rafmacalaba/datause-encoder-data huggingface dataset:
2. Using the Framework This section first discusses issues related to applying the framework, and then illustrates its use at the project level and at the country level.

# 2.1 Applications

The ME framework provides an analytic structure that can be used to (1) monitor change and evaluate the impact of a specific project that has empowerment as one of its goals, (2) conduct in-depth research, (3) monitor national-level concerns, and (4) track relative changes in empowerment among different countries. The objective and context of each activity will determine which aspects of the framework to use and how to collect and analyze data.

To illustrate, the framework can be applied to assess whether empowerment objectives are being reached and what specific factors associate with related outcomes. The five case studies of the ME study, for example, use the framework to evaluate the empowerment impacts and identify causal relations in Bank-financed interventions. Each of the five case studies has adapted the framework to fit the nature, context, and objectives of the intervention. For example, the community-based education project in Honduras collects empowerment data for the state and society domains at the local and intermediary levels. The market domain and the state sub-domains of justice and politics are not priorities for investigation, and the intra-household domain is of limited interest.

An in-depth research exercise could seek to understand whether, how, and to what extent marginalized people, such as Indian women, can be empowered. To develop policy and practice recommendations, such research would need to focus on all sub-domains and levels identified by the analytic framework. The framework has yet to be tested in this kind of analysis.

A national-level monitoring exercise can use the ME framework to identify key indicators in each domain and undertake an analysis of empowerment at different levels. Section 2.3 of this paper discusses work currently in progress on developing a survey module that can be added to a Living Standards Measurement Survey (LSMS) or other national household questionnaires.

Finally, using the ME framework concepts of domains and levels allows a comparative assessment of empowerment across countries. While some argue that inter-country comparisons are pointless, others indicate that information of this kind can encourage governments to improve performance. In fact, this has already happened as a result of the international governance reviews and databases now available to compare performance in governance across countries (Kaufmann, Kraay, and Mastruzzi 2003). Such information, particularly on trends, also helps international policymakers and donors decide strategy and financing priorities.

Two points are important in relation to these various applications. The first concerns the need for context-specific data collection and analysis. The second relates to conceptual 15
36 To more formally assess the model’s ability to explain the observed post-ban changes in night lights, we run a dynamic event study analysis at the district-month level, assigning districts to the treatment or control group depending on whether they were above or below the median of fertilizer importance at baseline (see Figure VIIIc). The outcome variable is (the log of) average night light intensity and the treatment period is defined as starting in May 2021 (i.e. the month when the ban was implemented). The results, displayed in Appendix Figure A15, show that night lights decreased by 26.7 log points in treated (i.e. relatively fertilizer-dependent) districts relative to control (i.e. less fertilizer-dependent) districts eight months into the ban, corresponding to an income loss of 7.7%. Using the same assignment of districts into treated and control groups, model-predicted real income losses (Figure VIIIb) are 6.3 percentage points worse in treated districts than in control districts, on average. Therefore, by comparing the model’s predictions to the night lights evidence, we conclude that the model can explain 82% of the differential real income loss of fertilizer-dependent districts observed in the data during the relevant time period.

Finally, we take advantage of our night lights data to further confirm that our main results are not driven by the potentially confounding influence of the Covid-19 pandemic (see Section IV.A). Figure VIIId shows that the spatial pattern of income losses (proxied by night lights) in the period following the pandemic’s arrival in March 2020 was very different from the pattern of losses in the post-ban period (Figure VIIIa), indicating that potentially lingering effects of the pandemic cannot explain the negative economic effects that we attribute to the fertilizer ban.

# **VIII Conclusion**

Trade policy and geopolitical events increasingly affect the ability of many countries to access critical intermediate inputs. This paper quantifies the costs of a nationwide fertilizer shortage and explores its interaction with domestic agricultural policies, such as subsidies. It does so by leveraging an unprecedented natural experiment, an abrupt and unexpected import ban that disrupted the supply of chemical fertilizers in Sri Lanka. Relying on novel high-frequency firm-level trade data, detailed agricultural ground production data, crop yield estimates from state-of-the-art remote sensing techniques, and cutting-edge event study designs, we show that the fertilizer import ban led to dramatic declines in fertilizer imports, agricultural production, and exports of fertilizer-intensive agricultural products.

To better understand the mechanisms and welfare implications of the ban’s effects, we propose and estimate a spatial general equilibrium model of agriculture and trade with quantitative restricexpenditure to aggregate income, as predicted by our model. This metric captures both the relative importance of the agricultural sector in the district’s economy and the agricultural sector’s degree of specialization in fertilizer-intensive crops.
**Intwnathonal Trae**

# **Policy Resurch**

WPS 1110 This paper-a product of the Intemational Trade Division, Intemational Economics Department- is part ofalargereffort In the department to analyzedevelopingcountrytrade in manufactures. Copies ofthe paper are availabie free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Dawn Gustafson, room S7-044, extension 33714 (March 1993,28 pages).

