rafmacalaba/datause-encoder-data huggingface dataset:
A closely related paper in the literature is the work of Eaton, Kortum and Kramarz (2011) (EKK from here on). EKK use customs-level data to understand the patterns of French ...rms’ exports. Their focus is on constructing the simplest model that ...ts most of the facts, rather than on trade policy. They also add a reduced form version of Arkolakis’s (2010) market access costs to explain the presence of many small ...rms with a limited attachment to the market, as well as ...rm and market speci...c demand shocks. We see their work as very complementary to ours. They look at the “big picture” and try to match the patterns in ...rm-level exports by all French ...rms, in all industries, to all countries. As a result, their model is unsuited to zooming in on a particular industry and incorporating the relevant trade policy details as our model is designed to do. Moreover, and perhaps more critically, their model, like that of Chaney (2008), assumes that the mass of potential entrants is ...xed. In contrast, we treat the mass of entrants as endogenous. Since we show that this entry margin does most of the heavy lifting in the adjustments that occur in response to policy, such di¤erence in our assumptions is worth emphasizing. Our paper is also related to Bernard, Redding and Schott (2011), which also features market demand shocks in order to determine the export behavior of multi-product ...rms.

Our model has two policy-relevant predictions. First, it suggests that a small country can increase its exports quite considerably if granted easily accessed preferences, and through cost-reducing policies. We explicitly show how to incorporate these preferences and the costs, both ...xed and variable, associated with obtaining them into a structural model that is suitable for estimation and policy analysis. Conversely, factors that raise export costs, like corruption or bad infrastructure, can really take a toll on exports. Second, the model suggests that preferences to developing countries can have a catalytic e¤ect. In our model, preferences given by one developed country can signi...cantly raise the exports to the other market rather than diverting trade away from other markets as predicted in standard competitive settings. This occurs because preferences raise the expected return to entry in the industry. Once a ...rm has entered the industry, it may end up exporting to markets other than those where it was given preferences if it gets an adequate demand shock. Our low estimate of elasticity of substitution between products means that entering ...rms make room for themselves in product space, so that greater entry does not quickly drive down pro...ts, which magni...es 5
<!-- Start of picture text --> ~ wy 0.10<br>0.04 0.16 0.18<br>0.03 0.060.02 0.32<br>=6 0.07oor Cen<br>= 008 067 “<br>no] =<br>a<br>5<br>Ss ot 0.85 082<br>2 0.69 0.07<br>= on oy 0.08<br>a 0.51 047 0.03<br>N<br>0.23 0.25<br>o<br>2001 2018 2001 2018 2001 2018 2001 2018<br>No education Fund. Secondary Superior<br>GE 4c, SR in Const’) «=ES Senvic.<br><!-- End of picture text --> <!-- Start of picture text --> ~<br>—5 0.57 054 0.55 054<br>5 0.69<br>no] 0.74<br>a<br>5<br>os oT 0.11<br>© 0.13 0.19 on<br>=<br>wn 0.05 on<br>N 0.20 0.16 0.30<br>“ 0.19 0.18 — one<br>0.10 0.11 0.10 0.10<br>o 0.04 0.05 nn 0.02 nna nine<br>2001 2018 2001 2018 2001 2018 2001 2018<br>No education Fund. Secondary Superior<br>GE 4c, SR in Const’) «=ES Senvic.<br><!-- End of picture text -->
## **Measuring aggregate poverty levels**

Aggregating across all households, we calculate poverty figures associated with each micro simulation for total population and various groups of households. The poverty lines used in our calculations, reported in the World Bank’s PovCalNet, were introduced into our household surveys in order to replicate the most recent available published rates of extreme poverty.<sup>1</sup> Using an elasticity of 0.6 for the cost of living with respect to household size estimated by (Lanjouw and Ravallion 1995), we identified the effective per-capita expenditure level of the households at the poverty line and used this estimate as the poverty line throughout the study. If, as a result of a simulation, the effective per-capita expenditure of a household crosses the poverty line, we account for this and update the list of households in poverty. Using the survey household weights and household size information, we then translate the list of households in poverty into the corresponding poverty rate and poverty gap measures defined by Foster, Greer, and Thorbecke (1984).

