rafmacalaba/datause-encoder-data huggingface dataset:
for time-invariant differences between different categories of cells. The coefficient on ActiveAid _ikt_ captures whether _Yikt_ is higher when a project is active (relative to periods before the arrival of aid) and _β_ whether it is more so in cells that hosted a historical mission. The coefficient on CompletedAid _ikt_ captures whether _Yikt_ is higher after the completion of a project (relative to periods before the arrival of aid) and _δ_ whether it is more so in cells that hosted a historical mission. The coefficients of interest are _β_ and _δ_ , because it is not a priori clear when the effects of aid should materialize.

It is important to stress that the main goal here is to test whether mission cells cause higher or lower aid effectiveness, and not to test whether aid is effective per se, as this is outside the scope of the article. As such, the identifying assumption is that conditional on EverAid _ik_ = 1, cells with and without missions have parallel trends in _Yikt_ . Identification of _β_ and _δ_ (the coefficients of interest) does not require any assumption about parallel trends between cells that received aid at different points in time or between cells that ever/never received aid. These assumptions would be arguably very demanding, as they amount to saying that the timing of aid implementation is random. In contrast, the identifying assumption used here is much less stringent, and it states that development trends (not levels) between cells with and without missions as of 1903 are the same in the period 1995–2014.

To implement this strategy it is necessary to have a measure of economic development at the cell level observed at several points in time. Proxies of development are constructed from individual-level georeferenced survey data from the DHS. Specifically, this test uses the individual recode of the DHS, which includes women of reproductive age (15–49), as it has the highest country coverage (in some countries the DHS surveys only women). Four questions are selected that cover different dimensions of development and are asked consistently across countries and time. Answers are collapsed at the cell level to obtain four variables measuring the fractions of cell respondents with certain characteristics. The characteristics are the following: having piped water as the main source of drinking water,<sup>27</sup> access to electricity, owning a television and/or radio, and having a floor made of modern material.<sup>28</sup> These measures are not necessarily representative at the cell-year level, but the number of respondents is high (with cell mean 190 and median 105), and the geographical and temporal coverage is also good (1,900 cells, 4,200 27 Other sources are worse: well water, surface water, or rainwater.

> 28Non-modern floors could be made of, for example, leaves or sand.

29
We further estimate another set of regressions using the financial development index compiled by Sahay et al. (2015), and find positive and statistically significant coefficients for exchange rate regime ranging between 0.61 and 1.53 as reported in the appendix (Table A4). The estimates indicate there is a coexistence between higher level of financial development and more flexible exchange rate regimes, which suggests that the absence of flexibility in the exchange rate may not be conducive to the deepening of the financial system.

# **5 Concluding Remarks**

This paper examines the impact of exchange rate regimes, and hence exchange rate policy on economic growth in Africa. We study the dynamics of this relationship along two dimensions: (i) the level of financial development, and (ii) the degree of trade openness. The main results suggest that the development of the financial system facilitates a positive relationship between exchange rate flexibility and both economic growth and total factor productivity growth. Moreover, the results show a positive impact of trade openness on the relationship between exchange rate flexibility and growth. In other words, an increase in trade openness enhances the potential benefits of flexible exchange rates on growth. Furthermore, the paper finds a positive and strong association between exchange rate flexibility and financial development.

A fixed exchange rate regime insulates an economy with an underdeveloped financial system from the risks associated with exchange rate variability and its negative consequences on growth. Yet, studies have shown that there is a threshold of financial development beyond which countries inevitably enjoy positive effects of financial development on economic growth, even under exchange rate variability. Thus, the potential negative effect of exchange rate flexibility is, perhaps, due to some structural features of developing economies, for example, an underdeveloped financial sector, as indicated by the findings. In essence, our paper suggests that exchange rate policy should be conducted with consideration for structural policies such as those that address the development of the financial sector.

