rafmacalaba/datause-encoder-data huggingface dataset:
|(5)<br>ln(light)|-0.443*<br>[0.237]<br>-1.240**<br>[0.610]<br>4,879<br>263<br>inear time trends.<br>es over the period<br>city, and averaged<br>optop20%) are the<br>ion of at least 100<br>nces are measured<br>FOB) in hundreds<br>f the nearest large<br> Robust standard<br>respectively.|
|---|---|
|(4)<br>ln(light)|-0.503**<br>[0.228]<br>-1.205**<br>[0.485]<br>4,879<br>263<br>y-specifc l<br>te countri<br>els in the <br>distance(p<br>a populat<br>ely. Dista<br>pot Price <br> are one i<br>light=5.5.<br>ent level,|
|(3)<br>ln(light)|-0.573**<br>[0.225]<br>-0.888<br>[0.736]<br>4,879<br>263<br>ts, and cit<br>stal prima<br>oss all pix<br>00k), and<br>ntry) with<br>, respectiv<br>e Brent S<br>3) and (5)<br>it cutof is<br>d one perc|
|(2)<br>ln(light)|-0.623***<br>[0.226]<br>-0.535<br>[0.501]<br>4,879<br>263<br>y fxed efec<br>es in 15 coa<br>ummed acr<br>nce(pop_≥_1<br>he same cou<br> population<br>erage Europ<br>n columns (<br>te. The tob<br>ten, fve, an|
|(1)<br>ln(light)|-0.683***<br>[0.223]<br>4,879<br>263<br>year and cit<br>l of 287 citi<br>l number, s<br>mate), dista<br>st city (in t<br>’s 1992 city <br>e annual av<br> distances i<br>t the prima<br>ance at the|
||isn’t primate)<br>% isn’t primate)<br>includes country*<br>nced annual pane<br>of the lights digita<br>ble. Distance(Pri<br>country, the neare<br>tile of the country<br>oil (specifcally th<br>eracted with these<br>in column 5) is no<br> *** mean signifc|
||ate)*_Poil_<br>100k)*_Poil_<br>100k)*_Poil_*1(nearest pop_≥_100k <br>op20%)*_Poil_<br>op20%)*_Poil_*1(nearest poptop20<br>ases<br>a separate tobit regression that<br>alysis is the city-year, for a bala<br>e dependent variable is the log <br>years within a year when applica<br>istance to the largest city in the<br>the nearest city in the top quin<br> kilometers. _Poil_ is the price of <br>arrel. The dummy variables int<br>column 3, or in the top quintile<br>d by city, are in brackets. *, **,|
||m<br>_≥_<br>_≥_<br>t<br>t<br><br> c<br>s<br>n<br>h<br>e-<br>d<br> <br>of<br> b<br>n<br>e|
||i<br>p<br>p<br>p<br>p<br>s<br>d <br>i<br>a<br>T<br>t<br> <br>d<br> <br>r <br>i<br>r|
||r<br>o<br>o<br>o<br>o<br>on<br>e<br>n<br> <br> <br>li<br>rk<br>n<br>s<br>e<br>k<br>te|
||P<br>p<br>p<br>p<br>p<br>i<br>r<br>m<br>of<br>. <br>el<br>o<br> a<br>d<br> p<br>0<br>s|
||distance(<br>distance(<br>distance(<br>distance(<br>distance(<br>Observat<br>left censo<br>Each colu<br>The unit <br>1992–2008<br>across sat<br>road netw<br>thousand, <br>in thousan<br>of dollars <br>city (of 10<br>errors, clu|

34
where C0A = I0 −∑pn0 tEEte−rt + ∑cn0 tAe−rt and where ctA are the variable costs associated with abatement.

In the next section, we focus our attention on the non-joint costs function given by equation (6). However, we also consider as an alternative the joint cost function given by equation (4). As it turns out, there are few practical consequences from choosing version of the applied model over the other.

