rafmacalaba/datause-encoder-data huggingface dataset:
displayed in Fig. 2. Vogt-Schilb et al. (2012) provide some numerical simulations calibrated with IPCC (2007) estimates of costs and abating potentials of seven sectors of the economy.

# **4. Marginal levelized abatement costs (MLACs)**

In this section, we solve for the optimal marginal investment costs in the general case when the depreciation rate is positive ( _δi >_ 0). Then, we show that the levelized abatement cost is not equal across sectors along the optimal path, and in particular is not equal to the carbon price.

# _Optimal marginal investment costs_

**Proposition 2.** _Along the optimal path, abatement in each sector i increases until it reaches the sectoral potential a_  ̄ _i at a date denoted Ti; before this date, marginal investment costs can be expressed as a function of a_  ̄ _i, Ti, the deprecation rate of the low-carbon capital δi, and the social cost of carbon μ:_

# Proof. See AppendixB.

Equation 10 states that at each time step _t_ , each sector _i_ should invest in lowcarbon capital up to the pace at which marginal investment costs (Left-hand side term) are equal to marginal benefits (RHS term).

In the marginal benefits, the current carbon price _μe_<sup>_rt_</sup> appears multiplied by a positive time span: _δ_ <u>1</u> _i_ �1 _− e_<sup>_−δi_(</sup><sup>_Ti−t_)�</sup> . This term is the equivalent of ( _Ti − t_ ) in the case of infinitely-lived abating capital (9). We interpret _K_ as the marginal benefit of building new low-carbon capital. The longer it takes for a sector to reach its potential (i.e. the further _Ti_ ), the more expensive should be the last unit of investments directed toward low-carbon capital accumulation. _K_ reflects a complex trade-off: investing soon allows the planner to benefit from the persistence of abating efforts over time, and prevents investing too much in the long-term; but it brings closer the date _Ti_ , removing the option to invest later, when the discount factor is higher. This results in a bell-shaped distribution of mitigation costs over time: in the short term, the effect of discounting may dominate<sup>8</sup> and the effort may grow exponentially; in the long term, the effect of the limited potential dominates and new capital accumulation decreases to zero (Fig. 3).

Marginal benefits also have another component, that tends to the marginal cost of maintaining abatement at its maximal level: _e_<sup>_−_(</sup><sup>_δi_+</sup><sup>_r_)(</sup><sup>_Ti−t_)</sup> _c_<sup>_′_</sup> _i_<sup>( ̄</sup><sup>_ai_).As the</sup> first term in (10) tends to 0, the optimal MIC tends to the cost of maintaining low-carbon capital at its maximal level: After _Ti_ , the abatement is constant ( _ai,t_ = ̄ _ai_ ) and the optimal MIC in sector _i_ is simply constant at _c_<sup>_′_</sup> _i_<sup>(</sup><sup>_δia_ ̄</sup><sup>_i_).</sup>

> 8 if either _Ti_ or _δi_ is sufficiently large

8
countries, Dollar et al. (2013) show that growth of the incomes of the bottom 20 percent, and bottom 40 percent of the income distribution on average move one for one with average growth. As a result, we should expect on average the incomes of settled migrants to broadly move with those of the countries they are working in, even if these migrants are working in jobs earning below average incomes.

However, temporary migrants moving on work visas often face binding minimum wages that exceed the market-clearing wage. The result is that adjustments to rising incomes at destination can take the form of increases in the numbers of temporary migrants, but little change in the wages these migrants earn (McKenzie et al. 2014). If this is the case, our estimates will be an upper bound on the growth in migrant income.

A further concern could be that the selection of migrants changes over time. For example, if in 1990 it was largely low-skilled migrants moving, and in 2010 the migrants moving were of higher skill, then the incomes earned by migrants may increase as a result of the increased skill level, in addition to any increase in wages for individuals of a given skill level. This would only bias our results if migrants were becoming skilled at a different rate than the native population. Using data from Brücker et al. (2013) on the educational breakdown of migrants to 20 OECD countries, we calculate that the share of developing country migrants who were low-skilled (having no schooling, primary, or lower secondary education only) fell from 48.6% in 1990 to 34.4% in 2010. So it is the case that migrants have become more skilled. But during the same period, the share of the native-born in these 20 OECD countries with low-skill fell from 35.2% to 16.6%. Thus in relative terms, migrants became increasingly low-skilled relative to natives over the period we look at. This suggests that growth in migrants’ earnings should not be greater than the growth of their destination economies, a hypothesis we can test.

