rafmacalaba/datause-encoder-data huggingface dataset:
||(0.00733)|(0.00731)|(0.00733)|
|---|---|---|---|
|Improved water|-0.0136|-0.0111|-0.0126|
||(0.0178)|(0.0185)|(0.0179)|
|Water treatment|0.0379|0.0382|0.0382|
||(0.0270)|(0.0271)|(0.0270)|
|Arsenic (WHO)|-0.0343***|||
||(0.00791)|||
|Arsenic (BD)||-0.0330***<br>(0.00999)||
|Arsenic - medium (11-50 ppb)|||-0.0316***<br>(0.0113)|
|Arsenic - high (51+ ppb)|||-0.0424***<br>(0.0123)|
|Observations|7,968|7,968|7,968|
|Standard errors in parentheses||||
|*** p<0.01, ** p<0.05, * p<0.1||||

**Table 6. Correlates of child physical development including three-specifications of arsenic exposure (N=7,906 children aged 3-5 years)**

|VARIABLES|(1)<br>WHO|(2)<br>Bangladesh|(3)<br>Risk Levels|
|---|---|---|---|
|Child ever breastfed|0.0198|0.0212|0.0213|
||(0.0139)|(0.0138)|(0.0138)|
|Child is female|-0.00666|-0.00675|-0.00675|
||(0.00508)|(0.00507)|(0.00507)|
|Child's age (log)|0.0813***|0.0802***|0.0802***|
||(0.0177)|(0.0175)|(0.0175)|
|Mother's education (primary/some secondary)|0.00202|0.00184|0.00181|
||(0.00593)|(0.00591)|(0.00591)|
|Mother's education (secondary or higher)|0.00522|0.00545|0.00529|
||(0.0105)|(0.0104)|(0.0105)|
|Urban|-8.13e-05|-0.00159|-0.00160|
||(0.00881)|(0.00883)|(0.00882)|
|Household size (log)|-0.00373|-0.00254|-0.00250|
||(0.00729)|(0.00734)|(0.00735)|
|Quintile 2|-0.0131*|-0.0129*|-0.0128*|
||(0.00716)|(0.00717)|(0.00717)|
|Quintile 3|-0.0156**|-0.0146**|-0.0145**|
||(0.00747)|(0.00741)|(0.00741)|
|Quintile 4|-0.00832|-0.00749|-0.00745|
||(0.00800)|(0.00795)|(0.00796)|
|Quintile 5|-0.00967|-0.0106|-0.0104|
||(0.0108)|(0.0109)|(0.0110)|
|Improved sanitation|0.00888|0.00863|0.00868|

25
Walker, P. A. 2004. Roots of crisis: Historical narratives of tree planting in Malawi. _Historical Geography_ , vol 32: 89-109.

World Resources Institute (WRI) 2003. Earth Trends Table, Forests, Grasslands and Drylands. <u>http://earthtrends.wri.org/pdf_library/data_tables/for1_2003.pdf</u> 29
information from Timilsina et al. (2019) that links top-down CGE models to the bottom-up TIMES model and estimates the baseline correction factors.

The study shows that China would be required to reduce almost 30% of its CO2 emissions from the business as the usual situation in 2030 to meet its nationally determined contribution (NDC) targets or pledges under the Paris Agreement. Note that required emission reductions from the baseline are highly sensitive to the projection of the baseline. When the information from the bottom-up TIMES model is used, the baseline emission drops by about half. Consequently, the required emission reductions to meet the NDC targets also drop, and so do the corresponding economic impacts.

The study finds that meeting China’s nationally determined contribution (NDC) or pledges under the Paris Agreement would cause 0.4% to 1.5% reduction of national economic output (GDP) in 2030 depending on (a) allocation of distribution of emission allowances and (b) baseline setting (i.e., utilizing or not the baseline correction factor derived from the linking of the CGE model with the bottom-up model). For some provinces, the ETS provides an opportunity to increase their economic outputs (e.g., Fujian, Guangdong, Guangxi, Liaoning) under all schemes of allowances allocation/distribution. Provincial economies of Inner Mongolia, Ningxia, Shanxi and Shaanxi that highly rely on the coal mining sector and coalintensive manufacturing sectors (electricity, non-metallic minerals, metals) would face relatively higher economic loss under each scheme of allowances allocation considered. Provincial GDP impacts widely vary depending upon economic structures and energy supply systems. Allowances allocation rules change not only the magnitude of the impacts, but also the direction of the impacts.

