rafmacalaba/data-use-mentions-extended huggingface dataset:
||**India**|**Pakistan**|**Bangladesh**|**Sri Lanka**|**Nepal**|
|---|---|---|---|---|---|
|Share of Total Exports Representing Top 50<br>Products|62.5|99.0|98.0|90.0|80.0|
|Share of Competing Products in Total<br>Exports*|32.1|82.7|89.8|77.1|73.9|
|Share of Competing Products with RCA ›1|29.7|82.3|89.6|75.7|73.8|

Notes: *The number products exported at SITC 4-digit level matching the exports of at least one regional trading partner, as a share of total exports.

Source: WITS and SIMA databases, World Bank.

When comparison is made between those products in which RCA > 1, the conclusion above remain unchanged. More than 89 percent of Bangladesh’s globally competitive exports coincide with those of at least one regional partner. The extent of competition among Nepal, Pakistan, and Sri Lanka is marginally lower than that of Bangladesh. This characteristic in export profiles underscores the extent of complexity involved in creating an environment that encourages regional competition for those products with global efficiency. Only India offers any noteworthy share of efficient exports that are dissimilar to others in the region, and therefore offers the prospects for significantly expanding regional trade.

The lack of complementarity and high degree of competition in export structures imply daunting prospects for expanding regional trade in South Asia. It seems that general liberalization efforts, which helped countries to increase efficiency, have evolved among a narrow range of products largely dominated by textiles and apparel. Ironically, these are precisely the products for which the countries have demonstrated a reluctance to totally dismantle existing trade barriers. For instance
39%|63%|39%|40%|41%|
|_PSP in Distribution_|0%|0%|1%|9%|16%|
|_PSP in Transmission_|0%|0%|2%|8%|14%|

Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform PSP = private sector participation While the 11 GW of capacity added by the IPPs in the two decades since the sector was opened has to be seen as a success for the power reform policy adopted, in fact it was inadequate to meet all of Pakistan’s needs. Despite this increase in capacity from the private sector and an increase of 4GW from the public sector, there have been power shortages and blackouts for much of the period.

The balance between the potential supply of electricity and the peak demand for it during a year is taken as an indicator of the adequacy of supply.<sup>25</sup> The data sources for Pakistan measure potential supply by generation capability (availability) rather than by capacity so that the difference between capability and peak demand is the system’s surplus or deficit.<sup>26</sup> Peak demand and generation capability are measured over NTDC’s system and KE’s system, and data were available for the 2007–2017. In addition, predictions made in 2017 for the period 2018–2021 are provided in the most recent State of Industry Report published by NEPRA. Figure 6 shows that since 2007 and until 2017 there had been a substantial and persistent deficit with a maximum of

> 25 Fuel shortages as well as capacity constraints also contributed to the deficit.

> 26 Availability includes plants that are both functioning and are not currently off-line for maintenance. The reserve margin equals 1 minus peak demand/available capacity.

25
starting from a random point.<sup>16</sup> Because we needed a larger sample for the interventions, in October 2008 we selected a set of GNs neighboring those in the original panel survey. We used a similar screening survey to identify male self-employed workers with fewer than 2 paid employees, boosting the sample by 816 individuals. Because of the way they are constructed, both subsamples are representative of the areas from which they are taken. However, there are some differences in the manner of constructing them, so we add a control for the enterprises in the booster sample in each of the regressions.<sup>17</sup>

# **Appendix 3: Details of Supplementary Interventions**

_Savings Intervention:_ In November 2008, we notified those assigned to the savings treatment that they had been selected to participate in a program designed to encourage them to build savings balances. The participants were not told about the other two interventions in November even if they had been assigned to one of the other two treatments. As a part of the savings incentive program, we offered to make the initial deposit in a savings account at the National Savings Bank (NSB) and then to match deposits made into that account up to a certain limit each month and at a pre-announced match rate. The account would remain ‘locked’ until August 1, 2009. The initial match rate was set at 50 percent for deposits of up to 1,000 Sri Lankan Rupees (LKR)<sup>18</sup> made by the end of December. The match rate was kept at 50 percent through July, but the maximum amount we would match was increased to 2,000 LKR in January and to 4,000 LKR in May 2009. In July, we raised the match rate to 100% and the maximum to 5,000 LKR. Finally, just before the accounts were unlocked, we added 5,000 LKR to every account, regardless of previous deposit patterns. The
may have affected access: whether the compound had a pour-flush toilet and piped water connection at the time of the endline, whether the water connection was working, the main source of water used by compound residents, the amount of time spent fetching water in the last week, the amount spent on water in the last month, and the overall piped water consumption in the compound. We also capture a range of outcomes that may be affected by the intervention and changes in water and sanitation access. Here we combine like outcomes into weighted, standardized indices following Anderson (2008) to reduce the potential for false positives from multiple hypothesis testing. We generate the following indices (full list of sub-indicators is presented in the appendix Tables A1 and A2): _Tenant-Property owner relationship_ : For the property owner index this includes property owner perceptions on whether tenants complain about the water and sewer facilities or about the general conditions of the compound, and whether tenants keep the compound in 15
However, there are many issues that the analysis is unable to address more fully. First, given the lack of a detailed consumption and expenditure survey from which we could construct a new estimate of monetary poverty, we were forced to impute poverty using non-monetary indicators and the relationship between poverty and those indicators in the last survey from which poverty was measured. However, for a more complete estimate the change in monetary well-being, we would need to utilize an actual measure of poverty instead of a proxy.