# **Data**

We base our work on an extensive dataset of household expenditure and income patterns which we have constructed using available household survey data for the 31 developing countries listed in Table 1 These 31 countries are the ones for which we have been able to obtain post 2000 household data for both expenditures on and income derived from the food commodities of interest. In terms of population, our sample covers about half of the low- and middle-income countries and includes some large developing countries (e.g. India) as well as a number of small ones (e.g. Belize, Albania). Due to the availability of suitable data, our sample covers best the regions of South Asia (nearly 100 percent coverage) with other regions being represented by a smaller fraction of countries; however, each World Bank developing country region is represented by at least one country.

The data collected in our data set include household-level production and expenditure data for 39 distinct food and agricultural items, and total household incomes and expenditure. The information on household finances is supplemented with a number of variables describing

> 1 We used the PovCalNet web-based tool as at February 2012 to obtain estimates of the poverty rates at the $US1.25/person/day poverty line definition.

13
**Table 2.2: Insurance** Sector **Regutory Characteristics in the East Caribbean Markets (US**

||Antigua<br>and<br>Barbuda|Barbados|Dominica|Grenada|St.Kitts<br>and<br>Nevis|St.<br>Lucia|St.Vincent<br>**&**the<br>Grenadines|Trinidad<br>&<br>Tobago|
|---|---|---|---|---|---|---|---|---|
|Minimum<br>Capital||||||Local:|||
|Requirement<br>(US$ million)|0.07|1.5|0.07|0.09|0.07|0.10<br>Foreign:<br>0.20|0.07|0.16|
|Solvency<br>Margin<br>(Assets-<br>Liab.)above<br>premium<br>income|Minimum<br>capitalor<br>10%of<br>premium<br>income|$0.25m<br>or25%<br>of<br>premium<br>income|Minimum<br>capitalor<br>10%of<br>premium<br>income|$0.06m<br>or10%<br>of<br>premium<br>income|Minimum<br>capitalor<br>10%or<br>premium<br>income|$0.07m or<br>20%of<br>premium<br>income|Minimum<br>capitalor<br>10%of<br>premium<br>income|Minimum<br>capital or<br>20% of<br>premium<br>income|
|Reserve<br>Requirement|na|40%of<br>annual<br>premium<br>income|30%<br>annual<br>premium<br>income|na|10%of<br>annual<br>premium|40%of<br>annual<br>premium|na|40%of<br>premium<br>income|
|Premium<br>Tax(%)|na|5|None|na|5|Local:3<br>Foreign:5|3|6|
|Corporate<br>Tax(%)|40|40|30|30|38|33.3|40|35|
|Tna=-not~avai<br>SourcePollner,|lable<br> 2001,p. 37||||||||

**Table 2.3: Structure of** the East **Caribbean General/Pro erty Insurance Market (1998)**

||Antigua<br>and<br>Barbuda|Barbados|Dominica|Grenada|St.<br>Kitts<br>and<br>Nevis|St.<br>Lucia|St.Vincent<br>and the<br>Grenadines|Trinidad<br>and<br>Tobago|Total|
|---|---|---|---|---|---|---|---|---|---|
|No.ofGeneral<br>Insurance<br>Companies|16|22|13|19|9|21|12|29|141|
|Foreign<br>Companiesas<br>Percentageof<br>Total'|88|45|95|76|67|86|85|12|66|
|Property<br>Insurance<br>Premiums(Gross<br>US$million)|10.6|43.0|5.8|8.6|6.3|11.9|9.6|52.1|148|
|GrossPremiums<br>%ofGDP|1.8|3.0|2.4|2.7|2.4|2.1|3.3|0.9||

'Approximately 75% of these companies are companies from Barbados, Guyana and Tnnidad & Tobago Source Pollner, 2001, p 37 26
Policy Research Working Paper 6150

