rafmacalaba/datause-encoder-data huggingface dataset:
household surveys. In total, we collected data from 3,379 households (92%). These numbers are 814 out of 900 (90%) for those villages that participated in the gender parity lottery. Again **Table A3** suggests no differences by treatment status.

# **Table A3. Attrition by Treatment Status**

|Data Source|Target|Collected<br>Missing|Missing|Beta|(se)|
|---|---|---|---|---|---|
||<br>|<br>control<br>_Tuungane treatment_<br> <br>|<br>treatment<br>||<br>|
|Villages visited|781|735<br>22|24|-0.005|(0.017)|
|Village chief survey|735|714<br>11|10|0.003|(0.012)|
|Household survey|3,675|3,379<br>149|147|0.0004|(0.009)|
|Children’s exam|3,379|1,496<br>966|917|0.024|(0.017)|
|Primary school|735|610<br>67|58|0.0231|(0.027)|
|Health facility|735|504<br>114|117|-0.010|(0.034)|
|||_Genderparity treatment_<br> <br>||||
|Villages visited|190|180<br>6|4|0.0177|(0.032)|
|Village chief survey|180|176<br>2|2|-0.001|(0.022)|
|Household survey|900|814<br>42|44|-0.008|(0.019)|
|Children’s exam|814|338<br>236|240|-0.041|(0.034)|
|Primary school|180|147<br>16|17|-0.019|(0.058)|
|Health facility|180|112<br>33|35|-0.039|(0.072)|

Note: Table presents number of targeted observations, number of observations used for analyses, and difference between both across treatment condition. Standard errors clustered at the village cluster level. ∗ p ≤ 0.10, ∗∗ p ≤ 0.05, ∗∗∗ p ≤ 0.01.

# **Children’s Exam**

In those households with children of school-going age (between 6 and 11 years old), we randomly selected one child for a brief exam. In 1,496 of the 3,379 household surveys was the respondent part of a household with a child of school going age, and was the respondent willing to give permission for the exam. This was 338 out of 814 for those household surveys in villages that were part of the gender parity lottery. Again, we find no differences in exams conducted across treatment conditions.

7
of almost all foreign products for a 90-day period, pursued a standby package with the IMF, and raised tariffs.<sup>3</sup> Year Tariff 1961 31.5% <u>1962 49.8% 1963 47.3% 1964 70.4% 1965 55.3% 1966 36.1%</u>

# <u>India 1972</u>

Indian development post-independence was guided by a series of five-year plans. By the fourth plan (covering 1969-73), there was a deliberate attempt to steer the economy towards selfreliance through import-substitution.<sup>4</sup> As part of the plan, tariffs were imposed on all goods other than grain and a few smaller exceptions. Still, the more immediate reason for the protectionist spike was undoubtedly the Indo-Pakistani war of December 1971.

|1969|59.4%|
|---|---|
|1970|58.7%|
|1971|63.8%|
|1972|91.4%|
|1973|76.2%|
|1974|64.9%|

3 https://www.nytimes.com/1964/12/02/archives/colombia-curbs-all-free-imports-90dayban-is-imposedlatin-bloc.html; also see “The Political Economy of Exchange Rate Policy in Colombia” by Jaramillo, Steiner and Salazar, <u>IDB Working Paper No. 102, 1999.</u> 4 http://shodhganga.inflibnet.ac.in/bitstream/10603/32033/14/14_chapter%208.pdf 8
# **On the International Transmission of Shocks: Micro-Evidence from Mutual Fund Portfolios**

Claudio Raddatz Sergio L. Schmukler<sup>*</sup>

## **Abstract**

Using micro-level data on mutual funds from different financial centers investing in equity and bonds, this paper analyzes how investors and managers behave and transmit shocks across countries. The paper shows that the volatility of mutual fund investments is quantitatively driven by investors through injections of capital into, or redemptions out of, each fund, and by managers changing the country weights and cash in their portfolios. Both investors and managers respond to returns and crises, and substantially adjust their investments accordingly. These mechanisms generated large capital reallocations during the global financial crisis. Their behavior tends to be pro-cyclical, reducing their exposure to countries experiencing crises and increasing it when conditions improve. Managers actively change country weights over time, although there is significant short-run “pass-through,” meaning that price changes affect country weights. Consequently, capital flows from mutual funds do not seem to stabilize markets and instead expose countries to foreign shocks.

