rafmacalaba/datause-encoder-data huggingface dataset:
We may calculate the left hand side of equation 5, since the three values on the right hand side are known from equations 1, 2, and 3. Equation 5 implies that even if initial wages differ between the CGE and micro models, the percentage change in the wage gaps will be consistent across the two models. By passing on percentage changes in wage premia by type of worker, instead of percentage changes in wages, the possibility of wage gaps moving in opposite directions in the macro and household data is eliminated. Within each group of workers, distributional changes occur; but, on average, for any group of workers, the relative wages for each type of worker are constrained to be consistent with the corresponding growth rates from the CGE model.

Given the known values in equations 1-5, and defining average wages for unskilled labor in agriculture as numeraire in the GIDD, so that y′�,� �y�′�,�, it is possible to calculate the percentage changes in average wage income of households in sector _s_ and skill level _e_ that are consistent with wage gaps expressed in Equation 5: Note that Equation 6 only operates on labor income. In order to adjust the micro data such that the weighted average percentage change in the per capita income/consumption across all households matches the change in real consumption per capita in the CGE model, a subsequent adjustment is carried out. Define Y as real per capita income calculated from the CGE model in the benchmark and Y<sup>�</sup> as its predicted value in the CGE model simulation. Define γ�� �∑�∈� y�,�� /n� as the per capita income of household h in the benchmark equilibrium, where y�,�� is the income of the ith member of household _h_ , and n is equal to the size of household _h_ ; . similarly, define λγ��� �∑�∈� λy��,�� /n�where y��,�� and λy��,�� are the unadjusted and adjusted values, respectively, of the income of the ith member of household _h_ in the counterfactual of the micromodel; the role of λ is explained by equation 14 below. Then define Y<sup>�</sup> as the weighted average value of real per capita income across all households, i.e., � ∑ν� �γ� = Y<sup>�</sup> (7) where ν� is the weight of household _h_ in aggregate income in the benchmark. Correspondingly ∑ω� �λγ��� � Y<sup>��</sup> (9) <mark><note – skipped equation 8?></mark> is the weighted average per capita income value in the policy simulation. Note that ∑υ� � �1, ∑ω� � �1 and λ is a scalar. Equations 11 and 12 allow for different household weights since the weights of the households will typically change over time. So that the percentage change in the aggregate value of household income is consistent with the CGE model, we constrain Y<sup>��</sup> by equation 13: We implement this constraint in a distribution neutral way. That is, we adjust all household income in the counterfactual by a scalar λ such that per capita household income equals λγ��� : as a result, λ can be defined by: 32
the burden must be distributed among them, and that hardy perennial, ‘the lack of political will’, will smother efforts to take real action at higher levels of government. Yet India’s constitution provides for communities to raise taxes as part of its design to further local democracy. Since a start must be made sometime, why not now?

Enhancing the benefits generated by past investments requires exploiting certain opportunities that arise within public infrastructure as a whole. In the wider road network, for example, the miserable state of most district roads limits the gains from PMGSY, since most trips involve a stretch of travel on both. Rural roads complement certain other forms of investment, in the sense that the joint returns are larger than the sum of those yielded by the separate components. Intuition suggests that this is especially so for irrigation, but it is likely to hold also for electricity and drinking water. Rural roads are, in contrast, good substitutes for local health facilities and schools. There is no need to have a primary health centre in every cluster of villages when a taxi or ambulance can be quickly summoned by telephone, nor a middle and high school when adolescents can easily cycle up to 8 km in little more than half an – hour and all of them would benefit from such daily exercise. A planned system of hubs will produce big savings, even if the bikes are provided free from the public purse. _Is a continuation of PMGSY socially profitable?_ This question must be posed, even if a stop is politically unthinkable. For there are always alternative uses of public funds, and scrutinising any given proposal using a common procedure imposes a measure of discipline on the thinking that leads up to decisions, if not on the decisions themselves.

The calculations in Sections 4 - 5 indicate that providing roads to villages in the hilly states is probably not socially profitable at the discount rate of 10 percent. Yet the net returns in the other participating states easily make good these shortfalls, even without an appeal to ‘growth effects’ generated by the larger economy, or to the existence of net, positive external effects produced by rural roads on the larger economy. On balance, Section 6 suggests that there are such effects, but the magnitude of the net benefit is shrouded in uncertainty.