Table 2 uses data from the United States Census and American Community Survey to compare the income growth of migrants in the U.S. to U.S. GDP growth. The income of developing country migrants working in the U.S. rose 19.3 percent in real terms over 1990 to 2010, which is close to the growth in real GDP of 23.2 percent. When we look at the growth of the incomes of 15
Table B12: Asset ownership, by wealth quintile (continued)

||(1)|(2)|(3)|(4)|(5)|Factor|
|---|---|---|---|---|---|---|
|||||||loadings|
|**Main wall material**|||||||
|None|0.010|0.071|0.026|0.016|0.019|0.005|
|Cane|0.498|0.366|0.224|0.094|0.117|-0.235|
|Bamboo|0.000|0.087|0.169|0.188|0.127|0.050|
|Stone wood|0.000|0.071|0.175|0.320|0.299|0.185|
|Uncovered adobe|0.000|0.000|0.000|0.006|0.006|0.058|
|Plywood|0.000|0.000|0.006|0.000|0.000|-0.009|
|Carton|0.006|0.016|0.010|0.010|0.000|-0.028|
|Cement|0.000|0.000|0.003|0.013|0.023|0.096|
|Stone cement|0.000|0.000|0.006|0.036|0.097|0.173|
|Bricks|0.000|0.000|0.036|0.087|0.068|0.083|
|Cement blocks|0.000|0.000|0.000|0.003|0.110|0.408|
|Covered adobe|0.000|0.000|0.016|0.013|0.006|0.017|
|Wood planks|0.424|0.236|0.120|0.074|0.029|-0.235|
|Other|0.061|0.152|0.208|0.139|0.097|-0.026|
|**Main roof material**|||||||
|Leaf|0.702|0.680|0.510|0.456|0.386|-0.193|
|Cane|0.000|0.000|0.003|0.003|0.000|-0.004|
|Bamboo|0.006|0.000|0.006|0.003|0.003|-0.012|
|Stone mud|0.100|0.104|0.162|0.139|0.136|0.004|
|Uncovered adobe|0.084|0.061|0.156|0.178|0.133|0.033|
|Cement|0.000|0.000|0.000|0.000|0.198|0.396|
|Stone cement|0.058|0.052|0.091|0.104|0.068|-0.009|
|Cement blocks|0.000|0.000|0.000|0.000|0.003|0.062|
|Coverer adobe|0.000|0.000|0.000|0.000|0.010|0.348|
|Other|0.049|0.104|0.071|0.117|0.062|-0.025|
|**Window type**|||||||
|Any|0.000|0.078|0.341|0.570|0.513|0.269|
|Shutters|0.000|0.000|0.029|0.227|0.305|0.360|
|Glass|0.000|0.000|0.000|0.006|0.006|0.081|
|Screens|0.000|0.000|0.000|0.000|0.003|0.073|
|None|0.570|0.518|0.334|0.084|0.097|-0.297|
|Other|0.430|0.405|0.295|0.113|0.075|-0.237|
|**Other**|||||||
|Persons per room|3.935|3.972|3.973|4.055|3.794|-0.003|
|Livestock|0.550|0.553|0.588|0.602|0.539|-0.011|
|Observations|309|309|308|309|308||

Note: Columns 1-5 shows the share of households owning the good or having access to the utility for each of the five wealth quintiles. Wealth quintiles are determined using the sample distribution of the asset index produced using the Filmer-Pritchett procedure (Filmer and Pritchett, 2001) and using all the variables presented in the Table.