Despite the achievements of the -xport-oriented economies of East Asia, many policymakers doubt that a development path led by manufactured exports is feasible for all developing countries.

Martin examines whiat happens if all developing countries, rather than merely a few, expand manufactured exports. He considers two driving forces for export expansion: the liberalization of trade barriers, and productivity growth in the production of manufactured exports.

With **only** trade liberatioialization, the static welfare gains are small (with the standard Arming-on specifieation used in the analysis). Even the export owth rates are far too small to replicate the essanial East Asian experience. And when all developing countries participate in static trade liberalization. the small welfare gains diminish slightly.

Under the more realistic assumption of dynamic export growth driven by productivity gains for manufactured exports, the welfare effects are much greater and the efforts of developing countries are mutually reinforcing. Because of strong South-South trade links, and developing countries' dependence on manufactured imports, developing countries buy more manufactured goods from each otner.

Martin accepts the view of "export pessimists" that a country expanding its manufactured exports will receive depressed prices for those exports. But his results differ because he uses a general equilibrium frameworic with intraindustry trade rather than a partial equilibrium model of the export market. The general equilibrium model captures the fact that developing countries still import most manufactured goods, often from each other. They will suffer, but they will also benefit, from declining prices. So they are better off if they all expand those exports.

**ThePolicy ResearchWog PaperSenesdisred athe Po mdgs of wodccundy way** n **theiBanki Anobjetoveof e seCnes is to got these findings out quickly, even if presawatons are less than fully polishecL** Tile findings, **interpretations,** and **conclusions in dims papers do not neceaily repnt of ficial Bank policy.**

**Produced by the Policy Raesech Dissiiation Center**
school are likely to be among the poorest and most disadvantaged in the country.<sup>29</sup> In this regard, one of the results of the Aurino et al. (2019) study of the Ghana School Feeding Programme is that the impact on test scores is significantly higher for girls, for children living in poor households, and for children living in the northern regions (the most disadvantaged regions) of the country. This highlights the possibility that an intervention with a modest benefit-cost ratio overall can nevertheless be equity improving, something that may soften the dilemma, but not eliminate it.

There are two reasons to be cautious about this conclusion, however. First, the results for the quality-of-schooling interventions are for a _marginal_ expenditure that, one supposes, benefits from the existing expenditure on schooling in general, while the costs for the quantity-ofschooling analysis are _average_ expenditures. If we were to force the marginal intervention to pay some share of the average costs of schooling, it would lower the benefit-cost ratios for the marginal interventions. Unfortunately, we can think of no coherent way to do this. Second, the impact estimates on test scores are all short-run effects over one or two years which we assume persist through time and are reflected in higher labor earnings as estimated by Evans and Yuan (2019) who use a contemporaneous correlation between test scores and labor earnings for their estimates. Our estimates, however, are for changes in test scores while in school, the effect of which may fade over time. We have assumed that they do not in our calculations.<sup>30</sup> If that is wrong, our calculations would overestimate the future labor earnings gains and, thus, the benefitcost ratios for the quality-of-schooling interventions.

There is much else to be cautious of in these estimates. For our quantity estimates, the limitations of Mincer regressions are well-established and discussed. While it seems natural that OLS estimates should be biased upwards, in fact, there are theoretical reasons for the bias to go either way, as discussed in Card (1999). Further, the fact that these estimates vary in a fairly narrow range over time and across countries gives us some confidence in them despite the possible biases.

> 29 While none of the studies we use except Aurino et al. (2019) was designed to break down results by poverty status, it seems likely that students most in need of better remedial teaching methods are also among the poorest students.

> 30 Some studies find that these effects of interventions fade with time and others do not. We are unaware of any studies in Ghana that attempt to estimate the potential fading of effects of these interventions. Evans and Yuan (2019) discuss the literature on the problem of fading effects and how it affects calculations such as the ones we do here.

