rafmacalaba/datause-encoder-data huggingface dataset:
5 inflation to single digits; and countries with single-digit inflation initially often halved inflation;<sup>5</sup>

- the gradual liberalization of interest rates and of directed credit, policies that had tended to depress the interest rates paid to depositors;

- the use of foreign currency deposits in some of the countries;<sup>6</sup>

- the use of new instruments such as certificates of deposits and bonds; and

- the increase in monetization and financial intermediation. (Credit cards and other non-monetary forms of transactions grew over the period but were still small by the end of the 1990s in most countries.)

<!-- Start of picture text --> Figure 1. Deposits /GDP 1990-2000<br>160 160<br>China<br>140 140<br>120 120<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>1989 1991 1993 1995 1997 1999 2001<br>Total Deposits in a country Ave. Total Deposits<br>Percent GDP<br><!-- End of picture text --> The increase in the ratio of deposits to GDP occurred in all regions and in 22 of the 25 larger developing countries. China had by far the largest volume of deposits in the developing world in 2000, the largest ratio of deposits to GDP (137 percent in 2000), and the largest growth in the ratio of deposits to GDP (Table 1). Other East Asian countries were not far behind by any of these metrics. Interestingly, East Asian bank deposits continued to grow even after the crisis, albeit more slowly. In Korea and Thailand, this growth may partly reflect a shift out of non-bank financial intermediaries.

> 5 The only exceptions to this statement are Venezuela, where inflation was halved but remained at about 20 percent at the end of the 1990s; Turkey, where inflation fell only marginally from 1990 to about 50 percent in 2000 though the rate in 2000 was substantially below inflation in the mid 1900s; and Russia, where inflation was brought down from over 850 percent in 1993 to about 20 percent in 2000.

> 6 Honohan and Shi, 2003, report figures on dollar holdings for 12 of the 25 countries.  In addition, at least 3 more of the 25 countries have dollar deposits.  Note that the large real depreciations that began in 1997 would have tended to increase the ratio of deposits to GDP; nonetheless the ratio grew slower after 1997 than before.  Hanson, 2002, discusses the developments that led to the legalization of foreign currency deposits and their impacts.
The condition for a household to be in the study was that at least one of the members should be in the target age range of our intervention, i.e., 18-25 years old.

Second, the research team randomly selected one person age 18-25 in each household to be invited to the screening of a Nigerian movie, different from MTV Shuga. We denote this initial screening as Screening 0. The selection was stratified by gender, half males and half females. Out of 17 _,_ 224 people invited to Screening 0, 6 _,_ 613 attended, with a turnout rate of 38 _._ 4 percent. Appendix Table A1 compares those who chose to attend Screening 0 and those who did not, on a number of characteristics that we collected when we first visited the households, as well as the normalized pair-wise differences and the p-values for the test that the difference in means is zero.<sup>1</sup> The two samples are well balanced, with few variables showing significant differences, but of extremely small magnitude: _in all_ cases the normalized difference is _below the threshold of_ 0 _._ 25 _suggested by Imbens and Rubin (2015)_ . We thus conclude that those who attended Screening 0 were a representative subset of the households invited.

The third step was the selection of our baseline sample. In each location enumerators paid home visits to 64 individuals among those who attended Screening 0, randomly selected.<sup>2</sup> All were invited to participate in the study and administered the baseline survey if they agreed. _Appendix Table A2 shows balance in observable characteristics between the people who attended Screening 0 and were selected into the baseline sample and those that were not._ We also compared our sample to the 2008 Nigeria DHS, restricted to the South-Western region, and found that the two were quite similar in terms of religion (a third Muslim and two thirds Christian), years of education (around 11 years) and television-ownership rates (around 90 percent).

At the end of the survey, the main study participants received invitations to attend Screenings 1 and 2, organized in the two weekends following the interview. Those in the ‘Friends treatment’ also received invitations to bring up to two friends of their choice. Note that invitees were not told what they would watch, neither before Screening 0 nor before Screening 1. It is only when attending Screening 1 that they learnt they were watching Shuga or Gidi Up.