Additionally, we are only able to measure and compare PNG to the rest of the world in a limited number of coarse indicators given data limitations. Thus, we are unable to delve more deeply into particular dimensions or to measure the quality of services used by the population. More analyses into each of these specific areas would help to unpack more fully the potential degree to which resource-growth leads to improvements in household and individual-level outcomes. For example, although Howes and Pandey (2022) find limited evidence that household health outcomes improved following the resource booms in the country, the evidence is more mixed when looking at many different health indicators over longer time periods.

Furthermore, we need to understand in more detail why PNG’s experience is more extreme than other resource-intensive countries. The Introduction discussed some potential issues that likely helped to contribute to the lack of benefit from the economic growth that occurred, including a poorly developed private sector, aside from the resource-intensive activities, poor human capital outcomes that limit the ability of the population to take advantage of economic growth, and a host of governance issues. However, more work needs to be done to examine whether these issues are more extreme in PNG and what reforms might make the most meaningful improvement in household-level outcomes.

20
female employment, that is, trade liberalization is found to positively contribute to a convergence of male and female labor force participation and employment rates.<sup>21</sup> By comparison, using a panel of Indian formal manufacturing firms from 1998-2008, Banerjee and Veermani (2015) argue that, in spite of massive trade liberalization, India has witnessed a declining trend in female workforce participation rates since the late 1990s. Their results suggest that female employment intensity (FEI), defined as the share of all employees who are female, reduced as a result of trade liberalization. Reforms seem to have induced a skill-biased technological change, which promoted firms to hire more male workers at the expense of female

> 14 See Goldberg and Pavcnik (2007) for a survey on developing countries.

> 15 For related studies, see Porto (2004) on Argentina; McCaig (2011) on Vietnam; Goldberg and Pavcnik (2005) on Colombia.

> 16 Most studies in the field focus primarily on Latin America (e.g., Cragg and Epelbaum, 1996; Revenga, 1996; Hanson and Harrison, 1999), while there are some studies on, for example, Morocco (Currie and Harrison, 1997) and Brazil (Castilho, Menéndez and Sztulman, 2012; Kovak, 2013).

> 17 See, for example, Edmonds, Pavcnik and Topalova (2010); Kis-Katos and Sparrow (2011).

> 18 For instance, see Edmonds, Pavcnik and Topalova (2010) for India.

> 19 Hasan et al. (2012).

> 20 See, for instance, Fontana (2009); Nordas (2003) for a survey on the gender-based effects of trade liberalization in developing countries; for country-specific studies, see Ozler (2000) for Turkey; Ederington, Minier and Troske (2009) for Colombia; Aguayo-Tellez et al. (2013) for Mexico; Rahman and Islam (2013) for Bangladesh; Subramanian and Roy (2001) for Mauritius.

21 See for example
70 percent) in three provinces (Faryab, Badghis, and Kapisa). This under-sampling can be a potential source of bias. Various checks, including comparisons with the 2016-17 survey data and sensitivity analysis to see if poverty trends are dramatically different in under-sampled and other provinces, find little cause for concern.

A second issue arises from poorly identified geographical locations for 19 percent of the

> 7For a comprehensive review of data sources and weather indicators (satellite and non-satellite) used in economic development literature, please refer to Fisker et al. (2021); Dell et al. (2014).

> 8While land surface temperatures are shown to have a negative relationship with NDVI, Rousta et al. (2020) shows that lagged precipitation deficits (or surpluses), soil moisture, and other factors also affect NDVI.

> 9The 2007-08 survey was called the National Risk and Vulnerability Assessment (NRVA).

6
|**Sub‐Saharan Africa**|8604000|5895000|7055000|5406100|5068000|N.A.|
|---|---|---|---|---|---|---|
|MENA|6230000|8000000|6675000|8592900|10892000|N.A.|
|Asia and Pacific|4325000|2405000|3392000|2128800|5490000|N.A.|
|(excl. Australia, Japan, New Zealand)|||||||
|Americas<br>(excl. North America)|1126000|1280000|2176000|2900000|3661000|N.A.|

Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee‐like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http://www.systemicpeace.org/). As indicated in Figure A1, these data are much lower compared to those provided from 2003 by IDMC but provide a longer time series. UNHCR assisted‐IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR‐assisted IDPs are only IDPs who are protected/assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Since some adjustments could take place over time, we always use the figures from the last available report.

