rafmacalaba/datause-encoder-data huggingface dataset:
(2021a). Industries in the model correspond directly to the goods used in our empirical analysis: the 5-digit Standard Industrial Classification of Trade (SITC), revision 2. Firms in each industry differ in terms of productivity, as well as variable export costs which they reduce gradually through a risky investment.<sup>3</sup> The export entry decision and gradual reduction in export costs allow for the slow adjustment of individual and aggregate trade volumes to a change in tariffs. Thus, past policy can have effects long after its implementation.

Firms make decisions to start and stop exporting incorporating expectations about idiosyncratic shocks as well as aggregate shocks to trade policy. Non-exporters decide whether to enter the export market and exporters decide whether to continue exporting. Uncertainty about future tariffs, especially if tariffs are expected to rise, can stifle export entry and encourage exit, lowering current aggregate trade volumes. The model features two tradepolicy regimes, NNTR and MFN, with tariffs taken directly from the data. The probability of switching between regimes varies over time but is common across industries. The realized path of trade policy is identical to the historical experience: The model begins in 1971 in the NNTR regime and then switches to the MFN regime in 1980. We capture the initial lifting of the embargo by assuming that there are zero exporting firms in 1970.

The model is calibrated to match the historical dynamics of U.S. imports from China across industries as well as salient facts about the exporter life cycle. Most importantly, we calibrate the probabilities of switching between trade policy regimes so that the model matches our estimated time path of the annual elasticity of trade to the NNTR gap. This allows us to recover a forward-looking measure of the expected path of U.S.-China trade policy at each moment in time. Our identification works as follows. A higher likelihood of reverting from MFN tariffs to NNTR tariffs raises the expected present value of future tariffs, which lowers the expected return to exporting and thus reduces exporter entry and survival. This effect is stronger for high-gap industries than low-gap industries, reducing imports in the former relative to the latter.

We find the annual probability of China gaining access to MFN tariff rates during the 1970s was about 25 percent. Once China gained this access in 1980, the probability of losing

> 3Drozd and Nosal (2012), Fitzgerald et al. (2016), Piveteau (2021), and Steinberg (2021) develop similar models of slow firm-level adjustment to market entry through the accumulation of customers.

4
40 In sum, we find that structural reforms are key to stimulate firms’ productivity in developing countries. The design of reforms should be comprehensive and account for complementarities and nonlinearities between reforms. The standardized WBES data set used in this paper focuses only on the formal sector, while in low and low middle-income countries, an important share of the labor force works in the informal sector. Our findings capture, therefore, the productivity gain from reforms in the formal sector. As both the informal and formal sectors are interrelated in developing countries, we should expect the gains from reforms to spread over the informal sector through the externality effects and potential encouragement for informal firms to move into the formal sector. Similarly, this paper does not account for structural reforms not supported by IMF programs due to lack of data assessing successful reforms. However, our findings on complementary factors let us suggest that the coexistence of an IMF supporting program with other types of reforms will have a higher impact on labor productivity growth.
Finally, we apply our “top heavy adjustment with Pareto tail”, where within-country inequalities are allowed to increase even further. This is done by allocating the entire gap between private consumption and the survey mean to the top decile and applying (as in the previous case) a Pareto “elongation”. It should be intuitively clear that by increasing the share of the top decile, we make income inequality greater and the Pareto constant lower.<sup>69</sup> In some cases, as we discuss in Appendix 2, such an adjustment may seem excessive an issue which we need to address more comprehensively in future work. For example, if the survey mean is equal to only 50% of private consumption (which is similar to the value observed in India) then simply “ascribing” these 50% to the top decile is probably excessive. Incomes of lower deciles are likely to have been underestimated as well.<sup>70</sup> This is why we consider “top heavy adjustment with Pareto tail” to be an extreme case. The results (row 4, Table 4) show that the Gini increase (compared to the “proportional adjustment with Pareto imputation”, row 3) is now between 4.5 and almost 8 points. Again, the most dramatic change occurs in 2008. In Appendix 2 we test for the robustness of these values to a more plausible range of the Pareto coefficients.

In summary, global Gini calculated from survey data alone is reduced by between 1 and 2 Gini points when we replace survey means by private consumption from national accounts (thus allocating the gap proportionally across national income distributions). When we also assume a Pareto upper tail, the overall Gini barely changes: it increases by about 0.5 Gini points.<sup>71</sup> Only if we increase inequality further by not allocating the gap proportionally, but imputing it to the top decile only, does global Gini increases substantially, by between 4.5 and almost 8 points. This therefore, we believe, sets the range within which the “true” global Gini is likely to be. In 2008, for example, that range is between 68% and 76%. We tend to believe that it is closer to the upper bound but there is no way to prove it.

