rafmacalaba/datause-encoder-data huggingface dataset:
The first follow-up CATI was conducted in early March, the midpoint of the storyline, and the second one in early April, at the conclusion of the storyline. The final face-to-face follow-up survey was conducted four months later, in late July, 2012.

The results from these surveys were then analyzed and issues highlighted for the qualitative study. Three focus group sessions were finally conducted in mid November, 2012 to conclude the study. Figure 1 illustrates the study timeline.

# **5. Summary Statistics, Randomization, and Take-up**

Table 1 presents baseline summary statistics that were gathered as part of the listing exercise. The table first presents means and standard deviations for the full sample, then separately for treatment and control groups, and finally presents p-values for a test of differences in means between treatment and control groups. None of the differences are significant, which confirms that the randomization was successful.

Overall, the sample consists of 70% females, which is unsurprising since soap opera viewership is predominantly female. At the same time, however, we strived to include males in the sample and the surveyors made the extra effort to find males who watched soap operas during the listing phase. Since our sample is entirely urban, the level of formal education was relatively high, with 70% of the sample having at least graduated from secondary school. A little more than 50% scored above median in the financial literacy test, which highlights that the mean and median are almost the same, thus a nice bell-shaped distribution. A little less than half the sample (43-46%) belongs to the low LSM group, thus providing variation in income levels. Half the sample also has a full time job or other paid work. Nearly 60% of the sample had not borrowed money in the past 6 months at the time of the baseline survey, and finally, about 50% of the sample had watched Scandal in the past.

Table 2 presents attrition results and notes two important points. First, the general level of attrition is very low – in the final follow-up survey from which we obtain knowledge and behavioral outcomes for our analysis, we were able to interview more than 93% of the baseline 16
# **APPENDIX B: TANZANIA**<sup>**10**</sup>

Early efforts to establish cotton as a plantation crop in Tanganyika bore no fruit, but during the 1920s new efforts focused on smallholder production. Cotton production rose significantly following the development of local varieties along with a better organization of the sector, and by 1966, Tanzania’s cotton output was 80,000 tons of lint, almost 1 percent of global production.

During the mid‐1960s, most aspects of the country’s cotton industry, in‐ cluding ginning, marketing, and trade, were transferred to cooperative unions and the Cotton Board. These entities soon became large bureaucracies failing to respond to the needs of the sector and, not surprisingly, the cotton sector began deteriorating. Despite various reform attempts, for the next two decades the sec‐ tor performed poorly. Only after the privatization efforts of the early 1990s—set forth by the Cotton Act of 1994—did the sector get back on a sustainable path (see Baffes 2004 for an account of the cotton sector reforms in Tanzania and Poul‐ ton and Maro 2009 on recent policy developments and performance.) In recent years, Tanzania has produced an average of 100,000 tons of cotton lint, almost all in the Mwanza region (districts of Mwanza, Shinyanga, and Bunda). Tanzania has 73 ginneries.

Production of cotton by‐products has evolved similarly to ginning opera‐ tions. The 1994 cotton reforms also applied to by‐products in the sense that both ginning seed processing operations were privatized, while new entrants were allowed to enter the market. Currently, there are 20 cotton crushing operations in Tanzania, ten of which were visited for this study in May 2009.<sup>**11**</sup> Nine of these ten are associated with ginneries.

The key characteristics of the ten facilities are reported in Table B1. The ten companies employ an average of 120 temporary and permanent workers, ranging from a minimum of 35 to a maximum of 260, and their daily crushing capacity ranges from 30 to 200 tons of seed. Most of them operate on a 24‐hour basis during the cotton seasons. Only three of them crush other oilseeds.

> **10** Most of the information in this Appendix is based on Vasilaky (2009).