# **Figure A1: Refugees and Internally Displaced People in SSA, 1990 and 2013**

40
MONGOLIA'S INFORMAL SECTOR Notes 57 fully liable for the activities of the proprietorship, and proprietors were forbidden from being members of any other economic entity, except for joint-stock companies. Furthermore, a similar strand of the literature on the informal sector uses the number of employees as the defining characteristic of informality. The definition of sole proprietorship in the _Economic Entities Law_ was "an economic entity whose owner alone, not in connection with a cooperative or a company, regularly engages in production or provision of services in order to supply himself with a permanent source of income." (Article 8.) Second, it is instructive to consider data for 1994 for evaluating how informal the "sole proprietors" of the Annual Labor Force Balance are. In 1994 the SSO together with UNIDO conducted a Census of Industrial Establishments. They found only 69 industrial establishments registered as sole proprietorships in all of Mongolia. (Gereltuya, et al, 1995) In the same year, over 18,000 people in Ulaanbaatar alone reported that they were sole proprietorships, and nearly half of them were in the distribution sector.

29.For example, the low estimate is computed as follows. 17,706 accounted informals + (17,706*3) unaccounted informals = 70,824.

30.A quote from ILO 1993 is pertinent here: "the population employed in the informal sector comprises all persons who work in one or more production units considered to belong to the informal sector, irrespective of whether this work is their main or secondary activity." p.13.

# 3 l.Note that these estimates include pensioners, not only working age people.

32.A slightly lower estimate of the proportion of households with income from micro-enterprise was found by Centre for Social Development (CSD), 1996. Their survey of 174 households in the _Songino Hairhan Duureg_ during the winter of 95/96 found that 41% had some income from micro enterprise activities, as contrasted with the estimates of 47-51% presented in the text. The
br>IC|
|Enforcing Contracts|Time (days)<br>Cost(% of claim)|ECT<br>ECC|

Other indicators were not included due to limited perceived relevance (eg: closing a business) and due to potential multicollinearity (eg: time to start a business was selected over number of procedures required to start a business).

Determinants of governance were sourced from the World Bank’s Governance Indicators website. These include political stability and absence from violence (PV), corruption control (CC), voice and accountability (VA), regulatory quality (RQ), rule of law (RL) and government effectiveness (GE).

GDP growth data (GDPGR), GDP per capita (GDPCAP), Openness (OPEN), FDI inflow data (FDI_INF_Norm), the real exchange rate (REER), inflation rate (INF), the tax on corporate profits as a percentage of revenues (TAXPROFIT) and tax on international trade as a percentage of revenues (TAXINT) were sourced from the World Bank’s Development Indicators.

Any panel analysis with FDI inflows as the dependent variable would require instrumenting GDP growth, GDP per capita, the real exchange rate and openness given potential reverse causality between these endogenous variables. Further, it would have to instrument the exogenous variables while also considering country fixed effects (such as geographic location) that are unlikely to change significantly over the short time period in question.

As such, this paper follows a similar recent study undertaken on FDI flows (Walsh and Yu 2010) and uses the Arellano-Bond methodology that accounts for fixed effects in large cross-sectional and small time series panels. A gravity model (Dabla-Norris, Honda, Lahreche and Verdier 2010) was not used as it 5
higher than poverty in rural areas but this is not the case in urban areas, reflecting the fact that the household survey was conducted during a year of good conditions for rural households and very difficult conditions for urban households. Within rural areas, vulnerability is higher in areas prone to drought. However, much vulnerability is not geographically determined, but instead determined by other factors such related to gender and education. We show that the ranking of vulnerability across groups (e.g. rural female, rural male) are fairly stable across the choice of measure, though measures that are more sensitive to depth of potential deprivation should also be carefully checked.

The methodology used avoids a number of the limitations of cross-section data where possible but caveats to the analysis remain with regard to unobserved household characteristics. For this type of analysis, the data requirements are indeed high, and future challenges include generalizing estimates of consumption more reliably out-of-sample in order to improve the vulnerability estimates (Anttila-Hughes and Sharma, 2014).

