rafmacalaba/datause-encoder-data huggingface dataset:
on these existing strategies to design a blocked savings account to help shield savings from redistributive pressure.

# **4 Experimental Design**

We design an experiment to test whether redistributive pressures distort labor supply—and therefore output and earnings—among full-time factory workers in Côte d’Ivoire. Workers are paid piece rates for output, and receive their wages twice a month in cash. We provide an overview of the design in this section, summarize the implementation protocols in Section 5, and provide more details in Appendix A.4.

## **4.1 Blocked savings accounts**

We seek to construct a design that mimics the approach in the tax schedule in Figure 2B above: a tool to lower redistributive pressure on income gains. To approximate this approach, we introduce a blocked savings account into which workers can transfer earnings _increases_ . Workers who opt in choose a threshold, which must be weakly higher than their baseline earnings. In each biweekly paycycle, any amount they earn _above_ this threshold is automatically deposited by the factory into the blocked account; the remainder of their earnings is paid in cash as usual on payday.

Using administrative data from the factories, we compute baseline earnings to be the worker’s average earnings per paycycle in the past 3 months. The funds in the account cannot be accessed until the end of the blocked period (3-9 months). The savings product is administered by one of the largest banks in Côte d’Ivoire, Banque Populaire (BPCI).

Because the funds deposited into the blocked savings account cannot be accessed, they cannot be used to fulfill transfer requests—at least not until cash is available after the unblock period. This enables workers to potentially accumulate a large lump sum of savings. Consequently, if workers decide to increase their labor supply, the incremental earnings from that effort (which then get deposited into the account) will potentially be “taxed” at a lower rate than if the account were not available. In other words, the accounts are designed to make it more likely that any increases in productivity are retained by workers for their own future use.

## **4.2 Treatments**

Our goal is to develop a treatment design that isolates the role of redistributive pressure in labor supply decisions. While the blocked accounts reduce the scope for such pressure, they 14
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14 where D is the stock of debt. Consistent with<sup>the previous discussion the following sign pattern is</sup> expected for the coefficients: ey > **O,** **_Eo_** _<_ **O,**<sup>**eD**<0, eDX,</sup><sup>**EDM**></sup><sup>**°.**</sup>

# IV. Data and Results

The per capita GDP growth equation (5) and the<sup>per capita capital growth equation (9) were</sup> estimated using cross-country data for a set of 35<sup>developing countries using a Two Stage Least square</sup> method.' The period under analysis is 1975-85.<sup>The data on the trade regime is based on the</sup> assessment of various components of trade policies<sup>prepared in a cross-country study by Halevi (1989).</sup> The trade regime data collected by Halevi corresponds<sup>mostly to the period 1979-83. Since some</sup> countries in the sample have in part altered their trade<sup>regimes throughout the 1975-85 period, we also</sup> estimate the model for the period 1979-83 to check<sup>the robustness of the estimates.</sup> The indicator of instability (variable a) chosen was<sup>the real exchange rate variability defined as</sup> the coefficient of variability (standard deviation/mean)<sup>of the real exchange rate during the period under</sup> analysis.<sup>8</sup> The variable representing export restrictions<sup>(DX) is a dummy variable that equals to one for</sup> countries that, according to Halevi's analysis, have<sup>imposed a high degree of direct export restrictions.</sup> Similarly, import restrictions are represented by a<sup>dummy variable (DM) that equals to one for countries</sup> that exhibit a high degree of direct import protection.<sup>We note that, consistent with the taxonomy of</sup> trade regimes proposed in Section II, we are considering<sup>here only direct restrictions. That is, for</sup> example, a country is judged to have high export restrictions<sup>based only on direct disincentives to exports</sup> without considering indirect incentives such as the<sup>degree of protection to the import substitution sector.</sup>

- 7 See appendix for the lists of countries considered.

- 8 We measure the deviations from the fitted trend values<sup>of the real exchange rate.</sup>
**Income distribution data** . Based on Colquhoun 1801-3 social table revised by Lindert and Williamson. Available at

<u>http://www.econ.ucdavis.edu/faculty/fzlinder/Colquhoun180103.htm. The data which</u> were originally presented on per household basis are transformed on per capita basis (each individual is assigned per capita income of his/her household).

