rafmacalaba/datause-encoder-data huggingface dataset:
30 Table 8: Self‐reported impacts on consumption (marginal effects)

||Did household cut<br>No (0),<br>|back consumption?<br>yes (1)<br>|How far did shock affe<br>Not at all (1), some (<br>|ct household welfare?<br>2), much (3), a lot (4)<br>|How far has hou<br>from<br>Not at all (1), so<br>yes comp<br>|sehold recovered<br>shock?<br>me (2), a lot (3),<br>letely (4)<br>|
|---|---|---|---|---|---|---|
||Poorest 20%|Richest 20%|Poorest 20%|Richest 20%|Poorest 20%|Richest 20%|
|Mean|0.616|0.514|3.287|2.904|3.078|2.698|
|_Shocks_|||||||
|Floods, mudslides, storms|0.221|‐0.527|‐0.445*|‐0.116|0.408|‐0.684|
||(0.78)|(‐1.09)|(‐1.72)|(‐0.15)|(1.17)|(‐0.63)|
|Frost, extreme cold|1.121|‐1.103|‐0.529|0.827|‐0.135|‐1.772*|
||(1.31)|(‐1.20)|(‐0.83)|(1.14)|(‐0.11)|(‐1.94)|
|Pest infestation, etc.|0.128|‐0.619|‐0.149|0.051|0.053|‐0.771|
||(0.71)|(‐1.02)|(‐0.92)|(0.11)|(0.33)|(‐1.37)|
|Decline in selling prices|‐0.892|0.945|‐1.072|‐0.458|‐0.266|‐0.593|
||(‐1.62)|(1.12)|(‐1.47)|(‐0.78)|(‐0.69)|(‐0.82)|
|Impossible to find work|‐1.993|‐0.995|0.173|‐1.550***|1.339|‐1.145|
||(‐1.38)|(‐1.17)|(0.15)|(‐2.58)|(1.50)|(‐1.62)|
|Non‐payment of income|‐1.861|‐0.474|‐3.050**|‐2.413***|‐0.779|1.207|
||‐1.23|‐0.58|(‐2.09)|(‐2.84)|(‐0.78)|(1.42)|
|Crime (robbery, theft)|‐0.417|‐2.900***|‐1.637***|‐1.462**|0.247|3.268***|
||(‐0.90)|(‐2.89)|(‐2.97)|(‐2.09)|(0.44)|(3.10)|
|Divorce, etc.|‐0.820|1.452|‐0.116|1.038|‐0.129|‐3.052|
||(‐0.95)|(0.39)|(‐0.33)|(0.32)|(‐0.28)|(‐1.53)|
|Injury|2.846**|‐0.946|‐0.763|‐0.550|0.079|0.503|
||(2.14)|(‐1.10)|(‐0.91)|(‐0.89)|(0.13)|(0.60)|
|Illness|0.698***|‐0.964|0.438***|‐0.909*|‐0.198|0.171|
||(3.33)|(‐1.39)|(2.52)|(‐1.70)|(‐1.00)|(0.23)|
|Death|0.813**|‐1.545|1.090***|2.462***|‐0.760***|1.223|
||(2.14)|(‐1.62)|(3.35)|(2.87)|(‐2.98)|(1.21)|

Notes: First row shows the mean of the dependent variable. Remaining rows are the results of a regression analysis. The first model is a probit, the rest are ordered probits. All models include, in addition to the shock dummies, pre‐shock wealth, each of the shock dummies interacted with wealth, the age of the household head (entered as a vector of dummies), a dummy indicating whether the household head is female, the ethnic group of the household (four groups), the education levels of the household head and the spouse (a series of dummies), the marital status of the head (also a series of dummies), the size of the household (seven dummies), dummies indicating whether grandparents live in the house, and village fixed effects. The first number in the pair is the linear combination of the marginal effects on the shock variable and on its interaction with wealth, evaluated at the mean wealth score of the quintile in question, and for the highest category (i.e. 1 in the first, and 4 in the second and third variables). The first number in the pair is the linear combination of the coefficient on the shock variable and its interaction with wealth, evaluated at the mean wealth score of the quintile in question. Numbers in parentheses are t‐statistics. *** p<.01, ** p<.05, * p<.1.
Policy Research Working Paper 6444