# **2.2 The Empirical Emissions Abatement Cost Function**

Assuming fixed input prices, the basic expressions for the log-log and log-quadratic functional forms of the abatement cost for project _i_ can be given by: where C is the net present value of total abatement costs, A is total emissions abatement, and q is a vector of control variables (e.g., project duration, project types, and location).<sup>8</sup> Equation 7 is nested in 8 and the two are indistinguishable when γ is indistinguishable from zero. Given the parameter estimates, the marginal cost of abatement can be computed for different types of CDM projects corresponding to The vector of input prices, w, associated with the cost functions is not always observed; however, for a given time period and a given location, the prices of the inputs are likely the same. Thus, dummy variables for different project types, location, and time periods can be used as a proxy for the missing input price vector. Said more formally, let xm be the vector of inputs associated with a specific mitigation methodology, for example generating solar power. The associated vector of prices can vary by time (t) and

> 8 In the current setting, we suppress the time subscript assuming that the equilibrium level of abatement would be the same in each year. This restriction is consistent with the CDM pipeline dataset in which expected emissions abatement and investments are annualized based on the PDDs. We expect to relax this assumption in future work when data on actual abatement and investments are available.

8
# **Investigating the Impact of Climate Change on the Robustness of Index-based Microinsurance in Malawi**

S. Hochrainer<sup>1</sup> , R. Mechler<sup>1</sup> , G. Pflug<sup>1</sup> & A. Lotsch<sup>2</sup> 1 International Institute for Applied Systems Analysis 2 The World Bank Group, Agriculture and Rural Development Department

**Keywords: Microinsurance, agricultural risks, dynamic financial analysis.**
network of the communist era has not been tackled with the same vigor as have market-oriented reforms.

The loss of export markets in the East as a consequence of the Russian crisis in 1998 costs Poland around 3 percentage points of GDP and triggered a new round of enterprise restructuring to curtail falling profitability. This time, the resulting improvements in productivity were brought about in large part through reducing employment. This reduction, together with the increased numbers of newcomers to the labor market owing to the baby boom of the early 1980s, led to significant increases in unemployment. As of December 2002, over 3.3 million people (20 percent of the labor force) were unemployed.

These developments coincided with a shift in monetary policy in 1999, leading to the creation of an independent Monetary Policy Council (MPC). Inflation targeting became the main anchor of monetary policy, one of the means of facilitating accession to the EMU. When inflation rebounded in 2000 into double digits, the MPC progressively tightened monetary policy by raising its rediscount rate to a peak of 21.5 percent in August 2000. As a result, real lending interest rates increased to over 13 percent during 2001, up from around 10 percent during 19992000. In combination with possible over-investment during the high growth years of 1996-99, this led to a significant decline in domestic demand, and especially in investment, from the second half of 2000 onwards. At the same time, inflation declined impressively from 2001 onwards to less than 1 percent in 2003.

**Figure 2:  GDP Growth, Export, and Industrial Performance**

<!-- Start of picture text --> Real gro wth y/y, % GDP grow th<br><!-- End of picture text --> <!-- Start of picture text --> Industrial Production and Export Growth<br>12 30<br>10 25<br>Real growth y/y, %<br>8 20<br>6 15<br>4 10<br>2 5<br>0 0<br>-2 -5<br>Industrial Output (RHS) Export index growth (LHS)<br>1999 06 1999 09 1999 12 2000 03 2000 06 2000 09 2000 12 2001 03 2001 06 2001 09 2001 12 2002 03 2002 06 2002 09 2002 12 2003 03<br> Export grpowth<br><!-- End of picture text --> _Source_ : Staff calculations based on GUS data.

The macroeconomic environment deteriorated progressively at the end of the last decade. While monetary policy tightened during the second half of 2000, fiscal policy eased considerably during 2001. Not only did the general government balance widen from –3.0 percent for 2000 to -5.9 percent for 2002, but the structural deficit (i.e., corrected for business cycle effects) worsened by over 2 percent to over 5 percent of GDP by 2002 and 2003 (see Figure 3). This “tight monetary and loose fiscal” policy mix has additionally crowded out investment and moderated the potentially expansionary effect of fiscal policy.