# **Network members**

We complemented our data collection on the main study participants with a sample of con-

> 1The normalized difference is a scale-free measure of the difference in distributions, recommended by Imbens and Wooldridge (2009):

where _X A_ and _X B_ are the means of covariate _X_ in groups A and B, respectively, and _SA_<sup>2and</sup><sup>_S_</sup> _B_<sup>2arethe</sup> corresponding sample variances of _X._

> 2In some locations we have 65 respondents instead of 64, because our teams consisted of eight enumerators working in parallel. Enumerators were given a target number of respondents to interview per day and they did not know how many their colleagues had interviewed until the end of the day.

ii
- Shenoy, A. (2017). Market failures and misallocation. _Journal of Development Economics_ , 128, 65–80.

- Singh, I., Squire, L., & Strauss, J. (1986). _Agricultural household models: Extensions, applications, and policy_ . The Johns Hopkins University Press.

- Skoufias, E. (1994). Using shadow wages to estimate labor supply of agricultural households. _American journal of agricultural economics_ , 76(2), 215–227.

- Smith, C. (2019). Land concentration and long-run development: Evidence from the frontier united states.

- Suri, T. (2011). Selection and comparative advantage in technology adoption. _Econometrica_ , 79(1), 159–209.

- Udry, C. (1996). Gender, agricultural production, and the theory of the household. _Journal of political Economy_ , 104(5), 1010–1046.

- Udry, C. (1997). Efficiency and market structure: testing for profit maximization in african agriculture.

- World Bank (2007). World development report 2008: Agriculture for development.

50
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Given the current high costs of domestic thermal production, NIGELEC would not be able to counteract potential increases in Nigeria’s power price. NIGELEC’s cost of production per kilowatt-hour is among the highest in West Africa (figure 16)—on average, it costs $0.16 to produce power in Niger, of which only $0.06 is attributable to generation (NIGELEC 2009). The generation cost is composed of the cost of power that is

**Figure 16. Mid-range cost of power production in Niger**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Operating Costs Capital Costs<br>US cents per KwH<br>Mali Benin (SBEE) Togo (CEB) Cape Verde Niger Burkina Faso Ghana Senegal Cote d'Ivoire Nigeria<br><!-- End of picture text --> _Source_ : Briceño-Garmendia and Shkaratan (2010); AICD estimates.

imported from Nigeria (around 87 percent of all power produced, at $0.03 per kWh), power produced using diesel (around 8 percent of all power produced, at an astronomical $0.30 per kWh), and power supplied domestically from SONICHAR (around 4.3 percent of all power produced, at $0.12 per kWh). The other non-generation costs, at $0.10, can be attributed to expenditures related to labor and new investments in transmission, among others.

The high costs of domestic thermal power are related to high diesel prices. As noted, domestically generated power is particularly expensive, at $0.30 per kWh. Between 2004 and 2008, prices of diesel in Niger were high relative to some of its neighbors, ranging from $0.94 to $1.11 per liter (table 13). The lack of oil resources within Niger forced reliance on imported fuel that was transported to Niger via road. Transporting fuel to Niger is an expensive proposition, as is moving along West African corridors in general (costs are more than $0.11 per tonne-km, among the highest in the world).

Niger’s high reliance on Nigerian imports is due to its limited power infrastructure, in particular its generation capacity. Niger’s

**Table 13. High fuel prices escalate domestic power production costs**

|Country|Power costs of<br>production|D<br>|iesel price ($/l<br>|iter)<br>|
|---|---|---|---|---|
||($/ kWh)|2004|2006|2008|
|Burkina Faso|0.39|94|112|133|
|Mali|0.39|90|104|110|
|Niger|0.16|94|111|97|
|Nigeria|0.15|45|66|113|
|Ghana|0.12|43|84|90|
|Côte d’Ivoire|0.11|95|106|120|

_Source:_ GTZ 2009. _Note:_ kWh = kilowatt-hour.

installed capacity of 105 MW, primarily thermal, is only one-tenth of the capacity of comparable LICs. The installed capacity per million people is 8 MW, far below the 20 MW that is the average for LICs (table 12) and one of the smallest in the continent. Niger could look into developing its own domestic power sources, particularly hydropower. The Kandadji Dam—Niger’s first hydroelectric project **–** about to 29
yields, we feel that our approach of using percentage changes in yields, which relates the size of the change to the prevailing yield level, is appropriate.

