rafmacalaba/datause-encoder-data huggingface dataset:
factors are (i) whether or not an intervention area has been hit by cyclone; (ii) poverty rate; (iii) the length of the lean season; and (iv) accessibility to safe water. We exploit the large-scale aspect of the program and the large sample size of our monitoring data (representing the universe of participants’ communities) to re-estimate the returns to exposure for different subgroups of the targeted population and analyze whether the return differs across the subgroups.

Our results show that the returns are higher in poorer areas, in areas that have been exposed to cyclones, and in areas that have a longer lean season. The fact that the results vary significantly according to these key characteristics of the program sites has an important implication for the potential expansion of the program within the current districts of intervention as well as to the new areas in the rest of the country.

The rest of the paper is structured as follow: next section reviews the relevant literature, and section 3 describes the program and the monitoring data in details, highlighting the phase-in aspect observed in the data and the large variation in both initial nutritional outcome and economic characteristics. After fully describing the program features and the data structure, we will discuss the empirical method that we employ to estimate the return to the program duration, and our results are presented in section 6. Section 7 concludes the paper.

# **2.  Literature Review**

There exist only a few papers that focus on the _duration in program implementation_ as the primary parameter of interest. Behrman, Cheng and Todd (2004) studies the impact of a pre-school program in Bolivia and finds that the impact on early childhood development outcomes is observed among children who have been exposed to the program for more than one year, compares to those exposed for shorter durations. They develop empirical methods to estimate the differential effect of duration both using a survey sample of participants and nonparticipants as well as using a sample of participating children only. An analogous approach is followed by Armecin et al (2005), who find outcomes for young children that are increasing with duration of exposure using household data from an early childhood development program in the Philippines. Finally, Gertler (2004) shows that the improvements in terms of the incidence of illness of children in the Mexican anti-poverty program _PROGRESA_ is significant only for those villages with two years of program exposure, relatively to those with less than one year.

6
we also report matched difference-in-differences estimates. Both methods yield similar results. Further, our results suggest that this impact materializes through a budget shift from tobacco to communication.

The paper is organized as follows. The data and basic descriptive statistics are described in Section 2. The estimation strategy is presented in Section 3. Results are discussed in Section 4. The final section concludes.

# **_2. The Data_**

Our analysis relies on household panel data collected in 135 villages of the Philippines. The first round of data collection took place in the fall of 2003 and the sample included 2,400 households of which 2,092 were re-interviewed in the fall of 2006. The dataset contains detailed information on consumption patterns, mobile phone ownership, household structure, education achievements as well as asset and land ownership (Chase and Holmemo, 2005). Because consumption information was collected item by item, we can extract tobacco and communication consumption ( _i.e.,_ all phone-related expenses) from total consumption.

Over the survey period, mobile phone ownership spread quickly in the sampled communities. Indeed, while the proportion of households owning a mobile phone in 2003 was 8.4 percent, it rose to 35.4 percent in 2006.

Smoking is prevalent in our sample with 41.6 percent of households reporting some tobacco consumption in 2003. The average monthly tobacco consumption was about 31.2 Philippine Peso (PHP) per adult over the age of 15. This is equivalent to 1.24 packs of 20 sticks.<sup>4</sup> This rose to 75.9 PHP for households in which at least one member smoked in 2003. Overall, tobacco consumption represented 2.01 percent of their total budget.

> 4 According to WHO (2008), the price of a pack of 20 sticks for the most popular brand in the Philippines is 25 PHP.

3
# **Shared Prosperity: Concepts, Data, and Some Policy Examples**

Francisco H.G. Ferreira<sup>1</sup> , Emanuela Galasso<sup>1</sup> and Mario Negre<sup>2</sup>

