rafmacalaba/datause-encoder-data huggingface dataset:
their data availability (see Milesi-Ferreti and Razin **1996, 1998;** Easterly, Islam, and Stiglitz **1999;** Rodrik **1999).15**

# _Expected returns (RE):_

**-** Overall productivity (measured **by** per capita **GDP** growth)

**-** Absence of price distortions (measured **by** the inverse of the black market premium)

**-** Financial depth (measured **by** the ratio of quasi-liquid liabilities to **GDP)**

**-** Openness (measured **by** the ratio of real imports plus real exports to **GDP)**

**-** Public institutional quality (measured **by** theKaufman _et al._ index on governance and the Gastil

index on civil liberties commonly used in the growth literature)

**-** Low tax burden (measured **by** the inverse of government consumption/GDP)

**-** Size and scale economies (measured **by** population size)

_Risks (RI):_

**-** General macroeconomic instability (measured **by** the standard deviation of per capita **GDP**

growth)1<sup>6</sup>

**-** Lack of international risk sharing in the composition of external liabilities (measured the ratio

of debt liabilities to equity plus debt liabilities)

**-** Monetary and domestic-price instability (measured **by** the average and standard deviation of the

annual inflation rate)

**-** External sector instability (measured **by** the standard deviation of real-exchange-rate changes,

the standard deviation of terms of trade shocks, and the standard deviation of [real imports **+** real

# exports]/GDP)

**-** Low public institutional quality (measured **by** the inverse of theKaufman et al. governance

index and the inverse of the Gastil index on political and civil rights)

**-** Lack of financial depth (measured **by** the inverse **of** quasi-liquid Liabilities **_/GDP)_**

Having constructed the return and risk indices and estimated the level of wealth and **NFA,** we proceed to the empirical estimation of the model outlined in the previous sections.

> **15** Note that some variables (such as financial depth and governance quality) enter in both the return and risk measures. They do so because of their dual effect on the country's investment profile.

> **16** The standard deviation of all listed variables is calculated as the standard deviation of observations corresponding to the current and four preceding years for each of the sample countries.

**17**
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households: 58% of primary men or 11 percentage points less than UP women would be classified as depressed (not shown).

These results confirm that UP levels of consumption, human capital, asset ownership and psychological well-being are significantly lower than other households in their community and among the lowest in the world, indicating that the UP experience multidimensional poverty in both a relative and an absolute sense.

# **Labor Markets for UP and non-UP Households**

Afghanistan has one of the world’s lowest employment-to-population ratios at 41, and 21% of the working population are considered underemployed (working less time than they are willing to). Women’s labor force participation is low at 27%, and women’s unemployment extremely high at 41%. Therefore, the context is one with limited labor opportunities, particularly for women.

Using baseline data on labor participation and labor activities directly from the primary woman in UP and non-UP households and primary man in UP households we find that engagement in income-generating activities is similar to the national average in our study villages:<sup>9</sup> 31% of UP primary women in the control group (UP women) and 25% of non-UP primary women engage in income-generating activities (Panel B in Table 1). These activities include self-employment such as livestock rearing, work in own agricultural and nonagricultural businesses, as well as paid jobs in agriculture, maid services, formal employment, and other activities. These figures hide high levels of underemployment, with women working few full-time days in a given month, and also significant differences in activities across socioeconomic groups. Figure 2 describes the main labor activities in these villages, by showing the share of hours UP women and men devote to them. This figure reveals that UP women devote a higher proportion of their time to “other paid jobs” than non-UP women and substantially less of their working time to own livestock rearing, and other household businesses (23%) than non-UP primary women (58%). The relative time allocation for UP men resembles that of UP women; however, UP primary men are more than twice as likely to be engaged in income-generating activities compared to UP women (69% vs. 31%), and conditional on working, primary men work almost five times more hours than primary women, accounting for 14 vs. 3 full-time-day equivalents (not shown).<sup>10</sup>

