rafmacalaba/datause-encoder-data huggingface dataset:
4 Economically, increased intra-African trade is also a natural indicator of the extent of integration and of the achievement of the preconditions for realizing the theoretical advantages associated with greater integration. For example, if a theoretical advantage of integration consists in intra**regional product specialization to exploit scale economies"1, and/or to reduce the cost of achieving a specific level of industrial output for each member country", then greater product specialization would certainly be associated with greater intra-regional trade. Thus, the failed increase in intra-SSA trade is evidence of the failure of integration efforts in SSA<sup>9</sup> .

As is widely known, intra-SSA trade flows are small in absolute magnitude. Total exports from SSA in 1988 were $3.66 bn.; by comparison, Brazilian exports were $8.7 bn. to the US and $9.3 bn. to the EC. Intra-SSA trade is also a small fraction of their total trade -- only 5.3% in 1988, down from 5.7% in 1970'. However, to our knowledge only two recent studies attempt to quantify if actual trade is less than either potential or expected trade.

'See Corden (1972).

'See Johnson (1965) and Cooper and MasseU (1965).

9 Many proponents of RI in SSA remain silent on the fact that increased trade among countries adhering to a regional integration scheme does not necessarily indicate that the scheme itself is welfare enhancing for the welfare effect of such a scheme depends not only on how much trade has been created among partner countries but also on how much trade iias been diverted from the rest of the world. A prominent example is that of the Eastern European countries which were quite successful in increasing trpde among themselves, but their distorted pattern of trade was almost surely welfare reducing. See fo example Havryly.;hyn and Pritchett (1991).

**10** UNCrAD Handb-ook of Internation?l Trade and Development Statistics, 1990. Exports from Developing Africa to Dleveloping Africa.
# **Table 3.  Impact of schooling attainment on age at marriage and age at first parenting** **<u>(Censored regression with bootstrapped standard errors, with 1000 replications)</u>**

||<br>**Age at mar**<br>**Males**<br>|<br>**riage**||<br>**Females**<br>|||**Age at first**<br>**Males**<br>|**parenti**|**ng**|**Females**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Coeff**|**t**|**n**|**Coeff**|**t**|**n**|**Coeff**|**t**|**n**|**Coeff**|**t**|**n**|
|_(1) Schooling attainment only (not endogenous)_|||||||||||||
|schooling attainment|0.167|**1.96**|572|0.441|**5.70**|697|0.131|1.56|567|0.521|**7.53**|704|
|_(2) Endogenous schooling attainment_|||||||||||||
|schooling attainment|-0.006|-0.04|636|0.470|**2.62**|770|0.050|0.36|647|0.680|**4.47**|779|
|_(3) Endogenous schooling attainment and height_|_at age 18_||||||||||||
|schooling attainment|-0.087|-0.58|636|0.570|**2.89**|770|-0.045|-0.30|647|0.737|**4.78**|779|
|Height at age 18|0.177|1.33||-0.295|**_-1.79_**||0.221|1.57||-0.181|-1.39||
|_(4) Endogenous schooling attainment and height_|_, plus controls_|_for own_|_family_|_background_|||||||||
|schooling attainment|-0.076|-0.35|636|0.545|**_1.70_**|770|-0.084|-0.38|647|0.870|**3.81**|779|
|Height at age18|0.170|1.11||-0.267|-1.28||0.115|0.70||-0.171|-1.09||

t-statistics in bold are significant at 0.05 level except those that also are in italics that are significant at 0.10 level.

18
|EDU: up to<br>secondary|0.20|0.16|0.22|0.16|2.12*|0.096|
|---|---|---|---|---|---|---|
|EDU: Post-|||||||
|secondary|0.23|0.21|0.25|0.28|1.70|0.165|
|EDU: Bachelor’s|0.41|0.48|0.40|0.41|1.90|0.128|
|EDU: Professional|0.05|0.06|0.03|0.03|2.80**|0.039|
|EDU: Masters+|0.11|0.10|0.10|0.13|0.92|0.431|
|Low Income|0.29|0.26|0.26|0.25|0.44|0.722|
|Med Income|0.30|0.30|0.36|0.31|1.13|0.335|
|High Income|0.41|0.44|0.39|0.45|1.02|0.384|
|In Standard|||||||
|Employment|0.21|0.24|0.23|0.23|0.28|0.843|
|Time Preference|||||||
|Index|0.47|0.49|0.46|0.46|0.30|0.823|
|Risk Appetite (0-<br>10)|6.30|6.54|6.47|6.40|0.59|0.621|
|N|333|336|337|332|||

