rafmacalaba/data-use-mentions-extended huggingface dataset:
A detailed discussion of this procedure can be found in Appendix B. Depending on the data set (NFHS or TUS) these variables include individual and household demographics, education levels, 2-digit occupation and industry codes at the household and/or individual level, work status (school, domestic work, casual versus salary versus self-employment), labor-intensive “domestic” tasks typically carried out by females not in the labor force (e.g. collecting firewood or water, husking paddy and grinding grains), laborintensive agricultural tasks, land possessed, and total expenditures deflated using an all-India CPI (see Appendix Tables S.3 and S.4). When restricting estimation to the six states in the TUS, state fixed effects are also included. The _R_<sup>2</sup> from these predictive equations ranges from 0.14 to 0.35 for the NFHS REE estimates and from 0.37 to 0.63 for the TUS AL estimates, suggesting the variables in common with the NSS-E are rich enough to capture a lot of the sample variation in REE and AL from the other data sets. The estimation procedure here is adequate for estimation population or conditional means, which is the goal here, but it would need to be amended if one were interested in the variance or other moments of the distribution (e.g. fraction of households with TEE below some threshold).

# **2.4 Analysis of household characteristics**

While Table 1 makes it clear that demographic composition and rural/urban location will generate large differences in TEE (and its REE and AL components) across households, there is also considerable cross-sectional variation related to other variables. Before turning to estimates of population TEE over time, some interesting cross-sectional patterns are discussed to demonstrate the richness of these data. For this analysis, only the 1998-1999 Time-Use Survey and REE imputation procedure are used.

Figure 1 plots household TEE (averaged across individuals within the household) against mean years of schooling for members aged 18 and
(Kutcher and Scandizzo, 1979), countries (Berry and Cline 1979), or individual farms (Barraclough, 1970). 1 More recent contributions have emphasized the importance of asset ownership in situations characterized by incomplete contracting (Bardhan et al. 1998), elaborating on models such as Dasgupta and Ray 1986 and 1987 and Moene 1992. The underlying idea is that, in situations characterized by credit rationing, individuals may not be able to undertake indivisible investments in human capital (schooling) or productive assets (wells, bullocks, or perennials with a long gestation period) that need to be financed through credit. This idea has been

> 'One _of_ the first studies to provide both a theoretical model and an empirical investigation of the potential impact of land reform indicates that, based on district level data from India, land reform can indeed have a positive impact on wages and employment (Rosenzweig 1978).

3
force participation, income, and access to basic services. By 2003, the surveys covered over 25 million individuals.<sup>13</sup> Data from SIFA and _Sisben_ were carefully merged using confidential information on date of birth, full name, and national identification numbers. The resulting dataset (―SIFA +

> 11 Nearly 12 percent of the households interviewed in treatment municipalities were not registered with the program (Attanasio et al., 2005).

> 12 We merged the household survey data with administrative data from the standardized test score _Icfes_ . Using this merged dataset, we identified that 95 percent of the population that presented the test in the 2000-2009 period were born during the period1975-1994. In order to study the indirect effects of participant children on non-eligible young adults that were still in school or could rejoin, this sample also includes individuals 19 to 23 years old that were listed as dependents, but are not eligible to the program.

> 13 A new _Sisben_ survey was fielded between 2003 and 2007 to update the information, improve the effectiveness of the targeting scheme, and change the algorithm due to concerns regarding manipulation by local authorities (Barrera, Linden, and Urquiola, 2007; Camacho and Conover, 2011).

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exposure probabilities at various food insecurity levels supports these types of indicators and methods that monitor food security transparently and frequently in countries like Afghanistan.

The paper is structured as follows: Section 2 discusses the country context and related literature. Section 3 introduces the data. Section 4 presents the modeling framework. Section 5 outlines key findings. Section 6 discusses implications, and Section 7 concludes.