## **JEL Classification Codes** : F32, F36, G11, G15, G23

**Keywords** : contagion, crises, global financial crisis, international capital flows, mutual fund investors, mutual fund managers

> * This paper was part of the MIT Sloan School of Management-NBER project on the global financial crisis. For very useful comments, we thank Charles Engel (Editor), Kristin Forbes (Editor), Jeff Frankel (Editor), Tarun Ramadorai, Luis Servén, Ajay Shah, Jeremy Stein, two anonymous referees, and participants at the AEA Annual Meeting (Chicago, IL), the Bank of Spain-World Bank Conference (Madrid, Spain), the CREI-U. Torcuato Di Tella Workshop (Barcelona, Spain), the Darden International Finance Conference (Charlottesville, VA), the LACEA-LAMES Annual Meeting (Santiago, Chile), the MIT-NBER Global Financial Crisis Project (Bretton Woods, NH), and the NIPFP-DEA Workshop on Capital Flows (Delhi, India). We are grateful to Tomas Williams for truly outstanding research assistance and for computing most of the estimates for this paper. Francisco Ceballos, Ana Gazmuri, Julian Kozlowski, Laura Malatini, and Lucas Núñez also did an excellent job as research assistants, helping us at different stages of the paper. We are indebted to EPFR Global for giving us unique data that made this paper possible. We thank the World Bank Development Economics Department and Knowledge for Change Program for generous research support. The views expressed here do not necessarily represent those of the World Bank. Raddatz is with the Central Bank of Chile and the World Bank, Development Research Group. Schmukler is with the World Bank, Development Research Group, and affiliated with the Financial Sector Board. Email addresses: craddatz@worldbank.org and sschmukler@worldbank.org
**Table 1: Behavioral management programs**

|**Category 1**||
|---|---|
|Alternative 1|Pathways Communication Program (RBRI<br>score 20-29)|
|Alternative 2|School district behavioral management<br>teams (Inala Cluster)- known as Behavior<br>Support Team, Corinda District (RBRI 30-<br>39)|
|Category 2<br>||
|Alternative 3|Pathways Social Skills Program (RBRI 40-<br>49)|
|Alternative 4|Behavioral School (Tennyson Special<br>School) (RBRI 50+)|

The costs of the alternative programs estimated by Manning and colleagues (2006) are presented in Table 2.

**Table 2. Cost of alternative behavioral management alternatives**

|**Intervention**|**Budget Cost**|**No. of Participants**|**Per participant**<br>**cost**|
|---|---|---|---|
|Alternative 1|$47,861.41|125|$382.89|
|Alternative 2|$236,312.93|145|$1,629.74|
|Alternative 3|$13,999.93|100|$139.99|
|Alternative 4|$417,460.32|21|$19,879.06|

# **_Measuring heterogenous impact of PIP_**

In terms of our analysis of potential heterogenous outcomes, we use scores derived from the same data used by Homel and colleagues (2015). As described in brief above, RBRI forms a total measure of behavioral adjustment ranging from 12 (positive adjustment) to 60 (poor adjustment). In the longitudinal study undertaken by Homel and colleagues (2015) there were five measures of behavioral adjustment across the reference period, one for each academic year. Our model specifies behavioral adjustment for the fifth assessment period as the dependent variable, behavioral adjustment for the first baseline period as a control, and the 25
# **Table 5e—Trade Flows by Trading Partner of COMESA (in percentage)**