24
# **1. The ‘Unhappy Development’ Paradox in Developing Arab Countries**

In the 2000s, many developing countries in the Middle East and North Africa (MENA) did well according to the regularly tracked poverty statistics and human development indicators. Absolute poverty, measured at $1.25 a day, declined in all economies, except the Republic of Yemen, and was low on average. The incomes of the bottom 40 percent, measured as 2005 PPP-adjusted per capita expenditure, grew at higher rates than average expenditures in many developing Arab countries for which information was available (Ianchovichina, Mottaghi, and Devarajan 2015). The Gini inequality indexes were low by international standards and did not worsen in most MENA economies (Ianchovichina, Mottaghi, and Devarajan 2015). Importantly, the region made notable strides in reaching not only the Millennium Development Goals related to poverty and access to infrastructure services (especially drinking water and sanitation and Internet connectivity), but also in terms of reducing hunger and child and maternal mortality, and increasing school enrollment (Iqbal and Kiendrebeogo 2015).

Prior to the Arab Spring uprising, most developing MENA countries were seen as relatively stable places. Only two MENA countries—Iraq (7th) and the Republic of Yemen (15th) —made it to the top 25 of the 2010 Failed States Index<sup>1</sup> of Foreign Policy. Libya and Tunisia were ranked 111th and 118th of 177 countries, respectively, and so they appeared among the stronger and less fragile countries in the world (Goodwin 2011). With autocratic rulers in power for many years, the cracks in these countries’ models of government remained invisible to most observers, including political scientists (Gause 2011), and some even considered Islam a stabilizing force (Bromley 2014). Thus, the Arab Spring transitions of 2011 took most economists, political scientists, and policy makers by surprise (Gause 2011; Goodwin 2011; Bellin 2012; Bromley 2014).

Yet, the emergence of social discontent in the Arab countries could be detected using subjective data. Life satisfaction in many MENA countries was below the average for the group of countries at a similar level of development (figure 1a) and had dropped significantly in the years prior to the Arab Spring events (figure 1b). By the end of the 2000s, people in the developing parts of MENA, especially in the Arab Republic of Egypt, Iraq, the Syrian Arab Republic, Tunisia, and the Republic of Yemen, were among the least happy people in the world (see figure 2 and appendix A).<sup>2</sup> In Egypt, for instance, average life-evaluation levels plunged on a 0-10 scale<sup>3</sup> from 5.5 in 2007 to 4.4

> 1 The Failed States Index measures stability based on economic, political, and military indicators.

> 2 The incidence of depression was also observed to be high in MENA, according to Ferrari et al. (2013).

> 3 The two extreme ends of the range capture worst possible life (0) and best possible life (10).

2
conflict, leading to a national holiday and making it difficult for paperwork to get processed toward the end of May. We therefore decided to give all four treatment groups an extra month to carry out their registration if they desired, with a new deadline of the end of July 2009. Any firm which had completed its registration between the previous deadline and the new deadline was also eligible to receive the payment.

# **3.4 Quasi-registration and Actually Receiving the Treatment Offer**

Owners of the firms assigned to treatment were asked to confirm their registration status on a follow-up visit in which we made the offer corresponding to their treatment group. We were surprised to learn that a non-trivial number of the firms were already registered with the DS. Recall that all of the firms stated they were not registered with the DS in the baseline survey. A handful of these firms had registered in the 3 to 4 months since the baseline survey (this was confirmed by examining the BRC, which shows the date of registration), but the majority of those registered had either misunderstood the question in the baseline survey or were what we term ―quasi-registered‖. The latter refers to a number of cases where the business had been registered in some form, but the registration did not match the current circumstances of the business. For example, in some cases the business had been registered by another family member, who was no longer running it. In other cases the registration had been for another location. The firm thus had a BRC, but it didn’t match the firm in all details. This BRC could still presumably be used to access government contracts or in dealing with a bank. We therefore chose not to offer the treatment to firms in this category. We subsequently also re-visited the control group firms to determine which of them also fell into this quasi-registered status.

In total, 152 out of the 415 firms which were assigned to one of the four treatment groups did not receive the registration offer. The majority of time (106 cases) this was because the firm was alreadyregistered or quasi-registered at the time of the baseline survey, as just described. In 14 cases, the business had closed since the baseline survey, in 18 cases, the owner could not be found in the follow-up, and in 14 cases the business had registered on its own between the baseline survey and the intervention. Five of the owners rejected the offer outright; we count these cases as having received the offer. Follow-up visits with the control group revealed 30 firms which were already registered, and a further 12 had closed or moved or couldn’t be located.

