rafmacalaba/datause-encoder-data huggingface dataset:
- Weyant, J. P., 2004. Introduction and overview to the EMF 19 study on technology and climate change policy. Energy Economics 26 (4), 501–515.

- Wikibooks contributors, 2014. First order linear 1. In: Ordinary Differential Equations. Wikibooks.

- Williams, J. H., DeBenedictis, A., Ghanadan, R., Mahone, A., Moore, J., Morrow, W. R., Price, S., Torn, M. S., 2012. The technology path to deep greenhouse gas emissions cuts by 2050: The pivotal role of electricity. Science 335 (6064), 53–59.

- Williams, R., 2010. Setting the initial time-profile of climate policy: The economics of environmental policy phase-ins. Working Paper 16120, National Bureau of Economic Research.

- Wilson, C., Grubler, A., Bauer, N., Krey, V., Riahi, K., 2013. Future capacity growth of energy technologies: are scenarios consistent with historical evidence? Climatic Change 118 (2), 381–395.

# **A. Efficiency conditions**

**Table A.5:** Variables and parameters notations used in the model.

||Description|Possible units|
|---|---|---|
|_i_|technology index||
|_h_|high-carbon technology (_h_)||
|_l_|low-carbon technology (_l_)||
|_z_|zero-carbon technology (_z_)||
|_ki,t_|capacity of technology _i_ at time _t_|GW|
|_qi,t_|production of technology _i_ at time _t_|GW|
|_xi,t_|physical investment in technology _i_ at time _t_|GW/yr|
|_νi,t_|shadow price of new capacity _i_|$/GW|
|_μt_|cost of emissions|$/tCO2|
|_αi,t_|cost of resource used by technology _i_|$/MWh|
|_γi,t_|shadow rental cost of existing capacity _i_|$/(GW⋅yr)|
|_u_<sup>′</sup><br>_t_|output price|$/GWh|
|_ci_(_xi,t_)|monetary investment in technology _i_ at time _t_|$/yr|
|_mt_|stock of atmospheric carbon|tCO2|
|_δ_|depreciation rate|yr<sup>−1</sup>|
|_r_|discount rate|yr<sup>−1</sup>|
|_Fi_|emission rate of technology _i_|tCO2/GWh|
| ̄<br>_M_|carbon budget|tCO2|
|_u_(∑_i qi,t_)|consumer utility|$/yr|

23
Table 2 (continued)

|Commodity Model|Modeling Purposes|Modeling Structure|Empirical Scope|Model Contribution|
|---|---|---|---|---|
|IronandSteel:<br>WISE (1981)|Forecast prices, demand and<br>supply quantities of steel<br>and requirements for major<br>imports and investments in<br>steel production facilities,|Competitive market with<br>allocation achieved through<br>Linear cosplementarity<br>programfing (LCP). Investment<br>dependson rational<br>expectation<br>13 equations.|Market economies<br>plus steel baLance<br>incentrally<br>plannied<br>economies. Benchmark<br>s.<br>years: 1970,<br>1975,<br>1980, 1985, 1990.|LCPmodel<br>applie**d**ynamically<br>to achieve feedback from market<br>price expectations investment<br>behavior.|
|Iron Ore (1987)|Multiple nalysis of the impact<br>of iron ore capacity,<br>steel<br>production,<br>scrap<br>prices<br>andMNY<br>prices on iron ore<br>consumptionandproduction.|Competitive and non-competitive<br>markets where bargaining at the<br>contractural<br>stage<br>is a major<br>price-determining force.<br>Econometric model cou.pled with<br>theories of bargaining and<br>gaming.|World.huluaL<br>1965-19&8. Nultiple<br>nalysis based on<br>historical period,|Theories<br>of bilateral<br>oligopoly<br>and principles of game theory<sup>as</sup><br>wellas competition<br>employed<br>to<br>determine<br>price formation for<br>iron ore during contractural<br>negotiations.|
|Lead and Zinc<br>(1983)|Explain<br>and forecast<br>equilibriu<br>prices for lead and zinc, based<br>on arginal cost pricing,|m Competitive<br>market<br>with joint<br>production employing linear<br>programing (LP) such that<br>lead-zinc<br>production<br>capacity<br>matches<br>demand<br>requireuants.<br>Econometric<br>equations<br>estimate<br>production<br>and consruption<br>variables.|World<br>production,<br>demand equilibrium.<br>Benchmark<br>year 1980.<br>Structural<br>equations<br>estimated<br>for 1962-80.<br>Simulation.<br>1983-199|Determine<br>minimum prices that<br>will cover costs in development<br>projects to determine growth.<br> <br>0.|
|Copper<br>(1987)|nalyze evo(ution<br>of world<br>copper<br>market<br>in the medium<br>and long term based<br>on a<br>variety<br>of assumptions.|Competitive<br>market<br>Including<br>inventory<br>adjustomnta.<br>57<br>equations.|world. Anmual<br>1968-1983.<br>Historica<br>perspective<br>since<br>1990s. Projection<br>to 1990-1995.|Provides<br>detailed<br>impact<br>analysis<br>l<br>of structural<br>change<br>adjustments<br>in the world<br>copper<br>market.|
|Fertilizer<br>(1985)|Determine<br>Impat of price<br>expectations<br>on cyclical<br>fluctuations<br>in investment<br>andprices,|Capacity<br>adjustment<br>or_supply_<br>side"<br>model<br>based<br>on general<br>autoregressive<br>rational<br>**and**<br>static<br>expectations.|world<br>divided<br>into<br>four regions.<br>Arual<br>1960-84.|Evaluate<br>role of expectations<br>in long-term<br>commodity<br>market<br>adjustments.|
jobs within those firms). Our paper thus contributes to this literature by identifying the effects of a internal forced displacement shock from a low-skilled population that, before the onset of conflict, had been primarily concentrated in agriculture and that was thus mostly absorbed by the informal sector upon arrival in hosting locations in the short term.