**Keywords:** Shared prosperity; poverty; inequality **JEL Codes:** D30, D63, I30

> **1** Ferreira and Galasso are with the World Bank’s Development Research Group. Ferreira is also with IZA.  2Negre is with the German Development Institute and the World Bank Poverty and Equity Global Practice. This paper was commissioned as an article for the Oxford Research Encyclopedia of Economics and Finance. We are grateful to Shaohua Chen, Asli Demirguc‐Kunt, Karla Hoff, Christoph Lakner, Renzo Massari, Espen Beer Prydz, and Prem Sangraula for advice and/or comments on an earlier version of the paper. Any remaining errors are the responsibility of the authors alone. These are the views of the authors only, and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.
<!-- Start of picture text --> Return on Invested Capital<br>Includes PT (2017) correction for intangible capital<br>100<br>80<br>60<br>“° an a oe<br>20 Tee eetteee te ene ee Net — Me" Sarees<br>TN eea NN SN<br>-<br>;<br>--:-- >: 25th percentile ©——— median<br>--------- 75th percentile © ————— 90th percentile<br>Large firms<br><!-- End of picture text --> <!-- Start of picture text --> (OPEX-COGS)/COGS<br>too)<br>oO<br>=<br>5+<br>o<br>=<br>N<br>oS<br>fa8 $ $€ € € 8 8 &€ € 8 $€ = ¥<br>year<br><!-- End of picture text -->
- 20 -

Government's formal approval. The JPC-set prices comprise a retention (producer) price, and various taxes and levies (for the Steel Development Fund, the Freight Equalization Fund, and the Engineering Goods Export Assistance Fund) which increase user prices 30-35% above producer prices. The producer prices are determined on a cost-plus basis to allow for a reasonable return on investment. Such administered prices have not given adequate incentives for improved performance and modernization, and have diluted the competition. Despite pressures from major steel consumers like railways, steel prices experienced a rapid increase over 1975-1985 (11% p.a.) which slowed down slightly in recent years (8.5% p.a. over 1986-1987).

37. Prices of domestic mild steel, which were competitive up to the early 1980s, increased markedly above international prices in the mid-1980s. Due to increased efficiency and price restraint, real depreciati **o** n f the exchange rate, and increasing international prices of steel, Indian prices of most mild steel products now appear to be closer to international prices. The price ratios (ex-factory to FOB prices) are presented in Table 8.

||**MlId S**|**teel**|**TABLE 6t**|**NPC.FORST**|**EE**PROUDCTS<br>**Sgecial St**ll/Al|loyv|
|---|---|---|---|---|---|---|
|Product|1980|1985|<br>**198S**|1989|Product|**Mid-1988**|
|**Wire**Rods|**0.7**|**1.88**|**1.06**|**0.91-1.00**|Cutting<br>**Steel**|2.6-2.7|
|Plates|0.64|1.50|1.04-1.1|1 0.92-1.18|Stainless<br>**Steel**|1.6-2.9|
|**HR**Coll*|0.88|1.69|1.05-1.1|0 1.09-1.14|Spring<br>Stool|2.64|
|CR**Coils**|0.98|1.72|1.02-1.1|6<br>0.94-0.95|**Ball-Baring**<br>**Steel**|<br>2.24|
|Galvanized<br>Plates|0.98|1.84|<br>0.92|N.A.|Alloy Steels|2.2-2.8|

Sources: - Industrial Costs **and Prices,** CEI Study (August 1988). - International **Competitiveness** of Indian **Steel** Industry, Arvind **Pande** (1989). - Economic **Time,** August 12, **1988.** 38. On the other hand, prices of non-ISP producers, (in particular ministeel plants) and special steels/alloys, are unregulated and determined by the conditions of highly competitive domestic markets (except for ferro-chrome); taking into account the import restrictions (canalization) and the high protective tariffs for special steels and alloys (110% to 345% nominal tariffs, and 50% to 160% tariff collection rates). Given the permanent shortage of special steels on the market, domestic prices have been driven by
Are Banks Engines of Export? Financial Structures and Export Dynamics<sup>_∗_</sup> Raoul Minetti Alen Mulabdic Michele Ruta Michigan State University World Bank World Bank Susan Zhu Michigan State University Keywords: Export Dynamics; Financial Structures; Foreign Banks. JEL Codes: G21, D82, F10

> _∗_ Minetti (corresponding author): Department of Economics, Michigan State University, minetti@msu.edu ; Mulabdic: World Bank,1818 H Street NW, Washington, DC 20433; Ruta: World Bank,1818 H Street NW, Washington, DC 20433; Zhu: Department of Economics, Michigan State University. We are grateful to Ana Margarida Fernandes, Ina Jakel, Andrea Landi, Hibret Belete Maemir, Bill Maloney, and to several seminar and conference participants for helpful comments and conversations. 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. 1
Table N16: Estimates of demand elasticity, demand curvature, and pass-through rates for SNAP-eligible goods

