rafmacalaba/datause-encoder-data huggingface dataset:
Table 3. Estimated probit results of determinants of being laid-off of rural off-farm job in <u>China due to global financial crisis between September 2008 and April 2009.</u>

||Once lost job in<br>Apri|September 2008 –<br>l 2009|Once lost job and did not find a new one<br>in September 2008-April 209|
|---|---|---|---|
||(1)|(2)|(3)<br>(4)|
||-0.006|0.007|0.007<br>0.006|
|Sex (male=1, female=0)|(0.023)|(0.023)|(0.017)<br>(0.016)|
||-0.003***|-0.002**|-0.0005<br>-0.0002|
|Age (years)|(0.01)|(0.001)|(0.0008)<br>(0.0007)|
||<br>-0.011***|<br>-0.010***|<br> <br>-0.007***<br>-0.007***|
|Education (years)|(0.004)|(0.003)|(0.003)<br>(0.002)|
|Sector dummy (compared||||
|to commercial services)||||
|||0.08**|0.007|
|Industry||(0.04)|(0.023)|
|||<br>013***|<br>001|
|Construction||.<br>(0.15)|.<br>(0.03)|
|||<br>0.24***|<br>0.14***|
|Agriculture||(0.08)|(0.07)|
|||<br>003|<br>0006|
|Others||.<br>(0.04)|.<br>(0.023)|
|Wage-earning (compared||0.13***|0.10***|
|to self-employed)||(0.02)|(0.01)|
|Province dummy||||
|(compared to Hebei)||||
|<br>|0.08*|0.05|0.02<br>-0.003|
|Shaanxi|(0.05)|(0.04)|(0.03)<br>(0.022)|
||0.01|-0.02|-0.005<br>-0.02|
|Liaoning|(0.04)|(0.04)|(0.026)<br>(0.02)|
||<br>0. 03|<br>0.02|<br> <br>-0.06***<br>-0.06***|
|Zhejiang|(0.04)|(0.04)|(0.02)<br>(0.02)|
||<br>0.13***|<br>0.09**|<br> <br>0.03<br>0.002|
|Sichuan|(0.05)|(0.05)|(0.03)<br>(0.024)|
||<br>0.10***|<br>0.08**|<br> <br>0.006<br>-0.001|
|Hubei|(0.04)|(0.04)|(0.024)<br>(0.021)|
|Observations|1415|1415|1415<br>1415|

Note: Standard errors in parentheses 30
<!-- Start of picture text --> Annual percentage change (Constant 2000 US$)<br>20<br>15<br>.<br>H Africa) EMENA<br>10 L i saaNq rnra" J Se AW, Baseline—Asia BaselineLAC—<=<br>i " vi \ TNA vo Baseline Baseline<br>’ he he aay Noa — weeeeee<br>}<br>5<br>1965 70 75 80 85 90 95 2000 05 10<br>Constant 2000 USSB (log scale) Constant 2000 USSB (log scale)<br>| |<br>3000 2500<br>1990 92 94 96 98 2000 02 04 06 08 10 1990 92 94 96 98 2000 02 04 06 O08 10<br>Actual Low Case<br>Baseline Trend (1965-87)<br><!-- End of picture text -->
Policy Research Working Paper 10825

# **Abstract**

This paper provides the first measures of vulnerability to poverty in Latin America and the Caribbean at the household level looking at natural hazards such as floods, landslides, cyclones, earthquakes, and droughts. It considers seven countries in the region: Brazil, the Dominican Republic, Ecuador, El Salvador, Honduras, Mexico, and Peru. The paper constructs a unique data base that links household surveys from the Socio-Economic Database for Latin America and the Caribbean database to data on natural hazards from the ThinkHazard database and Emergency Events Database, which permits the first estimates of how household exposure to natural events raises the probability of being poor. The results suggest that vulnerability to poverty is related to households’ coping and adaptation strategies, and there is great heterogeneity depending on the natural hazard assumed. The evidence generated by this approach helps in understanding the lack of coping mechanisms and adaptation strategies among vulnerable households. This understanding can be used to mitigate the effects of natural disasters. The evidence-based approach is useful for identifying specific vulnerable communities in disaster-prone areas. Furthermore, the information can be presented in the most disaggregated representative unit that the data allow so that policy makers can build more efficient management policies in their location.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at gcanavire@worldbank.org, solivieri@worldbank.org, mserio@worldbank.org, and aconconi@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
9 products. The choices for developing countries have narrowed; either they increrse protection to intellectural property arnd in particular extend patent protection to pharmaceutical drugs, or otherwise suffer actual or threatened retaliatory actions by industrial countries.