0: B1 = B2 = B3 = B4): Hours of Work FF̅ FF̅ FF̅ FF̅ Table 3. F-test of joint significance of treatment arm means (H

||Worker De|cides Hours|Client/ Employer|decides hours|||
|---|---|---|---|---|---|---|
|**Variable**|10% lower<br>earnings<br>**Job B1**|20% lower<br>earnings<br>**Job B2**|10% higher<br>earnings<br>**Job B3**|20% higher<br>earnings<br>**Job B4**|**F-stat**|**P-value**|
|Male|0.54|0.52|0.53|0.55|0.29|0.832|
|Has Children|0.41|0.41|0.44|0.42|0.26|0.851|
|Single/ Unmarried|0.44|0.46|0.47|0.48|0.28|0.841|
|Age|35.67|36.24|35.55|35.39|0.42|0.741|
|Ethnicity: Chinese|0.15|0.19|0.16|0.15|0.93|0.423|
|Ethnicity: Indian|0.09|0.10|0.05|0.10|2.54*|0.055|
|Ethnicity:|||||||
|Bumiputera|0.73|0.70|0.78|0.72|2.06|0.104|
|EDU: up to|||||||
|secondary|0.15|0.19|0.20|0.20|1.31|0.270|
|EDU: Post-|||||||
|secondary|0.26|0.22|0.25|0.23|0.64|0.589|
|EDU: Bachelor’s|0.44|0.43|0.42|0.41|0.26|0.855|
|EDU: Professional|0.04|0.04|0.04|0.05|0.61|0.610|
|EDU: Masters+|0.12|0.11|0.10|0.11|0.36|0.781|
|Low Income|0.25|0.24|0.27|0.29|0.75|0.524|
|Med Income|0.32|0.34|0.30|0.31|0.48|0.698|
|High Income|0.42|0.42|0.43|0.40|0.19|0.902|
|In Standard|||||||
|Employment|0.22|0.23|0.22|0.22|0.03|0.992|
|Time Preference<br>Index|0.50|0.49|0.44|0.45|1.25|0.290|
|Risk Appetite (0-<br>10)|6.47|6.44|6.47|6.34|0.21|0.889|

9
# **Resolving Puzzles of Monetary Policy Transmission in Emerging Markets**

Jongrim Ha, Dohan Kim, M. Ayhan Kose, and Eswar S. Prasad<sup>1</sup>

**_Keywords_** : monetary policy; emerging market economies; price puzzle; foreign exchange puzzle **_JEL Classification_** : E31, E32, E44, E52

> * Ha: World Bank; jongrimha@worldbank.org. Kim: World Bank; dkim23@worldbank.org. Kose: World Bank; Brookings Institution, CEPR, and CAMA; akose@worldbank.org. Prasad: Cornell University, Brookings Institution, and NBER; eswar.prasad@cornell.edu. We would like to thank Evi Pappa, David Furceri, George Kouretas, Cristiano Cantore, and an anonymous referee for detailed suggestions. The authors are also thankful to Carlos Arteta, Jakob De Haan, and other participants at the ICMAIF 2024, the PIER-ADB International Conference 2024, and the World Bank internal seminars for helpful comments. We gratefully acknowledge support from World Bank RSB Funds. The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the institutions with which they are affiliated.
Table 3, there is no consistent pattern in terms of significance level for GDP per capita, land endowment, and the interaction of physical capital endowment at the country level with the physical capital intensity at the product level. By contrast, the direct effect of physical capital and the effect of the human capital country-industry interaction have a consistent sign and are statistically significant at the 1 percent level in both column (3) and column (4). The negative coefficients for these two variables suggest that exporting countries’ endowment in physical capital decreases the export hazard rate and that human capital endowment disproportionately promotes exports from industries whose production process is particularly intensive in the use of human capital.