# **Abstract**

Transport costs are widely considered an important barrier to local economic activity but their impact in developing countries is not well-studied. This paper investigates the role of inter-city transport costs in determining the income of Sub-Saharan African cities, using two new data sources. Specifically, it asks how important access to a large port city is for the income of hinterland cities in 15 countries. Satellite data on lights at night proxy for city economic activity, and shortest routes between cities are calculated using new road network data. Cost per unit of distance is identified by world oil prices. The results show that an oil price increase of the magnitude experienced between 2002 and 2008 induces the income of cities near a major port to increase by 6 percent relative to otherwise identical cities 500 kilometers farther away. Cities connected to the port by paved roads are chiefly affected by transport costs to the port, while cities connected to the port by unpaved roads are more affected by connections to secondary centers. These are important findings for economic development in Sub-Saharan Africa since the majority of its population growth over the next few decades is expected to be in urban areas.

This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at adam.storeygard@tufts.edu.

_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
**Table 4: Targeting performance of MGNREGS across states**

|State|Participation<br>rate for the<br>poor|Participation<br>rate for the<br>non-poor|Targeting<br>differential|Rationing<br>rate for the<br>poor|Rationing<br>rate for the<br>non-poor|
|---|---|---|---|---|---|
|Andhra Pradesh|0.513|0.322|0.191|0.215|0.259|
|Assam|0.233|0.149|0.085|0.523|0.590|
|Bihar|0.127|0.075|0.052|0.756|0.816|
|Chhattisgarh|0.571|0.386|0.186|0.260|0.366|
|Gujarat|0.298|0.185|0.114|0.319|0.490|
|Haryana|0.106|0.037|0.069|0.701|0.760|
|Himachal Pradesh|0.510|0.318|0.192|0.173|0.206|
|Jharkhand|0.237|0.163|0.075|0.613|0.641|
|Karnataka|0.126|0.065|0.061|0.503|0.703|
|Kerala|0.116|0.112|0.005|0.535|0.516|
|Madhya Pradesh|0.534|0.319|0.215|0.297|0.438|
|Maharashtra|0.096|0.025|0.071|0.738|0.898|
|Orissa|0.317|0.135|0.182|0.509|0.650|
|Punjab|0.145|0.035|0.110|0.729|0.872|
|Rajasthan|0.728|0.579|0.149|0.166|0.150|
|Tamil Nadu|0.484|0.302|0.182|0.093|0.219|
|Uttar Pradesh|0.242|0.120|0.122|0.483|0.582|
|West Bengal|0.559|0.379|0.179|0.282|0.376|
|All India|0.325|0.210|0.115|0.428|0.463|

_Notes:_ Households classified into poor or non-poor based on poverty lines for Schedule 10 that would yield the same state-specific poverty rates as estimated from Schedule 1, and reported in Table 1. All-India figures reported in this table include only the states shown. _Source:_ Authors‘ calculations from NSS (2009/10).

27
firms.<sup>9</sup> Bernard et al. (2007) indeed show that U.S. exporters are significantly larger than nonexporters, by approximately 97 percent for employment and 108 percent for shipments. Verhoogen (2008) reports that on average Mexican exporters are twice as large as their non-exporting counterparts in terms of employment, revenues, and domestic sales. Bustos (2011) documents that on average in Argentina continuing exporters are substantially larger than nonexporters, by approximately 150 percent for employment and 177 percent for domestic sales. Data from the Enterprise Surveys of the World Bank Group further confirm this observation especially for middle- and low-income countries: for example, in 2013 in Bulgaria export represented 4.5% of sales for small (5-19 employees) and medium-sized (20-99 employees) firms and 32% for large firms ( _>_ 100 employees); and in Peru it accounted for 0.7% of sales for small firms, 4.1% for medium-sized firms, and 16.1% for large firms.