3. The economic interests at stake: a preliminary discussion What would the pharmaceutical companies of industrial countries gain frorm the introduction of patent protection in developing countries? How important are these gains? The first question is easy to answer, but the second has only recently begun to be analyzed. Essentially, introduction of patent protection would put an end to the era of copying brand name pharmaceutical drugs by domestic companif3 of developing countries. After patent protection is introduced, only brand name companies will be the "legal" suppliers of patented drugs.

Regarding the importance of the pharmaceutical drug market and the loss to copiers, the PMA has made estimates of some countries, these are presented in Table 1. Part of the pharmaceutical drug market in these countries is supplied by patent owners, but part is also sdpplied by copiers; the proportion supplied by these latter type of firms varies betwecn countries, being highest in India (36.1%) and lowest in Brazil (5.5%).6 Overall, in these four countries, the PMA estimates that the sale of copied drugs amounts to US$1.4 billion per year. The speed at which domestic copiers would loose their market to patent owners depends in part on whether patent protection would be enforced

> 6 These proportions have varied quite drastically over time as economic conditions and regulatory behavior have come to benefit multinational companies or domestic companies. See for example Katz and Groisman (1988) for the case of Argentina.
The inverse demand function is assumed to be that of Bulow and Pfleiderer (1983): where _P_<sup>_T_</sup> is the price charged by the traders to the consumers. There is a fixed number ( _Nr ≥_ 1) of refiners who import crude palm oil from the world market at an exogenous price _P_<sup>_w_</sup> and produce edible oil with a Leontief production function; for simplicity, we assume that 1 unit of crude is transformed into 1 unit of refined oil. The refiners incur per unit cost of _C_<sup>_r_</sup> for labor and other intermediate inputs in addition to the crude oil cost. Refiners engage in Cournot competition; prior to the reform they sell to DOTs. The case where the refiners collude with one another is captured by a value of _Nr_ smaller than the true number of refiners.

There is a fixed ( _Nd ≥_ 1) number of DOTs in the market. The DOTs buy DOs for the refined oil from the refiner at price _P_<sup>_r_</sup> , and sell these to wholesalers. DOTs do not take physical delivery of the oil, so incur no storage or transport costs themselves. DOTs engage in Cournot competition with one another in selling DOs to wholesale traders. As with the refiners, the case where the DOTs collude is represented by a value of _Nd_ smaller than the actual number of DOTs.

Evidence from a recent survey of wholesalers and dealers indicates that an important role of the DOTs is to provide supplier credit to the wholesalers (Emran et al. (2015)). DOTs are usually quite wealthy, with access to short term capital at lower costs than is available to most wholesalers. The DOTs sell oil to the wholesale traders on credit. They bear an interest cost of _i_<sup>_d_</sup> , smaller than _i_<sup>_b_</sup> which either wholesalers or refiners would bear.

The DOTs purchase DOs from the refiners at per unit price _P_<sup>_r_</sup> at the beginning of the period. Upon selling DOs at price _P_<sup>_d_</sup> at the end of the period, the effective price received by the DOTs _<u>P</u>_<sup>_d_</sup> discounted back to the beginning of the period is 1+ _i_<sup>_d_.Afterthereform,wholesalersbuyoil</sup> directly from the refiners at the beginning of the period by taking a loan from the market at interest rate _i_<sup>_b_</sup> _> i_<sup>_d_</sup> , and they sell the oil to retailers at the end of the period. Thus they pay an effective price (inclusive of finance costs) per unit of oil equal to _P_<sup>_r_(</sup> 1 + _i_<sup>_b_)</sup> after the reform. The key assumption in this version of the model is that the wholesale traders are not subject 12
especially in light of the fast growing literature on product variety and the extensive margin of trade (e.g., Hummels and Klenow 2003).

Worldwide export data are drawn from the United Nations COMTRADE database under the Harmonized Commodity Description and Coding System (HS), available beginning in the early 1990s for most countries at the 6-digit level of disaggregation (approximately 5000 products). These data have been used widely to analyze export dynamism and growth, as well as geographic patterns in export growth, but until Klinger and Lederman (2004) had not been used to study the emergence of new exports.

To identify a discovery, we require three periods: an initial period used to confirm that the good was never before exported (1994-1996) and to differentiate a discovery from a potentially sporadic export product; a window during which time the discovery can emerge (1997-2002); and then a final period when the discovery is confirmed to be an established export as opposed to sending of product samples in preparation for exporting (exported for at least $10,000 dollars in both 2002 and 2003). After eliminating microstates and countries missing more than two consecutive years of export data during the window, we are left with a sample of 73 countries representing all regions and levels of development (see Appendix for sample composition). In Klinger and Lederman (2004) we discussed how the number of export discoveries (obviously) changes with the criteria used to identify an export discovery. But it turns out that the basic relationship between the frequency of export discoveries and GDP per capita, changes to the filtering criteria in terms of the trade classification that is used, the levels of and number of years of exports required to be classified as a discovery do not affect the results.