# 2. Background and Literature Review

## 2.1. Food Security Challenges in Afghanistan

Afghanistan faces challenges in achieving food security due to its terrain, varied climatic conditions, and socio-economic vulnerabilities. Over the past decades, the country has experienced a series of climatic and weather shocks—including prolonged droughts, erratic rainfall, and flooding—that have impacted agricultural productivity and food availability. These disruptions have been compounded by poverty, limited market access, and ongoing conflict, creating a web of factors contributing to food insecurity and economic instability.

Research indicates that both droughts and floods are major drivers of food insecurity in Afghanistan. The World Bank (2019) reported that approximately 45% of the Afghan population was moderately food insecure in 2016, a figure that has worsened in subsequent years, now exceeding 80% (IPC, 2023), due to climatic events and the economic disruption caused by the political events of 2021. In 2018, a severe drought further intensified food insecurity, particularly in rural areas dependent on agriculture for livelihoods. This event reduced cereal yields, pushing many households to crisis levels of food insecurity and triggering large-scale humanitarian interventions. Oskorouchi et al. (2021) and Yolchi et al. (2024), using cross-sectional surveys, show that flooding also has significant negative effects on food security. Additionally, Omerkhil et al. (2020) highlight that smallholder farmers are especially vulnerable to climate change, with those in hilly regions at higher risk than those in plains, and both groups showing low adaptive capacity.

Further studies point to the increasing severity and frequency of droughts in Afghanistan, predicting more severe droughts in the future
# **3 Empirical Methodology**

## **3.1 Econometric Model**

We formally explore the relationship between uncertainty in import clearance times and firm export dynamics by estimating the following baseline model: The dependent variable _FirmDynamicsijkt_ is the entry, exit or survival rate of manufacturing firms in sector _k_ of country _i_ and exporting to destination _j_ . _Uncertaintyikt_ is the interquartile range of the time to clear imports at the border, and varies across time by exporting country _i_ and sector _k_ . We include vector _Xikt_ to control for time-varying exporting firm characteristics taken from the WBES and averaged at the country-sector-year level. It comprises (i) firm export intensity defined as direct exports as a fraction of total sales, (ii) import penetration, captured by the proportion of firms that import material inputs or supplies, (iii) the number of years since the firm first started to export as a proxy for average export experience, (iv) the natural logarithm of the number of permanent, full-time employees as a proxy for firm size, and (v) firm total factor productivity (TFP). Consistent with the literature on export dynamics, _Wij_ is a vector of standard time-invariant bilateral gravity variables such as contiguity, distance, and a binary variable for colonial relationship taken from CEPII. _EIAijt_ is a time-variant dummy provided at Jeffrey Bergstrand’s website and indicating whether the pair is involved in an economic integration agreement. _Tariffijkt_ refers to bilateral applied tariffs derived from WITS.<sup>22</sup> We also include vectors _Fit_ and _Pit_ to control for country-specific characteristics such as GDP per capita and the Doing Business indicator of the cost of procedures to start a business as a share of GNI per capita drawn from CEPII to account for the level of development and the average quality of the business environment, respectively. Finally, _Oi_ , _Dj_ , _Sk_ and _Tt_ refer to exporter-, importer-, sector- and year-specific effects respectively.

Table A3 in the Appendix provides a detailed description of the variables,
ethnic background, migratory status and treatment intensity. It is important to remember that the treatment intensity data are the result of the application of the weights described in section 4.3.

# A. _Results of absolute mobility_

Chart 2 shows the distribution of youths and their providers on the different occupational strata. In the case of youths, the distribution of sex, ethnic background, migratory status and intensity of PROSPERA’s treatment subgroups is also presented. This descriptive analysis provides the first approach to intergenerational changes in the occupational stratum.

