rafmacalaba/datause-encoder-data huggingface dataset:
growth process and evaluate the fit depending on the period of simulation as well as the policy dynamics. Second, we evaluate the model as a prescriptive tool to identify the influences of various sources of growth via the lens of Korea’s 55 years of growth experience. Both types of calibration exercises illuminate the nature of the LTGM in analyzing the future growth process of developing countries. Section 5 concludes.

# **2 Neoclassical Growth Model as an Accounting Framework**

We consider the standard neoclassical growth model based on the aggregate production function, which was first proposed by Solow (1956) postulating the relationship between inputs and output at aggregate levels such that where _Yt_ denotes the aggregate output, _Xt_ the composite input (sometimes referred as ”total factor”), and _At_ the total factor productivity (TFP) at period _t_ . The composite input _Xt_ consists of capital _Kt_ and effective unit of labor _L_<sup>�</sup> _t_ such that where the production function satisfies the canonical properties of the neoclassical growth model, i.e., (i) monotonicity, (ii) diminishing returns and (iii) constant returns to scale.<sup>3</sup> The capital is accumulated according to the following law of motion where _It_ denotes the capital investment and _δ_ the depreciation rate of existing capital stock. The diminishing returns property is the key property of the neoclassical growth model which 3The property of ”monotonicity” means that addition of capital and labor contributes to increasing output (i.e., _∂K∂F_<sup>_≥_0 and</sup><sup>_<u>∂F</u>_</sup> _∂L_<sup>_≥_0 for all</sup><sup>_K_and</sup><sup>_L_).The property of ”diminishing returns” means that the marginal contribution</sup> of adding more inputs decreases as the amount of inputs increases (i.e., _∂K∂_ � _∂K∂F_ � _<_ 0 and _∂L∂_ � _<u>∂F∂L</u>_ � _<_ 0). The property of ”constant returns to scale” means proportional changes in all inputs at the same time induces the same proportion of changes in output (i.e., _F cKt, cL_<sup>�</sup> _t_ = _cF Kt, L_<sup>�</sup> _t_ for all _c >_ 0). � � � � 3
<!-- Start of picture text --> 1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>2005 2007 2009 2011 2013 2015 2017<br>—— Budgeted oil revenues<br>— Collected oil revenues<br><!-- End of picture text --> <!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2005 2007 2009 2011 2013 2015 2017<br>—— Budgeted oil revenues<br>— Collected oil revenues<br><!-- End of picture text -->
# **The Upstream Tariff Simulator (UTAS): A Tool to Assess the Impact of Tariff Reform on Input Costs and Effective Protection across Sectors**<sup>*****</sup>

Andreas Eberhard‐Ruiz<sup>*</sup> Gonzalo J. Varela<sup>†</sup> Lucia Casal<sup>‡</sup> Federico Ganz<sup>§</sup> JEL: C67, F10, F17 Keywords: tariff policy, input‐output economics, simulator, effective protection

> *We thank Juan Pedro Gambetta provided excellent support throughout the preparation of the tool and background material. Valuable comments were received from Jakob Engel, Ana Margarida Fernandes, Alen Mulabdic, Alberto Portugal, Hans Timmer, and Erik von Uekxull, as well as from policymakers in workshops delivered in Kathmandu and Islamabad, specially to Ms Robina Ather, Chairperson of Pakistan’s National Tariff Commission and active supporter of this tool. We acknowledge financial support from the Trade Multidonor Trust Fund for Trade and Development without which this undertaking would not have been possible. * Eberhard is with the Social Protection and Jobs Group of the World Bank Group.

> † Varela is with the Macroeconomics, Trade and Investment GP of the World Bank Group.

> ‡ Ganz is with the Macroeconomics, Trade and Investment GP of the World Bank Group.

> § Casal is with Cornell University.
risk, the same likelihood of civil war, while still going up, is increasing by a mere to 35%. Similarly, at medium levels of coup d’état risk, increasing Ross oil rents from the 75<sup>th</sup> to the 99<sup>th</sup> percentile increases the likelihood of civil war four times. At high levels of coup risk, however, being very rich in oil rents actually reduces the same likelihood of civil war by close to 40%.

On the other hand, when varying the Humphreys oil production from the 75<sup>th</sup> percentile to the 99<sup>th</sup> percentile, anocracies are about twenty times more likely to see civil, whereas for all other regimes (democracies and dictatorships) the risk of war is less than four times larger. The marginal effects are similar when varying the Ross oil rents. In the case of riot history, countries with no riot history, if anything, see a reduction of civil war risk when we vary oil production or rents. When a country has had three or five years with riots in the recent past, however, increasing oil production or rents dramatically increases the risk of civil war.