The magnitudes of economic impacts of the ETS are highly sensitive to the baseline emissions. Our study finds that if the baseline is corrected by using a factor derived by Timilsina et al. (2019) through the linking of a top-down CGE model and bottom-up TIMES model, the magnitudes of economic impacts drop by almost two-thirds. Correction of the baseline, however, does not alter the direction of the impacts.

The sensitivity of economic impacts of an ETS to the allowance allocation rules and also to the baseline setting suggests that design of ETS is critical for fair and equitable distribution 56
Durlauf, 2000). Clearly, Griffith et al. (2004), drawing on Aghion and Howitt (1992); Aghion et al. (1998) offer one example in arguing that _rR_ & _D_ = _ρ_ + _δ_ 2 _ln_ ( _A_<sup>_∗_</sup> _/A_ ) _−_ 1 where _A_ , _A_<sup>_∗_</sup> are TFP and TFP at the frontier respectively, the second term arising from the additional effect of R&D as facilitating technological transfer from the frontier to the follower country. More generally, the complementarities among productive factors may not occur at the same intensity in frontier and developing countries (due to institutional barriers, lack of education, multiple externalities and non-convexities reflected in evident market imperfections, etc.). Azariadis and Drazen (1990) suggest that countries that are identical in their structural characteristics but differ in initial conditions may cluster around different steady state equilibria in the presence of increasing returns to scale from some factor of production, market imperfections, non-convexities in the production function, etc. (Galor, 1996; Howitt and Mayer-Foulkes, 2002).

From the purely statistical point of view, Kourtellos (2002) notes that numerous studies show evidence of misspecification (generally in the form of nonlinearities) and suggest that the assumption of a single linear model when applied to all countries is invalid (see Durlauf and Johnson, 1995; Durlauf et al., 2001; Hansen, 2000; Liu and Stengos, 1999; Desdoigts, 1999; Kourtellos, 2001). In our case, we want to allow the return to R&D to vary, but we also want to ensure that we are really picking up variation in returns to R&D and not the result of excessive constraints on the other potentially variant parameter values. Hence, we follow recent work by Hastie and Tibshirani (1993) and Fan and Zhang (1999, 2000) that allows the full parameter vector to vary and Cai et al. (2006) who extend the methodology to accommodate endogeneity in the conditioning set.<sup>7</sup>

> 7The suggested approach to specify the determinants of economic growth dates back to Mansfield (1965) and Griliches (1986). As Jones and Williams (1998) note, most of the literature aimed at estimating returns in this tradition is based on neoclassical growth models in which R&D is simply an alternative form of capital investment. Such an approach ignores many of the distortions associated with research that are formalized by the new growth theory, including monopoly pricing, inter temporal knowledge spillovers, congestion externalities and creative destruction. We take advantage of this in the sense that while the differentiated effect among creators and absorbers, in part induced by the creative destruction process, is not captured by any argument in the last equation, it is reflected in the varying estimates of the contribution of R&D to growth. This variation is shown to depend on the degree of economic development which in turn is related to the clustering of originators and imitators of technological progress.

5
# **3.1. IDENTIFICATION ISSUES AND EMPIRICAL STRATEGY**

Estimation of the effects of MFI membership on the propensity to borrow from informal sources faces challenges arising from household self-selection, MFI placement and screening choices. For example, households in a village may participate more in MFI programs and also take more loans from the moneylenders, both driven by higher aggregate demand for credit due to higher productivity potential in that village. Selection bias can also be due to unobserved household characteristics, as the households that participate and that do not may be systematically different. Two of the salient unobserved household characteristics in the context of our analysis are entrepreneurial ability and risk preference. According to the standard models of occupational choice (Kanbur (1979), Kihlstrom and Laffont (1979)), less riskaverse and high ability households would choose to experiment with new economic activities such as non-farm microenterprises. Also, a household with higher entrepreneurial ability is more likely to join the MFI. Households with higher ability and risk preference would thus need more loans from the moneylender, especially if the investment projects are indivisible. The fact that it is impossible to find reliable information on household ability and preference heterogeneity implies that the OLS estimates are likely to suffer from omitted variables bias. For example, we do not have good measures of ability, it is subsumed in the error term, and the omitted ability can create a spurious positive effect of MFI membership on the probability of moneylender loans taken by the households. However, note that the direction of bias from unobserved heterogeneity cannot be pinned down from _a priori_ theoretical reasoning alone. For example, omitted ability heterogeneity can instead result in a negative bias if high ability reduces the probability of joining an MFI because the outside option is higher (for example, higher educated women becoming teachers in the village school).