It can be seen that the distribution of the providers in the study group is concentrated, largely, in the agricultural stratum, while the youths, although having an important presence in that stratum, are redistributed towards other strata higher up in the hierarchy. The comparison of sex groups shows relevant differences; women have greater presence in the stratum in the highest hierarchy (NM and Commerce), as well as in the LMS, while males continue to have a significant presence in the agricultural stratum. The differences between the two ethnic background groups was not clearly noticeable, except for there being a slightly greater presence of indigenous people in the agricultural stratum.

**Chart 2. Distribution of providers and youths by occupational stratum. Youths from 18-35 of**

**age of ENCEL 2017 and their providers.**

<!-- Start of picture text --> 2.4<br>2.9 9.7 14.7 6.6 9.2 9.9 12.4 8.6 10.7 8.8<br>7.7 7.2<br>10.0 11.9 10.8 12.3 9.0 12.3 11.5<br>14.9 18.2<br>19.6 12.1<br>8.9 14.2 13.4 14.5 15.1 13.3<br>12.5 13.0
larger countries. The magnitude of investments required for effective climate change adaptation, combined with the lack of economies of scale for many of these investments, unavoidably presents fiscal challenges, with most SIDS having limited fiscal space (if any) for expanding public investments.

Fiscal space in many SIDS is constrained by high public debt ratios. More than a third of the SIDS which are independent UN member states have relatively high public debt ratios (above 60 percent of GDP). In their most recent Debt Sustainability Analysis (DSA), 14 SIDS were assessed as being at high risk of debt distress, mainly because their debt ratios breach sustainability thresholds in DSA stress tests (see section 4 for more details). Consequently, some researchers and policy advocates argue that debt relief measures, alongside other reforms to the global financial architecture, are essential to restore debt sustainability and to enable SIDS to undertake the investments needed to strengthen their resilience (Fresnillio and Crotti, 2022; Rustomjee, 2017). This is the motivation for the SIDS Strengthening Resilience by Alleviating Debt (SRAD) program, which advocates for policy measures to improve climate resilience and increase fiscal space in SIDS. It focuses on debt alleviation measures such as: i)

> 1 For comparison, the average unweighted real GDP of all EMDEs which are not SIDS contracted by 1.8 percent in 2020 but then increased by 6.9 percent in 2021. The source of the data is the WEO database. The data for the SIDS includes the 36 states which are UN members and which are classified as EMDEs (all SIDS except for Singapore), except Cuba and the Cook Islands for which there are no data in the WEO database.

> 2 There are some types of adaptation investments which are private goods, such as strengthening privately owned buildings to make them more resilient to storms, but even these may require some form of public subsidy, either because private property owners do not have sufficient resources for the necessary investments (as may be the case for houses owned by
are the focus (as opposed to policy differences across countries in the cross‐section analysis). In order to analyze this latter aspect in more detail, our DL measure will now be lagged with 2 years when possible and 1 year otherwise.

In all, the baseline regression equation for the three countries looks as follows: (3) where INNO is comprised of the innovation variables recorded in each country‐specific data set for Malaysia, Vietnam and China. The DL term is exactly similar to the one in equation (2) where the data policy index is interacted with the data/software intensity. In our three country cases however, given the lack of data on this variable in the region, we are left with measuring the sheer proportion of data usage as part of total input use instead. We use national input‐output (IO) matrices to compute the proportion of ICT‐services in total input use for each sector in the three countries. The national IO tables are taken from the World Bank and are reported at 2‐digit ISIC Rev. 4 level. IO tables are available for each country and therefore represent a consistent source. For each regression, we take input coefficients at the domestic level (i.e. excluding imports) and for a year that falls at the beginning or in the middle of the time period of analysis.

Further, the terms μ� , θ� and γ� are the firm, sector and year fixed effects, respectively. Note that it follows naturally that due to the fact we have three country‐specific regressions, we are unable to include any country fixed effects. Finally, the ε��� is the error term, which now for the LPM regressions are clustered by sector. Of note, due to technical constraints we cannot run the Probit model and therefore perform LPM for all three countries.<sup>9</sup> In addition, for China we also perform OLS on several occasions as the type of data allows
migration for women, but increase migration of children for work, especially for boys. To carry out their analysis, they use the migration-related questions on where the individual lived before moving to the current area of residence, when he/she moved, and the stated motive for doing so. Although this allows one to retrieve the district of origin and destination as well as the time of migration of individuals interviewed, they notice that the lack of information at origin before moving is a limitation for their study.