4
overwhelmingly low-skilled; and the vast majority of foreign workers are, legally, allowed to remain in Malaysia for at most five years without the ability to bring any dependents, limiting assimilation.<sup>8</sup> The data for this paper come from the Malaysian Labor Force Survey (LFS), which provides detailed information on the sectoral, skill and geographic composition of the native and immigrant workers for the years 1990 to 2010.<sup>9</sup> Over this period the fraction of immigrants in the labor force increased from 3.6 to 10.6 percent. In terms of our structural parameter estimates, we find an elasticity of labor demand of 3.4, which is significantly larger than the elasticity of substitution between immigrant and native labor of 2.5.<sup>10</sup> Thus the scale effect outweighs the substitution effect in Malaysia. The IV estimates imply that, on average, an additional 10 immigrants in a given industry-region results in the employment of an additional 4.1 natives. Despite these very large reallocation effects across sectors or regions, immigration has a negligible effect on the relative native wages across local labor markets. This suggests that a large number of Malaysian workers are highly mobile. A 10 percent increase in immigration, roughly equivalent to a 1 percent increase in total the labor force, increases native wage levels by 0.14 percent, but decreases immigrant wages by 3.9 percent. As a result, the net effect on average wages (immigrants and natives combined) is also negative at 0.35 percent.<sup>11</sup>

> 8In addition, in Malaysia the minumum wage was only introduced in 2012, making it much easier to identify wage effects at the lower-end of the wage distribution during our sample period than would be the case in most OECD countries.

> 9A wage module was added in 2007 and we have detailed wage data for 2007-10.

> 10Our estimate is similar to the elasticity of substitution between high and low-skilled labor in the US (Autor and Acemoglu, 2012, provide a recent overview).

11Average wages (across immigrants and natives) fall due to immigration as a consequence of downwardsloping demand for output produced in Malaysia. If the scale effect were smaller than the substitution effect then immigration would result in both immigrant and native wages decreasing.

5
**<u>Figure 2. Exports and the real exchange rate</u>**

<!-- Start of picture text --> Dominican Republic Ecuador<br>38 4.80 55 4.80<br>36 4.75 50 4.70<br>34 4.70 45<br>32 4.60<br>4.65<br>30 40<br>4.60 4.50<br>28 35<br>26 4.55 30 4.40<br>24 4.50<br>22 4.45 25 4.30<br>20 4.40 20 4.20<br>Export/GDP Log of REER Export/GDP Log of REER<br>EL Salvador Guatemala<br>29 4.90<br>22 4.90<br>27 4.80 21 4.80<br>25<br>4.70 20 4.70<br>23 19<br>4.60 4.60<br>21 18<br>19 4.50 17 4.50<br>17 4.40 16 4.40<br>15 4.30 15 4.30<br>Export/GDP Log of REER Export/GDP Log of REER<br>Haiti Honduras<br>30 5.20<br>50 5.10<br>25 5.00 45 5.00<br>20<br>4.80 4.90<br>40<br>15<br>4.80<br>4.60<br>10 35<br>4.70<br>5 4.40 30 4.60<br>0 4.20<br>25 4.50<br>Export/GDP Log of REER Export/GDP Log of REER<br>65 Jamaica 5.10 27 Nicaragua 4.8<br>60 5.00 4.7<br>25<br>55 4.90 4.6<br>23 4.5<br>50 4.80<br>4.4<br>45 4.70 21 4.3<br>40 4.60 19 4.2<br>35 4.50 17 4.1<br>4<br>30 4.40<br>15 3.9<br>25 4.30<br>Export/GDP Log of REER Export/GDP Log of REER<br>Exports/GDP Log of REER Exports/GDP Log of REER<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Exports/GDP Log of REER Exports/GDP Log of REER<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Export/GDP Log of REER<br>Exports/GDP Log of REER<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Exports/GDP Exports/GDP Log of REER<br>Log of REER<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> Note: increases in the REER index indicate a real appreciation. Source: Author’s calculations.

11
22 _The presence of children in the household is positively correlated with younger and older women’s participation in the labor market._ Family reasons are reported as the chief motive for nonparticipation among both females aged 15 to 24 and aged 50 to 64, even if the proportion is smaller than that observed for the 30-to-49 age group (Table 11). However, contrary to prime-age women, they seem to increase participation when living with children (Table 12). In rural areas, this is probably explained by the fact that their labor is needed on the farm to compensate for labor losses due to women of childbearing age. Young women’s participation in the labor market appears to be affected by the presence of children to a lesser extent though. Other motives, such as schooling, might play a part in their non-participation decision. Indeed, as shown in Table 11, 21.8 percent of out-ofthe labor women aged 15 to 24 are in school.