# **6. Conclusion**

In this paper, we estimated the spillover effect of US monetary policy on emerging economies, during October 1, 2008 to September 1, 2016—the period when the US primarily conducted unconventional monetary policy. Using event study methodology, we ask what was the impact of the FOMC announcements on emerging economies’ exchange rates, equity prices, and bond yields. Our empirical approach rests on identifying the surprises associated with monetary policy announcements of the Federal Reserve Board. In accordance with the literature we use changes in 2-year Treasury yield on the FOMC announcement day as an indicator of the surprise change in the policy.

We find a large and significant impact of the surprise US policy announcements on asset prices in emerging economies. Our estimates show that a surprise monetary easing, estimated by a decline in 2-year Treasury yield on the day of the FOMC announcement, results in exchange rate appreciation, increase in equity prices, and a decline in bond yields in emerging economies. A surprise tightening on the other hand, similarly estimated by an increase in 2-year Treasury yield, has an opposite effect.

The effect is larger for a surprise policy tightening than for an easing. The more pronounced market reaction to tightening announcements is consistent with the financial literature which shows that financial markets tend to react more strongly to negative news than to positive news. The results possibly also imply that investors react more sharply to a rise in the Treasury yield by concentrating the rebalancing of their global portfolios away from a few large emerging economies, than to easing when they reallocate to a larger number of countries. The extent of spillover is larger for large policy surprises, defined as those in which changes in Treasury yields exceed two standard deviations of the mean change.

The impact of FOMC announcements on emerging markets is also economically significant. Our results show that the probability of emerging economies experiencing large daily movements in asset prices is almost three to five times larger following FOMC announcements that result in large changes in US Treasury yields than on non-FOMC days.

Evidence shows that the spillover of monetary policy surprises in other advanced economies such as the euro area, Japan and UK is smaller, presumably because of their weaker financial integration with emerging economies.

The results are robust to a number of methodological choices, including estimating policy surprise in alternative ways. Results are not driven by a few specific events such as the tapering 28
# 3.1.2 Production of energy

Section 2 described how sectors are modeled. This holds true for the electricity sector too in the absence of data on fixed and variable factors of production. Electricity production is a translog cost function, where the share in output is explicitly modeled where _ωt_<sup>_E_istheshareofelectricityintotalvalue-addedand�</sup> _i_ =1<sup>_β_</sup> _i_<sup>ln</sup> � _Pt_<sup>_SRV_</sup> _Pt_<sup>_i_</sup> <u>�</u> represent the coefficients on relative prices (cross and own-price elasticities), while ln � _YYt_<sup>_F CST_</sup> _<u>t</u>_<sup>_IO,E_</sup> � measures the impact of final demand (approximated using the input-output table) on electricity production. If the IO tables reflect that household consumption is the main user of electricity, then the household weight in _Yt_<sup>_IO,E_</sup> will be larger.

In value-added terms, we can then get total energy value-added: The price of electricity (in this case the deflator) is an aggregation of renewable prices ( _Pt_<sup>_REN_</sup> which will be a function of the levelized cost of electricity ( _LCOE_ )), and fossil fuel prices ( _Pt_<sup>_FOSS_</sup> which will be a function of LCOE estimates, or a commodity price proxy in cases where _LCOE_ estimates are not available), all indexed to a value of 1 in the base year. Given that fossil fuels and renewables are substitutes we express the price aggregation as: Fossil fuel prices are a weighted sum of oil prices ( _Pt_<sup>_OIL_</sup> ), coal prices ( _Pt_<sup>_COL_</sup> ) and gas prices ( _Pt_<sup>_GAS_</sup> ): Renewable price is a weighted sum of wind prices ( _Pt_<sup>_WND_</sup> ), solar prices ( _Pt_<sup>_SOL_</sup> ), hydro prices ( _Pt_<sup>_HY D_</sup> ) and biofuels ( _Pt_<sup>_BIO_</sup> ):<sup>5</sup> The prices of the respective indices are exogenous and assumed to be determined by

> 5The list of renewables may also include geothermal or other sources depending on data availability.

9
computer usage (significant at less than 1% level). Estimation results for the restricted sample (West Bengal and Gujarat dropped) are almost similar to the ones above.