**Population and area.** Current territory of England and Wales. Population:  As obtained directly from Colquhoun (coincides within 1 percent with the population for year 1800 from Maddison, 2001).

**Urbanization rate.** Estimated from Allen (2003, Figure 9, p. 428).

**Mean income in $PPP** .  Maddison (2001) for year 1800.

# **REFERENCES**

Robert C. Allen, "Progress and Poverty in Early Modern Europe" _Economic History Review_ 61, 3 (August 2003): 403-443.

Maddison, Angus (2001), _The World Economy: A Millennial Perspectives_ , Paris: OECD Development Centre.

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# Table 2.2: Selected Indicators of policy stance

||R<br>.|eal<br>O<br>efetiee<br>197Q4|xhno<br>0 1981-82<br>1983.44|at<br>¶985-8|8 1970-8|atooff0cld<br>0<br>981-8|eiittDP<br>1983-84|1A985-8a|
|---|---|---|---|---|---|---|---|---|
|EIAL|||||||||
|UC|97.9|116.5|111.5|83.5|<br>9.0|<br>9.8|7.4|5.7|
|SSA|93.8|120.3|126.3|88.5|<br>6.6|<br>10.7|7.3|5.8|
|OAL|||||||||
|LIC|98.7|104.3|111.7|81.2|<br>9.1|<br>8.8|9.8|8.2|
|SSA|93.1|106.8|108.8|94.4|<br>3.6|<br>6.8|7.5|9.0|
|NAL|||||||||
|LIC|97.5|105.6|114.5|106.4|<br>4.6|<br>7.7|8.5|7.7|
|SSA|98.0|106.7|117.2|123.7|<br>2.4|<br>7.5|11.1|8.2|
||R<br>-|at i of Impo<br>.--<br>-¶sio.- 1981<br>-82Q:|on<br>to GDP<br>1983-84|-|Rat-o<br>0-8|or resa|oure<br>balanc|e to GDP<br>1985-89|
|EIAL|||||||||
|UC|38.6|33.5|29.8|31.9|<br>-5.9|<br>-9.2|-4.1|-4.2|
|SSA|40.8|36.1|32.1|34.2|<br>-10.6|<br>-8.4|-4.5|-5.3|
|OAL|||||||||
|LIC|35.5|36.9|35.0|30.8|<br>-10.2|<br>-16.8|-15.2|-10.6|
|SSA|34.7|38.4|37.0|30.9|-13.6|<br>-18.7|-17.5|-11.8|
|NAL|||||||||
|LIC|32.9|34.8|29.5|27.4|-7.8|-16.1|-10.0|-4.2|
|SSA|40.3|40.5|33.7|32.7|-10.1|<br>-8.6|- 0.2|-2.3|
- 24 -

To deal with autocorrelation and censoring in data, the spatial tobit regression model was used in the paper. The results are consistent with prior expectation. Spatial autocorrelation matters, presumably because our agricultural production data are spatially connected to neighboring areas. In addition, transport infrastructure is a typical network industry. Therefore, it is methodologically important to take autocorrelations into account: OLS estimation is likely biased.

It is also found that the impacts of transport accessibility on agriculture production vary across areas and crops. However, the results suggest that both rural accessibility and market access are important to stimulate agriculture production. Particularly, for export crops, access to port is found to be crucial: In some cases (e.g., coffee in the landlocked countries, tea in Kenya and tobacco in Tanzania), the estimated elasticities exceed one, implying that public investments to improve transport connectivity for these crops are highly profitable from the economic point of view.

There are many possible investment options to improve transport connectivity in the region. For illustration purposes, the paper examined two particular scenarios under the assumption that the waiting time costs at the two regional ports, Mombasa and Dar-es-Salaam, would be halved. The expected impacts are different, depending on estimated elasticity and the current level of production. But the results indicate that infrastructure investment tends to have a spillover effect in the region. Therefore, strategic prioritization and coordination may be important not only within a country but also in the whole East African region.
empirical results. Section 5 assesses these results, including those for nonschooling variables, and section 6 discusses the implications with regard to education, comparing our findings with those obtained for some of the same people, considered as individuals, in other analyses.