5
19 integration efforts offers examples of some of these difficulties.14 Santomero (1989) notes that the United States itself had a tradition of regulation centering on product restrictions and geographic limitations, resulting in a fragmented industry with an excessive number of participants. He describes the 'long and tortuous road' followed by the industry in its efforts to move closer to full interstate banking.

The case of the developing countries is even more complex: few have a tradition of free capital movements and the foreign debt crisis has rendered their foreign exchange problems even more serious. Their indigenous banking systems have frequently grown within a framework of distorted signals, due to a combination of protection and governments' use of the banks as instruments to achieve non-economic targets. In most, p.-udential regulation and supervision are woefully inadequate. In such circumstances, adjustment to a fully competitive setting cannot be too abrupt. The speed of adjustment will reflect the costs of reorganization, of learning, and in general, of developing new instruments and practices within institutions.15 Liberalization of domestic financial markets may need to take precedence over liberalization of capital movements (i.e. external liberalization). Blejer and Sagari (1987) elaborate on the issues of sequencing in financial liberalization. What is needed are

> **1'** For instance, as of April 1989, only four European countries--Denmark, the Federal Republic of Germany, the Netherlands, and the United Kingdom--had fully liberalized capital movements with respect to both other EEC members and third countries. France still prohibited nonbank residents not involved in international commercial activities from holding deposits at banks in foreign countries or holding foreign currency deposits, other than those denominated in ECUs, at banks in France.

15 For an example of the magnitude of institutional reforms needed by large Indonesian banks, see World Bank (1989), box 7.5.
outcomes specific to social groups, to context and to specific intervention elements. Manning et al. (2016) provide a detailed discussion on these elements. However, the current version of the enhanced CBA APP is capable of more than what we have presented here. Below we describe three additional modules that are currently in various stages of development, testing and implementation.

Module 4 (as shown in Figure 4) improves upon earlier economic tools, such as the MCBT, by drawing upon and exploiting machine learning (ML) techniques. Here, we incorporate a ‘User Input Database’ module, where all data entered by users into the APP is stored as one set of records per project. The enhanced APP will allow the user to store their input data on their server, providing a single data resource for the ML module (i.e. Module 6). Here, we intend to provide source code to allow users to manage their data on their own server, utilizing their own security protocols. Specifically, since Module 1 identified and established a relationship between moderators and outcome, module 4 then captures those relationships allowing the system to learn from these relationships and identify moderators that are relevant to outcomes concerned. This allows for the creation of a more comprehensive database and the possibility of subsequent ML, as described below in Module 6 (e.g. imputation of missing data).

Linked to Module 4, Module 5 will provide code for users to build a database to store the calculated results after analysis. Storing the calculated results is crucial because it provides the ability to model the relationships between all input-relevant cost and benefit data (Module 1) and the output of CBA (Module 3). Therefore, the database in this module of the workflow diagram stores the benefits (that could also be weighted using a harm index) and analysis data, enabling the system to map the relations between input and output, and exploit this to learn and improve CBA over time.

Finally, Module 6 integrates ML techniques to achieve two main goals, namely: (i) to provide input support to the user by imputing missing values (currently in testing stage), identifying potentially erroneous values, and make suggestions about relevant contextual factors; and (ii) to improve the analytical capabilities of CBA by usefully reducing the number and types of variables to minimize user effort (e.g. time-consuming data entry) and develop better estimates (e.g. cost savings; crimes avoided), based on what the system learns from earlier projects.

29
# **Lessons from the 2014‐16 oil price collapse**

The policy response to the spectacular oil price collapse has been quite different across countries (Arezki and Blanchard, 2014; Obstfeld, Arezki, Ferretti, 2016). Conceptually, one needs to distinguish between countries that have buffers and those who have none. Those with buffers should use them to adjust gradually to the medium anchor. Those with no buffers have no choice and need to let the exchange rate depreciate. In practice, the differences in responses reflect different countries’ circumstances including the presence of buffers but also the share of imported goods in total domestic demand (e.g. 11 percent for Russia compared to 40 percent for the Gulf Cooperation Council (GCC)). Russia and Azerbaijan have either devalued or let their currency depreciate early on (some with risks of currency mismatch). Inflation from imported goods in turn pushed the CB to raise rates. GCC countries have kept their peg unchanged reflecting their very large share of consumption concentrated around imported goods and their open capital account. On the fiscal front, many GCC countries have however embarked on an ambitious reform program (subsidy cuts) to reduce spending and also diversify their economy (e.g. Saudi 2030 plan). Nigeria had initially opposed to devaluate its currency and hence lost most of its reserves, leading to an explosion of the black market premium. The authorities recently opted for a devaluation and a (managed) float.

