rafmacalaba/datause-encoder-data huggingface dataset:
**_Energy demand models for policy_** **_<u>formulation</u>_**

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108
# **Table C.10: Main outcomes – Wave 2 only**

## **Panel A: Effects on women’s promotions, income and decision-making**

||(1)|(2)|(3)|(4)<br>IV|(5)|
|---|---|---|---|---|---|
||Supervisor<br>(Dummy)|Personal<br>income<br>(Taka)|Household<br>income<br>(Taka)|Personal<br>income<br>(Taka)|Decision-making<br>(Index)|
|selected|0_._18***|428_._73|507_._52||_−_0_._12|
||(0_._06)|(692_._61)|(1542_._20)||(0_._11)|
|supervisor||||_−_40_._39||
|||||(3579_._42)||
|N|182|182|182|182|182|
|Nominated mean|0_._10|9608_._55|23230_._52|9608_._55|0_._22|
|Nominated SD|0_._30|3388_._45|7390_._32|3388_._45|0_._58|

## **Panel B: Effects on assignable goods expenditures and remittances**

||Women|Girls|Men|Boys|Remittances|
|---|---|---|---|---|---|
|||(All outcome|s as % in hous|ehold income)||
|selected|3_._02*|1_._58***|0_._71|_−_0_._63|3_._06|
||(1_._70)|(0_._43)|(0_._75)|(1_._19)|(1_._97)|
|N|182|74|182|81|181|
|Nominated mean|5_._46|0_._95|3_._61|2_._86|7_._06|
|Nominated SD|4_._39|0_._97|3_._79|4_._22|9_._10|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso (ivlasso for Panel A, Column 4) from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies. Where available, the baseline outcome is included in the amelioration set. In Panel A, Column 4 Supervisor is being instrumented by selected, for which the first stage is shown in Panel A, Column 1. The sample in Panel B, Column 2 (Column 4) is limited to those respondent with a daughter (son), or a female (male) child in the household.

**Table C.11: Effects on assignable goods expenditures and remittances**

||(1)<br>Women|(2)<br>Girls|(3)<br>Men|(4)<br>Boys|(5)<br>Remittances|
|---|---|---|---|---|---|
|||(Al|l outcomes in|Taka)||
|selected|395_._59 **|202_._22|76_._12|_−_45_._49|954_._15 ***|
||(186_._12)|(132_._86)|(113_._15)|(158_._78)|(324_._62)|
|N|363|146|363|161|362|
|Nominated mean|1201_._99|391_._78|861_._67|566_._15|1305_._97|
|Nominated SD|1050_._47|597_._52|876_._00|782_._66|1986_._61|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewingline operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies.

79
measured by third-party inspectors at landing, I multiply each set-level percentage juvenile value by the ratio of landing-level percentage juvenile to average set-level percentage juvenile.

Finally, I shift the length distribution of each set up or down in half-cm increments until I minimize the absolute difference between the implied percentage juvenile (updated percentage of individuals that are less than 12 cm) and the corrected percentage juvenile. I use the resulting length distribution to calculate the corrected number of individuals caught by each set. The number of juveniles caught by each set is the corrected number of individuals times the corrected percentage juvenile. In Appendix C I describe the construction of these data in detail and demonstrate robustness to the assumptions I make in doing so.

Figure C1 displays the average uncorrected length distribution, the average corrected length distribution, the reported percentage juvenile values, and the corrected percentage juvenile values. If I did not have landings data, I would mismeasure juvenile catch because vessels seem to underreport percentage juvenile in the electronic logbook data. The individuals-weighted average percentage juvenile is 40% lower in the electronic logbook data than in the landings data (11% compared to 18.3%). Vesssels might underreport percentage juvenile to avoid triggering a closure in the area they are fishing.<sup>14</sup> This phenomenon also occurs in other settings where agents may be regulated as a consequence of the data they report, such as industrial plants in India and car owners in Mexico (Duflo et al., 2013; Oliva, 2015). Matching the electronic logbook and landings data preserves the resolution of the electronic logbook data while ensuring that the outcome variable in my main regression—juvenile catch at a given location and time—is not systematically manipulated.

There are 246,914 sets reported by 806 unique vessels in the electronic logbook data. 95% of sets occur within 80 km of the coast (Figure 2). During a fishing season there are 572 sets per day on average. The average set catches 570,103 juvenile anchoveta and 2,508,788 adult anchoveta, which together weigh 50.2 tons. Vessels do not underestimate tons caught in the electronic logbook data, perhaps because this variable has little effect on whether the

> 14Closures provide valuable information to vessels regarding the location of anchoveta, but only to vessels who did not recently fish in that area (Section VI).