|||||I|mports|||||||||Exports|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Kenya|Tanzania|Uganda|Rwanda|SADC|USA|EUR|China|ROW|Kenya|Tanzania|Uganda|Rwanda|SADC|USA|EUR|China|ROW|
|**Business Services**|||||||||||||||||||
|Air Transport|0.2|0.0|0.0|0.0|0.8|8.3|50.4|1.2|39.0|0.0|0.0|0.0|0.0|1.2|20.2|39.3|2.6|36.7|
|Communication|0.5|0.1|0.0|0.0|0.0|18.3|57.2|3.1|20.8|0.1|0.0|0.0|0.0|0.6|11.9|55.4|2.4|29.5|
|Insurance|0.0|0.0|0.0|0.0|0.0|18.9|50.6|1.3|29.2|0.0|0.1|0.0|0.0|0.0|22.7|35.3|5.0|36.9|
|Business Services nec|0.0|0.0|0.0|0.0|0.4|12.3|53.0|2.5|31.9|0.0|0.0|0.0|0.0|1.1|12.1|49.2|2.6|35.0|
|Financial Services nec|0.1|0.0|0.0|0.0|1.1|0.2|37.7|4.4|56.4|0.0|0.0|0.0|0.0|0.6|14.9|46.9|2.7|34.8|
|Transport nec|0.1|0.1|0.1|0.0|1.2|11.7|34.7|5.3|47.0|0.0|0.0|0.0|0.0|0.4|26.0|42.4|3.2|27.9|
|Water Transport|0.2|0.0|0.0|0.0|0.7|0.0|64.5|4.0|30.6|0.0|0.1|0.0|0.0|0.9|10.4|44.7|3.1|40.8|
|**Dixit-Stigliz Goods**|||||||||||||||||||
|Chemicals Mineral and Metal Products|0.8|0.0|0.4|0.0|1.8|6.1|34.7|12.7|43.4|0.4|0.2|0.1|0.0|0.7|4.8|40.3|0.8|52.6|
|Energy and Minerals|0.1|0.0|0.0|0.0|0.2|4.8|10.2|0.9|83.6|0.0|0.0|0.0|0.0|0.0|4.2|11.9|34.5|49.4|
|Food Products|2.2|0.1|0.9|0.0|1.2|7.3|19.9|1.5|66.9|3.9|0.0|0.2|0.5|2.6|3.9|30.3|0.9|57.7|
|Petroleum and Coal Products|0.3|0.0|0.0|0.1|0.3|1.9|7.2|5.9|84.4|0.4|0.4|0.1|0.0|0.5|4.8|49.6|0.2|43.9|
|Other Manufacturing|0.3|0.0|0.0|0.0|1.0|11.4|41.2|14.4|31.7|0.6|1.1|0.4|0.3|1.1|7.0|36.2|1.1|52.3|
|Textile and Apparel|0.5|0.1|0.1|0.0|0.3|1.2|11.0|44.5|42.3|0.2|0.0|0.0|0.0|0.3|31.5|46.7|2.0|19.2|
|Wood and Paper Products|0.6|0.0|0.1|0.0|1.0|6.4|38.4|9.3|44.2|4.6|0.4|0.8|0.3|0.8|7.8|28.7|1.5|55.1|
|**CRTS**|||||||||||||||||||
|Agriculture and Forestry|4.8|0.1|1.1|0.0|1.1|34.3|8.2|1.0|49.3|0.3|0.0|0.0|0.0|0.4|4.5|38.6|4.1|52.0|
|Other Services|0.3|0.0|0.1|0.0|0.9|30.8|34.7|3.4|29.7|0.1|0.1|0.0|0.0|1.6|16.4|38.5|3.1|40.2|
|Trade|0.0|0.1|0.0|0.0|0.3|10.8|43.7|5.2|39.8|0.0|0.1|0.0|0.0|0.8|11.9|45.2|4.6|37.4|
|Utilities|0.1|0.1|0.1|0.0|2.0|8.2|52.7|3.9|32.9|0.0|0.0|0.0|0.0|1.6|5.5|49.8|2.1|41.0|

# **Source:** GTAP 8.1 data set.

63
(particularly investment) and assets if they were headquartered in municipalities with high exposure to banks that were more impacted by the decline in energy prices.

