rafmacalaba/datause-encoder-data huggingface dataset:
significant at the 99 percent level. When we add additional controls for education, age, and marital status, the coefficient reduces to 0.015 (p‐value < 0.01, table 3, column 3). This suggests that about 55 percent of the overall gradient is due to spatial sorting, and 45 percent is driven by local differences in the quality of care received.

Within communities, the two most likely mechanisms underlying the wealth gradient are self‐selection into different facilities and within‐facility wealth‐quality gradients. Table 4 reports the relationship between wealth and the facilities where women reported receiving care. We find that a one standard deviation increase in wealth is associated with a four‐percentage‐point increase in the likelihood of receiving care at a referral health center or hospital (p < 0.01, column 1). The coefficient decreases to three percentage points when including village dummies (p < 0.01, column 2). Columns 3 and 4 consider the probability of receiving care in the health center officially serving the catchment area in which the village is located. The regression results presented in column 3 are for the full sample, while those in column 4 exclude villages where none of the women reported using the catchment’s health center.<sup>14</sup> Overall, 73 percent of the women report receiving consultations in the catchment’s health center. But the rates differ significantly by wealth: 79 percent among women from households in the lowest wealth quintile use the catchment’s facility but only 64 percent of women in the top wealth quintile do so. A one standard deviation increase in wealth is associated with a 2.9 percentage point lower likelihood of using the assigned health center for the full sample and 3.4 percentage points for the restricted sample.

Data from the exit interviews conducted at the health facilities also contain some information on the rationales for the different choices made by women of different socioeconomic status. During the exit interviews, the women were asked the primary reason they chose the health facility where they received care. Proximity of the facility to their residence was the most frequent answer given by women on both sides of the wealth median. However, women above median wealth were four percentage points more likely to respond that the reason was the high quality of services and eight percentage points more likely to say they chose the facility because of the confidence they had in the staff. There is also a statistically significant relationship between wealth and using some mode of transportation to get to the consultation. Twelve percent of women above median wealth reported using transportation, while 5 percent of those below did so. This suggests that, at least to an extent, the choice of facilities by

> 14 Some villages were closer to a health post or center in a neighboring catchment area.

12
indistinguishable. The price data from DAM however are available for one additional year (2008) compared with the TCB data. Moreover DAM reports price data for a wider range of commodities compared to TCB. We thus use the DAM data for our empirical analysis. The daily international price data of wheat are derived from the data stream of Chicago Board of Trading.<sup>21</sup> Crude palm oil price data are taken from the Malaysian Palm oil Board. Lentil import unit values are taken from the National Bureau of Revenue daily import data. Our sample extends from January 24, 2008 to October 4, 2012. There are however some data gaps due to lack of price data during weekends and holidays as well as some missing data in the DAM original data set. Our final sample for palm oil and wheat includes 966 days spread over 57 months.

To provide a feel of the data used in the analysis, Table A.1 in the online appendix reports summary statistics for the prices and the margins for palm oil and wheat during pre and postreform periods. For palm oil, the world-wholesale margin, the focus of our analysis, has increased in the post-reform period. In contrast, the margin has declined for wheat marketing in the postreform period. In the following, we present the estimates of the policy effect on the marketing margin from formal econometric analysis.

# **(7) The Effects of the Reform: Empirical Evidence**

# **(7.1) Estimates from Before-After Comparison**

The estimates from a before-after comparison of the world-wholesale marketing margin are reported in Table 1.<sup>22</sup> We define the trading margin using alternative measures of crude oil costs. We report two sets of results, using the same-period (top panel) and two week lagged (bottom panel) values of world crude price as the relevant costs. The choice of two weeks lag is motivated by the fact that it takes about two weeks to transport crude oil from Malaysia and Indonesia to Bangladesh.

We report estimates from three different samples: the “full sample”, a “restricted sample”,

> 21Crude palm oil was listed in the TCB in January 2009. We compared TCB data with MPB data on daily palm oil prices, there are again nearly identical.

> 22We focus on the wholesale margin because the DOTs were located in between the refiner and wholesale traders in the marketing chain. The results on the world-retail margin are similar, and are reported in the online appendix Table A.2.