> **11** The ten seed processing companies interviewed were: Birchand Oil Mill Ltd.; Voil/Vegetable Oil Industries, Ltd.; I. C. K. Cotton Oil Co. Ltd (Mwanza District); Afrisan Ginning Ltd.; Gaki In‐ vestment Co. Ltd; Jambo Group of Companies Ltd.; Fresho Investment Co. Ltd. (Shinyanga Dis‐ trict); SM Holdings Ltd./Nkalalo Ginnery; Verrian Tanzania Ltd./Bunda Oil Industries; S&C Gin‐ nery/Balamba Ginnery (Bunda District).

**_Page 32_**
the 40% poorest population, according to a targeting index devised by _Secretaría Técnica del Frente Social_ [Technical Social Front Secretariat].

However, the constitutional amendment in year 2008 ordered a gradual expansion in social protection. In order to comply with this, in 2009 a revised policy was established in order to consider people age 65 or older living under poverty income threshold eligible. However, in 2012 this policy became more flexible to allow introducing the whole population in the social records.<sup>5</sup> The original transfer amount was really low (USD11.50, approximately 7% of minimum wages), which was then updated, and from 2013 on, it is USD50 (16% of the unified basic wages).

In terms of coverage, social pension implementation achieved a significant percentage increase in older people getting this social security benefit. In this sense, as discussed above, while in 2016 the contributory system covered 25.8% of older people, with the implementation of the non-contributory pension, the percentage of older people protected by a transfer scheme climbed up to 60%. In other words, the non-contributory pension managed to narrow the gap reaching out to 34% of older people (Figure 8). However, there is still a significant group of older people excluded from the pension system.

Figure 8. Percentage of Older people over 65 Years of Age Receiving a Social Security Benefit per Kind of Scheme. Years 2003-2016 <!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>20032004200520062007200820092010201120122013201420152016<br>Contributory Social Pension Total<br><!-- End of picture text --> Source: author elaboration based on ENEMDU

> 5 At the same time, the government has implemented a non-contributory pension within the framework of program _Mis Mejores Años_ [My Best Years]. This program is different from the traditional social pension in the benefit amount that is USD100. This transfer was announced at the latest presidential campaign and it is being gradually introduced. The eligible beneficiaries for _Mis Mejores Años_ are vulnerable 65-aged and older people according to the welfare index based on social records.

14
Policy Research Working Paper 10953

# **Abstract**

It is often argued that small firms are more flexible than large firms. As a result, small firms perform better in volatile markets compared to large firms. The present paper explores this idea for a representative sample of private hotels in Malaysia. Specifically, the paper estimates the impact of volatility in occupancy rates on the pure technical efficiency of small versus large hotels. A slack-based non-radial efficiency measure obtained from the data envelopment analysis methodology is used. The empirical results confirm that smaller hotels are better at dealing with volatility than large hotels are. That is, there is a positive and significant impact of higher volatility on the efficiency of relatively small hotels, a negative and significant impact on the efficiency of larger hotels, and no significant impact on the efficiency of the average hotel. Higher women’s ownership also helps hotels to deal with volatility. The paper discusses the policy implications of the findings.

This paper is a product of the Global Indicators Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at mamin@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
# **_II. Export Performance of Penang_**

Penang accounts for 31% of Malaysia’s exports and close to half of the electronics exports of Malaysia (see Table 1). The bulk of its exports are electronics products mainly shipped by air, reflecting their higher value-to-weight ratio. Below we examine the evolution of Penang’s export mix and possible future directions.

## **Table 1: Penang’s Share of Exports in Malaysia**

||1995|2000|2007|
|---|---|---|---|
|Penang's exports as a share of Malaysia's exports|23.6|27.7|31.0|
|Penang's electronics exports as a share of Malaysia's electronics||||
|exports|35.8|36.1|49.0|
|Source: UN Comtrade||||

# **Revealed Comparative Advantage**

Revealed comparative advantage (RCA) is a technique which is frequently used to analyze export competitiveness.<sup>2</sup> In 1995, Penang’s strength was in resource-based