The formal estimates of the benefits generated in the spheres of education and health are substantial in size, though the method used to arrive at them has its weaknesses. The respondents in the Orissa survey ranked these benefits roughly on a par with the commercial ones, as did many of the villagers with whom I spoke on my field-trip. When a passable road may well be decisive in life-and-death emergencies, people will indeed value it highly. At all events, it is reassuring that two such radically different 25
corresponding figures for the South are 39% vs. 24%. By contrast, in Western Europe and especially in the Nordic countries, distributions of formal and informal employees among four household income perception categories are rather similar.

<!-- Start of picture text --> Household Income Perception by type of Employment<br>In European Regions, 2004-2009<br>Employee with a contract Employee without a contract<br>East South West North East South West North<br>Living Very Difficult Living Difficult Living Very Difficult Living Difficult<br>Coping Living Comfortably Coping Living Comfortably<br>Regions sorted descending by share of low-income households among workers without a contract<br>Data not weighted by countries' population: A worker from any country is equally likely to be sampled<br>100 100<br>80 80<br>60 60<br>40 % of workers 40<br>20 20<br>0 0<br><!-- End of picture text -->

# **Figure 17 Perception of household financial situation among formal and informal employees in European regions** _Source:_ Calculation with ESS data.

The data mentioned above refer to the period between 2004 and 2009. For 20082009, round 4 of ESS provides also information on household income decile group within the country, although non-response to corresponding question (unlike the question on income perception) was quite substantial. Figure 18 summarizes this information (in quintile rather than decile form) by European region, separately for contracted and non-contracted employees. In Eastern and Western Europe, 11% and 13%, respectively, of non-contracted employees and just 6.5% of contracted employees live in the bottom quintile households. In Southern Europe these proportions are 9.9% vs. 5.4%, whilst in Northern Europe – 8.6% vs. 2.5%. In other words, while informal workers disproportionally suffer from absolute poverty only in the East and in the South, the incidence of relative poverty among informal workers in much larger than among their formal counterparts in all parts of Europe.

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sanitation bill, and these services are explicitly included in the rent in 93% of the compound rental agreements. Customers use Jisomee Mita, a web-based ICT platform that enabled property owners to use a mobile phone to self-read meters, receive and pay water bills, and check their current balance at any time. While 80% of compounds have a piped water connection, 7% report that their connection is not working. Because many households are unable to access piped water in their homes, the utility also operates water kiosks where residents can purchase clean water at a regulated price of US$ 0.2 per kiloliter.

# **3.3 Utility Charges and Reasons for Nonpayment**

Piped water is affordable relative to income in this setting. In 2016 the average monthly water service bill for Kayole Soweto property owners was US$ 4.20, which accounts for 3.6% of compound rent, 0.3% of total compound resident income, or 1.1% of property owner income. An additional US$ 6 per month was charged by the utility to repay the water connection loan (US$1.50 for 30 months) and the sanitation loan (US$ 4.50 for 60 months). Sanitation services were charged at 70% of the water consumption bill. Even with the inclusion of the loan repayment, the total monthly contribution is significantly lower than the 3% threshold used by the United Nations to assess water affordability and 5% threshold including sanitation (United Nations, 2010). However, even with reliable service and affordable tariffs, nearly 40% of property owners had yet to make a single payment for services since being connected 1.3 years ago on average and the average time since the last payment was made was 6 months. As a result of this nonpayment, property owners owed US$ 24 on average for past water use and behind on US$ 38 of their infrastructure loan repayments.

Why did property owners not pay their loans or consumption bills? Figure 2 presents property owner self-reported reasons for (i) not making a payment in the last 2 months and (ii) never making a payment based on self-reported data collected from a 2018 survey of 5,091 property owners in six settlements. Service quality was the most cited reason for nonpayment, reported by about half of respondents, lack of liquidity was the second most cited reason, and third was not knowing how to make payments. The payment system and billing infrastructure were not a perceived constraint.