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES||||Log|real sales|||||
|Log price|-0.605***|-0.659***|-0.709***|0.214|-0.118|-0.144|0.442|0.306|0.389|
||(0.185)|(0.222)|(0.233)|(0.370)|(0.457)|(0.485)|(0.422)|(0.446)|(0.469)|
|(Log price)<sup>2</sup>||||-2.042**|-1.389|-1.491|-3.631|-4.830***|-5.858***|
|||||(0.842)|(0.899)|(0.949)|(2.335)|(1.661)|(1.679)|
|(Log price)<sup>3</sup>|||||||2.920<br>(3.650)|6.505***<br>(2.390)|8.286***<br>(2.519)|
|Observations|838260|837168|827388|838260|837168|827388|838260|837168|827388|
|R-squared|0.934|0.946|0.954|0.934|0.946|0.954|0.934|0.947|0.955|
|Prob _>_ F|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Number of units|7004|6995|6914|7004|6995|6914|7004|6995|6914|
|Number of clusters|48|48|48|48|48|48|48|48|48|
|Time FE|X|||X|||X|||
|DMA x Time FE||X|||X|||X||
|Zip3 x Time FE|||X|||X|||X|
|Implied rate||||0.740|0.522|0.489|0.767|0.480|0.597|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Control variables as well as store and specified period fixed effects are included. DMA refers to Nielsen designated market areas. Zip3 refers to 3-digit zip codes. Implied rate refers to the unit-subsidy pass-through rates implied by the estimated coefficients assuming markups are 0.3, which implies a market conduct of about 0.16. The coefficients are used to calculate the elasticity of marginal surplus at the mean log price.

Table N17: Incidence of an additional dollar of SNAP benefits for SNAP-ineligible goods

|MPC elasticity<br>Pass-through elasticity<br>Shift magnitude<br>PS<br>CS<br>CS (SNAP)<br>CS (non-SNAP)|
|---|
|0.1182<br>0.0898<br>0.009<br>0.098<br>-0.1608<br>-0.013<br>-0.148|
|Notes: MPC elasticities are obtained from Section 4.2. A market conduct parameter of 0.5 is assumed using markups as shown<br>in Hottman (2016). Demand elasticity is estimated using panel variation as described in Section 5.3. Proportion of SNAP<br>sales of 0.168 is obtained from USDA data. Pass-through elasticity is obtained from Section 4.1 and the shift magnitude is<br>the predicted pass-through elasticity obtained using equation (8) assuming a unit-subsidy pass-through rate of 1.<br>Surplus<br>calculations are changes in surplus per marginal dollar of SNAP disbursed. PS refers to producer surplus and CS refers to<br>consumer surplus, CS(SNAP) and CS(non-SNAP) refers to consumer surplus for SNAP consumers and consumer surplus for<br>non-SNAP consumers, respectively.|

84
> where _Pf_ 1 is the price of services provided by foreign suppliers with restrictions and _Pf_ 0 is the price of services provided by foreign suppliers without discriminatory restrictions.<sup>11</sup>

# **2.1.2 Discriminatory versus Non-Discriminatory Barriers.** Many restrictions affect

both foreign and domestic suppliers of services. For example, a ban on entry for all firms could be non-discriminatory. Even if these barriers have a relatively more adverse effect on the costs of foreign firms, we call these non-discriminatory restrictions. If the non-discriminatory restrictions are also removed or reduced, the foreign supply price would fall further. In this case, the AVE of all the barriers that foreign suppliers of services confront can be decomposed into two parts: AVEs of discriminatory restrictions and those of non-discriminatory restrictions. An issue in the literature has been the disentangling of the impact on price of a service of the discriminatory and non-discriminatory components of services regulations. The data set we use in this paper provides information on discriminatory barriers only. Consequently, we only provide estimates of the AVE of discriminatory barriers.<sup>12</sup>

# **2.2 Data Sources**

With two exceptions mentioned below, the data for the estimates of the discriminatory barriers faced by foreign suppliers of services come from the recently released World Bank database of survey information on 11 services sectors mentioned above in 103 countries. See Borchert, Gootiiz and Mattoo (2014; 2012) for a guide to the database.