> 9 For non-UP households these data were only collected at follow-up and reported by the primary woman. We use data on control UP households (excluding treated UP households) and non-UP at follow-up to describe the labor market characteristics and analyze differences across socioeconomic conditions in absence of the intervention (UP control vs. nonUP) as well as across gender (UP men vs. UP women in the control group). Across each productive activity inside and outside the household, we asked for the number days worked, hours per day worked, and corresponding earnings. 10 A full-time-day equivalent is defined as 8 hours of work

8
- 6 -

Blejer and Khan's paper is one of the few that uses an optimizing framework<sup>for the firim</sup> to derive an aggregate investment function to evaluate the effects of government<sup>policy." The</sup> resultiug model is essentially a flexible accelerator that<sup>allows for government policy to affect the</sup> speed of adjustment to the desircd capital stock<sup>through a standard partial adjustment</sup> mechanism. **"** Blejer and Khan hypothesize that the factors<sup>that affect the speed of adjustment</sup> to desired levels of capital are the stage of the business cycle,<sup>the availablity of financing, and</sup> the level of public sector investment. They argue that it is<sup>important to distinguish between</sup> public investment in infrastructulre, which is more likely to "crowd<sup>in" private investment, from</sup> that in other areas. However, the empirical testing of this distinction is weakened<sup>by the</sup> empirical proxies used for infrastructure investment.'<sup>5</sup> Their empirical results from twenty-four developing countries found an important positive effect on private<sup>investment from the degree of</sup> capacity utilization and the availability of credit. They also claim evidence in support<sup>of their</sup> 1 Blejer and Khan, 1984.

14 Note that thlere are problems with such an approach that stem from the deficiencies<sup>of the standard</sup> partial adjustment model. Under partial adjustment, agents incur<sup>costs for any kind of change, even a</sup> desirable one. Consequently, in a growth situation, the<sup>model results in consistent undershooting of the</sup> desired capital stock. It is possible to address this by using<sup>a generalized version of the partial adjustment</sup> model, the error correction model, that will be described later.<sup>See Nickell,</sup><sup>_1985._</sup> i Blejer and Khan used two different proxies for infrastructure investment:<sup>(1) a proxy based on the</sup> premise that infrastructure investments have a long gestation<sup>period and therefore the tre-id level of total</sup> public inves,ment can represent infrastructure; and (2) a proxy<sup>that posits that because of its long run</sup> nature, infrastructure investment is more likely to be anticipated.<sup>However, infrastructure investment is</sup> usually very lumpy. Therefore, the measure based on the trend<sup>level of investment may be reflecting otber</sup> types of investment spending that are fairly stable over time.<sup>Similiarly, expenditure on infrastructure is</sup> often uncxpected since it can, by its nature, be postponed if neglect<sup>or deterioration is tolerable. Also,</sup> because public investment in infrastructure in developing<sup>countries is often associated with borrowed</sup> resources from banaks or donors, there is likely to be even greater<sup>uncertainty in formulating expectations</sup> about future outlays.
pre-requisite for improvements in the country's growth fundamentals to realize better development outcomes (Chen, Loayza and Reynal-Querol 2008). In other words, Sri Lanka will have to pay close attention to the growth fundamentals outlined in Box 1.

**Box 2: Doubling US dollar per capita GDP between 2009 and 2016**

<!-- Start of picture text --> Per Capita GDP, US$<br>3000<br>2500<br>2,089<br>2000<br>1500<br>1,032<br>1000<br>500<br>500<br>0<br>Source: CEIC data<br><!-- End of picture text --> Sri Lanka took 13 years to double its nominal dollar per capita GDP from US$ 500 in 1991 to US$ 1,000 in 2004. Five years later, in 2009, per capita income had doubled once again to roughly US$ 2,000 as shown in the figure. The Mahinda Chinthana‘s vision is to achieve another doubling of dollar per capita income by 2016.

To get a better sense of the challenge involved, we use the following identity: , where _G$_ is nominal US dollar per capita growth, g is real GDP growth, is the growth in the bilateral Sri Lankan rupee-US dollar real exchange rate (defined such that a positive value is an appreciation), is U.S. inflation and _pgr_ is the population growth (all compound annual average growth rates).