15
into the study. We conducted a listing exercise in these 100 villages to classify households as eligible or ineligible according to the VG criteria, and to identify key individuals.

This pipeline of 119 active groups was randomized at the village level in to two tranches. The Early group was to be treated right after baseline (59 groups receive TASAF early treatment), and the Late group to be delayed for a year (60 groups receive TASAF late treatment), with the 12month midline survey conducted immediately before the Late group was treated. This randomization was blocked by district and group type (livestock groups versus non-livestock groups). Importantly for the interpretation of our results, the timing structure of the experiment was common knowledge to the beneficiaries, introducing the possibility that the control group behavior in the midline was altered by the fact that they were about to receive their transfer. Anticipating this issue, we retained the Eligible Non-Beneficiary (ENB) households in the panel data set to have a second (albeit non-experimental) control group that is never treated.<sup>9</sup> The entire study timeline was shifted off for the districts of Moshi and Lushoto, which began first, relative to the remaining districts which trailed by roughly ten months. Because the experiments were blocked by district this does not affect results. Early treatment disbursements took place in December 2008/early 2009 in Moshi and Lushoto, and were made between September and October 2009 in the remaining three districts. Late treatment took place in early 2010 in Moshi and Lushoto and in December 2011/January 2012 elsewhere. Figure 1 shows the timing of the surveys and the interventions.

# **3.2. The Training Experiment**

Human and managerial capital is a critical input to the generation of entrepreneurial returns (Ibarrarán et al. 2019, Bruhn et al. 2010), and in the context of group investments training also has

> 9 These are households in study villages that met the technical eligibility for TASAF in containing an individual defined as ‘vulnerable’ by the rules of the program, but that did not in fact participate in any TASAF group.

11
A document from TecnoServe<sup>24</sup> argues that the country has the climate and altitude to produce specialty Arabica coffee (a type of quality that can be sold at premium world market prices). Before 2004, the country farmers used to sell their high quality but poorly processed coffee on the blended coffee market through a state-run auction: a system that did not reward quality. When the country started to receive assistance by several donors, a policy reform allowed a lowering of the farm-gate taxation and permitted producers to sell their product directly to foreign buyers. These reforms, together with technical assistance to the farmers, allowed a change in the sector from 2004 onwards such that the price of coffee is no longer determined exclusively on organized exchanges.

The case of coffee shows that the result in Table 8 could indicate that Tanzanian firms might charge different prices even in homogeneous products, like coffee. In fact when the regression is restricted to Coffee and Tea (HS2 code 9), the result is positive (even though weakly significant). This preliminary result might highlight that countries with a comparative advantage in agricultural goods might still be able to improve the competitiveness of their products and increase the quality and value associated to them, even if they are classified as homogeneous goods. In fact, the 2009 Tanzania National Competitiveness Assessment reported that tea and coffee were two of Tanzania’s most competitive exports in the 20032007 period.

An alternative explanation to the result that price differentiation is only significant for homogenous goods is that firms charge different mark-ups in different markets, rather than firms selling different qualities in different markets. A way to differentiate between “quality led” and “mark-up led” pricing-to-market would be to use an index of vertical differentiation, like the one built by Khandelwal (2010). The problem with this and other indices of vertical differentiation is that they do not include the products defined as homogenous by Rauch. As a consequence, further research is needed in order to establish whether price dispersion across destinations in homogenous goods is led by quality differences or different mark-ups.

Thirdly, Equation (3) takes into account how prices are linked to the characteristics of trade partners, namely: GDP, GDPpc, bilateral distance, and the remoteness index. This is calculated as _remotenessd_ = ∑ _GDPo_ * _distod_ for year 2009. Nevertheless, results do not change drastically if the remoteness index is calculated for every year (the reason being that the index does not change much overtime). The specification includes firm-product and year fixed effects, and the errors are clustered at the firm-product level.

> 24 - <u>http://www.value chains.org/dyn/bds/docs/519/Technoserve%20Tanzania%20Coffee%20Summary%20Case.pdf</u>

14
10 program concentrates its activities on a selected number of countries in which it can make a significant contribution. As institutions become more self-reliant, core support is gradually withdrawn and transferred to other countries, but many of the 'graduate' organizations compete successfully not only for funding from the lask Forces, but froia other agencies engaged in reproductive research. The original focus of the program was on individual institutions, but has recently shifted to building national capacity with the objective of identifying, and finding solutions to, locally prevalent problems in the broad field of reproductive health.