In addition to these firm-level results, we analyze the impact of the collapse of energy prices at the more aggregated state level. We run a similar specification using state-quarter-level data. In this exercise, we relate quarterly state GDP to the average ex-ante exposure to the energy sector of banks operating in a state. We use the following specification: where _ys,q_ is the total GDP of state _s_ in quarter _q_ . Furthermore, we study the decomposition of the GDP in the three sectors: primary, secondary, and tertiary. The regressor— _AvgExposureEnergys,Aug14_ —is the average ex-ante exposure to the energy sector of banks operating in state _s_ , weighted by loan value. Finally, we include state _γs_ and quarter _γq_ fixed effects to control for state-specific, time-unvarying variation as well as aggregate time variation affecting all states, and errors are clustered at the state level. Our coefficient of interest is _β_ , which indicates whether the aggregate production of a given state was differentially affected by the drop in energy prices as the average ex-ante exposure of its banks to the energy sector increased. To isolate the impact of the contraction in bank lending from the drop in energy prices, we present the results for all 32 states in Mexico and the 30 states in Mexico that do not produce energy.<sup>14</sup> Table 6 presents the findings. Focusing on the non-energy-producing states, an increase of one standard deviation in the exposure of a state to the energy sector reduces the state’s GDP by 1.8 percentage points. We interpret this finding as evidence that the reduction in output was caused by the contraction in lending of banks that were highly exposed ex-ante. Furthermore, as the results in the table show, the brunt of the impact was on the GDP of the secondary sector. Relative to the tertiary sector, the secondary sector tends to be more capital intensive and dependent on external financing (Buera, Kaboski, and Shin 2011). An increase of one standard deviation in a state’s exante exposure to the energy sector reduces the state’s GDP from the secondary sector by around

> 14 Tabasco and Veracruz are the main oil producing states in Mexico. In these states, oil extraction and production represent roughly 40 percent of state-level GDP. For the remaining five producers—Chiapas, Tamaulipas, Puebla, San Luis Potosi, and Hidalgo—energy production is residual and represents less than 2 percent of state GDP.

18
Lewis and Lockheed to lower average fertility. Women with secondary education reduce their fertility by two or more children compared with uneducated mothers (Subbarao and Raney 1995; Schultz 1993; Hannum and Bachman 2006).

Education also improves women’s status within the family, and gives them greater control over family finances. This directly affects children, as women are more likely to spend discretionary resources on investments in human capital—health, education, and food (Thomas 1990; Hoddinott and Haddad 1995; Bruce and Lloyd 1996; Morley and Coady 2003).

Moreover, education empowers women to protect themselves from infection and domestic violence. In Africa, encouraging evidence suggests that education can help halt transmission HIV/AIDS (Herz and Sperling 2004). Educated women are better able to reduce risky behavior by negotiating safe sex with their partners (Malhotra, Grown, and Pande 2003). Studies of domestic violence in Bangladesh and India find that women with some education experience fewer beatings from their spouses (Purna 1999; Jejeebhoy 1998; Bates, Schuler, and Islam 2004; Schuler 2006).

Maternal education shows universally positive impacts on children’s schooling. In analyzing Demographic and Health Surveys from 68 countries, the Unesco Institute of Statistics (UIS) found that the level of maternal education was a significant correlate of whether a child attended school in all but 5 countries (UIS 2005). Children of mothers who had ever attended formal schooling were much more likely to attend school than children of mothers who had not been to school (Behrman and Sengupta 2002, Schultz 2002, Alderman and King 1998). Filmer’s (2000) study of 14 countries suggests that an additional year of mothers’ education raises the likelihood of children’s school enrollment by 1–6 percentage points. Mothers’ education has a strong effect on girls, particularly where girls’ enrollment lags behind that of boys (Behrman and Sengupta 2002; Schultz 2002; Alderman and King 1998; Ridker 1997). ). In Lao PDR, children of more educated mothers were more likely to be attending school, and the effect was more pronounced for girls (King and van de Walle 2007). In Pakistan, maternal education was particularly important for daughter’s school participation (Lloyd, Mete and Grant 2007). Even in rural China, where school participation at the primary level is near universal, mothers’ educational attainment helps predict secondary school enrollment (Hannum and Adams 2007).