> 69 A smaller value of the Pareto coefficient implies higher inequality.

> 70 Of course, this is assuming away any definitional differences and other reasons why means in national accounts and household surveys might differ.

> 71 However, for other applications, such as the number of people around the world above a certain income level, the Pareto imputation might make a substantial difference (see Atkinson, 2007).

38
and the response rate was high at 94.23 percent, yielding a total sample size of 1,617 households (corresponding to 7,895 individuals), of which 809 lived in treatment villages and 808 resided in control villages. All present and consenting household members were tested for malaria using Carestart® RDTs and microscopy was used to validate positive RDT results.<sup>9</sup> Tables 1 and 2 present means and standard deviations for variables which are essentially pre-determined, and mean differences between the treatment and the control groups. Even though some of these variables could potentially respond to the intervention, it is unlikely that any response along these dimensions (household demographics and dwelling characteristics) took place between the time of the intervention (June-July 2009) and the time of the survey (October 2009). Table 1 shows individual-level variables and Table 2 shows household-level variables. All the characteristics of treatment and control villages are balanced with one exception: the Tigre tribe is over represented in the treatment group. We take this into account in our analysis by including in all regressions an indicator variable that takes a value equal to 1 if household _i_ belongs to the Tigre tribe and 0 otherwise. The exclusion of this variable does not affect our results.

Tables 1 and 2 also show joint tests that check the balance of several variables simultaneously. We consider three different sets of variables: those available for the whole sample, those available for respondents only and those available only at the household level. To conduct the test we run probit regressions of treatment assignment on the variables in each group and we test whether the coefficients in the regressions are jointly equal to zero. To be precise, let _Ti_ denote an indicator that takes value 1 if household _i_ belongs to a treatment village and 0 otherwise and let _Xi_ be a vector of variables in each group. Then we estimate: where Φ is the cumulative density function of the standard normal and we test whether 9A total of 5,502 people were tested with RDT. 1,120 people were absent at the time of the survey and they could not be tested. In addition, 651 people refused testing. Among those tested, 13 individuals tested positive in the control group and 17 tested positive in the treatment group. The difference between the share of positive RDTs in the two groups is 0.001 (st. err. = 0.003) and not significant (see Keating et al. 2011). Malaria prevalence was (unexpectedly) very low in the area under investigation. No other additional test, such as anaemia, was collected.

8
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23
|Cotton Sector Adjustment Credit Project||
|---|---|
|Cotton Sector Adjustment Credit Project||
|Cotton Sector Adjustment Credit Project||
|Enterprise Rehabilitation and Development Project|Andre Ryba|
|Enterprise Rehabilitation and Development Project|Andre Ryba|
|Enterprise Rehabilitation and Development Project|Andre Ryba|
|Enterprise Rehabilitation and Development Project|Andre Ryba|
|Enterprise Rehabilitation and Development Project|Andre Ryba|
|Enterprise Rehabilitation and Development Project|Andre Ryba|
|Enterprise Rehabilitation and Development Project|Andre Ryba|
|Enterprise Rehabilitation and Development Project|Andre Ryba|
|Enterprise Rehabilitation and Development Project|Andre Ryba|
|Enterprise Rehabilitation and Development Project|Andre Ryba|
|Enterprise Rehabilitation and Development Project|Andre Ryba|
|Technical Assistance(03)|Ellen Cohen|
|Technical Assistance(03)|Ellen Cohen|
|Technical Assistance(03)|Ellen Cohen|
|Technical Assistance(03)|Ellen Cohen|
|Technical Assistance(03)|Ellen Cohen|
|Technical Assistance(03)|Ellen Cohen|
|Technical Assistance(03)|Ellen Cohen|
|Technical Assistance(03)|Ellen Cohen|
|Technical Assistance(03)|Ellen Cohen|
|Natural Resource Management Credit Project|Claude R. Heimo|
|Natural Resource Management Credit Project|Claude R. Heimo|
|Natural Resource Management Credit Project|Claude R. Heimo|
|Natural Resource Management Credit Project|Claude R. Heimo|
|Natural Resource Management Credit Project|Claude R. Heimo|
|Natural Resource Management Credit Project|Claude R. Heimo|
|Natural Resource Management Credit Project|Claude R. Heimo|
|Natural Resource Management Credit Project|Claude R. Heimo|
|Agricultural Services Development Project|Jean J. Delion|
|Agricultural Services Development Project|Jean J. Delion|
|Agricultural Services Development Project|Jean J. Delion|
|Agricultural Services Development Project|Jean J. Delion|
|Agricultural Services Development Project|Jean J. Delion|
|Agricultural Services Development Project|Jean J. Delion|
|Agricultural Services Development Project|Jean J. Delion|
|Agricultural Services Development Project|Jean J. Delion|
|Livestock Development and Rangeland Management Project|Noel Rene Chabeuf|
|Livestock Development and Rangeland Management Project|Noel Rene Chabeuf|
|Livestock Development and Rangeland Management Project|Noel Rene Chabeuf|
|Livestock Development and Rangeland Management Project|Noel Rene Chabeuf|
|Livestock Development and Rangeland Management Project|Noel Rene Chabeuf|
|Livestock Development and Rangeland Management Project|Noel Rene Chabeuf|
|Social Dimensions of Adjustment and Development Project|Nadine T. Poupart|
|Social Dimensions of Adjustment and Development Project|Nadine T. Poupart|
effects of traditionally-cited variables such as maternal age and education, gender of the household head, household wealth, toilet facilities, drinking water sources and cooking fuels.