Before considering various alternative channels and performing other robustness tests, let us briefly discuss the economic magnitude of the mechanism captured by our main interaction term ( _StMLibc,t_ 0 _∗ distanceck,t_ 0, with the estimated coefficient _β_ 2). This term captures the differential impact of stock market liberalization on hazard rate of products that are far away versus products that are close to the comparative advantage of the liberalizing country. For any two products, this differential effect is _β_ 2 _∗_ ∆ _StMLibc,t_ 0 _∗_ ∆ _distanceck,t_ 0, which in case of liberalization zero-one dummy simplifies to _β_ 2 _∗_ (1 _−_ 0) _∗_ ∆ _distanceck,t_ 0 = _β_ 2 _∗_ ∆ _distanceck,t_ 0. Let us take as an example India, a country that granted foreign investors access to its domestic market in 1992. Five years before the liberalization event, one of the products from the 75th percentile of distance to comparative advantage (DCA) in the case of India was “hearing aids” (SITC code 89961, DCA value 1.471), and one of the products from the 25th percentile of distance to Indian comparative advantage was “refined copper included remelted” (SITC code 68212, DCA value 0.482). Using the above formula and taking the estimated coefficient for our main interaction term from column (4) of Table 3 implies that after India liberalized its stock market, the difference between the hazard rates of a product far away from its comparative advantage (hearing aids) relative to hazard rate of product close to its comparative advantage (refined copper) increased by approximately 15 percent.<sup>17</sup> Online

> 17 _β_ 2 _∗_ ∆ _distanceck,t_ 0 = 0 _._ 152 _∗_ (1 _._ 471 _−_ 0 _._ 482) = 0 _._ 15, which approximately equals 15 percent, when applying the Taylor expansion in the context of the exponential functional form in the Cox Proportional Hazard Model.

18
Policy Research Working Paper 5559

# **Abstract**

Does radio access improve public service provision? And if so, does it do so by increasing government accountability to citizens, or by persuading households to take advantage of publicly-provided services? Prior research has argued that citizens with greater access to mass media receive greater benefits from targeted government welfare programs, but has not addressed these questions for public services such as in education and health. Using unique data from Benin, this paper finds that literacy rates among school children are higher in villages exposed to signals from a larger number of community radio stations. The effect is identified based on a “natural experiment” in the northern communes of Benin where within-commune variation in village access to radio stations is exogenous to observed and unobserved village characteristics. In contrast to prior research, the authors find that this media effect does not operate through government accountability: government inputs into village schools and household knowledge of government education policies are no different in villages with greater access to community radio. Instead, households with greater access are more likely to make financial investments in the education of their children.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at skhemani@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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23
where the cost of risky sex becomes zero for the infected individual who may become fatalistic because she has ‘nothing to lose’. Gong (2013) presents a simple model to suggest that the effects of HIV testing are, a priori, ambiguous: the response of a utility maximizing individual to new information about HIV infection will depend on her prior beliefs of HIV infection and the degree to which altruism affects her behavior. Given the widely documented ‘transactional’ nature of risky sexual activity in Malawi (Poulin 2007; Swidler and Watkins 2007), the setting for this study, one might expect the direct benefits of risky activity to be high and altruism towards casual partners to be low for some individuals. In such circumstances, it is possible that individuals who find out that they are infected with HIV may increase their risky sexual activity.

Economic theory also suggests the possibility of changes for a broader set of behaviors in response to testing. For instance, if learning one’s HIV status leads to changes in subjective life expectancy, this may lead to changes in consumption, savings, and investments in human capital (Thornton 2012) – changes that are also likely to be moderated by people’s beliefs about HIV infection prior to testing (Yeatman 2009, Goldstein et al. 2013).

In this paper, we utilize objective outcome measures within a home based HIV testing and counseling (HTC) experiment to test these hypotheses among young females in Malawi. Specifically, we are interested in the effects of HTC on two outcomes: risky sexual behavior and investments in human capital. We first examine the overall effect of HIV testing on the population, and then motivated by theory, examine both the effects of testing by HIV-status (i.e. the effects of an HIV-negative/positive test), as well as the effects of being surprised by the test result.

In order to minimize the risk of bias from self-reports, we use HSV-2 as our primary outcome measure for sexual behavior and achievement tests in mathematics, English reading comprehension, and Raven’s Colored Progressive Matrices as our primary measure of investments in human capital.<sup>6</sup> We also collected data on the participants’ prior beliefs regarding HIV infection.

Overall, we find no significant effects of testing on HIV, HSV-2, or test scores. The lack of an overall effect is consistent with the existing literature that finds no effects among those who were

> 6 In our sample of young females, we consider marginal investments in schooling to be the most important long-term decision, and focus on changes in achievement test scores to capture this effect.