During the period 2004 – 2009, real GDP growth was 6 percent with the population growth rate at 1 percent, implying per capita growth of 5 percent per year. Yet dollar per capita GDP grew at a huge 15 percent per year. Assuming 2 percent US inflation, the above identity tells us that the big driver of _G$_ was a rapidly appreciating bilateral real exchange rate, recording an annual average appreciation of 7.5 percent.

Experience from emerging markets the world over demonstrates that sustainable public finances are an essential underpinning for fast growth. Achieving low inflation rates is not enough. Public debt has to be on a trajectory deemed sustainable by the markets. Otherwise, there may be concerns about monetization of the debt and a rise in inflation, which could keep nominal and real interest rates high. This section starts by discussing broad fiscal outcomes of the past with a view to better understanding what future fiscal outcomes might look like. Next it 9
# **Food security and storage in the Middle East and North Africa**<sup>*****</sup>

**Donald F. Larson, Julian Lampietti, Christophe Gouel, Carlo Cafiero and John Roberts**

_Keywords:_ food security, Middle East and North Africa, food security, price volatility, storage, strategic reserve, wheat.

_JEL classification:_ F1, O13, Q11, Q18.

_Sector Board:_ Agriculture and Rural Development

> * Donald F. Larson, Development Research Group, World Bank; Julian Lampietti, Middle East and North Africa Sustainable Development Department, World Bank; Christophe Gouel, Centre d'Etudes Prospectives et d'Informations Internationales and the World Bank, Carlo Cafiero, Statistics Division, FAO; and John Roberts, University of Maryland.
# **Appendix 4: Sample Means by Assignment to Mentoring or Training Only**

|**Appendix Table 1: Means by Trea**|**tment Status fo**|**r Mentoring**|**Intervention**|||||
|---|---|---|---|---|---|---|---|
||Assigned<br>Mentor|Training<br>Only|Spillover<br>for Mentor|Spillover for<br> Training Only|Pure<br>Control|P‐value|P‐value|
||(1)|(2)|(3)|(4)|(5)|(1)vs(2)|(3)vs(4)|
|Age|35.73|36.39|35.38|35.70|35.80|0.262|0.807|
|Years of Education|9.08|8.76|9.00|8.89|9.08|0.019|0.321|
|Married|0.69|0.65|0.68|0.64|0.68|0.166|0.999|
|Household Size|4.96|4.99|4.93|4.83|4.86|0.962|0.841|
|Age of Firm|6.36|6.82|6.53|6.73|6.31|0.310|0.491|
|Number of Employees|0.27|0.26|0.27|0.27|0.26|0.985|0.422|
|Weekly Profits|1124|1115|1132|1155|1085|0.782|0.643|
|Weekly Sales|5657|5054|6028|6016|5247|0.596|0.558|
|Capital Stock|37859|24222|39042|27324|26733|0.001|0.005|
|Ever Received Bank/MFI Loan|0.26|0.26|0.23|0.27|0.23|0.652|0.061|
|Keeps Records|0.36|0.37|0.33|0.36|0.34|0.967|0.318|
|Business Practices Score|0.53|0.52|0.53|0.53|0.52|0.153|0.920|
|Retail Firm|0.77|0.81|0.76|0.79|0.75|0.223|0.115|
|Registered with City Council|0.48|0.36|0.49|0.39|0.40|0.001|0.023|
|**Sample Size**<br>Notes: Means and Sample Sizes ar|524<br>e shown for Sa|521<br>mple in Non|459<br>‐Small Marke|442<br>ts, for which m|1158<br>entoring in|tervention|applied.|

40
_conflicti_ 60 − 05 = α + β 1 lg _dpi_ 60 + β 2 _lpopi_ 60 + β 3 _av_ inst_ + ε _i_ _av_ insti_ = δ 1 _col_ orig_ + δ 2 ln _mortal_ + ω _i_ In Table 6 we analyze the effect of the average protection against expropriation risk on the probability of civil war using the IV-2SLS specification. The first eight columns do not include per capita income and population. In the first column we use the log of European settler mortality as an instrument for institutions. The first stage results suggest that countries where Europeans faced large settler mortality rates have lower protection against expropriation risk than countries where Europeans faced a healthier environment. Results of civil war regression indicate that the higher the protection against expropriation risk, the lower the probability of civil war. In order to obtain more observations, we include the percentage of European population or of European descendents in 1900, instead of the European settler mortality, as an alternative instrument. Results are in columns 2 and 3. In column 2 the analysis is restricted to the sample for which we have mortality data to show that results when using European settlement in 1900 are similar to when using settler mortality data, and in column 3 we do not restrict the sample. As expected, European settlement in 1900 exhibits a positive and significant effect in explaining protection against expropriation risk.