To summarize, our results strongly suggest that drinking water salinity is a significant determinant of infant mortality in coastal Bangladesh. They also provide some new insights about the relationship between post-natal impacts and the timing of pre-natal ingestion of saline drinking water. However, we should add a cautionary note: Although our research has benefitted from unprecedented access to spatially-formatted salinity monitoring information, our estimation exercise is not based on direct, household-specific measures of drinking water salinity. We have constructed salinity estimates for each DHS cluster using spatial interpolation of information from salinity monitors that lie within 40 km of the cluster. In addition, our monitoring information is for soil salinity, not the salinity of drinking water sources. We provide a rationale for our approach in the paper, but we readily acknowledge the possibility that the resulting measures proxy actual drinking water salinity with substantial random error.

In closing, we believe that our results are sufficiently strong to encourage further empirical work on this issue. An ideal longitudinal study would track health outcomes for women and their children over an extended period, while measuring the salinity of their drinking water at frequent intervals. The study would also track other determinants of maternal and child health outcomes (e.g., mother’s education, household wealth, access to sanitation, drinking water sources and cooking fuels). Such a study might well seem infeasible as a stand-alone exercise, but we believe that a relatively low-cost expedient could contribute valuable information. Once sampling clusters are identified for future Bangladesh Demographic and Health Surveys, an independently-financed team could undertake monthly sampling of drinking water salinity in those clusters, both before and during the survey period. This would provide sufficient information to support more robust and detailed short-run analyses of health impacts and, if repeated in successive surveys, would ensure the growth of a spatially-formatted database for 16
_Choice: School Choice Policies in International Perspective._ New York: Teachers' College Press (in press).

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21
# **3. Simulations**

The simulations are designed to demonstrate the types of issues that can be analyzed in a GEM‐Core application to a macro database. The first section discusses the base scenario, the second section the non‐base scenarios.

## **Base scenario**

The base scenario represents a business‐as‐usual projection without policy changes. Drawing on projections from IMF’s World Economic Outlook (IMF 2017), we impose an average growth rate of 4.9 percent for the period 2016‐2030 – this figure corresponds to the projected average growth rate of low‐income countries. In the base scenario, GDP growth is imposed by endogenously adjusting labor productivity.<sup>7</sup> We assume that government demand for government services, transfers from government to households, and domestic and foreign government net financing are all maintained at their base‐year shares of GDP. Taxes are fixed at their base‐year rates, which means that they will grow roughly at the same pace as the overall economy.

At the macro level, GEM‐Core – like any other CGE model – requires the specification of equilibrating mechanisms (“closures”) for three macroeconomic balances: government, savings‐investment, and the balance of payments. For the base scenario, the following closures are used: (a) government: its accounts are balanced via adjustments in the direct tax rate; (b) savings‐investment: household savings adjust to generate exogenous GDP shares for domestic private investment while foreign (private) investment is financed via the balance of payments and government investment is covered within the government budget; and (c) balance of payments: the real exchange rate equilibrates this balance by influencing export and import quantities and values; the non‐trade‐related payments of the balance of payments (transfers and non‐government net foreign financing) are non‐clearing, kept fixed as shares of GDP.

For each simulation, base and non‐base, GEM‐Core provides the evolution over time for a wide range of indicators including: (a) macro outcomes: GDP at market prices (split into

> 7 In the non‐base simulations, labor‐specific productivity is invariably exogenous.

‐14‐