The World Bank survey employed a 169 page questionnaire of the regulatory regimes of these 11 sectors that was completed by law firms resident in each of the non-OECD 103 countries. For OECD countries, the questionnaire was completed based on a variety of available reports. The database focuses exclusively on the discriminatory barriers faced by foreign suppliers of services and, except for the European Union countries, ignores preferential

> 11 In some model applications, the AVE cannot be greater than one (e.g., Balistreri et al., 2009; Jensen et al., 2010), so these researchers may use the domestic price with restrictions in the denominator; then the AVE is less than or equal to one. It is straightforward to convert the ad valorem equivalent (AVE) with the domestic price with restrictions to one with an unrestricted domestic price in the denominator. In particular, if AVE is the ad valorem equivalent with restricted prices in the denominator and AVE* is the ad valorem equivalent with unrestricted prices in the denominator, then AVE* may be derived from AVE as AVE* = AVE/[1-AVE]; and  AVE = AVE*/[1+ AVE*].

> 12 In work in Armenia, Georgia, Russia, Kenya, Tanzania, Belarus and Malaysia, our colleagues have supplemented the data from the World Bank questionnaire with other sources which then allowed them to estimate the AVE of the non-discriminatory barriers as well. The first five of these papers are available at https://drive.google.com/?pli=1#folders/0B0V-t-Bs4-

hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2.

5
Table (F3) Impacts of the Program on Food Consumption and Food Security

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Total Consumption (Log USD PPP)|-0.05|(0.10)|[1.00]|4.54|(0.78)|1,316|
|Out-of-pocket Consumption (USD PPP)|-6.18|(11.63)|[1.00]|120.53|(87.30)|1,320|
|Assistance Consumption (USD PPP)|0.15|(0.53)|[1.00]|0.76|(2.85)|1,308|
|Home Production Consumption (USD PPP)|-0.65|(1.65)|[1.00]|2.34|(11.36)|1,321|
|Meat Consumption|0.08|(0.83)|[1.00]|3.38|(5.13)|1,286|
|# Meals Eaten by Respondent Yesterday|-0.18**|(0.08)|[0.12]|2.04|(0.61)|1,321|
|Respondent Hunger Last Week (Days)|0.59**|(0.28)|[0.12]|1.30|(1.72)|1,277|
|Adult Hunger Last Week (Days)|0.56**|(0.26)|[0.12]|1.06|(1.61)|1,233|
|Food Diversity|-0.03|(0.26)|[1.00]|5.35|(1.82)|1,313|
|Reduced Coping Strategy Index|0.04|(0.14)|[1.00]|-0.00|(0.99)|1,283|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022-2023 phone survey data. Each row is its own dependent variable.

- Log total consumption is the log of the sum of the following weekly variables: total food consumption from purchases, total food consumption from home production, and total food consumption from assistance. The individual components are listed in the 3 rows below. Meat consumption reports the number of JD spent on meat, fish and other seafood in the last 7 days. Meal consumption reports the number of meals FR ate yesterday. Respondent hunger (days) reports the number of days in the past 7 days that the FR went to bed hungry because there wasn’t enough food to eat. Adult hunger (days) reports the number of days in the past 7 days that other adults in the household went to bed hungry because there wasn’t enough food to eat. Food diversity is the total number of food categories (out of 7) that the household spent any money on in the past 7 days. Reduced coping strategies index is the world food programme’s reduced coping strategies index, defined as follows: 1*(days in last week relied on less preferred foods) + 2*(borrowed food) + 1*(limited portion size) + 3*(restrict adult consumption so children can eat) + 1*(reduce number of meals eat in a day)

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family xxx in the preanalysis plan.

