u>Table 2: Effect of computerization on credit access</u>**

||**De**<br>|**pendent variabl**<br>|**e: log of credi**<br>|**t disbursed by b**<br>|**anks**|
|---|---|---|---|---|---|
|Computerization|0.981|0.471|-0.003|-0.013||
||[0.011]**|[0.015]**|[0.010]|[0.011]||
|Computerization * Urban Share||||0.151<br>[0.040]**||
|Computerization * Q1-Q4|||||0.002<br>[0.011]|
|Computerization * Q5-|||||-0.022|
||||||[0.014]|
|Computerization * Urban *Q1-Q4|||||0.048<br>[0.024]*|
|Computerization * Urban * Q5-|||||0.184<br>[0.047]**|
|Quarter*Year Fixed Effects|No|Yes|Yes|Yes|Yes|
|_Taluk_Fixed Effects|No|No|Yes|Yes|Yes|
|Observations|47,871|47871|47,871|47,871|47,871|
|R-squared|0.21|0.42|0.97|0.97|0.97|

Notes: _Taluk_ and year*quarter fixed effects included throughout. Robust standard errors clustered at the _taluk_ level. * significant at 5%; ** significant at 1% 18
markets are exposed to more intense competition and must improve faster than firms who sell their products domestically only. Exporting makes firms more productive.

Summarizing the results from a comprehensive survey of the empirical literature that covers 45 studies with data from 33 countries published between 1995 and 2006, Wagner (2007) argues that, details aside, the big picture that emerges after some ten years of microeconometric research in the relationship between exporting and productivity is that exporters are more productive than non-exporters, and that the more productive firms self-select into export markets, while exporting does not necessarily improve productivity.<sup>2</sup> However, this big picture hides a lot of heterogeneity. Cross-country comparisons, and even cross-study comparisons for one country, are difficult because the studies differ in details of the approach used. Therefore, the jury is still out on many of the issues regarding the relationship between exporting and productivity, including the absolute size of the productivity advantage needed to clear the export market hurdle and the reasons for differences in this size between countries, the reasons for the existence or not of learning-by-exporting effects in some countries, the determinants of ex-ante productivity premia of export starters, and the mechanisms by which learning from exporting occurs.

One promising approach to generate stylised facts in a more convincing way suggested in Wagner (2007) is to coordinate micro-econometric studies for many countries ex-ante, and to agree on a common approach and on the specification of the empirical models estimated. The outcome of such a joint effort would be a set of results that could be compared not only qualitatively (i.e. with regard to the signs and the statistical significance of the estimated coefficients) but with a view on the magnitude of the estimated effects, too.

This paper reports the results of an effort to proceed in this manner. Teams working with micro level data for 14 countries joined to form The International Study Group on

> 2 For contemporaneous but less comprehensive surveys of this literature with a partly different focus, see López (2005) and Greenaway and Kneller (2007).

3
Small Area Estimation of Poverty in Four West African Countries by Integrating Survey and Geospatial Data _Ifeanyi Edochie_<sup>_1_</sup> _David Newhouse_<sup>_2_</sup> _Nikos Tzavidis_<sup>_3_</sup> _Timo Schmid_<sup>_4_</sup> _Elizabeth Foster_<sup>_1_</sup> _Angela Luna Hernandez_<sup>_3_</sup> _Aissatou Ouedraogo_<sup>_1_</sup> _Aly Sanoh_<sup>_1_</sup> _Aboudrahyme Savadogo_<sup>_1_</sup>

> 1 Poverty and Equity Global Practice, World Bank Group

> 2 Development Economics Data Group, World Bank Group

3Department of Social Statistics and Demography and Southampton Statistical Sciences Research Institute, University of Southampton

> 4Institute of Statistics, Otto-Friedrich-University Bamberg.

We thank Olivier Dupriez, Jed Friedman, Haishan Fu, Craig Hammer, Johannes Hoogeveen, Gabrela Inchauste, Talip Kilic, Johan Mistiaen, Pierella Paci, and Nobuo Yoshida for their support. This work was partially supported by the Knowledge for Change Program’s phase IV programmatic research project “Understanding Trends in Sub-National Differences in Economic Well-Being in Low and Middle- Income Countries” Program. The work of Tzavidis, Schmid, and Luna is supported by the Data and Evidence to End Extreme Poverty (DEEP) research program. DEEP is a consortium of the Universities of Cornell, Copenhagen, and Southampton led by Oxford Policy Management, in partnership with the World Bank - Development Data Group and funded by the UK Foreign, Commonwealth and Development Office. The work of Tzavidis is also supported by the UKRI-ESRC strategic research grant ES/X014150/1 for “Survey data collection methods collaboration: securing the future of social surveys”, known as Survey Futures. Survey Futures is a collaboration of twelve organizations, benefitting from additional support from the Office for National Statistics and the ESRC National Centre for Research Methods. Further information can be found at <u>www.surveyfutures.net.</u> Keywords: Poverty measurement, Small Area Estimation, Geospatial data JEL codes: C52, I32