rafmacalaba/data-use-mentions-extended huggingface dataset:
consumption). This requires panel data. For this to work, interview months must also differ across waves for a sufficient number of households since otherwise the effect of the month of interview cannot be identified. While less prone to bias, fixed effects estimation comes at the expense of precision, making it a more stringent test of seasonality. The availability of three waves of household panel data in Tanzania, each conducted throughout the year and with many households interviewed in different months across waves, enables this study to follow the fixed‐effects estimation route.

Second, seasonal factors are estimated on a limited number of observations for each month (three in the empirical application of this paper). The Monte Carlo results presented above in connection with price seasonality indicate that seasonal gaps estimated in short time series may be biased upwards. It is reasonable to suppose that this result will extend to panels with short time dimension. The more parsimonious trigonometric specification is therefore also explored here: Fixed effects estimation of (9) is the preferred specification. For comparison, the estimated seasonal gap using the dummy variable approach (with and without fixed effects) will also be reported. To control for sources of consumption seasonality that are unrelated to the production cycle, such as the effect of specific demand shifters related to religious events and holidays (e.g. Ramadan and Christmas), a December dummy, to account for possible additional Christmas expenditure, and a Ramadan dummy are added to the vector of controls _xhw_ .

Finally, understanding the extent to which seasonality in food prices transmits to seasonality in food consumption is the third core question of interest. It is natural to suppose that seasonality in food consumption is a consequence of seasonality in the prices of subsistence foods – see Dercon and Krishnan (2000), Dostie, Haggblade, and Randriamamonjy (2002) and Ellis and Manda (2012). High food prices generate both negative income and substitution effects for net food purchasers. For net food sellers, greater price seasonality induces larger fluctuations in income.
where is firm growth from period t to t+1 and is a firm-fixed effect. Under the null hypothesis that the crisis did not improve the allocative efficiency of employment re-allocation amongst continuing firms , whereas ( 0) under the alternative hypothesis that the crisis enhanced (diminished) the importance of productivity as a determinant of firm growth.

Serial correlation in the error term, in conjunction with the presence of lagged size as an explanatory variable would render OLS estimates of the employment growth equation biased. To address this concern, we also use a Fixed Effects estimator. The fixed effects transformation is biased due to the correlation between the transformed error and the transformed explanatory variables (Nickell, 1981), but as the OLS and Fixed Effects estimators are biased in opposite directions, they provide a confidence interval within which the true parameters lie (Bond 2002).<sup>6</sup>

# **4. Data**

The Indonesian Manufacturing Census (1991-2001) collected by the Indonesian Statistical Agency, BPS (Badan Pusat Statistik), provides the empirical basis for our analysis. It contains information on all Indonesian manufacturing establishments with more than 20 employees and spans the pre- and post- crisis periods, as well as the crisis itself. It has very detailed information on employment, inputs and outputs, industrial classification, exporting, ownership, investment behavior, and the capital stock, which we measure as the replacement value of machinery and equipment at the end of the calendar year. Employment is measured as the average number of workers per day. We augmented the data with industry-level measures of

> 6 While the Difference and Systems GMM estimators developed by Arellano and Bond (1991) are in principle capable of yielding unbiased estimates, these estimators are not well suited for our data;  the difference GMM estimator is likely to result in poorly behaved estimates when variables are highly persistent as is the case with our data (for surviving firms the correlation between lnLt and lnLt-1 is 0.98 – both in crisis and non-crisis years
|team 3|0.063|0.016|3.88|0.000|0.031|0.094|
|---|---|---|---|---|---|---|
|team 4|0.012|0.010|1.17|0.243|-0.008|0.031|
|open to vote for UPC|||||||
|team 1|0.368|0.018|20.95|0.000|0.333|0.402|
|team 2|0.162|0.018|8.97|0.000|0.127|0.197|
|team 3|0.163|0.021|7.70|0.000|0.121|0.204|
|team 4|0.282|0.044|6.45|0.000|0.196|0.367|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: The table reports for each team (1,2,3, and 4) the R<sup>2</sup> of a regression of each respondent’s answer (column 1) on the full set of enumerator fixed effects and a constant. The political preference questions refer to: the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC).