20. The Committee on Resources for Research makes recommendations on how to improve the developing countries' research resources. The main instr iment for research-capability strengthening is the Long-Term Institutional Development Grant. This type of grant covers several dimensions of institutional development: technical assistance in the drawing up of a nationally relevant research program, strengthening of material resources, and developing human resources through Research -raining Grants to individual scientists. Since 1986, 38 institutions in 29 developing countries have received long-term institutional development grants. Other grants are available to support scientists after their training abroad, to purchase equipment and supplies, to hold training courses and workshops, establish postgraduate degree programs and provide visits for exchange of experience.

21. Recently HRP carried out a survey of scientists and supporting staff who had received Research Training and Visiting Scientist Grants between the start of the program in 1972 and the year 1987. There were 954 such grantees, of whom _855_ came from developing countries. The encouraging finding was that two-thirds of those for whom information was obtained (84%) were still engaged in research.

# FINANCIAL SUPPORT FOR HRP

22. Over the years HRP has attracted funds of well over $250 rnillion. Table 1 shows the level of funding since its inception. (HRP became a Special Programme in 1972, but there was a pre-existing WHO program.) In the first two years of Bank financial support for the program, 23 governments and agencies (including WHO)
# **1. Introduction**

Many countries from the West Africa Economic and Monetary Union (WAEMU) have been hit hard by the recent increase in food prices. While the countries have to some extent been protected from the full impact of the increase in international cereals prices due to the appreciation of the Franc CFA which is pegged to the Euro, they have nevertheless been highly vulnerable due to the fact that a substantial part of the consumption of cereals is imported, especially in the case of rice. In Mali, the overall inflation rate for the last twelve months has reached more than ten percent, and food prices have increased even more (WAEMU, 2008).

It is well known that the increase in cereals prices is likely to have substantial negative impacts on the poor (for reviews and multi-country work on this, see Ivanic and Martin, 2007; International Monetary Fund, 2008; Wodon et al., 2008; Wodon and Zaman, 2008; and World Bank, 2008a and 2008b). However, detailed work at the country level on the likely impacts of the crisis is still scarce especially in West African countries were data are often weaker.

According to field data collected in Mali, the price of rice is today about 25 percent higher than it was a year ago (USAID, 2008). Using recent household survey data, and following a well established methodology in order to identify the likely impact of the food price crisis on both consumers and producer (on this methodology see among others Deaton (1989), Barrett and Dorosh (1996) and Budd (1993)), Joseph and Wodon (2008) find that an increase in the price of cereals (rice, millet, sorghum, corn and wheat) of 25 percent would lead to an increase in the share of the population in poverty of 1.7 percentage point (this would represents close to 300,000 persons falling into poverty). The increase in the price of rice alone would increase the share of the population in poverty by 1.5 percentage point.

2
advantage related to institutional environment is indeed an important driving factor in entry decisions made by foreign banks.

The rest of the paper is structured as follows. In Section 2 we describe the database used for foreign bank entry and show some statistics that indicate the potential importance of competitive advantage. In Section 3 we describe the methodology used and Section 4 includes the results. The last section concludes.

# **2. Data and importance of competitive advantage**

# _Bank Entry Data_

To test our hypothesis whether the location decision of foreign banks is driven by competitive advantage, we need to construct a database that contains information on both the host country of FDI as well as the source country of ownership. Our primary source of information is Bankscope. This is an international data set of balance-sheet items and ownership information of individual banks. All the main information on assets and liabilities and revenues is reported according to a common standard which is comparable across countries. The coverage is comprehensive, with banks included roughly accounting for 90 percent of the assets of banks in each country.

Foreign presence in the banking sector is highly concentrated among developed countries, which share almost similar levels of institutional quality. This presence also often dates back from entry long ago. To test our competitive advantage hypothesis we need variation in institutional quality between the possible host countries a foreign bank ⎯ can choose from. Furthermore, we want to study entry decisions rather than presence, which have been more intense in the last decade for developing countries than for developed countries. For these two reasons, we limit our sample to foreign bank entry in developing countries. The source countries in our sample include of course both developed and developing countries.

Countries with less than five active banks in Bankscope are excluded from our sample. A few developing countries are offshore centers (Barbados, Lebanon, Mauritius, and Panama). As decisions to enter those markets are often driven by tax incentives we exclude these countries from our sample. Information on ownership for banks located in 4
Figure _5_

# **CIILEAN ELECTRIClTY SECTOR: THE INITAL SITUATION**

## **ELECTRICITY CONSUMON**

<!-- Start of picture text --> 210 l<br>190<br>150<br>1988 1989 1990 1991 1992 1993 1994 1995 1996<br><!-- End of picture text --> SOVRCE: NATIONAL ENERGY COMMISION 18
are allocated a hunting quota by the government and locals who engage in two types of activities - they hunt wildlife (bushmeat) for consumption and farm.