# **Abstract**

Armenia meets about 75 percent of its energy needs through imports, with natural gas imports from Russia accounting for about 80 percent of total energy imports and 60 percent of total primary energy supply. Because of high dependence on imported energy, Armenia is vulnerable to external energy price shocks, which are often beyond the control of its policymakers. A most recent case in point was the 2010 Russian gas tariff increase, which led to a nearly 40 percent increase in the retail gas price for residential consumers. Coming on the heels of the global economic recession that hit Armenia’s economy hard, the price hike amplified the impact on households that rely primarily on gas for heating and cooking. Using aggregate energy consumption data and a nationally representative household survey immediately before the crisis, this paper provides an overview of household energy consumption patterns, highlights Armenia’s energy vulnerability, and estimates the direct poverty and distributional impacts of the increase in the cost of imported gas. The analysis shows that the gas price hike resulted in a significant increase in energy expenditures, with disproportionately higher impact on the poor and vulnerable households. The paper concludes with a discussion on the effectiveness of the mitigation measures employed by the Government of Armenia.

This paper is a product of the Human Development Sector Unit, Europe and Central Asia Region. 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 lersado@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> REAL PRIVATE IWESrMENT/REAL GDP<br>0.08<br>0.07<br>0.06<br>0.05<br>0.04'I<br>0.03 -<br>0<br>0.02-<br>1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986<br>YEAR<br>° PVI/GDP<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 4a. Poverty headcount ratio<br><!-- End of picture text --> <!-- Start of picture text --> After top-up transfer (with change in poverty headcount ratio)<br>Headcount among 60+ (no means test) Change in headcount (no means test)<br>Headcount among 60+ (means-tested) Change in headcount (means-tested)<br>Argentina Bolivia Brazil ChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHonduras MexicoNicaraguaPanamaParaguay PeruUruguayVenezuela, RB<br>.2 0.160 0.151 0.156 0.154 0.169 0.148<br>0.136 0.136<br>0.116<br>.1 0.036 0.042 0.088 0.027 0.048 0.034 0.093 0.065 0.084 0.058 0.082 0.047 0.045 0.059 0.051 0.056 0.051 0.059 0.081 0.070 0.096 0.042 0.086 0.018 0.022 0.076 0.088<br>0<br>-0.012<br>-0.023<br>-0.033<br>-.1 -0.055 -0.059 -0.055<br>-0.083<br>-0.098 -0.092 -0.101<br>-0.114 -0.148 -0.135 -0.128 -0.113 -0.115 -0.119 -0.145 -0.139 -0.147 -0.133 -0.122<br>-.2 -0.172 -0.172 -0.159 -0.177 -0.159 -0.167 -0.164<br>-0.188 -0.191<br>-0.199 -0.205<br>-0.229<br>-0.237<br>-.3 -0.257<br><!-- End of picture text --> Poverty line = 50% of median per capita income, OECD equivalency scale adjusted household size The poverty profiles after top-up transfers were estimated using the pre-transfer poverty line 32
used to improve the credit market but to bring to life a credit market that would otherwise be moribund.

The kind of joint liability contract that can enhance efficiency is however difficult to implement in the Eurozone countries because of restrictions placed by the laws and treaties that bind the European Union, and in particular, the Treaty of Lisbon. Hence, what this paper argued for is the need for amending the Treaty of Lisbon, which would empower the European Central Bank to _de factor_ provide certain guarantees and also permit one nation to stand guarantee for another nation’s borrowings directly, instead of using convoluted means which do not work very well and merely create a source for financial intermediaries to earn additional money.

# **References**

Akerlof, G. (1976), “The Economics of Caste, and of the Rat Race and Other Tales of Woe,” **Quarterly, Journal of Economics** , vol. 90.

Arnott, R. and Stiglitz, J. (1991), “Moral Hazard and Nonmarket Institutions: Dysfunctional Crowding Out or Peer Monitoring,” **American Economic Review** , vol. 81, No. 1.

Basu, K. (1986), “One Kind of Power,” **Oxford Economic Papers** , vol. 38.

Basu, K. (1989), “Technological Stagnation, Tenurial Laws and Adverse Selection,” **American Economic Review** , vol. 79, No. 1.

Basu, K. (1991), “International Debt Problem, Credit Rationing and Loan Pushing,” **Princeton Studies in International Finance** , No. 79.