Next, we look at correlates with payment behavior within a regression framework, using the 2018 survey data merged with the utility administrative billing data. The sample consists of compounds that (i) have a water connection and (ii) have tenants. The dependent variables include whether the property owner ever made a payment; (ii) the proportion of bills paid; (iii) the outstanding proportion of water connection loan; and (iv) whether the loan has been fully paid.

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# **Table 7: Factors Associated with Market Pressure Index I (consisting of Exchange Rate Depreciation and Reserve Loss)**

|**Dependent Variable:**|**Composite**|**Index of**<br>**Loss betw**|**Percent Ch**<br>**een April-**|**ange in N**<br>**August, 20**|**ominal Ex**<br>**13**|**change Ra**|**te and R**|**eserve**|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Increase in Current Account|||||||||
|Deficit 2010–12, over 2007–09|0.06|0.07|0.05|0.05|0.03|0.06|0.02|0.05|
||[0.65]|[0.69]|[0.52]|[0.50]|[0.34]|[0.56]|[0.22]|[0.43]|
|Avg. Annual % Change<br>in RER, 2010–2012|-.34***|-.37***|-.45***|-.46***|-.39***|-.36***|-<br>.35***|-.36***|
||[2.98]|[3.22]|[3.01]|[3.57]|[3.34]|[3.19]|[3.03]|[3.39]|
|Size (External Financing, 2010–|||||||||
|<br>12, Log)|0.56**|0.61**|0.58**|0.47*|0.42*|0.54*|0.36|0.57**|
||[2.08]|[2.43]|[2.30]|[1.77]|[1.96]|[1.99]|[1.09]|[2.20]|
|Reserves/M2 Ratio, 2012|1.41|2.27|3.96|1.74|2.74|1.28|0.71|1.53|
||[0.49]|[0.81]|[1.47]|[0.66]|[1.01]|[0.43]|[0.25]|[0.55]|
|Real GDP Growth, 2012|0.05<br>[0.28]||||||||
|General Public Debt, 2012||0.02<br>[1.14]|||||||
|Fiscal Deficit 2012, % of GDP|||0.24*<br>[1.69]||||||
|Inflation, 2012||||0.08*<br>[1.88]|||||
|Capital Control Index, 2012|||||0.03||||
||||||[1.53]||||
|Increase in Capital Controls,|||||||||
|<br>2009-12||||||0.03|||
|||||||[0.51]|||
|Exchange Rate Regime, 2012|||||||0.38*<br>[1.93]||
|World Governance Indicator,<br>2012||||||||-0.62|
|||||||||[0.80]|
|Observations|41|40|41|41|41|41|40|41|
|R-squared|0.36|0.39|0.41|0.40|0.40|0.36|0.42|0.37|
|Adj. R-squared|0.27|0.31|0.32|0.31|0.31|0.27|0.33|0.28|

Note: An increase in real exchange rate (RER) is depreciation; we take average annual percent change in RER during 2010, 2011 and 2012. Current account deficit (CAD) is current deficit as percent of GDP; we take average annual increase in CAD during 2010–12 over 2007–09. Robust t statistics are in parentheses. *** indicates the coefficients are significant at 1 percent level, ** indicates significance at 5 percent, and * significance at 10 percent level.

# **5. Conclusion**

Our exploration of the effects of the Fed’s tapering talk in the summer of 2013 yields the following conclusions. First, emerging markets that allowed the largest appreciation of their real exchange rates and the largest increase in their current account deficits in the prior period of quantitative easing saw the sharpest currency depreciation, reserve losses and stock market declines when talk turned to tapering. Second, measures of policy fundamentals and economic performance (the budget deficit, the public debt, the level of reserves, and the rate of GDP 14
# **4. Benefits and Costs of Port Infrastructure Improvement in East Asia**

_What Are the Benefits and Costs of Port Construction?_ With a considerable surge in demand for exports and imports, port authorities in the developing countries in East Asia rapidly expanded the capacity of their container ports in the 2000s. However, serious congestion remains. Our regression analysis suggests that the expansion of port infrastructure would _ceteris paribus_ reduce the import charges / trade costs, ultimately paid by the importers. In turn, reduction in the transport costs may lead to an expansion of trade through the ports. The consumer surplus for the importers should increase.<sup>29</sup> The partial equilibrium framework illustrated in Figure 2 above helps consider what happens to the welfare of the port users and port authorities. In the diagram, the increase in welfare is brought about by the decline of the port-related total transport cost from P0 to P1. The decline in the costs for port services is to pass through to the reduced charges of the international transportation services, such as forwarders, to the traders, which are recorded by the import charge statistics as import charges.