> 2 Revealed comparative advantage (RCA) is often used to measure the export competitiveness of a commodity (or an industry) of a country.  It is a ratio of two shares: the share of a commodity’s export in the overall exports of a country; and the share of the same commodity in global exports (see the equation below).

where i denotes the commodity and j denotes countries (over the set of commodities, i = 1...I, and over the set of countries, j = 1...J). An RCA greater than one means that the country has a “revealed comparative advantage” in that commodity. This is assuming that the numerator and the denominator are increasing. If that is not the case, greater care is needed in interpreting the results. Rearranging the equation, one can obtain, The numerator is now country j’s market share of commodity i in the world export market and the denominator is country j’s share of exports in overall world exports. Thus, even if country j is losing 4
Ware NC, Idoko J, Kaaya S, Biraro IA, Wyatt MA, et al. (2009), “Explaining adherence success in sub-Saharan Africa: An ethnographic study.” _PLoS Med_ 6(1).

Weiser S., W. Wolfe, D. Bangsberg, I. Thior, P. Gilbert,J. Makhema,P. Kebaabetswe, D. Dickenson, K. Mompati, M. Essex & R. Marlink, (2003), “Barriers to Antiretroviral Adherence for Patients Living with HIV Infection and AIDS in Botswana,” _AIDS_ , 34:3.

WHO Expert committee on nutrition and physical status, (1995), _Uses and interpretation of anthropometry_ . Geneva: World Health Organization.

World Bank, (2008), “Guidance for responses from the human development sector to rising food and fuel prices”, Working Paper 45652.

22
which is 10% of a 600 kWh household monthly bill. However, lighting is disproportionately used “on peak”. Therefore, the resulting reduction is actually 21% of the household peak load demand.<sup>51</sup> For a transformer with about 20% of all households treated, this would equal approximately a 4% reduction in transformer peak load.<sup>52</sup> Using household survey data on the number of days without electricity (due to outage) during the month prior, we provide suggestive evidence of an aggregate impact of transformer-level treatment on unplanned outages.<sup>53</sup> In Figure 4, we plot the reported outages at follow-up by treatment group. As shown, the distribution of reported outages among households in treated transformers is shifted leftward (towards zero outages) in comparison to the graphed responses of households in the control transformers. This figure motivates the regression analysis that follows.

# **6.2 Estimating aggregate impacts**

We use the transformer-level randomization to estimate the impact on reported unplanned electricity outages using the following equation: where _Oig_ is the number of days without electricity due to outages in the month prior to the follow-up survey as reported by household _i_ in transformer _g_ ; _Hig_ indicates if household _i_ is in a higher-saturation transformer, with between 15 to 18 percent of all households assigned to treatment; _Lig_ indicates whether household _i_ is in a lower-saturation transformer, with between 10 to 14 percent of households assigned to treatment;<sup>54</sup> _Treatig_ remains the household’s own treatment status; and _Xg_ is a vector of transformer-level controls. Standard

> 51According to the utility, times of peak demand are between 6 to 9 am and 6 to 10 pm. The calculation assumes a reduction of peak demand of 1.5 kW peak, and switching from 100 W incandescent bulbs to 21 W CFLs (Appendix Calculation 3).

> 52We “ground-truth” these results by discussing them with our utility collaborators. Engineers indicated that this would be a substantial reduction in peak demand and would very likely reduce transformer outages.

> 53The follow-up survey occurred in March and April 2014, so the months in which we are measuring days without electricity due to outage include February and March.

> 54In higher (lower) saturation transformers, 80% (60%) of study households were assigned to treatment. There is heterogeneity in the number of households across transformers, and we sampled 20% of all households within each transformer to include them in the study. Therefore, the randomized study saturation proportions result in between 15 to 18 (10 to 14) percent of all households in high (low) saturation transformers actually receiving the CFL treatment

21
FTGURE 4 NIGERIA: FINANCIAL TAXES <!-- Start of picture text --> Mecaur I<br><!-- End of picture text --> <!-- Start of picture text --> 7<br>a.<br>a<br>I<br>1.2 ~ -,s iea 1e 1s e-ls e7 lS<br>2-<br>ioai 19a2 18 1984 1GM 1986 1G97 lGOs<br>v/i cc,r RR = Ge Nse<br><!-- End of picture text --> 44
ment’s Covid-19 policies and improve how citizens perceive the state’s capacity to implement these policies. We focus on two types of information: _(i)_ information about past state success in managing crises, and _(ii)_ information about non-state actors’ support for state policy. Governments frequently deploy both strategies to spur citizens to support their policies or build citizens’ trust in the state.