# 2. Description of Nonfarm Family Enterprises

The Peru Living Standards Survey [Grootaert and Arriagada (1986)1 generated information on 3,158 nonfarm family businesses nationwide and on 4,652 family members working in such businesses. Just over half (2,526) of the households in the sample owned and operated at least one such business. Nonfarm family enterprises are nearly equally divided among Metropolitan Lima, other urban areas, and rural areas (35 percent, 38 percent, and 27 percent, respectively) -- see table 1.

Four activities are predominant among nonfarm businesses in Peru: (1) retail trade, including both food services (street kiosks as well as sitdown restaurants) and nonfood merchandising; (2) textile manufacturing, including both the weaving of cloth and the sewing of clothing; (3) other manufacturing, i.e., all types of goods-producing enterprises other than textile manufacturing, such as food processing and furniture making); and (4) personal services, such as domestic work, laundering, auto repairs, and barbering. The analysis here, of education's contribution to business earnings, will be conducted separately for these four principal sectors as well as for the entire nonfarm family business sector.

4
25 doubling in car pools, results in benefits of $2.67 per vehicle trip eliminated. We will return to this example to examine the environmental link.

This environmenta **l** ink may be modeled using information from MOBILE4, which provides equations relating vehicle speed to emissions, by model year. A graph of some of these equations for HC emissions of autos is reproduced as figure 2, which provides speed correction factors to MOBILE4, based on a factor of 1.0 for the average vehicle speed of 19.6 mph. These factors are multiplied by the estimates of emissions per mile, which are calibrated to a 50,000 mile vehicle traveling at 19.6 mph. Note that the functions are highly non-linear in the 10 to 30 mph range and that model year has little effect on these relationships. (The same is true of earlier model years). Also note that MOBILE4 omits any estimates for emissions when accelerating or decelerating since it is based on average speeds.15 Returning to the above example, assuming all vehicles are 1981+ with 50,000 miles on them, the increased speed of the 1,000 vehicles remaining over the 5-mile stretch of freeway "saves" 3.35 kg of HC in total. Valuing the health benefits at a very generous $10,000 per ton HC reduced (five times higher than the "high" estimates for such benefits in Krupnick and Portney, 1991),16 results in benefits per trip eliminated of only $0.06.

15. Faiz et al (1990), p. 46, provides comparisons of emissions (in ppm per volume) while cruising, accelerating and decelerating. Emissions while cruising are far lower (for hydrocarbons, 1000 ppm while cruising versus 1,600 ppm while accelerating and 10,000 ppm while decelerating).

16. These estimates should be lower in a developing country than a developed one because of the expected lover WTP for health reductions in the former. At the same time, the reduction in health impacts for a given reduction in air pollution may be larger.
balance. In summary, our DCGE model is well suited to capture path dependent effects within a consistent macroeconomic framework.

# _Baseline scenario_

In order to estimate the economic impact of climate change for Tanzania, we first specify a baseline scenario that reflects development trends, policies and priorities in the absence of climate change. The baseline provides a reasonable trajectory for growth and structural change of the economy from 2007 to 2050 that can be used as a basis for comparison.

Economic growth in the DCGE model is determined by rates of factor accumulation and technical change. For population and labor supply, we assume that Tanzania’s population will continue to grow but at a decelerating rate (i.e., 2.0 percent today falling to 0.3 percent by 2050). We assume that the expansion of cultivated crop land will slow such that growth in agricultural production becomes increasingly dependent on the adoption of improved technologies rather than land expansion. As described earlier, the crop models use historical climate data to define year-on-year yield fluctuations in the baseline for each crop and region. Exogenous long-term agricultural productivity growth is set at 0.8 percent per year in agriculture and 1.2 percent in non-agriculture. Improvements in the education levels of Tanzania’s workforce are assumed to continue, with supply and productivity rising faster for skilled and semi-skilled workers than for unskilled workers (i.e., at 2.0 and 1.5 percent per year, respectively, compared to 0.5 percent). Under the above assumptions, Tanzania’s economy gradually develops, with agriculture’s contribution to gross domestic product (GDP) falling from 27.8 to 14.1 percent during 20072050. Overall, per capita GDP grows at an average 2.2 percent per year in the baseline, leading to significant improvements in average household welfare.