In column 4 we include the dummies of colonial origin as an instrument for economic institutions. The first stage results indicate that countries with Napoleonic colonial origin have lower protection against expropriation risk than countries of English colonial origin. In column six, we include both instruments together, and find that the first stage relationship between economic institutions, colonial origin and settler mortality is strong. The F test for excluded instruments is large (F= 8.74), which implies that the instruments do not seem to be weak. In columns 7 and 8 we substitute the settler mortality variable with the European settlement in 1900 in order to have more 11
out transactions and, to a lesser extent, savings rather than credit (Buri et al., 2023).

# **3.2 Assessing Tailored Product Interventions with Randomized Field Experiments**

At the simplest level, innovations that have brought under-served market segments into closer physical (and socio-cultural) proximity to financial services providers have spurred deeper financial inclusion and greater usage of financial services. This includes not only the aforementioned studies of branch/agent network expansion in Africa (Allen et al., 2021; Buri et al., forthcoming; Giné and Goldberg, 2023), but studies from African and other countries showing that physical proximity to agents can promote usage of savings accounts (see, e.g., Ashraf et al. 2006; Brune et al. 2016 and Bachas et al. 2021).

In part, the importance of physical proximity to an outlet of a financial services provider stems from clients’ needs for cash-in/cash-out transactions. Those transactions, in turn, have fostered financial resilience by enabling households to draw on their social networks for support in trying circumstances. For example, Jack and Suri (2014) have shown that mobile telephony reduces the costs of such within-network transfers in Kenya and find that proximity to ‘mobile money’ agents has helped households to smooth consumption in the face of economic shocks. Relatedly, Blumenstock et al. (2016) showed that Rwandan households affected by an earthquake received increased amounts of cellular “airtime” (a simple precursor to mobile money) from members of their social network, especially those with whom they had already established reciprocal relationships.

At the same time, increased within-social network demands for assistance may make it more 28
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

and that vulnerable income groups are protected. This type of question can be addressed through case studies involving cost-benefit analysis and distributional impacts. The main core indicators with reference to the components of our reform model are price and consumption data for income groups (Table IX), changes in access and quality of service (Table V), regulatory framework (Table IV), and where possible environmental impacts of reforms (Table X).

# **5.3 Electricity sectors indicators**

# **I. Sector endowments and characteristics**

|**Focus area**|**Indicator**|**Data Source**<br>**(UK as example)**|**Reference**|
|---|---|---|---|
|_? Resource mix_|**? Electricity generation mix:**<br>**(i) net generation capacity (MW)**<br>**(ii) energy supplied (MWh)**<br>? Primary energy supply mix (MTOE)|IEA and OECD||
|_? Energy security and_<br>_resource independence_<br>_? Reserve generation_<br>_capacity_|**? Electricity consumption, domestic**<br>**production, import, export (MWh)**<br>? Energy consumption, domestic production,<br>import, exports (MTOE).<br>**? Self-sufficiency in electricity – domestic**<br>**production GWh / domestic production + net**<br>**imports (%)**<br>? Energy self-sufficiency – domestic/total (%)<br>? Reserve capacity – at maximum demand<br>(MW, and as % of total installed capacity)||Drillisch et al.<br>(1998)|
|_? Electricity and energy_<br>_consumption per capita_|**? Electricity consumption per capita**<br>? Energy consumption per head||Zhang et al. (2002)|
|_? Unserved demand_|**? Households without electricity (number**<br>**and % of total)**<br>? Non-commercial energy (amount)|||
|_? Energy and electricity_<br>_intensity of GDP_|**? Electricity use per GDP unit (kWh/$)**<br>? Energy use per GDP unit (TOE/$)||Zhang et al. (2002)|
|_? Overall system price-_<br>_cost relationship_|? Price/cost (values per kWh, ratio)|||
|_? Number of customers_|? No. of residential, industrial, and commercial<br>customers. customers|||
|_? Natural gas (domestic_<br>_availability) _|? Proven reserves / annual production ratio|||