25
# **Figure A4: Impact of U.S. interest rate shocks on EMDE financial markets**

A. Impact of 25 basis point shock on 10-year bond yields B. Impact of 25 basis point shock on 3-month bond yields <!-- Start of picture text --> Basis points<br>Mean 90% confidence interval<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction<br><!-- End of picture text --> <!-- Start of picture text --> Basis points<br>Mean 90% confidence interval<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction<br>D. Impact of 25 basis point shock on capital<br>inflows<br>Percent of GDP<br>2 Mean 90% confidence interval<br>1<br>0<br>-1<br>-2<br>-3<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction<br><!-- End of picture text --> C. Impact of 25 basis point shock on sovereign risk spreads (EMBI+) D. Impact of 25 basis point shock on capital inflows <!-- Start of picture text --> Basis points Percent of GDP<br>140 Mean 90% confidence interval 2 Mean 90% confidence interval<br>120<br>100<br>80 1<br>60<br>40<br>20 0<br>0<br>-20 -1<br>-40<br>-60<br>-80 -2<br>-100<br>-120<br>-140 -3<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction Real Inflation Reaction<br>E. Impact of 25 basis point shock on real F. Impact of 25 basis point shock on equity prices<br>exchange rate<br>Percent Percent<br>Mean 90% confidence interval Mean 90% confidence interval<br>3 15<br>2 10<br>1 5<br>0 0<br>-1 -5<br>-2 -10<br>-3 -15<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction Real Inflation Reaction<br><!-- End of picture text --> F. Impact of 25 basis point shock on equity prices _Source:_ World Bank _Notes:_ Panel local projection models with fixed effects and robust standard errors. See table A2 for details. Models estimated over periods as long as 1997Q2-2019Q4; they exclude observations during global financial crisis (2008Q4-2009Q4) and the COVID-19 pandemic. EMBI = emerging market bond index.

D. Positive values reflect an increase in net liabilities of portfolio and other investments for EMDE.

E. Positive values reflect an appreciation in the exchange rate. Figure excluded fixed exchange rate economies.

39
-9-

14. However, in the third sub-sample period, Barnhart found significant negative reactions of commodity prices to unanticipated money but no reaction to unanticipated discount rate changes. This result **is** puzzling given the fact that the Fed did not have any specific target for MI in that period and was targeting the federal funds rate. Barnhart also found significant positive reactions of commodity prices to unanticipated surges in economic activity in the third sub-period.

15. In analyzing the whole sampl1, Barnhart found that **six** of the commodities reacted significantly to both discount and surcharge rate surprises, while ten commodities reacted significantly to money supply announcements. These reactions were all predominantly negative. Furthermore, he found a significant delayed reaction to several of the news components and like FH attributed it to market inefficiency.

16. Barnhar.'s 1989 article was an extenLion of his 1988 article; in the latter paper he considers the same commodities with a few more announcements over the period 2/15/80-12/28/84. Several of the news elements considered (e.g., consumer installment credit, manufacturers' orders for durable goods, housing starts, retail sales and the trade deficit) are not predicted by the theory 1/. However, it is possible that surprises in these variables might indirectly affect the terms on which speculators hold contracts to commodities

> 1/ Only news from the credit market (interest rates), the foreign exchange market, real activity, inflation, and money stock are predicted by the theory. See Cilbert (1985, 1987) for a discussion of the first four news components and how they affect commodity prices. Frankel and Hardouvelis (1985) discuss the theoretical links between unanticipated money and comodity prices.
implementation of agreement is assumed to start in 2017 and import tariffs among the 16 members<sup>8</sup> are gradually reduced starting with a decline by 24% in 2017 up to 89% in 2027. In case of FTAAP, its implementation is assumed to start in 2021 and import tariffs among the member countries<sup>9</sup> are also gradually reduced starting with a decline by 46% in 2021 up to 94% in 2030. The EU-IDN CEPA is simulated using the assumptions for the EU-Vietnam FTA provided by Petri and Plummer (2016). Hereby, the initial GTAP tariffs are gradually reduced starting with a fall by 55% in 2018 and up to 94% in 2030.

**Table 1. Indonesian and foreign trade-weighted** **<u>NTMs before and after trade liberalization</u>**

||**TPP11**|**TPP15**|**RCEP**|**FTAAP**|**EU‐IDN**|
|---|---|---|---|---|---|
|**Initial trade weighted average NTM in IDN**<br>**(over PTA members)**|3.28%|3.18%|3.02%|4.26%|26.29%|
|**Final trade weighted average NTM in IDN**<br>**(over PTA members)**||1.99%|2.46%|2.50%|15.38%|
|**Initial foreign trade weighted average NTM**<br>**that IDN faces on PTA markets**|6.84%|5.53%|7.10%|6.70%|15.42%|
|**Final foreign trade weighted average NTM**<br>**that IDN faces on PTA markets**||3.26%|5.46%|3.40%|7.96%|

# TPP -11 (CPTTP)

This scenario models the planned tariff cuts in accordance with the provisions of CPTPP among the members and on several key assumptions about the theoretically desirable and politically feasible (“actionable”) cuts in NTMs and the actual cuts likely to follow from the implementation of the CPTPP. Although the agreement has to be ratified by all member countries, executed simulations assume its implementation will begin in 2017.