4
two-week period and between 2.2% and 4.2% of VCs having any battles with fatalities and an insignificant and negative correlation of this measure with imagery-based analysis, the two variables indeed seem to measure very different aspects of violence.

# **3. Assessing the effects of the conflict on area cultivated with agricultural crops**

Our outcome variable is the area cultivated with winter or summer crops in 2019–23 aggregated at the level of more than 10,000 VCs nationally. The regressions for cultivated area that adjust for time-varying factors such as weather point toward significant conflict effects that persist and even grow in size during the periods for which data are available.

# **3.1 Data on crop cover**

To analyze the impact of the conflict on cropped area nationwide, we use the area cultivated with the main winter or summer crops at the VC level in each year during 2019–23. These crops traditionally made up more than 95% of Ukraine’s agricultural output value (Deininger et al. 2018). Cultivated area is of first-order importance and, as the statistics in appendix table A1 illustrate, can be reliably predicted using existing data, in contrast to yield. Exploring the impact on winter and summer crops separately is justified as both differ in their vegetation periods; the temperatures most conducive to their growth (Fang et al. 2023); and as farmers may substitute summer for winter crops if the latter failed, were damaged by fighting, or faced less favorable terms of trade (for example, due to higher transport costs).

Crop maps for the entire territory of Ukraine in 2019–23 were obtained using on the methodology discussed by Kussul et al. (2017), Shelestov et al. (2017), and Shelestov et al. (2020). In each year except 2022, when in-situ data were collected only for summer crops as the conflict made collection in the spring impossible, training data were gathered in two rounds of in-situ data collection along main roads following standard guidelines (Waldner et al. 2019). Optical data from Sentinel-2 and Synthetic Aperture Radar data from Sentinel-1 during the vegetation period were then used for a convolutional neural network that was run on the Amazon Web Services cloud computing platform.<sup>7</sup> Table 2 summarizes the data at the national level, showing that total area cultivated ranged from 27.3 million ha in 2019 (9.4 million and 17.9 million ha of winter and summer crops, respectively) to 24.88 million ha in 2023 (6.8 million and 18.1 million ha of winter and summer crops, respectively). A modest reduction in cultivated area (of less than 0.5 million ha) for the 2022 winter crop is consistent with the fact that this crop had been sown before the conflict. The fact that the 2023 winter crop area was 2 million ha

> 7 Appendix table 1 shows the number of training samples collected each year as well as the F1 scores for the winter and summer crop maps, which exceed 95% in each of the years. Deininger et al. (2023a) provide a more detailed description of the methodology, including maps of the roads traveled in different years and a confusion matrix.

8
# Three feet under: The impact of floods on urban jobs, connectivity, and infrastructure

## **Jun Rentschler**<sup>**1**</sup> **, Johannes Braese**<sup>**1**</sup> **, Nick Jones**<sup>**1**</sup> **, Paolo Avner**<sup>**1**</sup>

_1Global Facility for Disaster Reduction and Recovery, World Bank, Washington DC, USA_

**Acknowledgements:** This working paper contributes to the World Bank’s 2019 flagship report _Lifelines_ on resilient infrastructure. The authors thank Charles Fox, who provided invaluable support and developed the GostNets network analysis tool used in Section 4. The study has also benefited from helpful comments, feedback, and inputs by Andrii Berdnyk, Marga Cantada, Scott Ferguson, Stephane Hallegatte, Steven Rubinyi, Ben Stewart, Mercedeh Tariverdi, and Joshua Wimpey.