# **4.  Robustness**

In column 3 we add the control for _Regulation_ . As expected, _Regulation_ has a negative effect on computer usage (significant at less than 1% level) and controlling for it increases the coefficient of the labor regulation index from .189 to .346. In column 4 we add controls for overall development: _BI_ index, literacy rate, labor force participation rate, total adult population (size of the city) and share of services in total employment.<sup>7</sup> Population and participation rates also control for differences in labor supply across cities in which our stores are located.<sup>8</sup> The impact of these controls is an increase in the estimated coefficient of labor regulation (column 3 vs. column 4) which supports our claim of the downward bias. In column 5 we control for a host of store level characteristics: floor area of the shop (thousand square feet), age of the store, duration of power outages per day ( _Outage_ ) and five dummy variables capturing (equal to 1) if a store owns a generator ( _Generator_ ), has a checking/savings account ( _Checking_ ), a line of credit, overdraft facility ( _Overdraft_ ) and felt no need to borrow from external sources in the last fiscal year ( _Liquid_ ). With these controls added the coefficient of the labor regulation index remains positive and significant at less than 5% level.<sup>9</sup> It drops in magnitude from .412 to .369 which is partly due to the difference in sample size between

> 7 Apart from the _BI_ index, all other development proxies mentioned here are at the city level, for the year 1991 (lagged) and taken from Census of India (1991). Our results are same if we use 2001 values instead. 8 Population of the city is expressed in thousands.

> 9 This result holds with a host of other controls too. Examples include: a dummy for the metropolitan cities of Mumbai, Kolkatta, New Delhi, Chennai, Bangalore and Hyderabad, per capita income of the state (2001, 1991 values), number of telephone lines per capita at the state level (city level data is not available) and store level variables for: current sales, days of inventory, store is audited or not.

5
regressions as well as linear regressions.<sup>6</sup> The results, reported in Table 3, are similar for both specifications. Family and hired labor inputs per unit of output are generally lower for IPM production, suggesting that time savings from reduced pesticide applications more than compensate for reallocation of some labor to IPM-related activities. Seed inputs are also significantly lower per unit of output. As expected, pesticide inputs per unit of output are significantly lower for IPM production in both the linear and log models. However, IO coefficients for land, capital, irrigation and fertilizer are not significantly different in the two modes of production.

# **Table 3:  Impact of IPM on Input-Output Ratios for Rice Production**

||||Li|near Estima|tes||||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Land|(2)<br>Family<br>Labor|(3)<br>Hired<br>Labor|(4)<br>Capital|(5)<br>Irrigation|(7)<br>Seed|(8)<br>Fertilizer|(9)<br>Pesticide|
|IPM|0.000|-0.006|-0.005|-0.054|0.143|-0.049|0.000|-0.001|
||(0.25)|(3.01)**|(1.85)|(1.21)|(1.68)|(2.28)*|(0.09)|(6.02)**|
|Constant|0.001<br>(45.65)**|0.018<br>(23.81)**|0.022<br>(18.76)**|0.662<br>(35.76)**|1.077<br>(30.81)**|0.303<br>(34.61)**|0.088<br>(40.88)**|0.001<br>(21.89)**|
|Obs<br>R<sup>2</sup>|829<br>0.00|829<br>0.01|829<br>0.00|829<br>0.00|829<br>0.00|829<br>0.01|829<br>0.00|829<br>0.04|
|||||Log Estimate|s||||
|IPM|0.049<br>(1.33)|-0.237<br>(2.48)*|-0.213<br>(3.01)**|-0.056<br>(0.98)|-0.056<br>(0.87)|-0.130<br>(2.16)*|0.009<br>(0.16)|-0.662<br>(3.75)**|
|Constant|-7.669<br>(512.47)**|-4.474<br>(115.18)**|-4.058<br>(139.82)**|-0.611<br> <br>(25.92)**|-0.014<br>(0.50)|-1.418<br>(57.40)**|-2.571<br>(112.49)**|-7.243<br>(132.87)**|
|Obs<br>|829|807|798|828|753|829|812|659|
|R<sup>2</sup>|0.00|0.01|0.01|0.00|0.00|0.01|0.00|0.02|

Absolute value of t statistics in parentheses significant at 5%; ** significant at 1% Our results suggest that IPM may be more profitable than conventional farming, since no IO coefficient is significantly higher for IPM and several are significantly lower. For pesticides, the savings are clear: Conventional farmers use an average of 2.33 kg of

> 6 We have also estimated these regressions for a sample limited to the five regions where we collected data on IPM farmers, and for specifications that include the IPM determinants in Table 2.  The results are indistinguishable from the full-sample estimates in Table 3.