Furthermore, given that financial systems are generally underdeveloped in the region, es20
Figure **4:** Governance and Adult Literacy <!-- Start of picture text --> Voice and Accountability Political Instability and Violence<br>100 * l*w*~f*:~<br>so so_r .<br>80 70a<br>80 20<br>70 40<br>80 3<br>80 2<br>30 1<br>20<br>-25 -1.5 -0.5 0. 1.5 2.5 -2.5 -1.5 -0.5 0.5 1.5 2.5<br>Voice and Accountbility Poltical Violence and IndabIlity<br>Government Effectiveness Regulatory Burden<br>100 too*:~.-.~- 80 * - ~ q<br>90 go<br>80 ~ S 70<br>30 * 0<br>40 so 550 ,t. O<br>- ~ ~~~~ Rul ofLw* rf<br>'1 _ _ _ _ _ _ _ __ _ _ _ _ _ _ _ __ _ _ _ _ _ _ _ 100<br>so Ru2e of Lw 8~~~~~~~~~~~~~~~~~~~~0 Gr<br>-270. 05 . . . 720-15 0.851.25<br>8so* e* *.: 2 80 * *<br>70 U 0 -<br>20 2 0<br>covered by thegovernaent fctene ReguItogyaurde<br>10 10<br>0 I8<br>-2.5 -1.8 -0.5 0.5 1.5. 5 5- -28 1.8 -0.5 0.5 1.5 2.5<br>RuleO oLaw Gran<br><!-- End of picture text --> Notes: Each graph plots the iindicated governance aggregate (on the horizontal axis) against the adult literacy rate in percent (on the vertical axis) for the sample of countries covered by the governance aggregate.

25
regions. However, all regions (particularly AFR and LCR) include countries that have no lending, despite significant environmental problems.

**Figure 5.2:  World Bank Environmental Lending by Region**

**vs. Overall Environmental Problems (Log Scale)**

<!-- Start of picture text --> AFR EAP ECA<br>LCR MNA SAR<br>-4 -2 0 2 4 -4 -2 0 2 4 -4 -2 0 2 4<br>Log Environmental Index<br>Graphs by region<br>8<br>6<br>4<br>2<br>0<br>8<br>6<br>Log Environmental Lending 4<br>2<br>0<br><!-- End of picture text --> The number of such zero-lending cases suggests that the Bank's interaction with these countries has been affected by other factors. We introduce broader considerations into our regressions by including the Bank's total country lending and countries' OED project success rates, as well as regional differences. The results in columns 2 and 3 of Table 5.1 suggest that the Bank's overall lending relationship with a country and the country's project success rate are both significant determinants of environmental lending. The results in column 3 also indicate a significant component of environmental lending to two regions (ECA, MNA) that is not accounted for by our environmental problem indicator, project success rates, or other Bank lending.

14
enter both terms into the regressions separately. If the significance of policy in our basic specification reflects only the fact that distress is more likely to occur in countries with persistently poor policies, then we should find that average policy is significant but the deviation from average is not. In columns (3) and (4) we find that both average policy and the deviation of policy from its time average are significant predictors of debt distress. This casts doubt on the hypothesis that our policy variable matters only because it is picking up long-run differences across countries in the quality of policy. Moreover, in column (3) we find that we cannot reject the null hypothesis that the coefficient on average policy and on the policy deviation are equal, justifying the inclusion of simply the level of policy as we do in our core specification. In column (4) in our larger sample including the 1970s, we do find a statistically significant difference between the effects of average policy and its deviation, but we still do find a significant effect of both.

For completeness, in the next two columns of Table 8 we perform the same decomposition exercise, but now for real GDP growth. The rationale for this robustness check is the same as before. It is possible that our growth variable is simply picking up the difference between fast-growing and slow-growing countries, and the probability of distress is higher in the latter. In this case, however, we find no evidence that our growth variable is picking up these persistent cross-country differences. We find that the deviation of real GDP growth from average is highly significant, while averages over time of real GDP growth are not significant determinants of debt distress.