Chart 4 –Hard to forecast <!-- Start of picture text --> Actual and Forecast Oil Prices 1979<br>180<br>1982<br>160<br>1983<br>140<br>1984<br>120<br>100 1985<br>80<br>1986<br>60<br>1987<br>40<br>20 1989<br>0<br>1990<br>1979 1984 1989 1994 1999 2004 2009 2014 2019 2024 2029 2034 2039<br>Source: Energy Information Administration.<br><!-- End of picture text --> 10
Although subject to data limitations, the ratio of total governrent expenditures to GDP is perhaps a mcre complete proxy for the relative size of the government in economic activity than the ratio of government consumption expendicure to GDP. We compute the extreme bounds on the ratio of total government expenditures to GDP for 1974-1989. The partial correlation with the average annual growth rate of per capita GDP is not robust. The sign of the coefficient remains negative but becomes insignificant when other variables are added.

The effect of government expenditures on economic growth, however, may depend on the way in which funds are allocated, not merely total expenditures by the government. Barro (1991) attempts to capture this difference empirically by removing education and defense expenditures from government consumption. In Table S-A, we provide extreme bounds results using the ratio of government consumption less defense and education expenditures to GDP over the 1974-1989 period during which IMF data is available for a broad range of countries. Barro (1991) finds this measure using Summers and Heston data to be negatively correlated with growth measured over the period 1960-1985. We find that the coefficient on government consumption expenditures less defense and education payments is always negative but becomes insignificant when the standard deviation of domestic credit growth and the Africa dummy are added.14 Continuing to examine the effects of the disaggregated components of total government expenditures, we tested in turn the ratios o1 government capital formation, government education expenditures, and government defense expenditures to GDP. None of these variables were robustly correlated with 14 We find that relationship between growth measured over the 1960-1989 period and the government consumption less defense and education indicator measured over the 1974-1989 period is robustly negative as in Barro (1991).

16
**Table 2 Government Spending and Fiscal Oil Revenues – Nigeria (1970-2005, levels in constant $)**

||**with Overha**<br>|**ng Variable and 1984 dummy**<br> <br>|
|---|---|---|
||**E. 1a**|**E. 2a**<br>**E. 3a**|
||<br>TotalGovernmentExpenditure|<br>CurrentExpenditure<br> <br>Capital Expenditure|
||1.49|0.88<br>0.56|
|Oil Revenue|(16.6)|(14.74)<br>(10.36)|
||2.69|1.45<br>0.97|
|Constant|(2.11)|(1.70)<br>(1.26)|
|Overhang<br>|0.52|0.31<br>0.23|
|variable<sup>10</sup>|(4.70)|(4.29)<br>(3.56)|
|Dum84*Oil|-0.63|-0.30<br>-0.37|
|revenue|(-5.93)|(-4.36)<br>(-5.82)|
|No. of Obs.|35|35<br>35|
|R-sq. overall|0.92|0.90<br>0.84|
|DW|2.22|2.24<br>1.38|

_Note: Data source: IMF World Economic Outlook database. 2. t-statistics in parentheses._ For more details, see Annex A2 The results come out very strongly, but this time without indications of serial correlation (except for the investment equation). Consider first the regression taking total public expenditure as dependent variable. Before 1984 the spending coefficient in good years is still high at 1,49, and significantly higher than one. Thus at least before 1984, the fight for resources was clearly settled by shifting the burden out to the future, on the margin spending exceeded revenue. This changed in 1984, however. The 1984 interaction term suggests that the marginal spending propensity ( _msp_ ) dropped by a substantial margin, bringing the coefficient in good years down marginally (although not significantly) below one, to 0,97. Thus the analysis suggests that there was a voracity effect operative before 1984, but there is no sign of excess spending (on the margin) afterwards. Again there is a bias in favor of current over capital expenditure.

Most interesting are maybe the coefficients on the overhang dummy, which should be negative for the voracity effect to be dominant, and positive if the DO hypothesis is to be accepted as the dominant explanation of excess volatility. The overhang-dummy is strongly positive and significant in all three regressions. So the coefficient during upturns (increases in oil revenues) is lower than the coefficient in downturns (decreases in oil revenues). Moreover, the value of the _msp_ in bad years is clearly and significantly larger than one both for total spending and for government consumption, as the DO

> 10 The overhang variable is the product of the overhang dummy and real oil revenue.

20