A hypothetical policy simulation can assess the net benefit of port capacity expansion in East Asia in terms of percentage change in trade costs. In our partial equilibrium framework, the net welfare gain due to the expansion of the port capacity equals to the sum of the increase in consumer surplus (the trapezium P0 P1 E1 E0) and increase in the port tariff revenue net of the marginal capital and operation costs from the expansion. The increase in consumer surplus can be estimated by means of the transport cost regressions undertaken above. The policy assumptions on the capacity expansion of the ports will imply the target point change of our port congestion index. Multiplying these point changes with the estimated coefficient of the index, around 0.0737, gives the estimates of percent changes of transport costs. As actual transport costs are largely unobservable, except for U.S. and Japan, the amount of gain in consumer 29 Another important question, beyond the scope of this analysis here is how to measure to what extent port infrastructure is a bottleneck or major constraint in a specific country. This question would require additional data and modifications to the analytical approach taken here.

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however, always matters: _g_ = 1 _/k_<sup>2</sup> generally leads to smaller mean model sizes and results in very different conclusions on which regressors are important than _g_ = 1 _/n_ , especially for random _θ_ .

Given each choice of prior, the results vary quite a bit across subsamples, especially for the inclusion probabilities where the interquartile ranges can be as high as 60%. It is interesting how the combinations of _g_ = 1 _/n_ with fixed _θ_ = 7 _/_ 41 (the setting implicitly favored in SDM) and _g_ = 1 _/k_<sup>2</sup> with _θ_ = 0 _._ 5 (the prior of FLS) lead to very similar results, both for model sizes and inclusion probabilities, as also noted in Section 4. Overall, the difference between fixed and random _θ_ cases is small for _m_ = 7, but substantial for _m_ = _k/_ 2.

For the MP data, results on posterior model size are quite similar as for the FLS dataset, and we can draw the same conclusions on the basis of the inclusion probabilities.

Finally, Figure 7 shows the robustness results for the SDM data set, where we have _k_ = 67 potential regressors. As before, the choice of _m_ critically affects the fixed _θ_ results, but not the ones for random _θ_ . Differences between fixed and random _θ_ cases are large for _m_ = _k/_ 2, but relatively small for _m_ = 7, as with both previous datasets. Again, the choice of _g_ always affects the results, and inference using _g_ = 1 _/n_ in combination with _θ_ = 7 _/_ 67 is quite similar to that using _g_ = 1 _/k_<sup>2</sup> with _θ_ = 0 _._ 5, for both model size and inclusion probabilities. For _g_ = 1 _/k_<sup>2</sup> inference on model size is quite concentrated on small values, with the exception of the case with fixed _θ_ and _m_ = 33 _._ 5.

Even though many results are not very robust with respect to the particular choice of subsample, it is clear that the differences induced by the various prior assumptions largely persist if we take into account small changes to the data.