Prior literature documents that net entry rates into export mask a substantial turnover of exporters, and especially so in middle- and low-income countries, the bulk of our sample. Eaton et al. (2008) find that, in a typical year, about half of all Colombian exporters were not exporters in the previous year. Volpe Martincus and Carballo (2009) confirm similar large exit, entry and turnover rates of exporters for Peru. In line with this finding of the literature, in our sample the mean entry rate into export roughly equals 58% while the mean exit rate from export equals 57%. There is significant variation in the entry and exit rates of exporters. As shown in Figure 1, Panel C, countries with very similar net entry rates into export exhibit very different export sector dynamism, as captured by the degree of turnover of exporters. And Supplementary Table S1 reveals that the interquartile range for the share of exports of the 5% largest exporters exceeds 20%, signaling quite a diverse degree of concentration of the export sector across countries.

# **4 Baseline Results**

In this section, we investigate the impact of financial structures on export dynamics. In Section 5, we examine possible mechanisms that can drive this impact.

In Table 2, we study the estimates of equation (1) for the impact of financial structures on export dynamics by using measures of the relative importance of banks versus capital markets in the economy and measures of the relative importance of foreign banks within the banking sector of the country. Specifically, we consider the ratio of credit granted by banks over the sum of stock market capitalization and bank credit, the share of total bank assets accounted for by foreign banks, and the ratio of bank credit over the sum of bond market capitalization and bank credit. In all the regressions, we control for a measure of the overall financial development of the country, the ratio of total credit in the economy over GDP.

> 9See also Fernandes et al. (2016).

11
The AA group had higher mean growth over the whole period 1975-2005 (0.85 percent) than BB (0.39 percent). Growth was more erratic in the poorer countries. The coefficient of variation of growth is 4.67 for the BB category and 1.37 for the AA group. Given the high variance of growth rates, we cannot reject the hypothesis of equal mean growth rates in AA and BB countries between 1975 and 2005. Between 1995 and 2005 the two groups had virtually identical growth rates of about 1.60 percent per year. Within each group there is a wide diversity of growth performance. Growth rates in the AA group for example range from 6.24 percent for Botswana to -1.26 percent for Zimbabwe. The range of growth outcomes is somewhat more compact for the BB group, varying between 2.07 percent for Mozambique and -1.38 percent for Madagascar. All five of the region’s fastest growing economies for the period 1975-2005 are in the AA group.

One striking – and disappointing - feature from the point of view of hunting for leopards is the small number of transitional cases (BA or AB). BA countries comprise only Equatorial Guinea and Sudan, both oil exporters. These economies grew on average by 4.4 percent a year over the entire period, but their expansion was driven by very rapid growth in 1995-2005, when annual growth was 8.3 percent (from 1.8 percent in the first period). That boom enabled their GDP per capita to increase by 60 percent between 1975 and 2005. The AB economies – the Central African Republic and the Democratic Republic of the Congo – collapsed, mainly as a result of conflicts, leading average GDP per capita to shrink by more than half.

Given the diversity of growth experiences within our stable lower and higher income groups, it is possible our leopards are emerging within rather than between the groups. To continue our search for growth leaders we use a variation of the methodology developed by Hausmann, Pritchett, and Rodrik (2005) to identify growth accelerations (good times) and decelerations (bad times). Our approach (Arbache and Page, 2007) differs from theirs and that of researchers applying their method to Africa (IMF, 2007) in two important respects. First, it identifies both growth accelerations _and_ decelerations in a cross-section of countries. Second, it does not use a common threshold growth rate to identify growth accelerations. Instead, it defines good and bad times relative to each country’s long run economic performance. In Africa’s volatile, low growth environment this seems appropriate.

21
43

## FOOTNOTES

<!-- Start of picture text --> Jj<br><!-- End of picture text --> <!-- Start of picture text --> y<br><!-- End of picture text --> Applications to international trade and taxation issues are surveyed in Shoven and Whalley (1984). For models with a focus on energy policies see Hudson and Joergenson (1978). An exception is Dixon et al (1982) where a CGE model for Australia is used for analyzing a wide range of macroeconomic and microeconomic issues.