**Table 12. Rural Labor Force Participation by Gender and Family Status* (in %)**

||||_Female_<br>||
|---|---|---|---|---|
||_Male_|_All_|_With_<br>_children_|_Without_<br>_children_|
|_Age group_|||||
|_15-24_|52.5|24.6|25|23.3|
|_25-49_|92.1|28.5|27.7|31.6|
|_50-64_|85.8|24.1|28.2|20|

Source: EPCV 2004. Figures are displayed in percentage.* Whether or not there are children under 10 living in the household.

_Nearly two-thirds of employed women in rural areas are self-employed, a proportion which is similar to that of employed men_ (Figure 10). However, wage earners only accounts for 9 percent of female employment compared to 22 percent for males. Women are also much more likely than men to serve as unpaid family labor (respectively 26 percent and 10 percent).

**Figure 10.Types of Employment in Rural Areas by Gender (%)**

<!-- Start of picture text --> Females Males<br>Self- Self-<br>employed employed<br>63% 64%<br>Unpaid family Wage earner<br>apprenticeworker/ 9% Unpaid family Wage earner<br>26% worker/appre 22%<br>Day laborer3% ntice10% Day laborer4%<br><!-- End of picture text --> Source: EPCV 2004.
damage the environmenL The remainder of this part assesses the evidence on the various channels through which aid may affect the environment.

# _Govermnent intervention and the environment_

The argument for government intervention on the enviromnental front derives from the notion of "market failure" kWinpenny, 1990; and Girma, 1992). Market prices do not incorporate environmental information, large parts of the environment are common property and liable to excessive use (the "tragedy of the commons"), environmental effects are not experienced directly by their instigators and private decisions do not normally take the interests of future generations into account. Thus, free market prices do not reflect the environmental external effects like pollution and the resource depletion which is inherent to certain forms of production, so that market intervendon is required to protect the environment.

On the other hand, goverment interventions may worsen environmental conditions - for example tax incentives for deforestation, subsidies for pesticides and fertilizers, and the underpricing of energy. However, as pointed out by Reed (1992), "getting prices right" will not ensure sustained natural resource management if the underlying institutional structure (uncertainty; pricing refonrs not implemented or sustained) does not enable economic agents to respond to the new price incentives. Jansen (1991) argues that the case is for better government intervention rather than for less, since manv of the existing forms of government intervention in developing countries are outdated and should be adjusted to the changed international conditions. But what is 'better intervention"? To answer that question requires a closer look at the links between economic policy and the enviromment.

## _Modelling the inkrs: the analyticalfiramework_

Table 6.2 (based on Mearns, 1991) lists several policy instuments and their likely environmental consequences, which are the channels through which policy is often considered to affect the environment. One clear conclusion is that the effects are very specific to the precise nature of the intervention and local environmental conditions, so that analysis must necessarily proceed on a country-specific basis. Mearns points to several weaknesses in this approach: principally that it ignores the problem of uncertainty for scientific explanation. Many complex nonlinear interrelationships world are reduced to simple causal links between just two key variables: economic policy reform and environmental impact. The linkages are identified _ea ante_ and at a very generalized level, assuming that structural adjustment policies are actually, and _105_
|12. Institutional Development ...........................|30|
|---|---|
|<br>13. Level and Mix of External Funding ....................|31|
|14. Government Objectives -- Poverty Commitment .............|31|
|15. Central Budget Per Capita Infrastructure Expenditures: Summary||
|and Conclusions ...............................|33|
|C. Determinants of Per Capita Consolidated Budget Expenditures on||
|Infrastructure<br>.....................................|37|
|1. Stock of Infrastructure .............................|38|
|2. Population Density ...............................|39|
|3. Urbanization ...................................|41|
|4. Sectoral Balance .................................|42|
|_5._Labor Force Participation Rate ........................|45|
|6. Level of Development .............................|45|
|7. Intemal Balance .................................|46|
|8. External Balance ................................|47|
|9. Size of the Foreign Sector ..........................|47|
|10. Terms of Trade Shocks ............................|48|

|11. Debt Obligat|ions<br>.........................................|48|
|---|---|---|
|12.|Institutional Development ...........................|49|
|13.|Level and Mix of Extemal Funding ....................|49|
|14.|Government Objectives -- Poverty Commitment .............|50|
|15.|Consolidated Budget Per Capita Infrastructure Expenditures:<br>Summary and Conclusions .........................|51|
|D. A Com<br>Cen|parison of Analyses of Per Capita Infrastructure Expenditure Using<br>tral Budget vs. Consolidated Budget Data .................|55|
|REFERENCES ....|.........................................|59|
|ENDNOTES..|............................................|62|