All respondents received basic information about the government’s directives as part of the survey. We provided additional information to respondents assigned to a treatment group. Referring to truthful information about how the government successfully managed a large-scale dengue fever outbreak in 2011, the first treatment informs respondents that the government selected the right policies to address a public health emergency in the past, and that it possessed the capacity to successfully implement these policies.

The second treatment complements the first treatment. It emphasizes that, according to experts’ assessments, the government successfully tackled the dengue outbreak because, in addition to the government identifying and undertaking appropriate actions, citizens substantially cooperated with the government’s policies.

The third treatment represents an appeal to religious authority, a common strategy in Islamic countries’ politics. The treatment provides information that religious authorities support the government’s policy. We informed respondents about a fatwa issued by Al– Azhar University in Egypt considered one of the highest authorities on Sunni Islamic – thought by many Muslims permitting a ban on congregational prayers and urging citizens to hold confidence in their respective government’s Covid-19 policy.

Given concerns about potential experimenter demand effects in phone surveys, we include an additional treatment group that allows us to isolate experimenter demand effects from estimated treatment effects. This condition precludes misleading interpretations of estimated treatment We collected rich demographic data (e.g. on partisanship, belief in conspiracy theories, religiosity and economic exposure to the Covid-19 crisis) before sharing any information with the respondents. After providing respondents the respective informational treatments, we collected data on three sets of outcomes: support for the government’s Covid-19 related directives and policies, perceptions of state capacity, and trust in the state. We specified all outcome measures and the variables used in the heterogeneity analysis in a pre-analysis plan.

We find that, on average, the information treatments have little effect on support for government policy. Informing respondents that the state succeeded in managing a health crisis in the past does not affect their attitudes towards the government’s current directives on hand-washing, social distancing, and avoiding congregational prayers. Stressing that 3
Table 5: **The State sector is big in production, but small in employment**

||Industrial<br>growth (%)<br>1995-98|Share in<br>Industrial<br>growth (%)<br>1995-98|Share in<br>industrial<br>GDP (%)<br>1998|Share in<br>industrial<br>employment<br>1997/98 (%)|
|---|---|---|---|---|
|State sector|10.2|37.2|46.2|24.2|
|Foreign investment|22.7|46.5|31.8|11.5|
|Domestic non State sector<br>Of which|9.2|16.2|22.0|64.3|
|-Private and mixed|21.6|11.1|7.9|25.2|
|-Collective|8.7|0.3|0.6|1.3|
|-Households|4.0|4.7|13.5|37.8|

_Source:_ column 1 and 2 are from GSO (1999); column 3 is from VLSS 2

# _c) The domestic private manufacturing sector_

So far in Vietnam there are only 457 private domestic manufacturers with more than 100 full-time workers (MPDF, 1999). As can be seen in table 6, most of these companies operate in labor-intensive sectors (mainly in textiles and garments or in primary processing), specializing along the lines of the country's comparative advantage. They export on average around three quarters of their production. This contrasts sharply with State enterprises, where only about 12% of production is exported (according to information from the Ministry Of Finance). Thus, while the capital-intensive State sector is mainly inward-looking, export markets are clearly the main engine of private sector growth.