Third our paper, contributes to the literature on the economic impacts of armed conflict (see Abadie and Gardeazabal, 2003, Guidolin and La Ferrara, 2007; Camacho and Rodriguez, 2013; and Serneels and Verpoorten, 2015 for examples and Br ̈uck et al., 2013 and Blattman and Miguel, 2010 for literature reviews on this topic). We contribute to this group of studies by focusing our analysis on the impacts of IDP migration caused conflict within a state.

# **II Colombian Context**

According to information from the Colombian Human Rights Observatory, between 1995 and 2010 approximately 5.8 million people were internally displaced by violence in Colombia, accounting for approximately 11 percent of the total Colombian population of 2010 (see Figure I). The number of displaced individuals, however, may be even higher, since these figures only include individuals who searched for governmental support when arriving at their new locations. The same data suggest that, between 1995 and 2010, 82 percent of municipalities received at least one of these migrants (see panel b of Figure I).

The escalation of the Colombian conflict —-fought between the country’s guerrilla groups, paramilitary vigilantes, and armed forces-— was the main reason for forced displacement in the late 1990s and the early 2000s (Engel and Ib ́a ̃nez, 2007).<sup>6</sup> Forced displacement, more specifically,

> 6As documented in Rozo (2018), the Colombian internal armed conflict intensified in the middle of the twentieth century with the formal creation and growth of illegal armed groups. In 1964, adherents of a Cuban-style revolution founded the National Liberation Army (known by its Spanish acronym, ELN). Later, in 1966, a second left-wing group, the Revolutionary Armed Forces of Colombia (FARC in Spanish), was founded as the union of all the remaining communist guerillas. Initially, both groups claimed to defend the interests of the rural poor, aiming to overthrow the government and to install a Marxist regime. In time, however, the motivations of both groups became primarily economic. Paramilitarism began in the late 1980s as an anti-insurgent response by landowners and drug traffickers to left-wing guerillas’ actions in areas where the state was unable to provide security. In 1997, the paramilitary forces coalesced into the United Self-Defense Organization of Colombia (AUC in Spanish). By 2003, the AUC had declared a partial ceasefire, and some paramilitary blocs agreed to participate in a “disarming program” that concluded in

7
associated with substantially higher upfront investments (and longer durability) than latrines.<sup>10</sup>

**Table 2: Water and Sanitation Categories**

|**Water Infrastructure**|**JMP ladder**|**Revised**<br>**Water**<br>**ladder for**<br>**this paper**|**Sanitation**<br>**Infrastructure**|**JMP ladder**|**Revised**<br>**Sanitation**<br>**ladder for**<br>**this paper**|
|---|---|---|---|---|---|
|Piped water into hh|piped|piped|Flush to sewage|improved|flush|
|Public tap|improved|improved|Flush to septic tank|improved|flush|
|Tubewell/ Borehole|improved|improved|Flush to pit latrine|improved|improved|
|Protected well|improved|improved|VIP latrine|improved|improved|
|Protected spring|improved|improved|Pit latrine with slab|improved|improved|
|Unprotected well|unimproved|unimproved|Shared latrine|shared|improved|
|Unprotected spring|unimproved|unimproved|Traditional/open pits|unimproved|unimproved|
|Rainwater|unimproved|unimproved|Pit latrine without slab|unimproved|unimproved|
|Tanker-truck|unimproved|unimproved|Hanging latrine|unimproved|unimproved|
|Bottled water|unimproved|unimproved|Bucket|unimproved|unimproved|
|Surface water|unimproved|unimproved|No facilities|open|unimproved|