Tariff cuts under the existing PTAs as well as tariff commitments under the TPP follow the published schedules under the agreements as documented in ITC and MAcMap (2015) and MAcMap (2016). The authors document tariff reductions due to the existing PTAs signed by CPTPP members up to 2031 as well as the CPTPP commitments up to 2046 at the HS6 digit level.

Reductions in actionable NTMs follow the approach of Petri and Plummer (2016) and are assumed to be similar to the agreement between Korea and the United States (KORUS), including some

> 8 The member countries of RCEP include Australia, Japan, Malaysia, New Zealand, Vietnam, Singapore, Brunei Darussalam, China, Korea, Indonesia, the Philippines, Thailand, India, Cambodia, Lao People’s Democratic Republic, and Myanmar (due to the aggregation of regions in GTAP we also include Timor-Leste together with Myanmar).

> 9 In our simulation we include 18 of 21 countries negotiating FTAAP: Australia, Brunei Darussalam, Canada, Chile, Japan, Malaysia, Mexico, New Zealand, Peru, the United States, Vietnam, Singapore, Korea, Indonesia, the Philippines, Thailand, China, and Russia. We are not able to include Taiwan, China; Hong Kong SAR, China, and Papua New Guinea, as those are part of the rest of the word (ROW) in the model aggregation.

8
36 school students are at least a year younger than the other three groups would indicate that these girls had few interruptions in their school careers, or were less likely to have repeated a grade, or both. Since non-rural residential location was totally confounded with girls' school status, it is also possible that this particular residential status somehow favored these girls in ways not included in the statistical adjustment for this difference. Although all the schools in this study were government-run, it is also possible that some of the schools -- possibly all the girls' schools -- were boarding schools, an important school characteristic not measured in the IEA survey. Such an uninterrupted effect of school on students in a boarding environment could be particularly favorable for cognitive development. Another unavoidable analytic difficulty in the study results from the fact that there were only four girls' schools in the study (and 78 students), whereas the other school groups were more numerous. While this is not a serious statistical problem (with analysis at the student level), random selection is always less likely to produce representative samples when school samples are small. That is, the four girls' schools in this study may be special in some way not tapped by the measures we have included. On the other hand, the sixteen boys' schools could also be different. While there is no particular reason to suspect sampling bias in one direction or another, we mention this possibility.

# Education of Females

Since secondary school participation rates for females are so much lower than for males in developing countries like Nigeria, both educational officials and educational researchers should pay special attention to those
b) In real terms over time The comparison of minimum wages across countries refers to period 1985-93, but most of the minimum wage data actually correspond to the years 1985-88. The issue is whether a similar analysis using more recent data would'have yielded the same conclusions. After all, per capita output measured in real terms declined substantially in most CFA countries during the 1980s and early 1990s. In C6te d'lvoire, it decreased at an annual rate of 4.6 percent, and in Niger it did it at an annual rate of 4.1 percent. Although in a less dramatic way, per capita output also decreased in Cameroon (-2.2), Togo (- 2.1), Central African Republic (-1.6) and Gabon (-1.6 too).8 The current misalignment of minimum wages could therefore be much higher than suggested by the results in Table 6.

Minimum wages declined in real terms during this same period though, as shown in Figures 5 and 6. Their annual rate of variation per country, from the time when the terms-of-trade levels reached their peak until before the devaluation of the CFA Franc, is indicated in parentheses in the Figures, Although the specific dates chosen for the calculation of these rates (1977 and 1992 respectively) are somewhat arbitrary, the results would be similar if a few years were added or subtracted at both ends of the interval. What these rates show is that inflation allowed a substantial decline of real minimum wages in all countries where there are data, with the exception of Niger. In most cases, the minimum wage declined at a faster pace than per capita output. So, by the time the devaluation of the CFA Franc took place the misalignment of minimum wages was probably not larger than indicated by the international comparison in Table 6.

With the exception of Chad, which experienced a positive growth rate of 3.2 percent per year during this period, all of the other countries in the region had annual growth rates of per capita output in the range of -1.0 to 1.0 percent.