Di Falco et al. (2022) use LSMS-ISA data to construct a multi-country panel data set covering Ethiopia, Malawi, Niger, Nigeria, and Uganda that is merged with high-resolution gridded precipitation historical records from the Climate Research Unit to analyze the effects of cumulative drought shocks on the decision to migrate in rural households. While confirming the existence of an immediate, although small, impact on migration decisions in the aftermath (i.e., the subsequent year) of a severe and extreme drought shock, they interestingly show that this impact is long-lasting, increasing migration for at least five years after the shock occurs, and not even fading or diminishing over time. Furthermore, they find that the effect of multiple recently experienced droughts (past five years) accumulates over time, which results in a much higher number of migrants than one would expect based on the immediate effect of the shock only. The authors emphasize that this has relevant implications for the study of climate-induced migration and make a plea for advancing the research on the cumulative impacts of climate change on determining migratory flows in the long-run while at the same time improving the availability of detailed data on migration.

The fact that out of the vast and growing literature reviewed before, only five studies employ LSMS-ISA data (and even with conflicting findings), is a clear indicator of the currently limited capacity of the LSMS-ISA data sets to provide a basis for meaningful analysis on climate change and migration. Figure 1 below provides an idea of the type of migration tracking that is
WPS3653

# **Regime-Switching in Exchange Rate Policy and Balance Sheet Effects**

# **© Norbert Fiess**<sup>*****</sup> **and Rashmi Shankar**<sup>**#**</sup>

# **Abstract**

It is well accepted that exchange rate policy in many emerging markets has been characterized by shifts between a stronger and weaker commitment to peg. This raises the following questions, which we address in our paper: Does intervention policy exhibit clearly defined and periodic shifts? What drives this policy variability? We identify clearly defined switches between high and low intervention in all the countries in our sample. We also find strong evidence that balance sheet effects, proxied by the stock ratio of external liabilities to assets, and economic performance, as measured by GDP and stock market indices, determine the likelihood of the regime shift. Specifically, an increase in reserve currency debt raises potential capital losses from devaluation and reduces the probability of switching to a low intervention regime. We use a panel of quarterly data starting 1985 through 2004 for a sample of 15 countries, mostly from East Asia and Latin America. We adopt a novel two-step empirical strategy in this paper. First, we measure the policy response of the central bank in two ways, both derived from the monetarist model: a standard exchange market pressure index and a model-based volatility ratio that is endogenized relative to Japan, our “benchmark” floater. We apply regime-switching methods to these “policy response indices.” This generates a time-series of unconditional probabilities of switching between high and low intervention. In the second step, we establish a set of variables that explains changes in these probabilities.

**_Key words_** : Markov regime switching, balance sheet effects, exchange market intervention, panel estimation, emerging markets **_JEL Code_** : F310, F340

# World Bank Policy Research Working Paper 3653, July 2005

_The Policy Research Working Paper Series disseminates the findings of
areas) after the harvest. Based on the available early warning systems, it is possible to define triggering events that would set off a drought response. Potential triggers that could be considered include, for instance, LEAP’s intermediary outputs (WRSI or crop yield reduction estimates), or estimated beneficiary numbers based on either the LEAP or LIAS tools, or a combination thereof. In particular, by combining data obtained from the different early warning systems, it is possible to triangulate data sources to obtain a more accurate estimate of drought-related food insecurity. Based on pre-defined, objective triggers, the timeliness of drought response through Ethiopia’s PSNP could be enhanced, as detailed in section V below.