4
infrastructure<sup>11</sup> , countercyclical shifts in cropping patterns to offset agricultural losses immediately following the flooding. The people of Bangladesh also have adapted over generations to reduce exposure to flood risks and to maintain their livelihoods. For example, in areas where inundation is a risk, people have raised their houses on mounds above the normal flood level, and adjusted their cropping patterns to take advantage of the flood water. Rural roads and other infrastructure, such as schools, and medical centers, are also raised above normal flood level, where it is feasible. While these measures have significantly reduced damages and losses from floods over time, especially in terms of deaths and injuries, the challenge now that Bangladesh faces is to scale up the investments to prevent the potential drastic consequences of climate change.

# 3. Flooding in a Changing Climate

The IPCC AR4 and research conducted on the future climate of South Asia is indicating climate change will increase the vulnerability of Bangladesh to severe floods.

The IPCC AR4 predicted that monsoon rainfall will increase this century in South Asia resulting in higher flows in the rivers of Bangladesh from Nepal, India, China and Bhutan (IPCC 2007). Although Bangladesh accounts for only 7% of the GBM basin area, it drains the entire GBM basin (1.72 million km<sup>2</sup> ) because of its location at the mouth of the basin. Hence, the timing, location, and extent of flooding in Bangladesh depend on the rainfall in the entire GBM basin.<sup>12</sup> Mirza and Dixit (1997) have estimated that a 2°C warming combined with a 10 percent increase in precipitation would increase runoff in the GBM Rivers by 19, 13, and 11 percent respectively. In Bangladesh, increased depth of flooding will be pronounced in the lowlands and of the Faridpur, southwest Dhaka, Rajshahi, Pabna, Comilla, Sylhet, and Mymensingh greater districts. In a more recent study, Mirza et al., 1998 reported that an increase in precipitation over the GBM basins of about 5 percent combined with a temperature increase of around 1°C could result in up

> 11 Bangladesh Water Development Board (BWDB) has developed design manuals for embankment and flood control & drainage infrastructures.

> 12 On average, an annual water flow of 1,350 billion cubic meters of the GBM basin drains through Bangladesh. In addition, nearly 80 percent of the country’s annual precipitation occurs during the summer monsoon season, when these rivers have a combined peak flow of 180,000 m<sup>3</sup> /second, the second highest in the world.

7
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Positive coefficient estimates for _α_ 1 (and _α_ 2) are obtained but negative coefficient estimates for _α_ 3 (and _α_ 4). As shown in the simulation results presented in the appendix, this outcome arises when transfers serve to equalize land across the whole village.<sup>25</sup> This confirms that there is no more land equalization within ethnic or kinship groups than across the entire village. In other words, ethnicity and kinship are not what drives the lack of land equalization in the village. This is consistent with Arcand and Jaimovich (2014) who also conclude that ethnic divisions do not reduce land transfers in the Gambia Networks Data 2009.

One possible interpretation of these findings is that lineage heads, who are entrusted with the land of their lineage, choose to co-opt newcomers into the village by granting them secondary or temporary rights on land. Since these newcomers often are from a different ethnic and kinship group, this means that sizable land transfers take place across ethnic and kinship boundaries. If land transfers from these outliers are ignored, more evidence of land equalization within ethnic and kinship groups may be found.

To verify this interpretation, the regressions are estimated without land-rich outliers. In practice, outliers are winsorized by replacing values of ( _ai −_ _<u>ak</u>_ ) _/Nk_ and ( ̄ _ak − aj_ ) _/Nk_ below the first percentile and above the 99th percentile with the value of the first and the 99th percentile, respectively. Two findings come out of this exercise (see appendix S2.3). First, when estimating regression model (4) (columns 1 and 2), point estimates for _α_ 3 and _α_ 4 increase markedly: they more than double for ethnicity and nearly double for kinship. This suggests that, when transfers from outliers are omitted from the analysis, the rest of the transfers within co-ethnic or kin farmers serve a more effective role in within-group land equalization. However, when model (6) is estimated, again negative estimates for _α_ 3 and _α_ 4 (albeit not significantly so in the case of co-ethnicity) and positive estimates for _α_ 1 and _α_ 2 are found. This indicates that the negative coefficients found in Table 6 are not purely due to large land 26