The other insight from inspecting at the size of the mid-point predictions is that while the predicted values for the risk of civil war when varying the mitigating circumstances are relatively low, the same predicted values when varying the aggravating circumstances are very high. This indicates that once a coup prone regime rich in natural resource rents starts to experience protracted street protests or other forms of instability, it is very likely that the mitigating effect of rent distribution and spending on the military will be overcome by the aggravating perception that the regime is weakening.

In many countries adverse and potentially mitigating context conditions have co-existed with natural resource endowments. For example, two conflict countries – Nigeria and Sudan – have been ruled intermittently by authoritarian/military regimes with a history of coup d’état. These countries, however also experienced bouts of political instability with transitions to democracy and numerous riots. In terms of resource endowments, in Nigeria oil was discovered and 24
of time): once the amount of time spent on a specific activity is determined (which is, of course, subject to recall bias), the activity has to be matched to a list of activities in various categories (e.g., occupational, home, leisure/recreation, travel or commuting), each with its own energy expenditure conversion factors.<sup>18</sup> Because respondents are unlikely to recall whether such activities were consistently performed at peak intensity or involved rest periods, estimates of energy expenditures using these methods are likely to be fraught with error.

# **6. CONCLUSION**

In this paper, we discuss the challenges associated with collecting time use data in developing countries and suggest potential solutions, concentrating for the most part on the two most common time use methods used in development settings: stylized questions and time diaries. We identify a significant lack of rigorous empirical research on the accuracy and feasibility of these methods, and begin to fill this gap using data from WEAI surveys in Bangladesh and Uganda, which collected stylized questions and time diary data for the same individual. We find that stylized questions are sometimes less efficient (in terms of interview length) than time diaries—a result which we attribute, based on qualitative evidence, to the greater cognitive difficulties faced by respondents when answering stylized questions compared to time diaries. We also find evidence of significant differences in the time use estimates provided by stylized questions and time diaries, which can be linked to enumerator fixed-effects (in both countries) and respondent characteristics (in Uganda alone). The evidence of significant enumerator fixed-effects, in particular, should be carefully noted: if enumerators’ abilities help to determine respondents’ ability to accurately recall their time use, then proper enumerator training is paramount. Further research, at a greater scale than offered here, is needed to determine the broader validity of our results.

Considered together, no one method stands out as universally superior to the others. Each has its strengths and weaknesses. Traditional methods that account solely for the quantity of time still have to

> 18 There are several questionnaires, mainly from the US and Europe that include links to physical activity questionnaires, for example: http://appliedresearch.cancer.gov/resource/collection.html.

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and household consumption improves with age of the household head, but at a diminishing rate. In 2005, _female-headed_ urban households are likely to have _lower_ consumption than male-headed urban households, while no significant effect is observed for rural households. In contrast female headed rural households were at a clear disadvantage in earlier survey years, while in only one of the five surveys which Wodon (1999) examines were urban female headed households significantly worse off compared with male headed households. Consistent with what was argued in section II, households headed by women where the men are migrants (and likely to send remittances) are likely to be better off on the average than other households whether male or female headed – this result is consistent with those found between 1983/84 and 1995/96.

_Education_ levels of all household members have positive and significant association with per capita consumption, with the household head’s education having the highest effect. The education “premium” increases with the level of education among household heads. The premiums for education of fifth grade or higher are larger for urban than rural households, reflecting greater opportunities for educated workers in urban areas – this is consistent with Wodon’s (1999) analysis of earlier survey years. Spouse’s (of the household head) education and the maximum education level among other adult members of the household have positive but smaller effects on per capita expenditures. These indicate positive externalities of education among household members, in line with evidence from an earlier study (Basu et al, 2001).

_Agricultural land ownership_ is positively and significantly correlated with household consumption in rural areas. All categories of land ownership raise the level of consumption (compared to the reference group of landless households), and the 12
(8) This expression can be used to trace out the relationship between μ and q for a fixed value of the mean difference in height between adults who were and were not stunted as children, d . Both of these methods can be contrasted with the assumption in Galasso and Wagstaff (2016) in which the only effect on adult average height comes through a reduction in the stunting rate, i.e. μ�z�dq

# **Figure A3.4.1  Calibrated Relationship Between Adult Height and Stunting**

<!-- Start of picture text --> 166<br>164<br>162<br>160<br>158<br>156<br>154<br>152<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8<br>Fraction Stunted (q)<br>Holding σ fixed Holding d Fixed Galasso‐Wagstaff<br>Mean Adult Height in cm (μ)<br><!-- End of picture text --> Figure A3.4.1 plots the relationship between average adult height (on the vertical axis) and stunting (on the horizontal axis) implied by these three methods. To plot these graphs, set z�156 centimeters, corresponding to the WHO height‐for‐age z‐score of ‐2 for 19 year‐olds (average across male and female). The thin blue line plots the relationship between mean adult height and stunting holding fixed the standard deviation of height.<sup>25</sup> The heavy black line shows the same relationship in