Bobinson (1986) surveys CGE applications for developing countries. In that survey, the emphasis is on macroclosure and applications rather than on international trade. The two surveys are therefore coaplementary.

Devarajan, Lewis and Robinson (1986) and Decaluwe and Martens (1986) provide bibliographic surveys.

...

<!-- Start of picture text --> 3/<br>-<br><!-- End of picture text --> <!-- Start of picture text --> 4/<br>-<br><!-- End of picture text --> A recent example of pass-through, though for a developed economy, is the dollar appreciation during 1981-85. Dornbusch (1987) observes that a 20 percent appreciation in the dollar only resulted in a 5 percent increase in the index for manufactured imports. He goes on to show that this relative price behavior among traded goods is consistent with a number of competing hypotheses about firm behavior and product characteristics including the one presented here. Krugman (1986a) also develops other pricing models that are consistent with this observation.

The presentation here follows de Melo and Robinson (1986).

The CET and CBS formulations in this context were first introduced by Powell and Gruen (1967) and Armington (1969) respectively.

Edwards and Ng (1985) discusses alternative definitions of the real exchange rate and of the indices used in computing it.

# 7/ -

The analysis below draws on de Melo and Robinson (1985).

This is what was done by Taylor and Black (1974) in one of the first trade policy simulations with a CGE model.

de Helo and BDbinson derive these expressions and compute values of Ee and s• for different parameter configurations in equations (4) and (5). They also show that this formulation can easily be extended to accommodate a less than infinitely elastic foreign demand for exports, with or without product differentiation on the export side, thereby extending the analysis for homogenous primary commodities.

- 10/ Chenery (1975) reviews the structuralist debate.

Feinberg (1986a, b) uses the model presented here augmented by a tera that captures the presence of oligopolistic interactions on the pricecost margin in a Cournot model to estimate econometrically the pass

> ~' '.
require that the calculation of PWs include a fair actuarial factor to account for this risk. The government also tops up any annuity payments that fall below the PBS level. In addition the Government provides a pension supplement to pensioners in the lowest 60 percent of the income distribution. This is equal to the PBS less 29.4 percent of the pension income of individual pensioners. The supplement is effectively eliminated when pension income is close to 60 percent of the average wage (the exact point of elimination depends on the level of the PBS relative to the average wage). This level is known as the maximum pension with solidarity support (PMAS). PMAS is gradually increasing and it is expected to reach approximately USD 600 in July 2011. The new universal pension effectively covers uninsured workers, who represent a significant proportion of all workers because of the continuing large relative importance of the informal labor market.

In Sweden a minimum pension guarantee of about 30 percent of the average wage covers the combined benefits from the new NDC and FDC schemes. In Switzerland, benefits from the first pillar are subject to a minimum level that is about 18 percent of the average wage. However, an additional means-tested supplement is paid from general tax revenues to pensioners with no other sources of income. This is estimated at 6 percent of average earnings, bringing the level of the combined benefit to 24 percent of average earnings.

Only Sweden and Switzerland have a first pillar (Table 4). These are contributory public schemes with earnings-related benefits.

**<u>Table 4: Structure of First Pillars</u>**

|Country|Type|Contribution Rate<br>(Percent ofcovered earnings)|Targeted Replacement Rate|
|---|---|---|---|
|Australia|No|||
|Chile|No|||
|Denmark|No|||
|Sweden|NDC|16%|39%|
|Switzerland|DB|8.4%|33%|

Sources: Andersen and Skjodt (2007); Brunner and Thorburn (2008); Buetler and Ruesch (2007); Palmer (2008); Rocha and Rudolph (2010); Rocha and Thorburn (2007); and Vittas (2008).