Table 6: Private manufacturers are labor-intensive and export-oriented

|Nu|mber of firms|Share of total<br>firns (%)|Exports/output (%)<br>(unweighted average)|
|---|---|---|---|
|Garments and Textiles|159|34.8|80.5|
|Food and Beverages|71|15.5|63.2|
|Wood Products|65|14.2|75.1|
|Other Non-metallic Products|39|8.5|73.2|
|Leather Products|34|7.4|85.0|
|Rubber and Plastic Products|22|4.8|75.0|
|Basic Metals|9|1.9|n.a.|
|Chemical Products|9|1.9|20.0|
|Others|49|10.7|74.4|
|TOTAL|457|100|75.3|
|_Source:_Data from MPDF (1999)||||

Under what conditions do domestic private companies compete in international markets and whom do they employ? A visit to factories in Haiphong shows that many Vietnamese private companies producing garments or footwear for export operate as subcontractors for foreign companies located abroad, which later re-export their products to Europe and to the U.S. This means that many foreign investors associate themselves with domestic firms without formally registering as joint-ventures. Given the present dual pricing system in Vietnam -which requires foreign investors to pay higher prices for land, utilities, labor, and transportation- this substantially reduces production costs. In addition, it avoids 7
Matsumoto, Tomoyama, and Takashi Yamano. 2009. _Soil Fertility, Fertilizer, and the Maize Green_ _Revolution in East Africa_ . Policy Research Working Paper 5158. Washington, DC: World Bank.

- Matsuyama, Kiminori. 1992. “Agricultural Productivity, Comparative Advantage, and Economic Growth.” _Journal of Economic Theory_ 58, 2: 317–34.

- McArthur, John W. 2015. "Agriculture’s role in ending extreme poverty.” In _The Last Mile in Ending Extreme Poverty._ Edited by Laurence Chandy, Hiroshi Kato and Homi Kharas. Washington, DC: Brookings Press , 175-218.

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- Pinto, Emmanuel, and Nihal Bayraktarb. 2005. _A Macroeconomic Framework for Quantifying Growth and Poverty Reduction Strategies in Niger_ . Policy Research Working Paper 3506. Washington, DC: World Bank.

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- ———. 2011. “Aid, Dutch Disease, and manufacturing growth.” _Journal of Development Economics_ 94, 1: 106–18.

Robinson, Sherman, and Hans Lofgren. 2005. “Macro models and poverty analysis: Theoretical tensions and empirical practice.” _Development Policy Review_ 23, 3: 267–83.

Rockström, Johan, Malin Falkenmark, Louise Karlberg, Holger Hoff, Stefanie Rost, and Dieter Gerten. 2009. “Future water availability for global food production: The potential of green water for 34
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**Figure 20. Market accessibility and firm agglomeration by sector, 2018**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Market Access Index (0-1)<br>Agriculture Fitted values<br>Manufacturing Fitted values<br>Retail & Wholesale Fitted values<br>1500<br>1000<br>500<br>Number of firms per district<br>0<br><!-- End of picture text -->

# **III. Empirical Models**

As discussed above, one of the most important empirical issues is the endogeneity of infrastructure placement. While public infrastructure is considered to have various positive impacts on an economy, it is often placed where productivity is already inherently high because of unobserved reasons for researchers. To deal with this problem, the paper uses the ArellanoBond (1991) dynamic panel data regression. The fixed-effects regression model can mitigate the endogeneity bias under certain assumptions (e.g., parallel trend assumption) but cannot eliminate it in general (e.g., Nickell, 1981).<sup>8</sup> Using the same data as the current paper, IMF (2020) estimates the impact of transport infrastructure under Nurly Zhol on firm revenues and profits by the fixed-effects method. However, the fixed-effects model may lead to biased estimates if there are any time-variant individual effects that are correlated with the residuals.

In addition, the previous study does not necessarily explain why the close presence of improved regional transport corridors increased firm revenue and profit, but not employment. It may be more useful to build an empirical model based on some economic theories so that the underlying results chain could be understood better.

> 8 As will be shown below, the dynamic panel model estimator is more suitable to our data where there is a strong first-order serial correlation in the first-difference residuals.