Basu, K. (2000), **Prelude to Political Economy: A Study of the Social and Political Foundations of Economics** , Oxford University Press, Oxford and New York.

26
million in 1995-98 respectively.<sup>7</sup> On the other hand, the two main groups of products imported from China to Sub-Saharan Africa during 2015-18 were “ _machinery and transport equipment_ ” and “ _manufactured goods classified by material_ ”. Each group represented US$ 18.7 and US$ 9 billion in 2015-18, respectively —a sharp increase from US$ 433 and US$ 416 million in 1995-98, respectively.

# **_5.3 Is there greater scope for intra-regional trade?_**

Across the world, countries have increasingly engaged in regional trade agreements (RTAs). For instance, the number of RTAs in force worldwide has increased from 50 in 1990 to 280 in 270. Currently, the most notable examples around the world are the Trans-Pacific Partnership (TPP), the Transatlantic Trade and Investment Partnership (T-TIP), the Regional Comprehensive Economic Partnership (RCEP) in Asia, the Pacific Alliance in Latin America, and the Tripartite Free Trade Agreement in Africa.<sup>8</sup> These agreements have also become more complex in nature. Regional trade negotiations go beyond tariffs and include policies that boost trade and investment in goods and services such as competition policies, government procurement rules, and intellectual property rights. These more complex agreements —also known as _deep_ agreements— are found to boost trade, foreign investment, and global value chain participation (more than less complex trade agreements). On average, deep trade agreements increase goods trade by more than 35 percent, services trade by more than 15 percent, and GVC integration by more than 10 percent (Mattoo et al. 2017, Osnago et al. 2017, and Ruta 2017).

Negotiating and enacting regional trade agreements can help: (a) promote long-term growth by enabling countries to exploit comparative advantage and economies of scale, and (b) accelerate the process of structural transformation by facilitating the transmission of knowledge and technology and the development of new products. In the case of Sub-Saharan Africa, a question that emerges is how trade can be further increased within the region and exploit the scale of a larger and unified regional market to create opportunities for economic transformation.

> 7 The export product categories are based on COMTRADE SITC Revision 1.

> 8 The Tripartite Free Trade Area (TFTA) was signed in 2015. It proposed a free trade agreement between the Common Market for Eastern and Southern Africa (COMESA), Southern African Development Community (SADC), and East African Community (EAC). Overall, it enabled the participation of trade between 27 African countries.

33
-ii-

vi. A number of lower-middle income (and some larger low-income) countries are at an earlier stage of systems development. The experiences of more advanced countries will be useful to continuing investments; these will include support for policy and institutional development. Key policy issues will be clear structural separation of training from general education and the development of alternatives to diLect government financing.

vii. In small low income countries, recent Bank investment experience suggests strategies that concentrate resources, and that emphasize nonformal training centers, training quality, development of management capacity both in training institutions and in enterprises, and aggressive marketing of training opportunities and services. Regional professional support institutions merit consideration.

viii. For investments supported by the World Bank, at least, the vocational secondary school has become a less attractive mode of training. Vocational secondary schools can be effective in middle income countries when they acquire the characteristics of effective nonformal centers -- strong linkages with enterprises, and the ability to respond klexibly to changing labor markets and to offer salaries and incentives sufficient to attract and retain qualified instructors. In low income countries, weak implementation capacity and the nascent stage of enterprise development, coupled with the curriculum and staffing rigidities and recurrent cost constraints characteristic of Ministry of Education operations, have made it difficult to adapt the secondary model effectively in these directions.

ix. At the same time, strong secondary education is important to trainability, either after employment in enterprispi or before in nonformal centers. In this context, the decline of Bank investment in general secondary education over the past decade needs re-examination. A new concept of secondary education in the low income countries is needed.

x. Thus the Bank faces both an opportunity and a challenge in prQviding support for vocational education and training over the balance of the century. The opportunity is to capitalize on -- and to extend -- ;he generally successful pattern of training systems development in middle income countries. The challenge is posed by the problems of developing cost-effective training systems in small low-income countries, notably in Sub-Saharan Africa. Successful investment will require a very high level of policy and institutional development content; of special importance will be continued development of alternative financing arrangemenus.