In terms of policy, the results highlight the need for caution in using poverty data collected once every five years to target programs, as underlying rates of vulnerability can be quite different from the poverty rate captured at one point in time. More frequent collection of poverty data, and vulnerability analysis such as performed in this paper can 18
Almost all of the microenterprise owners draw income from farm and/or other nonfarm activities. For example, in 2000, 69.5 percent of the enterprise owners draw income from farm activities. The corresponding figures for 2005 and 2010 are 81.5 percent and 82.0 percent, respectively. Income figures are CPI-adjusted (2000=100). Figures in parentheses are t-statistics of the differences between microenterprise owners and non-owners. Source: HIES 2000, 2005 and 2010 39
# Figure 11. Institutional reforms

A. ICRG indicators around sustained B. Cumulative change in EMDE reform advances and setbacks in EMDEs investment and TFP two to four years after a sustained change in institutional quality <!-- Start of picture text --> Median<br>Setback Advance<br>0.51<br>0.49<br>0.47<br>0.45<br>t-4 t-3 t-2 t-1 t=0 t+1 t+2 t+3 t+4<br>Sources: Penn World Tables, World Bank.<br><!-- End of picture text --> <!-- Start of picture text --> Percent Percent<br>5 30<br>3 15<br>0 0<br>-3 -15<br>-5 -30<br>2 4 2 4 2 4 2 4<br>Advance Setback Advance Setback<br>TFP Investment (RHS)<br><!-- End of picture text --> Note: EMDEs = emerging market and developing economies; TFP = Total factor productivity. Sustained institutional advances or setbacks are defined as an increase or decrease, respectively, in the unweighted average of four ICRG indicators—bureaucracy quality, law and order, corruption, and investment profile— provided the increase is not unwound for at least three consecutive years. A detailed methodology is available in annex 5B.

A. Average of four indicators: bureaucracy quality, law and order, corruption, and investment profile. t=0 indicates the year when a sustained reform advance or setback started.

B. Sample starts in 1985. Chart shows regression coefficients on TFP and investment growth with dummies for the start of sustained reform advances and setbacks from local projection estimation for lags of two and four years. Vertical lines show the 90 percent confidence intervals.
**_Proposition 3 Effect of Liquidity Constraint:_** _In the presence of borrowing constraint (i.e., farmers cannot borrow), the marginal effect of_ E[p2|Ω1] _will decrease._

In the following sections, we describe our data and empirical strategies to verify the above theoretical predictions.

# **3. Empirical strategy**

We use the IMDG data to test the implication of our theoretical model on the expectation formation and liquidity constraints. In our empirical model, we assume that farmers are price takers. Using the exogenous variations of food price shocks to households, we estimate the relationship between agricultural investment and food price using a first-differencing (FD) model.

We define food price variable ∆P as a growth rate of food prices between 2007 and 2009. The price index is constructed as follows. We start from the provincial capital producer prices of five main food items: rice ( _c=1_ ), maize ( _c=2_ ), cassava ( _c=3_ ), estate plantation crops ( _c=4_ ), and horticulture crops (c=5). We use an aggregate price measure of estate plantation crops which include major crops such as coffee, cocoa, and coconuts. Our price measure of horticulture crops also includes both vegetables and fruits. Since many farmers produce multiple crops in our sample, the price of each food item is weighted by the revenue share of each main crop among total revenues in 2007 ( φ 2007 )<sup>7</sup> , and the weighted prices are summed. This method

> 7 Revenue shares are the proportions of production revenues of rice, maize, cassava, estate crops, and horticulture crops.  If farmers do not market the crops and thus only report the volume of crop production, we imputed the production revenue using the median price of each crop at the village level.

10
will likely sort into better schools (where ���� is higher). Estimating the equation by simple OLS would therefore likely lead to biased results because there are unobserved student (and school) characteristics that are correlated both with the teacher quality measures and the left-hand side variable. In addition, if there are other dimensions of teacher quality that matter for student learning that are unobserved but correlated with effort, knowledge and skills, then the estimated coefficients will be biased.

If we want to retain all the teacher quality measures in the estimation, the best we can do is to try to control for all the relevant inputs in the education production function. Since the SDI data contain a wealth of information on students, teachers and schools, we can make some progress here. In particular, we include a student’s non-verbal reasoning score, a (admittedly) crude measure of innate ability, whether they had breakfast and their age and gender in the regression. For the teacher we include the same set of characteristics used in the previous section, namely gender, age, experience, and whether they have a university and/or education degree. For the school, we include its location, the pupil-teacher ratio and indices for teaching equipment and infrastructure. In addition, we add country-district fixed effects and day of week and month of survey fixed effects.

If we consider only a subset of the teacher quality measures, namely subject content and pedagogy knowledge, the prospect for identification is improved, because we can now exploit within-student variation. Specifically, we take first differences of equation (1) to arrive at where the dependent variable, ��� ����, is now the difference in student test scores in language and math. Note that content knowledge ��� differs both across and within teachers while pedagogical knowledge is teacher specific. Thus, �� is identified from the sub-sample of students with different teachers in the two subjects, while �� is identified off of variation across and within teachers.