Third, we directly investigate the role of a country’s history of default on its external obligations as a predictor of debt distress. In particular, we use the database of default episodes compiled by Reinhart, Rogoff and Savastano (2003) to identify the fraction of years between independence (or 1824, whichever is later) and 1980 in which a country was in default on its external borrowing.<sup>8</sup> We then add this variable to our core specification covering distress episodes in the 1980s and 1990s. The results of these regressions are reported in columns (7) and (8) of Table 8. In column (7) we report a

> 8 Since the Reinhart et. al. dataset is a comprehensive list of external debt default episodes, we set this variable to zero for those countries not appearing in their dataset.

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# _5.5 Impact of the INDH on ECD_

- 5.5.1 OLS and Logit Regression Results

We turn now to the impact of the INDH on ECD. It may be the case that the impact of the INDH on this dimension of human development is greater than the economic impact (since ECD could be improved both directly through ECD interventions and indirectly through economic channels). The first results with respect to prenatal care, skilled delivery, and preschooling are derived from a logit model. We present marginal effects for the probability of receiving a service in Table 7 below. These results show that for the two health indicators, the INDH had near zero, insignificant effects in 2008 and 2011 and negative effects, which may be spurious, in 2013. It should also be noted that the relationship between the poverty rate and the likelihood of accessing health care services, which ought to be negative, was certainly negative in 2008 and 2011 but not in 2013, when it became positive and significant. This suggests some challenges in estimation with respect to this particular wave and the two indicators. With regard to preschooling, all the INDH parameters are small negatives but statistically insignificant for all the waves.

**Table 7. ECD Outcomes Logit (Marginal Effects) Regression Results by Outcome and Round**

|||**Prenat**<br>|**al**<br>||**Deliver**<br>|**y**<br>||**Prescho**<br>|**ol**<br>|
|---|---|---|---|---|---|---|---|---|---|
||**2008**|**2011**|**2013**|**2008**|**2011**|**2013**|**2008**|**2011**|**2013**|
|**INDH**|0.020|0.103|˗0.300***|˗0.014|0.058|˗0.244**|˗0.042|˗0.036|˗0.021|
||(0.093)|(0.096)|(0.100)|(0.093)|(0.082)|(0.101)|(0.046)|(0.039)|(0.031)|
|**Community poverty rate**|˗0.009|˗0.037|0.109***|˗0.013|˗0.051**|0.072**|0.005|0.010|0.001|
||(0.029)|(0.032)|(0.030)|(0.031)|(0.025)|(0.032)|(0.015)|(0.011)|(0.010)|
|**N (Observations)**|678|604|608|678|604|608|638|642|657|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Notes:_ *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors clustered on commune level. Quantile regression standard errors are bootstrapped with re-sampling at the commune and household levels. Logit marginal effects estimated with observed values for all other characteristics.

The results with respect to nutrition and anthropomorphic measurements are shown in Table 8. It should be noted that (almost) all the coefficients are statistically insignificant. Consequently, the INDH appears to have had no significantly positive impact on the anthropomorphic measurements or on the nutritional status of the children studied. The only parameter that emerges as significant and positive is the height-for-age parameter in the 2013 wave. It should also be noted that the relationship between the poverty rate of the rural commune where the household is located and the anthropomorphic measurements are not all negative as they ought to be. Given these results, the INDH has not had any positive impacts, either directly or indirectly, on the nutritional status of children living in INDH-targeted communes.