Table 1 reports summary statistics for the full sample according to assignment to treatment, and also for the subsample which actually received the treatment offer (or was eligible to do so after rechecking in the control group). The randomization succeeded in achieving balance for both the variables explicitly stratified or matched on, and for other key variables. With the possible exception of treatment group 1 (the information and reimbursement only group), there is also balance in the share of 12
39 APPENDIX I

# The Theory PE ORtIMMlTAxation: Theory and Tests

An important question is whether the observed pattern of the inflation tax can be explained as the optimal government response to a politically desired path of public spending. The modern theory of public finance lends some support to this point of view. Under the eminently plausible assumptions of tax evasion, and administrative tax collection costs it is optimal for the government to rely (at least partially) on the inflation tax (see, for example, Aizenman (1987)).27 Suppose that the government can use the inflation tax (i) and other tax rates on output (r) to finance its expenditures. Both taxes are distortionary and impose a welfare cost that is increasing on their rate. The cost of the output tax rate is f(r) while that of the inflation tax is h(ff). Then Mankiw (1987) shows that the optimal tax policy implies:

- h'(wf ) kf'(r ) (A.1)

where k is a parameter of the money demand function. Thus, at the optimum the marginal cost of each tax has to be equated in every period. This implies that as government expenditure changes, inflation and non-inflation taxes move together. Mankiw (1987) tests this implication using U.S. data for 1951-82; his results show that there has indeed been a positive relationship between inflation and the tax rate. Mankiw interprets this finding as providing support for the theory of optimal taxation as a positive theory of policy behavior.

27This result is true even if money facilitates the process of exchanige and reduces transaction costs, as in the models of Faig (1988) and Kimbrough (1987), so that money essentially acts like an intermediate input.
to date, and the first to enable the formal estimation of a key elasticity in the market for ivory. The price data are then combined with a panel dataset on elephant poaching, based on surveys of elephant carcasses undertaken in 30 countries since 2002.

In order to reliably estimate the price elasticity of illegal ivory supply, we must address three issues. First, the price data are collected from multiple sources and prices are recorded at various points along the supply chain. We thus partial out some of the observable covariates of prices before combining them with poaching measures. Specifically, we regress prices on variables capturing the source of data (survey, government, industry, etc.), and the location along the supply chain (poacher, middle-man, exporter, etc.), and use the residual price variation in the analysis. To further reduce the noise in the price data, we construct a countryyear panel of raw ivory prices by taking the median across individual price observations in each country and year.

Second, market prices and quantities are jointly determined by the intersection of supply and demand. To identify the supply elasticity, we thus have to find a potential demand shifter. Our instrument for the price of ivory is based on the fact that ivory is storable and thus subject to the Hotelling condition (Kremer and Morcom, 2000). The behavior of ivory prices is then potentially influenced by factors that also affect other stores of value such as gold. Thus, gold prices are potential instrument for ivory prices, under the assumption that shocks to gold prices do not affect poaching other than through changes in ivory prices. We address several scenarios where that might not be the case.

Third, the Hotelling condition implies that prices and hence quantities are non-stationary. We are indeed unable to reject the hypothesis that ivory prices measured in China have a unit root. Gold prices and poaching at the aggregate level are also found to have a unit root. Non-stationarity of individual variables is not a challenge to our estimation strategy. On the contrary, Phillips and Hansen (1990) establish that IV estimates are consistent when the instrument and the variable being instrumented are both non-stationary. Furthermore, we argue that these processes are trending jointly by testing for co-integration. We reject the null of no-cointegration between gold and ivory prices and between gold prices and poaching. We fail to do so for ivory prices and poaching, possibly because of the small sample size of only 13 observations.