# **LIST OF TABLES**

|Table 1|Correlation Between PCOM & Poverty Indicators .....<br>..<br>.........|14|
|---|---|---|
|Table 2|Means of Central Budget Infrastructure Expenditure Data ....<br>.......|15|
|Table 3|Means of Consolidated Budget Infrastructure Expenditure Data ...<br>.....|16|
|Table 4|Central Budget Regressions: Number of Observations by Country ......|18|
|Table 5|Consolidated Budget Regressions: Number of Observations by Country . .|19|
|Table 6|Central Budget Base Infrastructure Regressions .......<br>..........|21|
|Table 7|Central Budget Infrastructure Regressions With Time Dummies .<br>.23||
|Table 8|Central Budget Infrastructure Expenditures Summary Overall Results<br>....|34|
|Table 9|Consolidated Budget Base Infrastructure Regressions<br>.<br>.40||
|Table 10|Consolidated Buget Base Infrastructure Regressions With Time Dummies .|43|
|Table 11|Consolidated Budget Infrastructure Expenditures Summary Overall Results .|54|
<mark>As noted by scholars and practitioners,</mark><sup>15</sup> <mark>adding modules to existing surveys such as the LSMS-ISA has a non-negligible advantage in terms of both timing and cost, as it involves only the relatively small marginal cost (compared to the total survey cost) of adding questions to an ongoing survey program already funded and underway. In the medium term, solving technical and ethical challenges related to the interoperability with non-conventional data sources would unleash even more innovations and opportunities. F</mark> or the moment, we see clear scope for general multi-purpose household surveys to play a leading role in the climate-migration field, as these tools <mark>hold key advantages over censuses and other administrative data sources due to their comprehensiveness and flexibility to collect more detailed data. Our review calls for the exploitation of these advantages.</mark>

> 15 See, for example, here: https://migrationdataportal.org/blog/household-surveys-key-potential-source-data-migration.

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# **2 Facts on Reserves and Central Bank Independence**

This section presents empirical evidence on the interaction between reserves and central bank independence. Subsection 2.1 describes the data sources used. Subsection 2.2 documents that central banks with more independence tend to accumulate more reserves. Finally, subsection 2.3 provides anecdotal evidence to support a key assumption of the model.

## **2.1 Data**

I use annual data from 1970 to 2017 for 11 Latin American countries.<sup>11</sup> As is common in the literature, I exclude data for default episodes, following Catao and Mano (2017).

For central bank independence, I use a _de jure_ CBI index from Garriga (2016). Following Cukierman, Webb, and Neyapti (1992), the index is based on 16 criteria coded on a scale from 0 to 1 (lowest and highest independence, respectively) and reflects the central banks’ political, financial, and policy independence from the government. For international reserves, I use data from the International Financial Statistics. As defined by the IMF (2001), reserves are "official public sector foreign assets that are readily available". This definition includes foreign currencies and foreign-currency deposits and securities, special drawing rights (SDRs), and the reserves position at the IMF. Following the standard convention, I exclude gold. For public debt, I use data from the IMF Historical Public Debt Database for the 1970-2012 period, and from the IMF World Economic Outlook Database for the 2012-2017 period. In line with my motivation, I consider all forms of defaultable public debt. This includes all maturities, and debt denominated in domestic and foreign currency as well as issued domestically or externally. For GDP, I use data from the World Development Indicators Database. For spreads, I use the Emerging Market Bond Index Plus (EMBI+) for the 1994-2017 period. For inflation rates, I use data from the International Financial Statistics.

> 11I focus on this region because, in the early 1990s, most countries in the region approved central bank independence reforms at the same time. The full sample consider for this study includes Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Venezuela.

7