35
initial marital status. Figure 2 clearly shows that, conditional upon staying in the U.S., the probability of a change in marital status is substantially higher for initially unmarried than for initially married migrants, with the same pattern applying to both men and women. In other words, getting married after migration for initially unmarried migrants is substantially more likely than getting divorced/separated from one spouse for initially married migrants. This (admittedly intuitive) stylized fact is obtained under the assumption that initially married individuals who are separated or divorced at the time of the survey dissolved their marital union after migration, thus magnifying the probability of a change in their marital status.<sup>17</sup> Figure A.1 in the Appendix also reveals that this stylized fact is independent of the assumption that we need to introduce on the (unobserved) relative timing of an earlier marriage with respect to migration for individuals married more than once (representing 13.0 percent of the sample, see Table 1).<sup>18</sup>

# **4.2 Transnational marriages are largely a temporary phenomenon**

Panel B of Table 1 reveals that married migrants from developing countries mostly co-reside with their spouses; this finding, which also applies to migrants from Mexico, differs from the evidence provided in McKenzie and Rapoport (2010), who draw on earlier rounds of the ACS to show that two out of three married Mexican male immigrants have their spouse absent, while the same does not apply to married Mexican female migrants, consistently with the predominant sequential nature of the migration of married Mexican couples (Cerrutti and Massey, 2001). Crucially, McKenzie and Rapoport (2010) focus only on recently arrived migrants (no more than two years before the survey). Figure 1 reconciles these different findings, as it reveals that, among migrants married before migration, the share who do not co-reside with their spouse sharply declines with the time since migration, in particular for men. The share who are
because shareholders of MFIs generally have difficulty in quickly responding to calls for additional capital infusion. For a number of reasons (see Christen and Rosenberg 2000), self-sufficient MFIs (other than rural banks) in the Microfinance Information Exchange database maintain a higher ratio of capital funds to total assets compared to commercial banks, ranging from 17 percent for rural banks, 24 percent for regular banks, 31 percent for CFIs, and 40 percent for non-bank MFIs.

# **_Asset Quality_**

61. In principle, recognizing and setting aside provisions for delinquent loans should be handled more aggressively for MFIs than for commercial banks, given the relatively short-term nature and unconventional collateral typical of microfinance portfolios. Benin, Ghana, and Tanzania vary significantly in how they classify loans as delinquent and in how they provision for possible losses. Only Benin requires provisioning at the time a loan is classified as delinquent, that is, when a payment is overdue by 90 days or more. A loan is delinquent when overdue by 31 days or more in Tanzania, but provisioning is required only when a loan is overdue by more than 180 days. Ghana is the most restrictive, classifying a microfinance loan as delinquent when a scheduled payment is 1 day overdue and requiring provisioning after 60 days. Provisioning at 40 percent is required when a loan is delinquent for a period between 61 and up to 90 days in Ghana and for 181 days or more in Tanzania. Provisioning at 100 percent is required when a loan is delinquent for 151 or more days in Ghana, and for 360 days or more in Benin and Tanzania (table 6).<sup>13</sup>