# _Economy-wide modeling results_

The DCGE model uses the crop yield results from Section 2 to estimate the economy-wide impacts of climate change. We first discuss the macroeconomic results from the model, which are summarized in Table 3. We focus on changes in “absorption”, which is the broadest measure of national welfare. Absorption tracks an economy’s use of goods for household consumption (C), investment (I), and government expenditure (G). Absorption is closely related to GDP growth. Formally, absorption (A) is defined as A=C+I+G. Recalling that GDP=C+I+G+X-M, where X is exports and M is imports, we can write that A=GDP+M-X. In other words, absorption is the volume of goods produced by the economy plus the goods that foreigners supply to the economy (imports) less the goods sent out to foreigners (exports). One advantage of measuring outcomes based on aggregate absorption is that it is less sensitive to our choice of “closure rules”, which influence the relative sizes of absorption’s C, I and G components.

# [Insert Table 3]

9
paid for schooling of the children can thus be a substantial burden on the poorest households.

The NHSC 2010 collected information on household size and composition, household head’s education and employment. We use this information to de...ne control variables for our regression analysis. The survey also collected information about household’s total monthly income and expenditure. Summary statistics for all of these variables are provided in the online appendix Table A1:

## (5.2) Rainfall Data

In order to de...ne our identifying instrument, we need rainfall information which are not collected in the NHSC survey. The rainfall data are drawn from Bandyopadhyay and Skou...as (2012). The original data on rainfall come from the Climate Research Unit (CRU) of the University of East Anglia. The CRU reported estimated monthly rainfall for most of the world by the half degree resolution from 1902 to 2009. The CRU estimation combines weather station data with other information to arrive at the estimates.<sup>28</sup> To estimate the thana level rainfall from the CRU data, Bandyopadhyay and Skou...as (2012) uses area weighted averages.<sup>29</sup> To de...ne our instruments, we use average rainfall during the 2000-2009 period. As noted before, we do not include contemporaneous rainfall (2010) to avoid any potential direct e¤ect through factors such as mood of people. As a robustness check, we also use average rainfall over 1999-2005 period as the identifying instrument.

# (6) Preliminary Evidence

We begin with preliminary evidence on the extent and pattern of bribery in schools. The ...rst interesting thing to note is that the average per capita income of the bribe payers (Tk. 1930 per month) is much lower compared to the average per capita income of non-payers (Tk. 2560 per month). This indicates that on an average the households that end up paying bribes for their

> 28 Previous versions of the CRU data were homogenized to reduce variability and provide more accurate estimation of mean rain at the cost of variability estimation. The version 3.1 data is not homogenized and thus allows for better variability estimates. Also, the estimates of rainfall near international boundaries are not less reliable as compared with those in the interior of the country, as the CRU estimation utilizes data from all the weather stations in the region.

> 29 For example if an Upazila/thana covers two half degree grid cells for which CRU has rainfall estimates, then upzila/thana rainfall is estimated as the average rainfall of the two grid-cells, where the weights are the proportion of the area of the upazila/thana in each grid-cell. For details, please see Bandyopadhyay and Skou...as(2012).

25
22 economy story might suggest that NTMs are endogenously more prevalent in industries where trade is more important (i.e., trade volume is higher or has recently increased) if they are intended to protect domestic producers from foreign competition. In this case, empirical studies using industry panel data might understate the trade impact of NTMs (for an application see Ederington and Minier, 2003).<sup>12</sup> Thus, researchers must take into account that NTMs and trade flows are both endogenously determined in calculating the incidence and trade impact of NTMs.