To deal with the biases resulting from MFI placement and selection of households into MFI membership, we take advantage of a two-round panel data that span seven years, from 2000 to 2007. We implement household fixed effects in a difference-in-difference (DID) framework. Consider the following DID specification: Bit = θ0 + θ1d07 + θ2Ti + θ3(d07 ∗Ti) + eit (5) Where Ti is the treatment dummy which takes on the value of 1 if household _i_ is an MFI member in the year 2007, but was not a member in the initial survey year 2000, Bit is a binary variable which takes the value of unity if household _i_ borrowed from informal sources in 2007, but did not borrow in 2000, d07 is a dummy that equals 1 for 2007, and eit is the residual term. This specification exploits household fixed effects in a DID framework by defining the treatment and outcome variables appropriately. It effectively differences out the time invariant household characteristics (ability and risk aversion); it also wipes out the effect of time invariant village characteristics that may have affected MFI placement decisions.

20
||No Rights|Rights (License Required)|Rights (No License)|
|---|---|---|---|
|Backhaul|27.5%|63.75%|8.75%|
|Cabotage|82.5%|17.5%|0%|
|Transit|22.5%|71.25%|6.25%|
|Triangular|47.5%|47.5%|5%|

Source: World Bank Survey Notes: This table presents the percentage of countries which allow a type of travel (backhaul, cabotage, transit, or triangular), and the degree to which that travel requires prior licensing.

Table 2: Regulatory Practices foreign carriers cannot complete domestic jobs, and set minimums on the share of international jobs performed by truckers from each country (Teravaninthorn and Raballand (2009)). Similarly, in Central America and Belize, prevailing regulation prohibits cabotage, despite trucking being a major mode of international trade (Guerrero and Abad (2013)). In contrast, the European Union features a more integrated regional trucking market where truckers are allowed to pick up as many as three cabotage loads after completing an international trip (Blancas and Briceno-Garmendia (2020)). Even in this more relaxed context, Commission (2014) reports that vehicles traveling outside their registration country were empty more than 45 percent of the time, while vehicles within their registration country were empty just over 25 percent of the time. This difference is present in both specialized and general freight markets, suggesting that cabotage restrictions, rather than search frictions, is the culprit.

Based on a 2018-2019 World Bank survey of regulatory practices, restrictions on cabotage are very common - over 80 percent of countries do not allow cabotage rights. Backhaul, transit, and triangular rights are more common, but in the vast majority of countries, they require particular licenses.

Across countries, higher income countries tend to have more permissive regulation. This is especially pronounced for cabotage restrictions, which is driven by permissive regulatory regimes in Europe.

Within markets, the right to transport regulated commodities may be restricted to a protected set of carriers, such as in the United States prior to the 1980 Motor Carrier Act. Wilson and Dooley (1993) find that, unsurprisingly, carriers with the legal ability to carry a wider variety of products have lower rates of empty trips. Beilock and Kilmer (1986) find that carriers with the regulatory authority to carry regulated goods are 23 percent more likely to be loaded. Similarly, under the Economic Community of West African States’ IST Convention, bilateral treaties between states can define a list of “strategic” commodities which can only be hauled by trucks registered to the destination country (Zerelli and Cook (2010)). These lists can range from military supplies and food aid to fuel and building materials. As 22
a day. At US$1 a day, the average change in poverty deficit in the 20 most severely affected countries amounts to 0.5 percent against 2.2 percent at US$2 a day.

In all countries included in the study the change in the poverty deficit is mostly induced by the negative real income effect of those households who were poor before the price shock, while the change in poverty deficit attributable to new households falling in poverty is negligible. Thus, in countries already equipped with effective targeting mechanisms, such as conditional cash transfers, the strategy that would produce faster results at lower costs would be to scale up such programs rather than designing tools to identify new poor. The challenge in this case lies in the ability to adjust responses to the permanent vs. transitory nature of the shock, yet to a large extent unknown.<sup>11</sup> In other countries resources should be devoted to rapidly implement effective targeting mechanisms in cities. Workfare programs or direct hand-outs could constitute short-term responses to the crisis in this context (World Bank 2008b).