> 25 The value at which σ is held fixed matters for the calculation.  To make the series comparable, set σ so that Equations (6) and (7) hold at a stunting rate of q�0.25 and the observed height difference of d�6 in the study by Victoria et. al. (2008) cited by Galasso and Wagstaff (2016).  Victoria et. al. (2008) report on young adult health outcomes observed in the mid‐2000s in cohort studies that have tracked respondents since childhood.  Data from the WHO‐UNICEF‐WB Joint Malnutrition Estimates databased indicate that stunting rates in the early 1990s (when the respondents were children) in the five countries  were 19% (Brazil), 66% (Guatemala), 62% (India), 43% (Philippines) and 32% (South Africa).  This range of values for the stunting rate is represented in the horizontal axis of the figure.  In contrast, the value of z does not matter for the analysis since it only shifts the relationship between stunting and adult height up and down, without changing the slope.

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# **1.  Place-based policies: Introduction**

Place-based policies – such as transport improvements, special economic zones, and treatment of lagging regions – are an integral part of development policy. They are frequently very expensive and they require that choices be made between the places at which policy is implemented. Choices are particularly stark in developing countries as constrained resources and the lumpiness of many public investments mean that, of necessity, some places will be served with roads, telecommunications, power and other public services, before others. Informing these difficult choices with systematic ex ante appraisal of projected policies is complex because of two distinctive features of place-based policies.

The first is that place-based policies are typically expected to deliver two types of benefit. One is the direct effect; e.g. a transport improvement saves time and vehicle operating costs. The other is indirect; the transport improvement may induce private sector investment, job creation, and higher productivity.<sup>1</sup> The direct effect is the stuff of standard project appraisal techniques for which well-established techniques are in place, based around cost-benefit analysis. But indirect effects depend on responses of the private sector, often including major location decisions that incur sunk costs and are long-lasting. Such decisions and their economic impacts are hard to predict.

The second reason is inherent in the spatial context. The value of proximity – and the cost of distance – creates a high degree of complementarity in the location of investments, public and private. On the public side, having good roads but no electric power leaves a place unattractive for private investors; concentrated packages of investment matter. Private investment is encouraged by public infrastructure and also by investments made by other private investors – the location decisions of workers, customers and supplier firms. Expectations about the future development of a place are critical in shaping these decisions. These factors can create spatially focused increasing returns to scale (agglomeration economies) and virtuous or vicious cycles of development, yielding spatially uneven outcomes.<sup>2</sup> They create numerous market failures so there is a role for policy, but it is these factors that make the effects of policy particularly difficult to predict.

These two features pose a major challenge for appraisal of place-based policies (PBP). A conventional cost-benefit analysis would simply ignore indirect effects, but this is incorrect if – as is likely in the spatial context – there are important market failures. Ignoring indirect effects may lead to the unsatisfactory situation in which the strategic case for a project is presented in terms of these effects, while they are ignored in the narrow economic appraisal. Including them risks

> 1  Some literature refers to ‘wider benefits’; we will use the term ‘indirect effects’ throughout. 2  See World Bank (2009).

3
which corresponds to the ratio of credit market instruments to total non-financial assets in the business sector in the Flow of Funds data.

In our benchmark exercise, we assume _φ_ = 10% of firms are non-essential and shut them down for one quarter. This matches the fall in the US GDP in the second quarter of 2020. As for the unanticipated demand or preference shock accompanying the shutdown shock, we calibrate the preference shock _ξ_ 1 so that the ratio of the fall in consumption to the fall in investment in period _t_ = 1 is similar to the one observed in US data in the second quarter of 2020 (see Figure 2 )— i.e., _I_ 1 _/I_ 0 = 0 _._ 92 _C_ 1 _/C_ 0.

# **4 The Ripple Effect of a Lockdown**

In this section, we show the transition dynamics following an unexpected shock that shuts down _φ_ = 10% of firms for one period and a concurrent unexpected demand shock that lowers the marginal utility of consumption during the lockdown. Unemployed workers that were employed in these firms receive full unemployment insurance benefits financed with government debt and can be reemployed directly by their previous employers without going through the matching process in period 2.<sup>15</sup> During the shutdown, non-essential entrepreneurs have to pay the rental cost of capital, which can be viewed as a fixed cost, and decide how much to draw their assets down and how much to consume.

We first present the aggregate implication of the lockdown, and then dive into the microlevel implications for employment and consumption dynamics across different firms and individuals, respectively.