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The Impact of Early Childhood Education on Early Achievement Gaps: Evidence from the Indonesia Early Childhood Education and Development (ECED) Project<sup>∗</sup> Haeil Jung Indiana University Bloomington Amer Hasan World Bank World Bank Sector Board: EDU JEL Classifications: I21; I25; I28

> ∗ This paper builds on joint work with Sally Brinkman, Marilou Hyson, Angela Kinnell, and Menno Pradhan presented in Hasan, Hyson and Chang, (eds.), 2013. The registration number of the trial on which this paper is based is ISRCTN76061874. Preparation of this document received partial funding from the Government of the Kingdom of the Netherlands through the Dutch Basic Education Support Program. We would like to thank Luis Benveniste, Marilou Hyson, Menno Pradhan, Samer Al-Samarrai and Heather Tomlinson for detailed feedback on an earlier version of this paper. All errors are our own. 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.
Gertler, Paul, Manisha Shah, Maria L. Alzua, Lisa Cameron, Sebastian Martinez, and Sumeet Patil. 2015. “How Does Health Promotion Work? Evidence from the Dirty Business of Eliminating Open Defecation.” NBER Working Paper 20997, National Bureau of Economic Research, Cambridge, MA.

- Godfrey, Samuel, Matteus Van der Velden, Americo Muianga, Melinda Vigh, Jan W. Gunning, and Chris Elbers. 2014. “Impact Study of the One Million Initiative Rural Water and Sanitation Programme in Mozambique.” _Waterlines_ 33 (1): 35–44.

- Government of India. 2017. “Guidelines for Swachh Bharat Mission (Gramin).” Ministry of Drinking Water and Sanitation, Government of India, New Delhi.

- Guiteras, Raymond, James Levinsohn, and Ahmed M. Mobarak. 2015. “Encouraging Sanitation Investment in the Developing World: A Cluster-Randomized Trial.” _Science_ 348 (6237): 903– 06.

- Hadidjaja, Pinardi, Engelina Bonang, M. Arifin Suyardi, S. Alisah Abidin, I. Suhariah Ismid, and Sri S. Margono. 1998. “The Effect of Intervention Methods on Nutritional Status and Cognitive Function of Primary School Children Infected with Ascaris Lumbricoides.” _The American Journal of Tropical Medicine and Hygiene_ 59 (5): 791–95.

- Humphrey, Jean H. 2009. “Child Undernutrition, Tropical Enteropathy, Toilets, and Handwashing.” _The Lancet_ 374 (9694): 1032–35. doi:10.1016/S0140-6736(09)60950-8.

- Imbens, Guido W., and Jeffrey M. Wooldridge. 2009. “Recent Developments in the Econometrics of Program Evaluation.” _Journal of Economic Literature_ 47 (1): 5–86.

- Institute of Development Studies. 2018. “Countries.” Community-Led Total Sanitation, April 27. <u>http://www.communityledtotalsanitation.org/where.</u>

- Joint Monitoring Programme for Water Supply and Sanitation (JMP). 2017. _Progress on Drinking Water, Sanitation and Hygiene: 2017 Update and SDG Baselines_ . Geneva: World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF).

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- McKenzie, David. 2012. “Beyond Baseline and Follow-up: The Case for More T in Experiments.” _Journal of Development Economics_ 99 (2): 210–21.

- Patil, Sumeet, Benjamin Arnold, Alicia Salvatore, Bertha Briceño, Sandipan Ganguly, John Colford Jr., and Paul Gertler. 2014. “The Effect of India’s Total Sanitation Campaign on Defecation Behaviors and Child Health in Rural Madhya Pradesh: A Cluster Randomized Controlled Trial.” _PLoS Medicine_ 11 (8): e1001709. <u><mark>https://doi.org/10.1371/journal.pmed.1001709</mark></u> .

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percent to the market level (wholesale or retail). Country‐specific variation is not statistically significant.

Looking at each crop separately we find statistically significant country differences only for maize and plantain, while most seasonality is attributable to market location.<sup>26</sup> This suggests that differences in seasonality arising from geographical location are likely to be caused more by transport factors than by differences in government policies at the national level, , the single important exception being for maize where, controlling for the market level (wholesale versus retail) the Malawian seasonal gap averages 23 percent higher than the average of the other countries.