While it is unrealistic to envisage perfect targeting, the rough estimates provided in this paper nevertheless provide an order of magnitude of the cost (excluding operational and implementation costs) of an efficient transfer program from coverage and targeting perspectives. These estimates can also be used to benchmark the cost of alternative policy options.

# **References**

Atkinson, A.B. (1987) “On the measurement of poverty”, _Econometrica_ , Vol. 55, No. 4, pp. 749-764.

Ackah, C. and S. Appleton (2007) “Food price changes and consumer welfare in Ghana in the 1990’s”. _Centre for research in Economic Development and International Trade._ University of Nottingham. CREDIT Research Paper 07/03.

Baffes, J. and Gardner, B. (2003) “The transmission of world commodity prices to domestic markets under policy reforms in developing countries”, _Journal of Economic Policy Reform_ , Volume 6, Issue 3 September 2003 , pages 159 - 180 11 Current outlooks from most international institutions (OECD-FAO, 2008) now foresee a slow decline in food prices in 2008/9, stabilizing thereafter to a higher plateau than in 2005/6, yet remaining volatile. But such outlook still remains quite uncertain. Indeed, the transitory vs. permanent component of food inflation is yet to be quantified with more certainty. This is not an easy task as many important factors exerting influence on agricultural supply and demand can move in the near future in different directions, implying the existence of multiple equilibriums in the medium term. The policy response to the current crisis is itself one of these factors, as well as its interpretation by markets. Others comprise global macro-economic imbalances, the impact of climate change on agricultural yields and volatility, potential technological gains, energy prices, and possible remaining bubbles on financial and assets markets.
29 Table **6s** Public policy and<sup>private saving: effects of intervening variables</sup>

||_Infflaian_|_Aftat_<br>11_Prtet_|_Per cp_|_itadisp.f_|_gwo_|_Foreg_||||
|---|---|---|---|---|---|---|---|---|---|
|<br>p|<br>_widrdatlal _<br>,<br>_setabfity_|_ofram_on<br> _savin_|Growth|_Trend f_|_Deviat i_<br>_rom trend_|_resowe_<br>_constraint_|_Incma,_<br>_concentrat._|_OCpital_<br><sup>_flight_</sup>|_Total ect_<br>_on saing_|
|Effect of an inreasein||||||||||
|interverdng vadiable<br>on private saving|**-**|+|+|+|+|**-**|+|||
|Policy||||||||||
|FlnandalUberalizatlon||?||+<br>(Lrun)|||+||+ ?<br>nrun)|
|FLscal/monetary<br>stabilization|+|?||+<br>(Lnu)|+<br>(Lrun)|(Sxnm)|||+<br>(L_mn)_|
|Selective tax incentives<br>on partcula finandal<br>assets||+?|||||||+|
|Shift of taxation fom<br>corporadons to<br>houeholds|||||||+||+|
|Shift of txation from<br>higher to lower income<br>houeholds|||||||+||+|
|Shift fom income to<br>consumption tax|||||||+||t|
|Realexchange rate<br>depredation|||+<br>(Lrw')||(Srun)|||+|+<br>(L run)|
|Foreign capital Inflows|||+<br>(Lrun)|||||||

Note The agm in the **BUnt** line hndicate the effet of the inteveing varlable<sup>oh private saving, while the *sg in the rmainderof the table</sup> denote the fiscal effect of each pulcy rug the<sup>coresponding</sup> ltewevltig variable. For detild SCsio of the effects, aee text reason is that an increase in the real<sup>return on saving has two offsetting</sup> effects. First, a higher<sup>real interest rate decreases the present cost of</sup> future consumption, so that<sup>it is attractive to consume less now and more in</sup> the future and thus to save<sup>more today. Second, it is no longer necessary to</sup> save as much to achieve a<sup>target level of future consumption. A higher real</sup> interest rate therefore<sup>allows greater consumption both today and tomorrow and</sup> reduces the need to save today.