The accuracy of early warning information is dependent on the quantity and quality of input data, and the robustness of the tool’s methodology in predicting a disaster. The LEAP and LIAS tools are already based on a substantive amount of information<sup>52</sup> . However, the predictive accuracy of the tools will be enhanced further with time, as more and better data becomes available and the tools are being refined. With the development of the LEAP and LIAS tools, the GoE, WFP and wider donor community within Ethiopia have made significant advances in developing the necessary instruments to facilitate early action. The processes of further extending the LEAP tool and re-initiating the use of the HEA methodology as part of seasonal needs assessments would further enhance Ethiopia’s early warning framework, thereby facilitating early action to protect lives and livelihoods from the adverse impacts of droughts.

> 52 For instance, the LEAP tool makes use of different pre-loaded rainfall datasets, including the Rain Fall Estimate (RFE) datasets RFE 1 and RFE 2 produced by the United States Climate Prediction Center, ARC2 and TAMSAT data, as well as data produced by the NMA (Ethiopia’s National Meteorological Agency).

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Eurostat Household Budget Survey Database available at <u>https://ec.europa.eu/eurostat/web/household-budget-surveys/database</u> (accessed on November 5, 2020). Eurostat provides survey means per adult equivalent in constant currency. We use available data about household structure from the corresponding surveys, as well as deflators and exchange rates to recover per capita means in current local currency units. With this data we can also recover estimates in 2011 PPPs, using the methods applied in PovcalNet.

> 8 <u>https://www.bls.gov/cex/, accessed on March 28, 2017.</u>

> 9 The data from PovcalNet used for this version of the paper was accessed March 28, 2021. The ancillary data on deflators and exchange dates is available at <u>http://iresearch.worldbank.org/PovcalNet/data.aspx.</u> PovcalNet has data for 168 economies, of which Argentina and Taiwan are not included in our compilation due to partial national geographic representativeness of the surveys.

7
/ capita<br>Annual PA rent, $ / capita<br><!-- End of picture text --> Source: Derived from data published in World Bank (2011)

> 10 In practice, World Bank (2011) uses the lower of cropland and pastureland average per-hectare values in each country. For simplicity we refer to ‘opportunity costs’ in lieu of ‘quasi-opportunity costs’ in what follows. 11 World Bank (2011) attempts to value the wealth available to the current generation, and therefore calculates land asset values as the present value of land rents over a 25 year horizon.

11
Note that when substituting ti, _i_ e _A,_ B in (20), we are implicitly assuming that, when lobbying the government, lobbies do not take into account the second-round effects of the adjustment in tariffs on their production levels. This assumption is not only probably closer to reality than the alternative which would take second-round effects into account, but also it is the only possible assumption in our empirical application on Mercosur.'<sup>5</sup> Most importantly, to be able to substitute the existing tariffs will avoid excluding from the empirical analysis some important determinants of the tariff levels which cannot be directly measured.

To recapitulate, the simple political-economy model developed here shows that, if there is a possibility for exporters to obtain a duty-drawback, they will lobby to obtain the full drawback (which is what is usually observed), and that duty-drawbacks tend to raise the equilibrium rate of protection on intermediate goods. More importantly for our purposes, the model shows that under plausible conditions, a cu will alter lobbying incentives, leading to pressures to eliminate duty-drawbacks and to reduce the external protection on intermediate goods.

# 4 Application to Mercosur

We use Mercosur data to check whether the model's predictions are consistent with observed outcomes. Beyond data issues and the reasons stated in the introduction, there are at least two practical reasons for our choice. First, steps towards putting in place a CET have gone much faster than in other recent RTAs; recall that the agreement was signed in 1991 and that the **CET** came into effect in 1995. Second, Mercosur is the only cu whose members previously had duty-drawbacks and which substantially eliminated barriers on internal trade. We discuss first data and econometric issues, then turn to results.

> 15 This is because to our knowledge there exists no disaggregated production data available for Mercosur members after 1994, and Mercosur's **CET** was implemented in 1995.

17