37
7 Agrarian Services. There are about 15,000 such minor schemes, with about 50 percent of them currently operational.<sup>16</sup> Rainfed agriculture in Sri Lanka is still very prominent, with approximately two-thirds of crop cultivation being rainfed farming. The mean annual rainfall is approximately 1,900 mm. Rice farming is mostly irrigated, but about a third of rice production is still rainfall dependent. Most rainfalldependent farming occurs in the Maha season (September to March) during the north-east monsoons. Farming during the Yala season (May to August) mostly uses irrigation. Rice is cultivated at an annual average of 870,000 hectares, with 560,000 hectares during the Maha season and 310,000 during the Yala season.<sup>17</sup> Rainfall-dependent farming is exposed to various risks, such as changes in the seasonal rainfall pattern, dry spells, lower number of rainy days, late onset of the season, high intensity of rainfall, droughts in the dry zone areas, and also rain-triggered flash flooding.<sup>18</sup>

# **4. Development of Index Insurance for Sri Lanka**

One of Sri Lanka’s first experiences in index insurance for agriculture was a pilot for the tea sector by SANASA Insurance Company with technical assistance from the International Finance Corporation (IFC) in 2011-2014. The pilot was for smallholder tea farmers in Ratnapura district who relied entirely on rainfall as the source of water for their cultivation. This was an opportunity to experiment with an index insurance product based on rainfall data, which has been popularly deployed in other parts of the world. This pilot resulted in a commercially successful product with positive correlation to the farmers’ actual experience. The main prerequisite for the pilot was a reliable source of rainfall data.

> 16 http://www.ips.lk/irrigation-and-agriculture-in-sri-lanka/.

> 17 http://doa.gov.lk/rrdi/index.php/en/crop/42-crop-rice-cultivation.

> 18 Institutionalizing Agricultural Water Management at Local Level in Sri Lanka, Mrs. K.R.S. Perera, Ministry of Agriculture Development & Agrarian Services.
NTPs receive substantially more aid than those that have not done so. The substantial difference between the amount of additional aid allocated for civil and human rightsrelated projects to countries that sign non-binding NTPs and those that make binding commitments in this area is not a function of the type of NTP but a reflection of the fact that the EU and the US do not include enforceable NTPs dealing with civil and human rights in their PTAs.<sup>21</sup> Nonetheless, the difference between the pattern of aid and NTPs for environment and civil rights is noteworthy. Finally, in the case of labor rights, there is little difference in the average amount of ODA received by countries that have agreed to NTPs in this area and those that have not, nor does the type of NTP appear to matter much in terms of average dollar values disbursed.

To assess whether and to what extent signing a PTA affects official development assistance flows, we again use the SDID estimator (see Eq. 1). We are interested in determining whether signing an agreement affects the amount of development assistance received, and whether there are differences in this respect between signing binding provisions vs. non-binding ones.

Table 4 presents the SDID estimates for this second exercise. We report the ATT estimates of signing a PTA that includes a NTP of interest (covering environmental, labor, or civil rights protection) on aid flows targeting the associated policy domain, as well as on total aggregate ODA received. Given the focus on ODA, we restrict our sample to the set of countries that were eligible for receiving financial support during the period 1990-2015. To improve matching, we include per capita GDP, population, total trade, and share of trade with the US and the EU.<sup>22</sup> The results for binding (non-binding) provisions are reported in the upper (lower) panel of Table 4. The first three rows of each panel focus on the relationship between the amount of issue specific ODA received and acceptance of a NTP in the corresponding area. The last three rows of each panel report the impact of having accepted an issue 21There are other donor countries with PTAs that do have binding provisions in this area that are captured in the regressions.

> 22We consider all countries that received ODA at least once from at least one donor, and that did not lose the status of potential beneficiaries during the period considered. Country and year fixed effects included in the SDID estimator control for both time- and country-specific invariant factors that could also explain the allocation of ODA.

22
In terms of employment, heavy job losses are recorded for the construction and transport‐communication service sectors, about 0.9 and 1.2 percent of total employment, respectively (see Table _2_ , third column). These, translated in actual levels of employment, are close to 500,000 and 700,000 jobs, a considerable loss. However, new opportunities arise in import competing and export oriented sectors. A potential large gain is highlighted in the manufacturing sector which may create close to 800,000 jobs.

Table 2: Oil shock triggers sectoral adjustments in favor of tradables and against non‐tradables

||**Production**<br>(% change with<br>respect to no shock)|**Employment**<br>(% change with<br>respect to no shock)|**Employment**<br>(change with respect<br>to no shock, as % of<br>tot employment)|**Factor Intensity**<br>(number of skilled<br>per 100 unskilled)|
|---|---|---|---|---|
|Oil|‐13.4|‐18.9|‐0.4|211|
|Construction|‐5.3|‐9.7|‐0.9|182|
|Transport and Trade Services|‐1.3|‐6.0|‐1.2|139|
|Business Serv. & Publ. Admin.|‐0.4|‐0.8|‐0.4|182|
|Food products|2.3|3.6|0.1|145|
|Other agriculture|3.4|6.3|0.3|89|
|Other mining|5.1|20.4|1.2|145|
|Other manufacturing|8.2|8.6|0.3|89|
|Export agriculture|9.5|15.6|0.2|211|
|Export manufacturing|12.7|23.2|0.7|145|

Source: GTAP and ROSSTAT data, and CGE simulation results Sectors use different inputs in their production, and specifically employ skilled and unskilled<sup>11</sup> workers in different proportions; these reallocations produce imbalances in labor markets. In particular, as on average skilled workers are employed more intensively in shrinking sectors – such as Business Services and Public Services, Oil, and even in Construction (where for every 100 unskilled workers, 182 skilled workers are employed, see rightmost column in Table _2_ ) – job losses affect more severely skilled workers than unskilled ones. Similar imbalances influence the urban premium, defined here as the ratio of non‐ agriculture activities (essentially urban) wages over agriculture (mainly rural<sup>12</sup> ) wages. These imbalances in the labor markets put downward pressure on the skill and urban premia which, as shown in Table 1, decrease by a few percentage points.