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# ACKNOWLEDGEMENTS

The analysis benefitted from market intelligence and statistical information provided by following organizations Containerization Internationai Drewry Shipping Consultants Ltd., London Fairplay International Shipping Weekly Fearmleys and Fearnresearch, Oslo Hansa Zentralorgan fur Schiffahrt, Schiffbau, und Hafen Indian Shipping (Journal of the Indian National Shipowners Association), Bombay Institute of Shipping Economics and Logistics, Bremen Internationai Chamber of Shipping International Maritime OrganizaLion Organization for Economic Cooperation and Development Lloyd's Shipping Economist Lloyd's Register of Shipping Naftiliaki International, Pireaus Seatrade Organization Ltd., Colchester and Hong Kong SS&Y Research Services Ltd., London The author is grateful to a number of colleagues in and outside the Bank whose opinion and advice helped to interpret the observed developments and to sharpen the presentation. Special thanks are due to Esra Bennathan and Gregory Ingram o. **dhe** World Bank, William Bolitho of Australian National Lines (Melbourne), Karl-Heinz Breitzmann of Rostock University, John Harper of the International Finance Corporation, Nigel Hill of Hill Samuel Bank Ltd. (London), Georgy Levikov of Soyuzmorniiproekt (Moscow), Karl-Heinz Sager of Senator Line GmbH (Bremen), Deborah Seyman of Lloyd's Shipping Economist (London), Jayant Shakdher of Paramount Intermodal Transport Pvt. Ltd. (New Delhi), and Keith Trace of Monash University (Melbourne). Susanne Holste provided valuable assistance in compiling background information and data processing.
technologies are often inaccessible to smaller firms or those more severely exposed to credit restrictions. Infrastructure failures and credit limitations therefore interact in constraining firms’ development, both by making existing investments less productive and by discouraging new ones.

In this paper, we use a sample of 46,606 firms across 87 countries covering the period 2002-2006 to analyze the behavior of firms facing both infrastructure and financing constraints. In particular, our objective is to understand under which conditions they decide to invest in their own generating capacity, and how this decision is affected by the above mentioned constraints and their interactions with firm- and sector-level characteristics. The answers to these questions are important because they condition the policies towards industrial development and, in particular, the way policy makers in different environments should prioritize measures related to credit limitations versus those addressing investment in infrastructure.

A number of papers have documented the burden imposed on developing countries’ firms by an erratic, low quality supply of electricity. Early contributions include Lee, Anas and Oh (1996) and Lee, Anas, Verma and Murray (1996)—both of which use data from Nigeria, Indonesia and Thailand. Lee, Anas and Oh (1996) document the extent and incidence of public infrastructure deficiencies, the response of private entrepreneurs in terms of investment in private infrastructure and the private cost of generation. They conclude that the private costs of infrastructure deficiencies are substantial and that the burdens fall disproportionately on smaller firms, while pointing out large differences across these three countries, linked in particular to the regulatory environment. In Indonesia and Thailand, the opening up of infrastructure markets to private providers and the possibility of shared production appears to ease constraints on all categories of firms and to improve the reliability of service flows.

Lee, Anas, Verma and Murray (1996) develop a model of the firm in which electricity is produced internally, with scale economies to explain why firms supplement their purchases of publicly produced electricity with electricity produced internally (a practice that is prevalent in Nigeria, common in Indonesia, but rare in Thailand). The paper confirms strong scale economies in internal power production in both Nigeria and Indonesia and establishes that in both countries, smaller firms would be ready to pay higher prices for reliable public
in Nigeria, 96 kg/ha in Tanzania, and 38 kg/ha in Uganda (Sheahan and Barrett, 2017). Average use rates and even some application rates conditional on use are generally below government recommendations based on experimental trials; Tanzania for example recommends 150 kg urea and 100 kg DAP per hectare for maize production (Mutegi et al. 2015).<sup>8</sup> _Figure 1. Share of crop farming households using inorganic fertilizers. Source: RuLIS._

# **Pesticides**

Pesticides are agri-chemicals whose application protects crops from pressures that impede plant growth and development. Pesticides include insecticides, which protect against insect infestation and damage; herbicides, which kill weeds that compete with crops for nutrients, soil, and sun; and fungicides, which protect crops from fungi including rusts, mildews, and blights. These inputs are labor saving relative to the manual work of pulling weeds and dealing with insects and fungi (Tamru et al. 2017). Chemical pesticides can have human health consequences for agricultural workers and for individuals who are exposed to them through water pollution or through direct consumption in food (Jepson et al. 2014) and poor quality and improperly used pesticides can contribute to the emergence of resistance to active chemical ingredients among common weeds and insects (Cerdeira and Duke, 2006). Chemical pesticides have environmental effects as well, contaminating surface water and impacting aquatic life (Annett et al. 2014).

> 8 Fertilizer recommendations in many countries in Sub-Saharan Africa remain relatively spatially coarse, set at the region level within many countries.

6