Some of these problems are dealt with in the empirical studies discussed below. However, it is in general not possible to address all these issues at the same time. For this reason, any review of the landscape of non-tariff measures as well as the analysis of their impact on international trade is likely to be incomplete.

# **2.3 The trade impact of NTMs**

While governments impose non-tariff measures for a variety of reasons that may have nothing to do with international trade, NTMs may still have an impact on trade flows. The quantification of these effects is the subject of a large literature that is briefly reviewed below.<sup>13</sup> While frequency and coverage ratios are helpful in providing insight into the incidence of non-tariff measures (e.g., in which industries they are most common), they provide little guidance as to their trade restrictiveness. Thus, in the following subsection (2.3.1) we provide some general discussion of the main methodologies behind calculating the trade impact of NTMs. Obviously a significant issue in measuring the trade impact of NTMs is going to be _dimensionality_ : that NTMs can take many disparate forms that can have very different effects on trade flows. In light of this, one approach to measuring the trade impact of NTMs is to focus on a single type of NTM (e.g., TBT or SPS measures). The first subsection (2.3.2) provides an overview of empirical literature that investigates the trade impacts of particular NTMs. However, in many cases it is helpful to have an aggregate (country-wide) measure of NTM trade restrictiveness and, indeed, a large body of

> 12 Trefler (1993) first made this point showing that when trade protection is modeled endogenously, the impact of lowering non-tariff barriers such as quotas and antidumping duties is ten times the size from treating protection exogenously.

> 13 In addition to the aforementioned studies by the WTO and UNCTAD, there are a number of papers that provide in depth overviews of the methodologies used to assess the trade impact of non-tariff measures (Deardorff and Stern, 1998; Ferrantino, 2006) or of a specific methodological approach (e.g. Novy and Chen, 2012). A useful practical guide on how to measure the effect on NTMs is in UNCTAD and WTO (2012).
- Gill, P., Murray, W., Saunders, M. and Wright, M.: 1998, Users Manual for NPSOL 5.0, A FORTRAN Package for Nonlinear Programming, Technical Report SOL 86-1, Department of Operations Research, Stanford University.

- Guerrini, L.: 2006, The Solow-Swan Model with a Bounded Population Growth Rate, Journal of Mathematical Economics 42(1), 1421.

- Guerrini, L.: 2010, A Closed-form Solution to the Ramsey Model with Logistic Population Growth, Economic Modelling 27(5), 11781182.

- Hartley, P., Medlock III, K. and Rosthal, J.: 2008, The Relationship of Natural Gas to Oil Prices, The Energy Journal 29(3), 4766.

- Hertel, T.: 1997, Global Trade Analysis: Modeling and Applications, Cambridge University Press.

- Hertel, T., Rose, S. and Tol, R.: 2009, Economic Analysis of Land Use in Global Climate Change Policy, Routledge.

- Hertel, T., Stiegert, K. and Vroomen, H.: 1996, Nitrogen-land Substitution in Corn Production: a Reconciliation of Aggregate and Firm-level Evidence, American Journal of Agricultural Economics 78(1), 3040.

- Hertel, T., Tyner, W. and Birur, D.: 2010, The Global Impacts of Biofuel Mandates, The Energy Journal 31(1), 75100.

- IPCC: 2000, Summary for Policymakers, in N. Naki¢enovi¢, J. Alcamo, G. Davis, B. De Vries, J. Fenhann, S. Gan, K. Gregory, A. Grübler, T. Jung, T. Kram et al. (eds), Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change, Cambridge University Press.

- Jorgenson, D. and Vu, K.: 2010, Potential Growth of the World Economy, Journal of Policy Modeling 32(5), 615631.

- Judd, K. L.: 1998, Numerical Methods in Economics, The MIT press.

- Lee, H., Hertel, T., Rose, S. and Avetisyan, M.: 2009, An Integrated Global Land Use Data Base for CGE Analysis of Climate Policy Options, in T. Hertel, S. Rose and R. Tol (eds), Economic Analysis of Land Use in Global Climate Change Policy, Routledge, pp. 7288.

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