rafmacalaba/datause-encoder-data huggingface dataset:
concentrate on the 1998 financial crisis and, due to data limitations on the 2001 Mexican slowdown<sup>2</sup> .

Both the Indonesian 1998 crisis and the Mexican 2001 slowdown, had important effects in the labor market, but displayed very different adjustment mechanisms. While Indonesia witnessed a strong reduction in real formal wages, the Mexican economy adjusted mostly through an increase of informal employment. Unemployment instead, responded only modestly. What explains such different outcomes? There are two potential explanations for this i) both economies faced very different institutional arrangements and ii) The shock was qualitatively different. We will explore both explanations.

# **2.1 The Financial Crisis and Indonesian labor market**

The past two decades witnessed important changes in Indonesian labor market. The first half of the 90 ́s, prior to the financial crises, was characterized by rapid growth, an inflow of better educated workers to formal jobs, and a movement of unskilled labor away from farm jobs.<sup>3</sup> During the financial crisis of 97-99, inflation caused a huge drop in real wages in urban areas, pushing many workers into poverty, and forcing women to enter the labor market in record numbers. This decrease in real wages probably mitigated the effect of the crises on urban formal employment, which decreased only slightly. Wage and earning drops among agricultural workers were partly mitigated by an increase in the prices of rice. This drop in real formal wages coupled with increases in agricultural earnings and increased female labor force participation explains the shift of the labor force to informal and agricultural activities.

The Asian financial produced a large blow to the Indonesian economy. Per capita GDP contracted by almost 15% during 1998. Despite this huge drop in economic activity, by 2000 unemployment had only increased by less than 2 percentage points. Instead wages in the formal sector decreased by 10%. Additionally, the formal sector contracted by 5%, shedding labor into the informal economy. Surprisingly, the recession was not reflected in survey household average labor earnings, which, by the year 2000 was almost the same as in 1997, the pre‐crisis year. The main reason behind this apparent contradiction is that during the crisis

> 2 To calibrate the model we need either unemployment duration or employment duration. Unemployment duration is only  available beginning 1998 and employment duration beginning 2004.

> 3 World Bank  (2009)“Labor Market Trends in Indonesia. Jobs report” mimeo, World Bank, washington DC 2009. Pg. 3.
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14 The Brazilian data configuration is such, therefore, that the expenditure effect is computed for all households from one sample (POF), while the (market and transfer) income effects are computed for all households from another sample (PNAD). Although both are large and nationally representative surveys, they do not sample the same households. To combine the three effects in (6), we therefore look at their average values at each centile π of the pre-shock income distribution. Denote by the set of households in centile π of that distribution. Let each centile have _n(Π)_ households. We can then define: as the (proportional) expenditure, market income, and transfer income effects of the food price increases, for centile π of the Brazilian income distribution. Whereas equation (7) uses POF and INPC data, (8) and (9) are computed using PNAD data. Given (6), and the above definitions, we can also define the net price effect for each centile as: Equation (10), which is the centile-average equivalent of (6), describes our estimates of the proportional money-metric welfare effect of the price changes along Brazil‟s income distribution. It is closely analogous to the growth incidence curve of Ravallion and Chen (2003), which describes the proportional income growth for each percentile of the original income distribution. We accordingly denote the “price change incidence curve” (PIC). Equations (7), (8) and (9) define its three components: the expenditure effect incidence curve; the market income effect incidence curve, and the transfer income effect incidence curve.

In the following section we present our empirical results in two ways. First we graphically present the price change incidence curves, building on equations (7), (8), (9) and (10). The visual clarity and degree of distributional disaggregation of these figures are, in our view, important improvements in the analysis of the distributional consequences of food price changes. PICs are presented first for the eleven large urban areas from which detailed information on prices is
**Table 10. Differences in Fuel Exporter Effects on the Breakdown of Expenditures**

|Dependent variable:|Consumption<br>expenditure/GDP (%)|Capital<br>expenditure/GDP (%)|Wage<br>expenditure/GDP (%)|
|---|---|---|---|
|Constant|-7.40***<br>(-4.89)|6.49***<br>(8.45)|0.611<br>(1.09)|
|Dummy 2005–13|2.48***<br>(4.87)|-0.086<br>(-0.35)|0.291<br>(1.34)|
|Dummy fuel > 25%<br>exports|4.90***<br>(6.91)|1.72***<br>(4.50)|1.12***<br>(3.37)|
|Dummy fuel > 25%<br>exports *<br>Dummy 2005–13|-3.56***<br>(-3.72)|1.13**<br>(2.08)|-1.24***<br>(-3.11)|
|Dummy ores and<br>metals > 10%<br>exports|-1.52***<br>(-2.98)|0.920***<br>(3.62)|-0.308<br>(-1.48)|
|Hard peg dummy|-4.34***<br>(-8.60)|0.593*<br>(1.76)|-0.978***<br>(-6.44)|
|Political stability|0.455***|-0.0290**|0.0994***|
|(0 to 100)|(19.36)|(-2.58)|(11.80)|
|n|1,286|1,232|1,128|
|R-squared|0.275|0.083|0.107|
|RMSE|7.75|3.90|3.04|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

19
_Journal of Agricultural Economics_ , October 2020, _102_ (5), 1328–1351.