**JEL classification:** R41, O18, Q54

**Keywords:** Resilience; infrastructure; cities; urban flooding
7 systematically collected which will make it easier to achieve given product standards. But it is entirely possible – and indeed often the case – that producers have achieved the required process certification, but that this is not used systematically to improve quality and environmental performance.

# **MAJOR ACTORS IN THE SETTING OF STANDARDS**

Four major sets of actors are involved in the setting of standards – private sector actors, governments, civil society organizations and international industry bodies.

# <u>Private sector standard-setters</u>

In the private sector, individual lead-firms have developed standards to determine the efficiency of their value chain operations. Initially these corporate standards largely defined the nature of the product. They were initially generally unique to the firm. But in some cases, firms began to cooperate to widen the pool of suppliers on which they could draw (Box 1). The concept of “efficiency” also began to widen during the latter decades of the 20<sup>th</sup> Century. In addition to focusing on flexibility, inventories, quality and cost and focusing on product standards, lead-firms have increasingly also needed to respond to civil society pressures on labor standards and the environment (Box 2).

# **Box 1: Evolving supplier standards in the auto industry**

The dominant standard in the auto industry’s quality standard is ISOTS16949. This is an auto-specific standard administered by the ISO, but developed with a sub-committee of OICA (the international vehicle assembly association). It has replaced QS-9000 (the American standard), and VDA-6 (the German standard, previously widely used in Europe), as well as ISO9001/2 (designed generically for all industries). Certain assemblers have additional quality achievement levels based on their suppliers’ actual quality performance For example, Ford has Q1, identifying suppliers who have not had any returns for a period of time and who have passed Ford’s annual audit with a clean record. Toyota requires ISO accreditations: ISO14001 and ISO-TS16949, but has detailed firmspecific standards on which it rewards (and punishes) suppliers.

Source: Justin Barnes, Benchmarking Analysts, personal communication
as it reduces environmental pollution. Similarly, an overwhelming majority (92 percent) agreed or strongly agreed with the statement that having a toilet protects their family against illness such as diarrhea. Further, 78.4 percent of households reported that having a toilet reduces the probability of being a topic of gossip, and only 6.9 percent of households agreed with the proposition that open defecation is acceptable because their ancestors practiced it. Nevertheless, the figures also show a tolerance of those who need to engage in open defecation. For example, 31.3 percent reported that it is acceptable to practice open defecation if you don’t have a toilet, and only 34 percent reported that those who practice open defecation will be socially shunned. Table 11 disaggregates this information by treatment and control communities. There are no significant differences across the communities. Thus, it seems that knowledge of the health consequences of poor sanitation is relatively high across East Java, even in the absence of the program.

Table 12 examines caregivers’ perceptions of the causes of diarrhea. These data were collected in both the baseline and endline surveys. Almost all caregivers (approximately 97 percent) thought that diarrhea is caused by eating dirty food or drinking dirty water. Lower proportions thought that it is caused by not washing hands (about 82 percent). About 86 percent reported environmental contamination as a cause, although only 72 percent believed that it is a consequence of others defecating in the open, and a smaller percentage (60 percent) believed it is a result of others defecating in rivers. The belief that diarrhea is caused by unclean food and water increased in both treatment and control communities but by significantly more in treatment communities. This is also the case for the belief that diarrhea is caused by an unclean environment. Increases in treatment villages relative to control villages in the belief that diarrhea is caused by some vaccines and exposure to the sun are more difficult to explain. All of the differences are small, which is perhaps to be expected when so many households seem to have been relatively well-informed about the causes of diarrhea before program implementation.

Households were also asked how much it would cost to build or improve their own toilet and to state the maximum that they would be willing to pay. Fifty-seven percent of households reported a figure equal to the figure they had reported for constructing the cheapest latrine possible. However, 38 percent of households reported a higher figure, indicating that they were only interested in building a more elaborate toilet. Figure 5 shows the relationship between perceived affordability and household income. Many households with household income below the mean reported that the amount they would have to pay is many times the maximum amount they were willing to pay. This is less common among higher income households.

19