19
# **Figure 1. Convergence or divergence? Real income per worker and urbanization**

# **a. Real income per worker**

<!-- Start of picture text --> Restricted labor mobility Full labor mobility = no Hukou NEN - Hukou vs. no Hukou<br>30 50 200<br>25 40<br>150<br>20 30<br>15 Metro regionsNE 20 Metro regionsNE 100 Metro regionsNE<br>Coastal Coastal Coastal<br>Central Central Central<br>10 NW 10 NW 50 NW<br>SW SW SW<br>5 0<br>0<br>0 -10<br>-5 -20 -50<br>-14 -13 -12 -11 -10 -14 -13 -12 -11 -10 -9 -14 -13 -12 -11 -10<br>Ln of real income per worker, without expressways Ln of real income per worker, without expressways Ln of real income per worker, Hukou<br>b. Urbanization<br>5 Restricted labor mobility 2 Full labor mobility = no Hukou 70 NEN - Hukou vs. no Hukou<br>60<br>4<br>0 50<br>3 40<br>2 Metro regionsNE -2 Metro regionsNE 30 Metro regionsNE<br>CoastalCentral -4 CoastalCentral 20 CoastalCentral<br>1 NW NW 10 NW<br>SW SW SW<br>0 -6 0<br>-10<br>-1 -8<br>-20<br>-2 -10 -30<br>0 20 40 60 80 20 40 60 80 100 0 20 40 60 80 100<br>urbanization, without expressways urbanization, without expressways urbanization, no Hukou<br>% change in real income per worker % change in real income per worker % change in real income per worker<br>ppt change urbanization<br>ppt change urbanization, with expressways ppt change urbanization, with expressways<br><!-- End of picture text --> 26
## **5.3.2 Other manufacturing workers in treated districts**

We argue that the possibility that other manufacturing industries in treated districts were impacted by international scrutiny was likely small; the responses we describe in section 2 were closely targeted at the garment industry. Control workers may still have been affected by labor supply shifts, if workers fled the garment industry for other manufacturing sectors (corresponding to the inward labor supply shift in theoretical model we posit in section 6).

# **5.4 Evidence on labor supply shifts**

We can use our data to provide some additional evidence on the worker flows described in the prior subsection. We first consider the potential for changes in the extensive margin, i.e, the total number of garment workers in treated versus control districts over time, in in figure 8. It shows a short-term dip in garment sector employment (though more strongly appearing in non-treated districts<sup>10</sup> ), which appears to dissipate by 2015. Thus, we conclude that extensive margin effects are not a large part of the overall story.

Even if there are not large effects on the size of the garment sector workforce, there could be a composition change in the types of workers that remain in the garment industry post Rana Plaza (compared to those in control industries). Indeed, in section 6, we will argue that an inward shift in the labor supply curve is likely important in explaining how total compensation increased without an increase in the quantity of labor supplied. Of course, a shift in labor supply doesn’t automatically imply a change in worker characteristics, it could, if workers who are less likely to work for any given compensation after different than those still willing to work. While we can partially address this issue by controlling for observable worker demographics interacted with year and gender dummies in equation 1, we acknowledge that there might be changes in unobservable worker characteristics (such as ability) that are correlated with labor market outcomes. If so, treatment effects estimated by equation 1 will capture both compositional changes and causal

> 10Note that the drop off in garment employment in non-treated districts between 2010 and 2013 does not appear to be driven by Rana Plaza, because the decrease also appears in the first quarter of 2013, which is before Rana Plaza. The 2010 survey was at the peak period of Bangladesh’s low unit cost production and subcontracting, which likely affected non-treated districts particularly strongly (Farole et al., 2017).

23