Even though the water and sanitation coding slightly deviates from our previous paper, it is worth stressing that the differences are mostly conceptual – the impact of recoding on estimated coefficients is marginal at most. We provide a complete list of all water and sanitation infrastructure codes of the 38 DHS surveys, as well as the chosen categories in Appendices 1 and 2.

10 See Table 9 for details.

9
investments in African manufacturing have been held back by high risk rather than low returns on investments. We discuss this in detail in Section 3.4.

# _ii) Access to credit_

Bigsten et al. (2003) look at formal credit market participation and credit constraints, based on survey data on firms in Burundi, Cameroon, Côte d’Ivoire, Ghana, Kenya, and Zimbabwe observed between 1991 and 1995. The authors use data on firms’ demand for external funds and whether loan applications were approved or not. Table 1, originally Table 2 in Bigsten et al. (2003), displays summary data on the frequency of loan applications, and, conditional on an application having been made, the outcome. These data suggest the demand for formal loans among African manufacturers is low: less than 20 percent of the firms in the sample had applied for a formal loan in the year prior to the time of the survey. Among those applying, the majority of firms obtain loans. There are large differences across the size distribution: among small firms loan applications are less common, and the success rate lower, than among larger firms. This is another example of considerable heterogeneity across firms.

**Table 1. Formal Credit Market Participation by Firm Size (percentages of firms)**

||Micro|Small|Medium|Large|All|
|---|---|---|---|---|---|
|Did not apply|92|82|80|75|82|
|Applied and did not receive|6|11|9|5|8|
|Applied and received|2|7|11|20|10|

_Source_ : Bigsten et al. (2003) A firm may be credit constrained even if it does not apply for a loan. For instance, a firm may expect that an application will not be successful precisely because there are credit constraints, and may therefore decide not to apply in order not to incur the transaction costs. Based on information on why firms did not apply for a loan, Bigsten et al. identify three groups of firms: those without credit demand, and, among firms with credit demand, those constrained and those unconstrained. Table 2, which summarizes these data, shows that 55 percent of the firms in their sample have no credit demand, 33 percent are credit constrained, and 12 percent are unconstrained. Across the size distribution, the differences are large. Close to two-thirds of the micro 10
**Table 1. Assumptions Used in the Projections: Cumulative Growth Rates Over the Period 1992-2005 (Percentage Changes)**

|**_Regions_**|**_Populationa_**<br>**_(1)_**|**_Labor_**<br>**_Forcea_**<br>**_(2)_**|**_Capital_**<br>**_Stock_**_a_<br>**_(3)_**|**_Human_**<br>**_Capital'_**<br>**_(4)_**|**_k/l_**<br>**_(3)-(2)_**<br>**_(5)_**|**_hk/l_**<br>**_(4)-(2)_**<br>**_(6)_**|**_Real_**<br>**_GDP_**<sup>**_7_**</sup><br>**_(8)_**|
|---|---|---|---|---|---|---|---|
|United States & Canada (USC)|10|13|43|67|30|54|41|
|EU-12|2|2|19|167|17|165|33|
|Japan (JPN)|4|-2|52|61|54|63|39|
|Korea (KOR)|12|12|115|258|103|246|127|
|Taiwan (TWN)|11|18|136|112|118|95|126|
|Hong Kong (HKG)|10|9|118|112|109|104|90|
|China (CHN)|18|16|216|78|199|62|203|
|Indonesia (IDN)|23|30|132|230|101|200|117|
|C)<br>Malaysia (MYS)|32|41|131|299|90|258|166|
|Philippines (PHL)|25|40|51|71|10|31|75|
|Thailand (THA)|19|26|178|332|151|305|171|
|Latin America (LTN)|25|32|17|119|-15|86|59|
|Sub-Saharan Africa (SSA)|47|48|39|268|-9|220|64|
|South Asia (SAS)|26|36|144|74|109|39|93|
|Rest of World (ROW)|18|36|37|103|1|67|37|

**_Sources:(see_** appendix for computational details) International Economic Analysis and Prospects Division, World Bank. GTAP model simulation.
- 30 -