9
Table 7: Summary of systematic review and meta-analyses studies on burden of malnutrition/disease in adults associated with their diet

|Author (Year)|Study Type & Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key fndings|
|---|---|---|---|---|---|
|Nutrition.outcomes||||||
|Haider et al.<br>(2018)<br>Overweight.and.ob<br>|SRM of 27 studies (27<br>cross-sectional, 3 RCTs)<br>includes 1 in Thailand,<br>India, the Islamic Republic<br>of Iran<br>esity<br>|Adults >18y|Vegetarian diet<br>|Iron status<br>|Vegetarians had signifcantly lower<br>serum ferritin levels compared to non-<br>vegetarian controls (**−**29.71**μ**g/L; 95%<br>CI -39.69, -19.73). The impact was<br>more pronounced in men, than in<br>premenopausal women and all women<br>|
|Qorbani et al.<br>(2022)|SRM of 23 studies (22 cross<br>sectional, 1 cohort), 13 in<br>LMICs: the Islamic Republic<br>of Iran 7, Burkina Faso 1,<br>Ethiopia 1, Ghana 1, Sri<br>Lanka 1,Togo 1|Adults 18-67y<br>(n= 113-78235)|Dietary Diversity(note:<br>studies used diferent<br>DDS measurement range<br>scores from 5-24 groups)|Obesity and overweight,<br>diabetes, BMI, blood<br>pressure, lipid profle|Association of DDS with obesity,<br>abdominal obesity, overweight, body<br>mass index, diabetes, blood pressure,<br>and lipid profle (TC, LDL, HDL) was not<br>statistically signifcant.<br>|
|Salehi-Abargouei<br>et al. (2016)|SRM of 16 studies (all<br>cross-sectional) 6 in<br>Asia, 5 in Africa, 3 in North<br>America, 2 in South<br>America|Adults (n ranged<br>from 172 to<br>10,424)|Dietary Diversity|Overweight, obesity, BMI|Found no signifcant association on<br>either overweight/obesity (OR 0.72;<br>95% CI 0.45-1.16), or mean<br>diferences in BMI (MD 0.22; - 0.70,<br>1.14) when comparing highest and<br>lowest diverse diets|
|Moradi et al.<br>(2023)|SRM of 12 studies (9 cross-<br>sectional, 3 cohort)|Adults (n=140<br>577)|UPF|overweight, obesity,<br>abdominal obesity|UPF associated with increased risk of<br>obesity (OR 1.55; 95% CI 1.36, 1.77),<br>overweight (OR 1.36; 1.14, 1.63),<br>abdominal obesity (OR 1.41; 1.18,<br>1.68)|
|Lane et al. (2021)|SRM of 43 studies ((21<br>cross-sectional, 19<br>prospective, 2 case-control,<br>1 both prospective & cross-<br>sectional analysis) 17 in<br>Brazil,1 in Lebanon|Adults,<br>adolescents,<br>children<br>(n=891,723)|UPF|Overweight, obesity,<br>abdominal obesity|UPF associated with an increased risk<br>of overweight (OR 1.36; 95% CI 1.23-<br>1.51), obesity (OR 1.51; 1.34-1.70),<br>abdominal obesity (OR 1.49; 1.34-<br>1.66)|
|Pagliai et al.<br>(2021)|SRM of 23 studies (10<br>cross-sectional, 13<br>prospective cohort), 3 in<br>Brazil, 1 in Lebanon, rest in<br>HICs|General<br>population|UPF|overweight/obesity (5<br>studies), high waist<br>circumference (4<br>studies), hypertension|_Cross-sectional studies_: Highest UPF<br>consumption was associated with<br>increased risk of OWOB (OR 1.39, 95%<br>CI 1.29, 1.50), high WC or abdominal<br>obesity (OR 1.39,95 % CI 1.16,1.67).|

37
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