xi. These conclusions emerge from an analysis of World Bank investments in vocationally-specific education and training for industrial employment in the period FY63-86. The revieL is based on a
**Table 1** **<u>(continued):</u> Female Labor Dependence of Sectors**

|ISIC Code|Sector Name|Dependence|
|---|---|---|
|300|Ofce, accounting and computing machinery|0.34|
|311|Electric motors, generators and transformers|0.32|
|312|Electricity distribution and control apparatus|0.30|
|313|Insulated wire and cable|0.32|
|314|Accumulators, primary cells and primary batteries|0.26|
|315|Electric lamps and lighting equipment|0.34|
|319|Other electrical equipment n.e.c.|0.42|
|321|Electronic valves and tubes and other electronic components|0.46|
|322|TV and radio transmitters; telephony and telegraphy apparatus|0.38|
|323|TV and radio receivers, sound or video apparatus|0.43|
|331|Medical appliances and instruments|0.38|
|332|Optical instruments and photographic equipment|0.45|
|333|Watches and clocks|0.42|
|341|Motor vehicles|0.09|
|342|Bodies for motor vehicles; trailers and semi-trailers|0.08|
|343|Parts and accessories for motor vehicles and their engines|0.21|
|351|Building and repairing of ships and boats|0.09|
|352|Railway and tramway locomotives and rolling stock|0.08|
|353|Aircraft and spacecraft|0.15|
|359|Transport equipment n.e.c.|0.21|
|361|Furniture|0.20|
|369|Manufacturing n.e.c.|0.38|
|371|Recycling of metal waste and scrap|0.17|
|372|Recycling of non-metal waste and scrap|0.25|
||Mean|0.274|
||Min|0.08|
||Max|0.71|

**Table 2: Summary Statistics for Female Labor Need of Exports**

|||_OECD_|||_Non-OEC_|_D_|
|---|---|---|---|---|---|---|
||Mean|St. Dev.|Countries|Mean|St. Dev.|Countries|
|1960s|.251|.043|20|.271|.077|100|
|1970s|.239|.034|20|.260|.078|103|
|1980s|.244|.043|20|.269|.094|103|
|1990s|.262|.043|20|.302|.111|123|
|2000s|.256|.033|21|.293|.124|127|

30
# **5. Conclusion**

Rwanda has made major strides toward a sustained economic recovery following the genocide of 1994 and the subsequent formation of a civilian government. The inflow of large quantities of foreign aid into Rwanda has made the extent of recovery and growth possible. However, scaled-up foreign aid can have some potential adverse effects such as aid dependency. This may particularly happen if aid has a significant negative effect on tax efforts of the government and on public investments. In this paper, we carry out a theoretical and empirical study to examine the above issues as a first step toward designing the interventions to support the effective use of aid. The results from the paper hopefully provide insights to the effective use of aid to support growth.

The theoretical part develops an optimizing model in which the recipient government decides on the optimal level of tax and optimally allocates total government revenue (tax revenue and foreign aid) between current expenditure and public investment. Our theoretical prediction is that an increase in aid is likely to reduce the optimal tax rate and the proportion of public expenditure allocated to public investment.

The empirical analysis uses time series data on Rwanda, to estimate that an increase in aid is associated with a reduced average tax rate in Rwanda, but the magnitude of the effect is very small. A 1 percentage point increase in aid to GDP ratio has reduced the rate of taxation by only 0.05 percentage point. It would appear that the reforms put in place to strengthen the efficiency of revenue administration have mitigated the impact of ODA. As for public investment, we find that an increase in aid also had a negative effect on the proportion of public expenditure allocated to public investment 30
Figure 9 - TFP Growth 1970<!-- Start of picture text --> ior.<br>1%.<br>saeins<br>Lor.<br>oor.<br>Lo gO BP FP FB PPP PP RS TP<br>2s.<br>1%<br>som<br>“_<br><!-- End of picture text --> Figure 10 - TFP Levels 1970-2008 <!-- Start of picture text --> 150<br>os6<br>oS2<br>O8s<br>oOg4<br>O80<br>O76<br>13970 7975. 1320 1985 1950 1936. 2000 20065.<br><!-- End of picture text -->