Another relevant aggregate result estimated by the CGE model is the change in relative prices of goods and services. The oil price reduction and the real exchange rate depreciation make energy cheaper,

> 11 The partition of the population into skilled and skilled workers is based on the highest level of education attained; specifically, unskilled are all those who have completed, at most, secondary education while all the others are considered skilled (even if they did not completed tertiary education). Based on this definition, unskilled workers account for 40 percent of the population and skilled for 60 percent. Among the unskilled, 20 percent has only primary education and 17 percent has junior education; 20 percent have been enrolled in junior vocational education and the highest share (38%) has secondary education.

> 12 Agricultural activities are mainly (76 percent) located in rural areas.

13
# **_7.2 Exporter Entry, Exit, and Survival – Second Typology_**

As a second typology we classify sectors according to their comparative advantage in each country based on observed patterns of trade. To define comparative advantage we focus on the concept of net trade, taking exports and imports into account. Following Vollrath (1991) we computed a net-trade revealed comparative advantage index for each HS 2-digit sector and then categorized sectors into those with indexes greater than 0 - sectors with comparative advantage - and those with indexes smaller than 0 - sectors without comparative advantage.<sup>33</sup> Panels A and B of Figure 12 presents the exporter entry rates in sectors with and without comparative advantage in each country during 2005-2007 and 2008-2009. Despite heterogeneity in exporter entry rates across sectors and countries, a strong pattern emerges during the steady growth period: entry rates were higher in sectors that did not have revealed comparative advantage as an average in LAC and in every country except Costa Rica. This was, however, not a feature of LAC as it was also observed in Bangladesh, Cambodia, and South Africa. Ecuador and Peru exhibited the highest entry rates in the two types of sectors among natural resource countries, but Cambodia exhibited higher entry rates than any LAC processing country. Panel B shows that with the global recession average entry rates in the LAC region decreased in comparative advantage sectors but were unchanged in other sectors.

Exporter exit rates in sectors with and without comparative advantage in each country are presented in Panels C and D of Figure 12 for 2005-2007 and 2008-2009. Exit rates were systematically higher in sectors with comparative advantage in all LAC countries except Chile during the steady growth period. The differences were in some cases very large: e.g., in the Dominican Republic an 11 percentage point difference separated the exit rates in sectors with and without comparative advantage. Panel D shows that the financial crisis increased exit rates in both types of sectors and the increases were highest in the Dominican Republic.

> 33 We use export flows and import flows at the HS 2-digit sector for all countries in the world in 2005 from World Integrated Trade Solution (WITS) to calculate the relative export advantage indexes (revealed comparative advantage in Balassa (1965) terminology) and similarly defined relative import advantage indexes for each HS 2-digit sector in each LAC country. The nettrade revealed comparative advantage index for a sector is computed as the logarithm of the corresponding relative export advantage index minus the logarithm of the relative import advantage index. A net-trade revealed comparative advantage index greater than 0 indicates that the country has a comparative advantage in that sector.

36
11 -

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of the village income spectrum.

However, in the absence of smoothly functioning credit markets that give explicit preference to the poor - a condition characteristic of village economies in LDCs - households or individuals at very low levels of absolute income may be unable to engage in migration if migration is costly and the initial risks associated with it are high.<sup>2</sup> In addition, at incomes very near or below subsistence, relative income considerations are not likely to matter as much as concerns for mere survival. Thus, we would expect a small increase in income (and a small decrease in relative deprivation) to have a positive effect on migration from households at the very bottom of the village income distribution - owing, first, to a loosening of capital constraints on migration and, second, to the increasing importance of relative deprivation considerations ill these households' labor allocations. At higher income levels, in contrast, both the relative and absolute income hypotheses predict that increases in income will reduce the likelihood that households or individuals will engage in migration. It is therefore impossible on purely theoretical grounds to separate the effect of absolute income incentives from the effect of relative income incentives for migration, since, when credit markets are highly imperfect, absolute and relative income effects of changes in village incomes tend to move in tandem. Note that migration studies that ignore relative income effects may place undue significance on absolute income motives for migration.