In keeping with the existing literature the focus is on a single representative species - the wildebeest. This simplification is reasonable in the context of the Seregenti where wildebeest fulfill important ecological functions as ecosystem regulators, with significant impacts on the local economy.

Ecologically wildebeest are regarded as a keystone species, whose numbers regulate biomass growth, tree dynamics, predator populations and ungulate competitors (Sinclair _et al_ 2008). Reducing their numbers from habitat patches results in marked changes in biodiversity and community structure (Terborgh _et al_ 2002). All of this suggests that as a first approximation a focus on the dominant species is reasonable in a modeling context. Data on tourism indicate that tourist numbers closely correlate with wildebeest populations suggesting that they remain an important draw card for visitors, especially because of the migration.<sup>4</sup> For the locals, the wildebeest are an important source of protein and the migration periodically brings large numbers into proximity of humans and increases their vulnerability to hunting outside protected areas.<sup>5</sup> Due to the paucity of quantitative information, in what follows functional forms are used that economize on data requirements. Tourists are assumed to visit the area to view wildlife and their numbers<sup>_T_</sup> τ<sup>, depend on the stock of wildlife. For simplicity, wildlife stocks are proxied by wildebeest</sup> population _W_ (Sinclair _op cit_ ). The number of tourists is then given by<sup>6</sup> :

> <sup>4</sup> A regression yields the following log tourist numbers = 0.5 log(wildebeest) + 0.211 time trend, with an R<sup>2</sup> = 0.879  though the correlation need not imply casuality.

> (2.45)                                (0.81)

> 5 As noted earlier this species is disproportionately impacted by hunting leading to concerns that this could result in wider trophic changes with impacts across the food chain (Holdo et al) .

> 6 Note that it is possible to interpret this formulation as the outcome of a utility maximizing problem such that tourist utility 1 1 W<sup>b</sup> 1−b U(T) = b (WT)<sup>b</sup> −PT where P is price per tourist day, which upon maximization yields Tτ= � P ~~�~~ ; In equation (1) this

> 1 1 1−b ∂T 1 implies that A = �P ~~�~~ and _β = b/1- b,_ or equivalently _b = β/(1+β)._ Hence ∂P = − 1−b ~~(~~ W/P)<sup>(b/1-b)</sup> and finally for completeness we note that the price elasticity of _T_ is _–P/(1-b)_ .

5
The rest of this paper is organized as follows. Section 2 offers some background to the paper, providing context for Syrian refugees and their QOL in Jordan. Section 3 introduces a multidimensional quality-of-life indicator. Section 4 explains the methodology used in this paper, describing the data, identification strategy, and analysis method. Section 5 presents results of the statistical analysis, while section 6 presents conclusions and policy implications.

# 2. Syrian Refugees in Jordan

The war in the Syrian Arab Republic has turned into a decade-long crisis, and refugees from Syria form the largest share of international displaced persons. About 5.5 million Syrians are registered as refugees in Turkey (65%), Lebanon (16%), Jordan (12%), Iraq (4%), and other countries (3%). Many Syrian refugees settle in urban and peri-urban regions in these host countries (World Bank 2020). The number of refugees living in camps has declined since 2017. Many refugees seem to prefer living outside the crowded camps and to escape the precarious living conditions and enjoy the freedom to live with relatives and friends and to find work. According to the World Bank (2020), most Syrian refugees are under age 40 years (87%), which means they could be more energetic and likely to live in places where they can work. The World Bank (2020) study also showed that many refugee households have children, and about 70 percent of the refugees received at least some elementary education. Nevertheless, they are disproportionally poor compared to the host population and compared to the economic status of the refugees before leaving Syria (Verme et al., 2016).

Jordan has been receiving Syrian refugees since 2011, and it is currently hosting the secondhighest Syrian refugees per capita after Lebanon. A Jordanian Ministry of Planning and International Cooperation (MoPIC, 2020) report estimates that Jordan hosts about 1.36 million Syrians, covering registered and non-registered refugees, equivalent to approximately 15 percent of Jordan's total population. The report also estimates that about 90 percent of the refugees live out of camps, and some 10 percent live in camps. As of December 2020, 661,991 Syrian refugees had registered with UNHCR, of which about 126,832 (19%) live in camps (UNHCR, 2020c). This suggests that roughly 800,000 Syrian refugees are living among the host population. The majority of Syrian refugees in Jordan are working-age people and mostly settle in four governorates: Amman, Mafraq, Irbid, and Zarqa.

5