**Table 6.** Asset Quality Standards for Licensed MFIs, 2002

- 12 This capital adequacy ratio may be more appropriate for other categories of NBFIs that book considerably more assets than the typical S&L (or NGO). In general, Ghanaian rural banks and S&Ls have faced difficulties in raising additional funds from shareholders to meet increased paid-in capital requirements
|
|Observations|1314120|1313807|1313807|1314123|1313810|1313810|1314119|1313806|1313806|
||||||Urban|||||
|CatchmentArea X Post|0.0432|0.0369|0.0415|0.0367|0.0045|0.0085|0.0466|0.0545|0.0588|
||(0.0652)|(0.0599)|(0.0603)|(0.0626)|(0.0589)|(0.0594)|(0.0656)|(0.0667)|(0.0666)|
||[ 0.535 ]|[ 0.559 ]|[ 0.525 ]|[ 0.572 ]|[ 0.939 ]|[ 0.898 ]|[ 0.516 ]|[ 0.455 ]|[ 0.42 ]|
|R-squared|0.5513|0.5717|0.5718|0.5355|0.5554|0.5555|0.5253|0.5474|0.5475|
|Observations|172407|172371|172371|172407|172371|172371|172407|172371|172371|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Municipality X YOB FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
poorest households between urban and rural areas, several natural hazards threaten in higher proportion according to the respondents. For instance, hurricanes threaten 74 percent of rural and poor households, while only a 59 percent for urban and poor households, almost 15 percent higher. Likewise, vulnerability at the household level is also higher among households with a less educated head of household. Given the correlation between socio-economic status and education, the data by educational level of the household head was disaggregated to examine the vulnerability of households to natural hazards. Results show that certain natural hazards affect in higher proportion those with a lower level of education of the household's head. For instance, 61 percent of respondents that reported an educational level of the head of household equal to primary school or less reported living in a household exposed to cyclones, compared to 55 percent for those with heads of household with more than primary education (Table 6).

For earthquakes, data from the most recent earthquake on August 14, 2021, suggests that the educational level of the household head and the presence of children in the household correlate with the likelihood of being affected. In 2021, Haiti experienced an earthquake that resulted in 2,246 deaths, 12,673 injured and the destruction of infrastructure, with an estimated $1.6 billion in damage and losses.<sup>9</sup> We examine which socioeconomic factors are correlated with being affected by the earthquake in a multivariate regression framework. The results show that the households in the southern region are more likely to report being affected in all survey years, as expected, since the earthquake hit the southern peninsula of the country. The results in Figure 3a show that households where the household head has an educational level of secondary or tertiary or more decreases the chances by 9 and 10 percentage points of being affected by the earthquake, respectively; while living in rural area increases the likelihood of suffering from this hazard. The multivariate analysis for 2022 shows that households with 3 or more children have
which is around 11.4 percentage poinits. Table A5 indicates that there were 30.8 million poor people in February 19915. This means that there were additional 25 million people who fell to below poverty line during the period of February 1996 to February 1999.

The percentage point increase ifor rural areas, which is 13.6 percentage points, is higher than in urban areas, which experienced an increase of 9.1 percentage points. In relative terms, however, the increaLse in poverty incidence is much higher in urban areas than in rural areas. In urban areas, the poverty rate increased by 126 percent, while in rural areas it increased by 66 percent.

# **_1)_** _Poverty profile: Household characteristics_

In making use of the poverty line discussed above, we can also examine some of the characteristics of poor households. These characteristics will help in identifying the poor even though these characteristics are far too broad to be directly useful for targeting purposes.

_Poverty and sector of occupation._ Poverty profile across sectors is important to identify the poor. Table 5 shows the poverty incidence across sectors as well as the contribution of each sector to total poverty in both February 1996 and February 1999.5 These and hereafter are based on the regional poverty lines presented in tables A2 and A4.

19
goods.

Since many of the surveys are subject to confidentiality agreements, we aggregate the households and offer statistics for each percentile of the household per capita real income distribution. The database thus has 5,400 observations (54 countries and 100 observations per country), but is based on an underlying data set of 521,639 households which are, in turn, representative of approximately 1.8 billion people. The HIT database is best suited for country-specific analysis. In order to facilitate cross-country comparisons, we converted incomes to their constant 2010 USD equivalent by setting the survey mean of real expenditure 3
sporting goods|294.6|451.8|157.1|53.3|
|Garments,other fiberproducts,footwear|1336.2|2064.9|728.7|54.5|
|Leather,Fur,Feather & relatedproducts|578.6|906.5|327.9|56.7|
|Electrics and Telecommunications|1623.8|3250.8|1627.0|100.2|
|Furniture Manufacturing|180.5|364.4|183.9|101.9|

Source: Banister and Cook, Monthly Labor Review March 2011, and NBS, China Labor Statistical Yearbook 2010 (for 2009 data). Sectors marked in yellow are sunset sectors. The NBS stopped publishing statistics on rural enterprises in 2002, thus decomposition of employment by subsector for the entire economy is not available.