## **4.1 Aggregate Variables**

Figure 4 shows the trajectory of aggregate variables. The left panel shows GDP, investment, consumption, and total factor productivity, while the right panel illustrates the dynamics of unemployment. The most salient feature of the figure is the swift recovery of the aggregate output. After falling by 10% on impact, GDP bounces back to just 0.4% below the initial steady state level in the quarter following the lockdown. The behavior of the unemployment rate shows that, to a large extent, the swift recovery in GDP is a reflection of the swift recovery in employment. The decline in investment explains the slight delay in the recovery of output. The fall in _TFP_ on impact is simply a reflection of the unused physical capital by non-essential entrepreneurs.

> 15An equivalent interpretation is that the government provides locked-down firms with a 100% wage subsidy to finance the wage bill during the temporary shutdown, conditional on “employing” their existing workers.

18
14 (growth) is over one that is larger than the elasticity from our benchmark specification. The right hand-side panel in Figure 11 shows the impulse response from a tax shock using the narrative tax shock from Romer and Romer (2010). While Romer and Romer (2010) find that tax news shocks have a strong effect on US growth, we find that the effect on AE growth overall is negative but not statistically significant. That is perhaps due to the shorter sample period used in this paper but also perhaps because when considering AE as a whole a tax hike in the US may also lead to an increase in capital flows to AE therefore counterbalancing the negative growth effect from higher tax in the US. That said, the impulse response from a US tax news shocks show a negative and statistically significant response suggesting that the spillover from US tax news shock to EM is large. The elasticity of the spillover from US to EM is also above one.

Thus far, we have validated only one of our main results that is the spillover from AE/US spillover to EMs are large using a different identification strategy. To also explore whether the other direction of the causality running from EMs to AEs is much smaller we use arguably exogenous variation from natural disaster shocks measured as the damage from large natural disasters (larger than one billion USD) in the relevant group over the total group GDP. Natural disasters have significant consequences. EM-DAT report that the direct economic damage from natural disasters between 1991–2015 is estimated at around $2.5 trillion and led to 1.75 million deaths. In theory, the impact of natural disasters on GDP is unclear (Strömberg, 2007). On the one hand, the loss of productive physical and human capital may reduce GDP. On the other hand, the disaster may provide a positive contribution to measured GDP as reconstruction efforts and humanitarian aid. The net effect of natural disaster is thus an empirical matter.

Impulse responses presented in Figure 12 confirm that a natural disaster shock in AEs have a negative and significant effect on GDP growth in AEs. The results also show that AE natural
Nevertheless, this study, based on data from China, six major host African countries and 35 firm interviews, provides strong evidence that private Chinese investment in Africa is gathering momentum and starting to make an impact in some places. It reinforces what many have suggested, i.e., China’s overall role in global cross-border investment is likely to continue to grow in the near future, and the role played by its private sector especially deserves focused attention.

This phenomenon itself raises many more questions: Will private Chinese investment be a blessing or curse to Africa? Will it contribute to Africa’s industrialization or will it kill native “infant” industries? Will Chinese private businessmen provide valuable jobs for locals, or will they simply exploit the cheap labor? What influence will this investment have on the local business environment, fostering healthy law and order and economic competition, or aggravating local corruption? Finally, what impact will this new development have on China?

Answers to these questions have important policy implications for African host governments, as well as for China. Like any FDI, private Chinese OFDI carries not only new opportunities but also special challenges. How the host governments meet those challenges will determine the net impact of the investment for their economies. There are also implications for the home government as well as the involved Chinese firms. The following suggests several “do’s and don’ts” in terms of government policies, especially those for the host countries, that can influence the outcome of this investment.

# 1. Policy implications for host countries’ industrialization

First and foremost, the rising role of private Chinese investment appears to offer a great opportunity to African host countries, especially those which aspire to transform their traditionally resourced-based economies into more industrialized societies. Industrialization is a process involving many steps. The labor-intensity and relatively low technology associated with the current wave of Chinese firms relocating to Africa provides a desirable first step in this process. Such industries are easy for less developed host countries to absorb and adopt; it also allows them the chance to realize their comparative advantages when competing internationally.

The significance of this phenomenon, the so-called “flying geese” investment or investment from emerging markets, has been a subject of policy and scholarly attention for the last several decades, as summarized in Box 1 below. The positive impact of investment by the south to the south during the industrialization of Latin America and Asia in the 1960s-1970s is well documented. (Wells 1983) In the 1980s-1990s, in turn, China benefited tremendously during its economic take-off by attracting investors from Hong Kong SAR, China<sup>19</sup> ; Taiwan, China; and other Asian Tiger struggling to upgrade their industries at home and sending the “sunset” ones

> 19 Until 1997, Hong Kong was a British colony.

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