Given this theoretical ambiguity,<sup>the effect of the real interest rate on</sup> saving becomes an empirical<sup>issue.</sup> A large body of evidence for<sup>both</sup>
Table 10 reports estimation results on self-reported early season labor application intensity (i.e., labor per hectare, in person days). The first two columns include household and hired labor, while the last two columns show estimates for household labor only. There is some similarity with the observed spillover effects of plot size information on fertilizer use intensity. We see statistically significant asymmetric response, downward adjustment in self-reports of early season labor use intensity among those farmers who had overestimated plot size pre-treatment, but not among those who overestimated.

The labor use patterns differ in important ways from those with respect to fertilizer, however. In particular, the information treatment had no significant impact on labor intensity selfreports at mean plot size and pre-treatment measurement error. Furthermore, a significant inverse relationship exists between labor intensity and plot size in the absence of information treatment and is not statistically significantly affected by the corrective information. Measuring labor applications in smallholder farming is challenging (Arthi et al., 2018); the evidence here suggests one added complication: the possibility of correlated measurement error with plot size (Abay et al. 2019).

The findings in Tables 9 and 10 suggest that farmers employ simplifying mental models – e.g., optimal prediction error (Hyslop and Imbens, 2001) – to track some variables. This has two important implications in terms of understanding survey data generating processes. First, the presence of NCME in one production input likely propagates to other production inputs. Given the empirical regularity of NCME in smallholder plot sizes and farm sizes (Carletto et al., 2013; Carletto et al., 2015; Desiere and Jolliffe, 2018; Abay et al., 2019; Dillon et al., 2019; Gourlay et al., 2019), this implies likely NCME in fertilizer, labor, seed, and other inputs that have been less well studied. Second, simplifying mental models naturally generate correlated measurement error, which further complicates econometric correctives, as replacing an erroneous self-reported variable with an accurate measure of the same variable can aggravate rather than reduce bias in regression coefficient estimates due to correlated NCME (e.g., Abay et al., 2019).

We run several robustness checks to probe the robustness of our main results. Among these, we limit our sample to those cases where we have the same respondent in both the postplanting and post-harvest surveys. We re-estimate for these restricted sample involving the same respondent. Appendix Tables A6-A9 show adjustment in plot size self-reports in the post-harvest survey round. These estimates confirm those in Tables 7-10, confirming the already-discussed 28
In the next two columns of the bottom panel of Table 1, we report the lower bound and the average over time of the upper bound on the mobility parameter, ���� , which are 0.81 and 0.97, respectively. In Figure 1, we display the time-evolution of our estimated bounds on mobility based on the aggregate moments. Recall that the lower bound is �� , which is constant over time, while the upper ��� **�** ����� �� � � bound is equal to ���� = min �1, ��+ ����� �� **�** ~~�~~ and varies over time with the observed data on ���� . Purely for visual reference, the dashed line indicates the midpoint of the range between the lower and upper bound estimates.

The key question of interest is how these bounds on the mobility parameter ���� compare with actual mobility as measured at the household level. To answer this question, recall that ���� is simply the slope coefficient from an OLS regression of household-level log per capita income on its lagged value. Given the panel structure of the PSID, we can immediately retrieve a time series of estimates of ���� by regressing income on lagged income at the household level in successive rounds of the PSID. In our baseline specification, we estimate this series of OLS regressions in each period using all households with the requisite per capita income data in the current and previous period. These baseline estimates are superimposed on the macro estimates based on aggregate data in the top panel of Figure 1. These baseline micro estimates of ���� fall within the bounds estimated using the macro data for all but the last four years of the sample. Over the entire time period, the micro estimates of ���� average to 0.83, falling within the range of the average lower and upper bounds of 0.80 and 0.97. However, as is apparent from Figure 1, these baseline micro estimates fall closer to the bottom of the range based on the macro estimates.

There are two features of the PSID microdata that suggest relevant variants on these baseline micro estimates of mobility. The first is that the PSID is a rotating panel, and only around a quarter of the household-year observations correspond to households that appear in all 21 rounds between 1977 and 1997, while the median household is observed for 16 of the 21 PSID rounds. In our baseline specification, the number of households in each cross-sectional regression ranges from 5,570 to 7,587. This raises the possibility that at least some of the fluctuations over time in the micro estimates of ���� reflect changes in the composition of households in the PSID from year to year. To investigate the possible role of changing sample composition, we generate an alternative set of micro estimates of ���� by estimating the same series of cross-sectional regressions of household per capita income on lagged income, but restricting attention to the much-reduced sample of 1,637 households that appear in all 21 rounds of the PSID.

14