34
adaptations and improvements to their majority-owned subsidiaries than to their minorityowned subsidiaries.<sup>25</sup> Thus, our evidence provides some support to the idea that equity joint ventures transfer a more dynamic technology to local fi rms. This idea has been at the center of policies to attract FDI across many developing countries that favor joint ventures with local partners over majority foreign ownership. However, the evidence so far has been of stronger technology transfers to majority-owned subsidiaries (Mans fi eld and Romeo, 1980; Ramachandran, 1993; Javorcik, 2006).<sup>26</sup>

# 3.2. Robustness

Our fi ndings in Table 3 identify important associations between international technology diffusion through trade and FDI and fi rm-level technological innovations in developing countries. However, some of the estimates may be biased. First, if multinational parents tend to acquire the best - more innovative and productive - domestic fi rms, then the coefficients on foreign ownership could be biased upwards. This problem is particularly relevant for the coefficient on minority foreign ownership. Second, if technological innovations improve a fi rm’s ability to enter and remain in foreign markets or to buy from foreign suppliers, then our point estimates of the coefficients on exporter and importer status could also be biased upwards. Finally, it is possible that some unobserved variables, such as, e.g., managerial ability, that are correlated with the propensity to innovate also affect the fi rm’s global engagement. Since we do not observe fi rms for more than one period and valid instruments for our variables of interest are difficult to obtain, we address these selection and omitted variables’ problems making use of the rich set of information available in each cross-section of the survey and show the results in Table 4.

The effect of international activities on innovation may simply re fl ect better managerial

> 25The multinational parents’ fear of leakage of proprietary knowledge and of the imminent threat of imitation by domestic-owned fi rms in countries with weaker property rights mentioned by Saggi (2002), seems to in fl uence the nature of the technology transfers to majority-owned subsidiaries but not so much of those to minority-owned subsidiaries.

> 26Mans fi eld and Romeo (1980) fi nd that U.S. multinational parents transfer more advanced technologies to fully-owned subsidiaries than to minority-owned subsidiaries. Ramachandran (1993) fi nds higher technology transfers from multinational parents to fully-owned subsidiaries than to minority-owned subsidiaries in India. Javorcik (2006) fi nds that multinationals with the most advanced technologies tend to enter into Eastern European countries through majority foreign ownership rather than minority foreign ownership.

15
be partial equilibrium, focusing on labor demand/supply and the functioning of the labor market, with an emphasis on institutional factors such as minimum wages, existence of unions, incentives to pay efficiency wages, etc. In the latter literature unemployment is generally endogenous, whereas much of the trade literature assumes full employment or imposes an exogenous constraint such as a fixed minimum wage. It also differs from the trade literature by explicitly considering immigration in their analysis, whereas such mobility is assumed to be impossible in most trade analyses. Indeed, trade studies often assume that trade in goods and factors of production are substitutes, in that under a set of (restrictive) assumptions free trade in goods is predicted to equalize the factor prices across countries.<sup>8</sup> The “standard” prediction from endowment based theories of comparative advantage (Heckscher-Ohlin) is that the distributional impacts of trade and trade liberalization operate through the effect of changes in the relative price of tradable goods as a result of liberalization or other changes that allow trade or expand it. The basic result (prediction) is that once labor adjustment across industries has occurred, wage impacts depend only on the change in product prices induced by greater trade. The argument goes as follows. Since OECD countries have a more educated (skilled) labor force, they (should) specialize in products that use such factors relatively intensively. The relative price of goods that use less skilled labor more intensively should fall as trade is liberalized (and those of skilled goods increase), which in turn should reduce the relative wages of the factors used in producing these goods domestically. At the same time as unskilled labor-intensive activities are downsized and relative wages fall, there should be an expansion in the demand for such labor in all parts of the economy. Conversely, developing countries should specialize in goods that use less skilled labor more intensively and so liberalization should boost unskilled wages.

> 8 Lemieux (2003) is a recent investigation of whether the average wages for different classes of workers defined on the basis of their skills (education and experience) and other characteristics (gender in particular) in Canada and the United States have converged over the last two decades. He notes that aside from the restrictive conditions needed for factor price equalization to be observed, it is not very reasonable to expect national wages to be identical across countries if they are not equalized across regions of the same country (where labor and capital mobility should be much more powerful in equalizing factor prices). Using regional wage dispersion in Canada and the United States as a benchmark for assessing “how different” the wage structures in the two countries are, and controlling for national and regional differences in worker characteristics, he concludes that there has been _divergence_ between the wage structures in Canada and the United States over the last 20 years.

10