We performed two exercises in order to further explore the Malawian maize seasonal gap. First, using real local maize prices for Malawi spanning 23 years (1989‐2012) instead of 8 (2005‐ 2012), the preferred seasonal gap estimate is 39.5 percent (instead of 50.6 percent, Table A1).<sup>27</sup> The lower figure may in part reflect the use of deflated prices. Irrespectively, it remains higher than the wholesale estimates for all the other countries in our sample. Second, we compared the maize gaps across locations on both sides of the Malawian and Tanzanian borders where cultivation and meteorological conditions are similar. Mbeya, which is the capital of the Tanzanian region ( _mkoa_ ) of the same name contiguous with the border with Malawi, exhibits a maize seasonal gap of 22.8 percent. Chitipa (143km from Mbeya, maize seasonal gap 48.3 percent), Karonga (161km, 74.8 percent) and Misuku (180km, 71.6 percent) are the closest locations on the Malawian side of the border.<sup>28</sup> Maize price seasonality appears to change dramatically over these relatively short cross‐border differences.

The prevalence of high seasonal gaps throughout Malawi together with the sharp drop in the gap as one moves north into Tanzania suggests that the high Malawian gaps are the results of 26 This analysis requires data at both the retail and the wholesale level for at least one country. This prevents our performing this analysis for bananas, eggs, oranges, teff and tomatoes. For maize, we have gap estimates for all seven countries under analysis while for plantain information is confined to Ghana and Uganda.

> 27 The price data are from the dataset used by Dana, Gilbert and Shim (2006). Given rapid inflation in Malawi during the 1990s, prices were deflated by the CPI (IMF, International Financial Statistics). 28 Quoted distances are the distances for the fastest road connections.

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research and product quality information, health and quarantine measures to protect and improve the quality of the agricultural and livestock sector output. In a global economy entrepreneurs need to also access information on international industry standards, and applicable international trade agreements in order to compete in world markets and understand the implications of these agreements for their operations.

**Table 14. Indicators of innovation in Mongolia**

|**Number of:**|**2002**|**2003**|**2004**|**2005**|
|---|---|---|---|---|
|Exported goods|351|393|436|462|
|New exports||120|145|139|
|New manufactured exports||99|104||
|Discontinued exports||78|102|113|
|New exports discontinued next year||63|71||
|New manufactured exports discontinued nextyear||51|51||

_Source: Authors’ estimates based on UN COMTRADE data, HS 4 digits._ In developing countries poor quality or lack of such services is a serious obstacle to output expansion of existing products, improving the quality of existing goods, and new product development (“self-discovery”). In Mongolia poor transport logistics and limited understanding among firms of how to comply with international quality and environmental standards, poor ability to market products in domestic and foreign markets stem from coordination failures.<sup>49</sup> We use a few specific examples of coordination failures in Mongolia to clarify these points.

In Mongolia, the cashmere textile industry offers an example of coordination failures that have stunted its growth in recent years (World Bank 2003). The industry is an _oligopsony_ , (in which a few processors of cashmere have _monopsonistic_ power over a large number of herders supplying raw cashmere). Herders operate in remote areas, lack information, and are often forced to sell their cashmere either to traders at the farm gate or at informal provincial markets at 10 to 45 percent discounts from the prices in the main market in Ulaanbaatar. Herders realize that poor quality raw cashmere is a problem and are reluctant to allow buyers to separate their raw cashmere into different grades. They fear that they may not be able to sell the rejected cashmere, and therefore prefer to sell it in one lot. Buyers have responded by buying unsorted raw cashmere at lower prices in order to hedge the risk of poor quality.

> 49 For instance, the main obstacle to expanding meat exports to Russia is primarily the current ban on such imports from Mongolia that was put in place after the outbreak of “foot-and-mouth” disease in 2001.

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