# **<u>Table 12. Summary statistics of local business survey data</u>**

|<br>Variable|<br>Abb.|<br>Obs.|<br>Mean|Std.Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Number of employees||474|4.989|6.139|1|53|
|Dummy variable for program|D<sup>RSDP</sup>||||||
|beneficiaries from RSDP roads||474|0.234|0.424|0|1|
|Distance to all weather road (mi|nutes):||||||
|15 to 30 minutes|FEED15-30min|474|0.139|0.347|0|1|
|31 to 60 minutes|FEED30-60min|474|0.034|0.181|0|1|
|1 to 2 hours||474|0|0|0|0|
|2 to 3 hours|FEED2-3hr|474|0.053|0.224|0|1|
|More than 3 hours|FEED>3hr|474|0.015|0.121|0|1|
|Business perspective compared t<br>sales:|o last year||||||
|No change|NOCH|474|0.095|0.293|0|1|
|Increasing|INCRE|474|0.859|0.349|0|1|
|Business type:|||||||
|Shop||474|0.367|0.483|0|1|
|Hotel, restaurant, café|HOTL|474|0.340|0.474|0|1|
|Pharmacy|PHAM|474|0.293|0.456|0|1|
|Time fixed effects:|||||||
|_t_=2013|T2013|474|0.224|0.417|0|1|
|_t_=2014|T2014|474|0.184|0.388|0|1|
|_t_=2015|T2015|474|0.184|0.388|0|1|
|_t_=2016|T2016|474|0.173|0.379|0|1|

**<u>Table 13. OLS regression on size of firms</u>**

|<br>Coef.|<br>Std.Err.<br>Coef.|Std.Err.|Coef. Std.Err.|
|---|---|---|---|
|D<sup>RSDP</sup><br>0.310|(0.074) ***<br>0.304|(0.077) ***|0.197 (0.142)|
|FEED15-30min<br>-0.056|(0.077)|||
|FEED30-60min<br>-0.499|(0.155) ***|||
|FEED2-3hr<br>-0.026|(0.113)|||
|FEED>3hr<br>-0.468|(0.112) ***|||
|D<sup>Less than 30min</sup>|0.244|(0.089) ***|0.227 (0.102) **|
|D<sup>RSDP</sup>•D<sup>Less than 30min</sup>|||0.116 (0.151)|
|NOCH<br>0.067|(0.126)<br> <sup>0.057</sup>|<sup>(0.123)</sup>|<sup>0.060 (0.124)</sup>|
|INCRE<br>0.191|(0.119) *<br>0.191|(0.117) *|0.194 (0.118) *|
|HOTL<br>1.062|(0.071) ***<br>1.059|(0.071) ***|1.060 (0.071) ***|
|PHAM<br>0.336|(0.046) ***<br>0.346|(0.046) ***|0.347 (0.046) ***|
|T2013<br>0.254|(0.087) ***<br>0.251|(0.085) ***|0.251 (0.085) ***|
|T2014<br>0.101|(0.089)<br> <sup>0.097</sup>|<sup>(0.092)</sup>|<sup>0.092 (0.093)</sup>|
|T2015<br>0.047|(0.092)<br><sup>0.044</sup>|<sup>(0.096)</sup>|<sup>0.039 (0.097)</sup>|
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# **1. Introduction**

Emerging market economies (EMEs) have experienced a noticeable decline in inflation since the mid-1990s. Average inflation in these countries has declined from triple-digit figures in the late 1980s to low single-digit figures by the end of 2001 once a few outlier countries are excluded. This marked decline in inflation in EMEs is attributed to a more favorable external environment and the implementation of more prudent macroeconomic policies arising mainly from public discontent with high inflation.<sup>1</sup> More specifically, evidence suggests that fiscal consolidation—a prerequisite for macroeconomic stability—was one of the key contributing factors to disinflation: fiscal deficits in EMEs were lowered by one-half from the levels that prevailed in the 1970s and 1980s (IMF, 2001). External developments also contributed to the decline in inflation mainly through two channels: (i) the fall in global inflation; and (ii) the decline in oil prices in the first half of the 1990s. In addition to these factors, institutional reforms such as the enhancement of central bank independence, structural reforms in trade, product, and labor markets along with the improved access to global capital markets were among the forces behind the noticeable decline in inflation in EMEs.