We then estimate the impact of ivory prices – either local ones in each range state or in China – on poaching to measure the elasticity of poaching with respect to prices. The 2SLS estimates using gold prices as instruments are larger than their OLS counterparts but still 3
that aim at stimulating labor demand by reducing labor costs. A reduction of payroll taxes is the most straightforward of such policies, however one can also think of other polices that increase labor market flexibility and ultimately support labor demand. The costs of such policies need to be taken into account, especially in the case of a very highly indebted country such as Jamaica, and in our simulation a compensatory increase in sales tax rates maintains the government balance unchanged. A major result of this paper is that a revenue-neutral policy that reduces labor costs can almost completely sterilize the negative labor supply effect of rising remittances—an encouraging outcome for a country struggling with high unemployment rates.

The paper is organized as follows. The next section provides some background information on the Jamaican labor markets, remittance flows and on the still sparse evidence on the links between these flows and labor supply provided by the recent literature. Section 3 briefly presents the analytical structure of our model and the basic dataset it utilizes. Section 4 describes the scenarios and the results, both at the aggregate and detailed sectoral level. Section 5 offers concluding remarks.

# **2 Labor markets and remittances in Jamaica**

In the period between 1980 and 2000, Jamaica’s labor market performance has been mixed. The average urban unemployment rate has been around 20 percent, which is very high by both regional and global standards. In the same period, employment has grown at 1.6 percent per annum, at about the same pace as GDP, but with female participation lagging behind (see Table 1). However, real wages have grown decisively faster: between 1992 and 2002, their average yearly growth rate was 3.2 percent.

_Table 1: Latin America and Caribbean economic performance and labor indicators_

|GDP Growth<br>1980-2001|Employment<br>Growth 1980-<br>1999|Female Labor<br>Force<br>Participation<br>Growth 1980-<br>1999|Average<br>Urban<br>Unempl. rate<br>1980-2000|
|---|---|---|---|
|Argentina<br>1.13|1.16|1.12|9.30|
|Brazil<br>2.49|2.72|2.11|5.62|
|Chile<br>4.81|2.63|2.17|10.09|
|Colombia<br>3.09|3.23|3.56|12.10|
|Peru<br>1.55|3.52|2.27|8.03|
|Uruguay<br>1.80|1.43|2.37|10.62|
|Barbados<br>1.17|1.28|1.30|15.77|
|**Jamaica**<br>**1.56**|**1.60**|**0.89**|**19.40**|
|Trinidad and Tobago<br>0.11|0.78|1.30|15.85|
|Average<br>1.97|2.04|1.90||

Source: Heckman and Pages (2003) 3
insurance. Access to social insurance in Argentina typically requires that one has a job in the formal segment of the labor market, which is less than half of the workforce, and relatively few of the poor (World Bank, 1999).

There is evidence that spending on social services has responded more to changes in national income than has social insurance. Wodon and Hicks (1999) study the effects of changes in GDP on targeted spending on social services (about 4% of total government spending). They find that the ratio of targeted public spending to the number of poor had a positive elasticity (of about three) to GDP; in recessions, there were more poor people, and less was spent on them.

The political regime is also likely to matter. For a sample of Latin American countries (including Argentina), Brown and Hunter (1999) find that democracies are more likely to protect social spending in a recession, but that authoritarian regimes are more inclined to expand social spending when the crisis is over.5 Argentina has been a democracy since 1982. The data series I will use start in 1980, and so it is reasonable to ignore the change in regime.

In this setting I examine how changes in the government's total budget affected the level and composition of social spending. Did budget cuts have similar effects to budget expansions? Were the categories of spending that are known to matter to the poor more protected than other types of spending?

Figure 1 offers a direct test for whether social spending has been protected from budget cuts. The figure plots the time series of changes in the log of total public spending ("Gasto Puiblico Consolidado") and the log of social spending (both in 1997 prices) as compiled by Government of Argentina (1999) and covering the period 1980-97.6 Unlike 5 This study does not, however, control for the level of total spending, so it is unclear whether the identified effects operate through the composition of spending or its aggregate level; "non-social spending may well behave the same way as social spending.

6 Both time series are highly serially correlated; indeed, augmented Dickey-Fuller tests do not reject the unit root hypothesis for either variable at even the 10% levels. So the following analysis will focus on changes from year to year rather than levels.

9