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20
Our attempt to evaluate the effects of those reforms contains both good and bad news. On the positive front, Madagascar has been able to regain some of the competitive ground it lost during the disastrous Ratsiraka era. However some of this competitive recovery simply reflects the (exogenous) difficulties that affected Indonesia, then its main competitor, in the second half of the 1990s. More ominously, following the recent price hike, new entrants are appearing on the horizon, together with bio-engineered substitutes. It remains that little of this ‘improvement’ in the policy environment can be traced back to higher farmer incomes in the household surveys. Nor have presumably better incentives for farmers translated into a noticeable producer response, at least according to FAO data which suggest that yields and planted areas have remained about flat.

Because large price fluctuations in a market that has always been very volatile render any interpretation of prima-facie evidence difficult, we turned to a counterfactual experiment to assess the effect of policy reform “ceteris paribus”. The results, again, contain good and bad news. Producer prices seem to have benefited from a substantial narrowing of intermediation margins after the reforms and phase-out of export taxes which stood in the early 1990s at absurdly high levels. However, once introduced into household survey data, this improvement does not translate into large effects on measures of poverty and inequality simply because cash income from export crops is too small relative to self-consumption.

In a nutshell, thus, our main result is that the vanilla sector reforms may have had a large and positive effect on producer prices (travelers in the vanilla producing region report a change in consumption to beer and to the purchase of durables like radios and bricks for construction), but the reductions in intermediation margins (i) were dwarfed by the volatility of world prices and (ii) could not have a very large impact on farmers’ consumption simply because the latter is essentially self-produced.

For market reforms to take hold and make a difference, two conditions must be met. First, for impact effects to be substantial the economy needs to “remarketize”, a process that can only take time after decades of retrenchment under strongly anti-market policies. This is an important point. In Madagascar, decades of bad policies and predatory taxation have raised transaction costs to such prohibitive levels that the market economy has largely shrunk to urban areas, leaving rural households isolated and forced to rely on themselves. Once this has happened, reforms affecting the price signals faced by rural farmers can 31
# 7. **How Robust are these Findings** ?

To this point, we have reported results from OLS and IV regressions. In both the size and growth regressions, the estimates were found to be stable. In the life expectancy and infant mortality regressions, the OLS and IV produced qualitatively similar results; and the variables that were statistically significant under OLS continued to be significant under IV as well. We now undertake two further exercises. The first involves the joint estimation of size, growth, infant mortality and life expectancy regressions. The second looks at how sensitive our results are to measurement error and the specification of the regression model.

# 7.1. System **Estimation**

The system of four equations represents a simultaneous equation system. Growth depends, among other things, on government size; government size is in turn determined by another equation; and in the remaining two equations, growth affects life expectancy and infant mortality. In addition to the four left-hand side variables, there are many other endogenous variables in the system. In terms of the empirical work, the resulting endogeneity needs to be addressed, otherwise it will lead to biased parameter estimates. So far, we have taken care of the endogeneity problem by applying the IV technique on each equation separately. Although preferable to OLS, the IV technique suffers from two shortcomings. First, even in the absence of simultaneity, it is more efficient to use system-wide estimation techniques than single-equation techniques since errors can be correlated across equations. Sources of cross-equation correlation can be the impact of 39
headcount index of the initially poor household group shifts from 100.00 to 99.64 percent, indicating that 23,000 formerly poor people have escaped from poverty. People who escape poverty belong to households whose head is involved in agriculture, fishing, hunting, food industry, or in public service. Households entering poverty have a head who is involved in transports or miscellaneous services. Headcount indices of all other household groups (according to the main activity of the head) remain unchanged (Table 4).

# **4. World and Domestic Free-Trade Simulations**

Three scenarios are performed in this section and involve the complete elimination of import tariffs: first in the ROW, then in Cameroon, and then in both regions. The macroeconomic closure of the model for these scenarios is the following: employment, real investment, real public expenses and the trade balance are fixed. To compensate lost tariff revenue, we introduce a neutral production tax evenly levied on locally sold production and imports. Moreover, we assume endogenous terms of trade (FOB price possibly different to corresponding world export price).