Sweden implemented a radical reform of its pension system in the mid-1990s. A new public unfunded NDC scheme was introduced to replace the pre-existing flat and earnings-related (ATP) DB pensions. The contribution rate for the NDC scheme is 16 percent of covered earnings. Out-of-employment spells are covered by government contributions and thus most workers are likely to have full contribution records. The targeted replacement rate for people on average earnings and retiring at normal retirement age amounts to 39 percent.

In the NDC scheme, the retirement decision is left to individual workers (within certain limits) but the pension benefit is adjusted to take account of the remaining life expectancy on retirement. NDC balances earn notional interest at 1.6 percent in real terms and NDC 5
**Table 4. Sex ratios at birth, 1995-2000, for the Sub-Saharan African countries with high hepatitis B infection rates as listed in Oster (2005), Appendix B**

||**DHS**|**UN**|
|---|---|---|
|Botswana|0.98|1.03|
|Liberia|1.05|1.03|
|Malawi|1.01|1.03|
|Mali|1.02|1.05|
|Togo|1.02|1.02|
|Zimbabwe|1.02|1.02|
|Sub-Saharan Africa||1.04|

_Sources_ : Col 1: Garenne (2002), calculated from the Demographic and Health Surveys conducted on national samples of these countries. Where there was more than one survey, that from the most recent year is shown. Col 2: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, _World Population Prospects: The 2004 Revision_ and _World Urbanization Prospects: The 2003 Revision_ <u>, http://esa.un.org/unpp, 15 June 2005; 11:19:25 AM</u> 10
25

- (ii) For longer-term hedges, i.e., more than a year, the use of exchange-traded futures/options contracts can only provide very limited coverage. Currentiy, all the open interest in crude oil futures contracts of more than a year is about equal to two month's of Venezuela's oil exports. For refined products, longer-term futures/options hedges can provide even less coverage. But, liquidity for longerterm hedges can be enhanced by using the OCM. The liquidity of the OCM can be assessed by approaching various brokerage firms or banks. It should be noted that in the case of longer-term hedges, the access and terms of using the OCM will depend on the perception in international financial markets regarding Venezuela's creditworthiness.

- (iii) There are few instances where hedging all the commodity price exposure is advisable. In most cases, partial hedges covering a faction of the exposure are carried out. The fraction of the exposure covered is determined by an analysis of the existing market circumstances, the amount of risk that is acceptable, the cost of hedging, the possible offset between price and quantity fluctuations as well as other circumstances specific to the company's or the government's interest.
conditions, Ricardo-Barro equivalence applies: a tax increase today, matched by lower domestic borrowing, will lead agents to expect lower future taxation and will be fully matched by a decline in saving. Therefore, total aggregate demand will be unaffected. Similarly a decline in government spending will not affect, by assumption, private marginal utility, but will induce private agents to expect lower future taxation. The consequent increase in private consumption will again fully offset the decline in government spending. Suppose however, as in Blanchard (1985) that, because of a finite probability of dynastic extinction, private agents discount the future at a higher rate than the government. Then, expenditure cuts and revenue increases may have different macroeconomic implications. More specifically, because of the existence of a finite probability of death, increases in tax revenues today will not be fully discounted into lower future taxes and will therefore lead rational private agents to reduce consumption. Similarly reductions in government spending will not be fully offset by increases in private spending. Which of the two strategies is more contractionay will depend in a complex way on the time pattern of future government surpluses. The earlier taxation is expected to decline in response to the drop in public spending, the more lLmited will be the recessionary impact.

The previous discussion abstracts from several complicating factors. For instance, it does not allow for the effect of liquidity constraints on households which would prevent the intertemporal smoothing of consumption in response to variations in disposable income. The models also postulate the existence of lump-sum taxation. Financing choices become therefore irrelevant. This result would no longer hold if the government had to rely on distortionary taxation (Rodriguez, 1989). The problem becomes more complex yet, if government expenditures affect private marginal utility. Then a reduction in public spending will lead to more (less) private spending, provided the two bear a substitute (complementary) relationship.

Empiricai av5.dence on the importance of Ricardian equivalence in developing countries is scant. Haque (1988) finds little empirical support to _14_