In view of the adverse consequences of high inflation (Bruno and Easterly 1998, Ghosh and Phillips 1998, and Khan and Senhadji 2000), the observed decline of inflation in EMEs is clearly a positive development. However, whether this salutary development of a stable price environment in these countries is likely to be permanent and what kind of policies need to be pursued to maintain price stability still remain questions of considerable policy relevance. The objective of this paper is to shed some light on these questions by investigating the factors associated with the start of 24 inflation episodes in 15 EMEs between 1980 and 2001. To this end, we rely on a cross-

> 1 IMF (2001).

2
roots, forest floor shape, bathymetry, spectral characteristics of the incident waves, and the tidal stage at which the wave enters the forest. Even though additional studies are needed to define the specific details and limits of this protective function, experts and scientists agree that coastal forest belts, if well designed and managed, have the potential to act as bioshields for the protection of people and other assets against the above mentioned coastal hazards and some tsunamis (FAO, 2007; Das and Vincent, 2009; Arkema et al. 2013).

The latest global estimates of the total area of mangroves range from approximately 137,000 sq. km (Giri et al 2010) to 150,000 sq. km (Spalding et al 2010). Over the past century, mangrove forest cover has declined significantly. Although figures are not available for global mangrove forest cover loss over the century, estimates indicate the amount of loss to be approximately 35,600 square kilometers from 1980 (FAO 2007; Spalding et al 2010), with an average annual loss rate of 1.04 percent from 1980 to 2000, and 0.66 percent from 2000 to 2005. Rates of average loss may have stabilized or declined further between 2000 and 2012 with a few exceptions, mainly in Southeast Asia (Hamilton and Casey 2014).<sup>3</sup> Most of this loss is a result of mangrove clearing for aquaculture, tourism, industrial/urban development, and overexploitation of mangrove timber. In addition, urban and industrial pollution has contributed to degradation. (For example, see Tanaka 1995; Primavera 1997; Wolanski et al. 2000; Saito and Alino 2008; Giri et al. 2008; Feka and Ajonina 2011; Shahbudin et al. 2012; Munji et al. 2014; Nguyen 2014.) While significant losses due to human actions are likely to continue in the future, it is projected that stresses on mangroves may be further aggravated in the 21<sup>st</sup> century due to climate change. Continuation of the present rate of global warming may even threaten the survival of mangroves. Climate change poses a number of threats to mangroves: rise in sea level, rise in atmospheric CO2, rise in air and water temperature, and change in frequency and intensity of precipitation/storm patterns due to climate change (discussed in Alongi 2008). Among these threats from climate change, sea level

> 3 Data for extended periods are available for some countries.  For example, coastal development in the Philippines has led to more than a 50 percent loss of mangroves since 1900, mainly due to conversion for aquaculture (Primavera 2005, Primavera et al., 2014). Vietnam’s mangrove forests declined about 75% from 1950 to 2000, falling from roughly 400,000 hectares to 100,000 hectares (MONRE 2002).

3
on socioeconomic inequalities in health within a developing country. Though incomes are not available, Hurt et al., (2004) report death rates among those with no education that are about double those found for people with five or more years of formal education.<sup>9</sup> Estimates with more comprehensive coverage are provided by Gwatkin (2000), who reports death rates by age group for the poorest 20% of the population of the developing world. This follows a method outlined in Gwatkin et al. (1999) in which the country is the unit of observation for income and death rates. However, this method has the drawback that differences <u>within</u> countries are ignored; the differentials are driven entirely by between-country differences in average incomes (though states were used for China and India). By this method, deaths in the poorest 20% account for 35% of all deaths at age group 0-4 years, falling to 32% for ages 5-29, 27% for 30-44, 23% for 45-59 and 20% for 60+. The implied values of _DRD_ for each age group are 1.8, 1.6, 1.2 and 1.0 respectively.

Recognizing the aforementioned data problems in studying the socioeconomic differences in social indicators, Bidani and Ravallion (1997) use an indirect method of inferring the conditional means from cross-country (or cross-regional) aggregates. An econometricdecomposition method on cross-country data is used to estimate the differences in various social indicators between the poor and non-poor; hereafter this is termed the BR method. In essence, the regression coefficient of the social indicator on the poverty rate across countries estimates the difference between the mean social indicator for the poor and that for the non-poor.

Bidani and Ravallion did not implement their decomposition for overall death rates. However, it is not difficult to do so for the purpose of this paper. Figure 2 gives a scatter plot of the CDR for 2002 against the latest Chen-Ravallion “$1 a day” poverty rates across the 83

> 9 Significant income effects on mortality arising from higher prices for foodstaples in Matlab thana are also reported by Ravallion (1987).

11