33
the Bank of Korea intends to shift to the restrictive monetary policy, it usually absorb bank reserves by resorting to the (outright) sales of RPs with the banks involving mainly Monetary Stabilization Bonds and government bonds. But up until recently, open market operations have been undertaken primarily with a view to absorbing short-term liquidity supplied through the automatic rediscount facility necessitated by policy-based loans and by foreign sector when the current account ran a substantial surplus. This kind of quasi open market operations have frequently restricted banks' autonomous portfolio management and led banks to hold some unwanted proportion of those securities.'<sup>6</sup> <!-- Start of picture text --> The Cumulative Response of Large and Small Bank Securities Growth to a<br>Call Rate Shock<br>1.8<br>~Smnall Bank<br>1.6 Securities<br>1O4 evidence of in favor the theoretical i F prediction 1 a that both lending volume and Large BankSecurites<br>1.2<br>0.6<br>0.4<br>0.2<br>months after shock<br>< Figure 13 ><br><!-- End of picture text --> On balance, the results in Figure 12 and 13 would seem to provide some positive evidence in favor of the theoretical prediction that both lending volume and securities holdings of small banks tend to be more sensitive to a monetary contraction than by large banks if the loan supply effects become more pronounced for the small banks facing typically inelastic loan demand schedules and higher costs (tighter liquidity constraints) in attracting non-deposit sources of external finance such as CDs.

We also reran all the equations corresponding to the above VAR specification replacing the December 1997 episode as an alternative indicator of monetary policy for the call rate to ensure the robustness of our results. If the call rate used as our measure of the monetary shock adequately proxies for the change in the stance of monetary policy, then one should expect the cumulative impulse responses estimated with the alternative VAR specification including the December 1997 episode as a measure of the apparently severe monetary contraction to give some results similar to those in Figure 12 and 13.

> 16 In accordance with the Article 22 of the General Banking Act in Korea, a banking institution's investment in stocks or debentures and other securities with maturity exceeding three years should not exceed one hundred per cent of its equity capital. However, note that this stipulation is not applied to government bonds and the Monetary Stabilization Bonds of the Bank of Korea.

19
**-** **<u>Table 7: Impact of DPIP on new participants, converted participants and non participants in the project area</u>**

|**Variables**|**Trimme**|**d sample, P**|**S weighte**|**d**|**Trimmed**|**sample, Ke**|**rnel matc**|**hed**|
|---|---|---|---|---|---|---|---|---|
|**Tr**|**eated**<br>|**Control**|**Diff.**||**Treated**|**Control**|**Diff.**||
|**Change in female social capital**|||||||||
|New participants|0.71|-0.53|1.24|***|0.71|-0.47|1.18|***|
|Converted participants|1.24|-0.25|1.49|***|1.24|-0.18|1.42|***|
|Non-participants|0.82|-0.33|1.16|***|0.82|-0.39|1.21|***|
|**Change in female economic emp**|**owerment**||||||||
|New participants|0.33|-0.33|0.66|***|0.33|-0.32|0.65|***|
|<br>Converted participants|0.72|-0.21|0.93|***|0.72|-0.19|0.91|***|
|Non-participants|0.30|-0.20|0.50|***|0.30|-0.26|0.56|***|
|**Change in female political parti**|**cipation**||||||||
|New participants|0.09|0.00|0.09|***|0.09|0.00|0.09|***|
|Converted participants|0.09|-0.01|0.09|***|0.09|-0.01|0.09|***|
|Non-participants|0.05|0.01|0.04|***|0.05|0.01|0.04|***|
|**Energy intake p.c. (kcal/day)**|||||||||
|New participants|2172|1987|185|**|2172|1979|193|***|
|Converted participants|2076|2069|7||2076|2076|0||
|<br>Non-participants|2070|2049|21||2070|2060|10||
|<br>**Protein intake p.c. (g/day)**|||||||||
|New participants|51.57|44.19|7.38|***|51.57|44.06|7.51|***|
|Converted participants|49.64|46.02|3.62|**|49.64|46.17|3.48|**|
|Non-participants|48.47|46.29|2.19||48.47|46.55|1.92||
|**Consumption p.c. (Rs/year)**|||||||||
|New participants|7271|6198|1073|***|7271|6274|997|**|
|Converted participants|7269|6810|459||7269|6863|406||
|Non-participants|7107|7468|-360||7107|7349|-241||
|**Income p.c. (Rs/year)**|||||||||
|<br>New participants|5224|5737|-514||5224|5658|-434||
|<br>Converted participants|5809|6026|-217||5809|6049|-240||
|Non-participants|6347|5953|393||6347|5948|399||
|<br>**Asset p.c. (Rs)**|||||||||
|New participants|691|379|313||691|410|282||
|Converted participants|426|715|-289||426|680|-254||
|Non-participants|549|743|-194||549|783|-234||

Significance of difference is as follows: * significant at 10%; ** significant at 5%; *** significant at 1% 23