Tables 8 – 9 compare the impacts of full liberalization in ROW with that of domestic trade reform in Cameroon – focusing on prices and volumes of goods and services flows, as well as factor markets. According to results from the GTAP world model (Table 8), a complete liberalization in ROW would lead to a non negligible increase in world import prices, especially for foodstuff agriculture (7.08 percent) and food processing (4.76 percent), as well as an increase in export prices of foodstuff agriculture (3.20 percent), cash crops (1.86 percent) and of food processing (1.75 percent). On the other hand, unilateral liberalization by Cameroon means a 100 percent reduction in domestic tariffs from an average tariff rate of 11.79 percent and a maximum 13
# **Introduction**

Distance and location are important determinants of many choices that economists study. For example, in the von Thünen model, distance to market determines land owners decisions about what crop is most profitable to produce. In studies of child labor market activity, distance from urban areas is shown to be an important determinant of both schooling and work decisions (Fafchamps and Wahba, 2006). In migration models, greater distance between origin and destination implies larger migration costs and reduces migration flows (Borjas, 2004).

While location and distance can sometimes be obtained from secondary data, economists often rely on information that is self-reported by respondents in surveys. These self-reports are especially used for the distance from households or community centers to various features such as roads, markets, schools, clinics and other public services. There is growing evidence that selfreported distances and areas are measured with error (Goldstein and Udry, 1999) and that these errors are correlated with outcomes of interest (Escobal and Laszlo, 2005). In contrast to selfreports, the Global Positioning System (GPS) can determine almost exact location (typically within 15 meters).

These GPS locations are determined from satellites (currently 30) with highly precise atomic clocks that orbit about 20,000 kilometers above the surface of the earth and send out a unique radio signal with a time-stamp. A GPS receiver uses the time delay between transmission and reception to calculate the distance to each satellite, and calculates the latitude and longitude of the current location by using triangulation. More precise calculations, including elevation, can be made if four satellites are in view (El-Rabbany, 2006). Accuracy depends partly on whether anything obscures the GPS receiver’s view of the sky and the quality of the receiver used to process the satellite signal. Consumer-grade GPS receivers are accurate to within 15 meters, 95 percent of the time,<sup>1</sup> with further improvements in accuracy to about three meters achieved by using _differential GPS_ where information from a local reference station augments that from the satellites.<sup>2</sup> Two principal factors have dramatically increased the feasibility and usefulness of collecting GPS information in household surveys. First, on May 1<sup>st</sup> , 2000, the U.S. military

> 1 <u>http://www.garmin.com/support/faqs/faq.jsp?faq=582&webPage=Main%20web%20page</u>

> 2 Specifically, a ‘base station’ GPS receiver is set up on a precisely known location and used to compare position based on the satellite signals with this known location. The difference is then applied to other GPS receivers in the area to correct their calculations of their unknown locations.

- 2 -
Reducing Emissions from Deforestation and Forest Degradation (REDD+) is a payment for ecosystem services (PES) system created under the Framework Convention on Climate Change (FCCC) tasked with creating markets for carbon sequestration services. Developing appropriate REDD+ carbon markets is a potentially important tool to give value to forest ecosystem services and create incentives for those who control forests to sequester carbon and for those who emit carbon into the atmosphere to pay for sequestration services (Bluffstone 2013).

From an economic point of view, carbon prices, payments to communities and the opportunity costs of carbon sequestration drive the decisions to adopt or not adopt REDD+. To achieve a welfare gain from the creation of the REDD+ carbon markets, the sellers (those sequestering carbon, typically in non-Annex 1 countries) must receive a price that is greater than the direct and indirect costs of participating in the carbon market by engaging in carbon sequestration. At the same time, the buyers in the carbon market (those paying for carbon sequestration services, typically in Annex-1 countries) will only engage in a REDD+ market if the price of carbon sequestration is competitive compared to the cost of alternate methods such as fuel switching and efficiency improvements (Bluffstone 2013). Therefore the costs borne by participants in non-Annex-1 countries, particularly the opportunity costs of participating in REDD+, will determine the success of a global REDD+ carbon market.

Despite the acknowledged importance of local level opportunity costs and preferences (e.g. McKinsey & Co, 2010; Gregorsen _et al._ 2011), the literature on opportunity costs of carbon sequestration in developing countries and preferences for programs to prevent deforestation like REDD+ is extremely limited. This paper attempts to partially close this critical research gap using choice experiment surveys conducted in three regional states in Ethiopia.<sup>1</sup> The main goal 3