54
|1.14|1%|

Source: Computations based on UN COMTRADE Statistics.

As for footwear and furniture, the ratio of imported parts to total exports of footwear chain products fell from 38 percent in 2000-01 to 34 percent in 2003 and in exports of furniture from 23 percent in 1998 to 12 percent in 2003. Since in both networks this was accompanied by a quicker growth in exports of parts than of final products, this suggests that Bulgarian producers of are firmly embedded in EU-driven footwear and furniture chains. This also may suggest the
>**

<!-- Start of picture text --> Fabricated Metal Products Manufacturing<br>Chemical Material Manufacturing<br>Computers, Electronic and Optical Products ...<br>Electrical Equipment Manufacturing<br>Basic Metal Manufacturing<br>Non-metallic Mineral Products Manufacturing<br>Machinery and Equipment Manufacturing<br>Plastic Products Manufacturing<br>Electronic Parts and Components Manufacturing High Value Added<br>Wholesale and Retail Trade Service sector<br>-20 -15 -10 -5 0 5 10 15 20 25<br>Source: Rosen and Zhi Wang, 2011. Page 35 based on data from the MOEA, Taiwan (China), approved direct<br>investment to mainland China- rapid growth of service sectors.<br><!-- End of picture text --> In sum, in this section we have argued that industrialization failed in many parts of the world for various reasons. Import substitution and protectionism in the 1970s and 1980s played an 41
impact of the brain drain, as suggested by Commander, Kangasniemi and Winters (2004); and extension of the cross-country comparisons. In particular, due to data limitations, existing empirical studies (Beine et al., 2001 and 2003) are based on cross-section regressions, meaning that they neglect the dynamics of migration rates as well as the dynamics of education levels; in addition, in the absence of a time series dimension, it is impossible to control for individual-country e¤ects in the regression estimates. Given the strong heterogeneity of developing countries in terms of sizes, levels of development, etc., such country-...xed e¤ects are likely to play some role in the value of the estimates. However, the new estimates computed for 1990 and 2000 by Docquier and Marfouk (2004) should make it possible for further empirical research to go part of the way toward addressing these concerns satisfactorily.

# 6 References

Adams, R. (2003): International migration, remittances and the brain drain: a study of 24 labor-exporting countries, World Bank Policy Research Working Paper, n. 2972 Arora, A. and A. Gambardella (2004): The Globalization of the Software Industry: Perspectives and Opportunities for Developed and Developing Countries; in A.B. Ja¤e, J. Lerner and S. Stern, eds.: Innovation Policy and the Economy, Vol. 5, Cambridge, Ma.: The MIT Press, forthcoming.

Azariadis, C. and A. Drazen (1990): Threshold externalities in economic development, Quarterly Journal of Economics, 105, 2: 501-26.

Barro, R.J. and J.W. Lee (1993): International Data on Educational Attainment, Journal of Monetary Economics, 32, 363-394.

Bauer, T., Epstein, G.S., Gang, I.N. (2002): Herd e¤ects or migration networks? The location choice of Mexican immigrants in the US, IZA Discussion
# Results

## **Crop losses are widespread and significant**

The data used in this analysis is from the Living Standards Measurement Study-Integrated Survey on Agriculture (LSMS-ISA) in Ethiopia, Malawi, Mali, Niger, Nigeria, and Tanzania. These data were harmonized across countries and cover close to 120,000 fields on around 30,000 farms. The data show that crop losses due to disasters and adverse climatic events are widespread and significant in African smallholder agriculture. Farmers report crop losses on between 11% (Nigeria 2018/19) and 90% of plots (Niger 2011), depending on country and year (Figure 1, Panel A and Table 1). Overall, 36% of plots in our sample report a crop loss. Farmers reported losing, on average, 53% of their harvest on plots affected by crop shocks (Panel B and Table 1). Losses vary across countries and years, ranging from 48% of harvest (Ethiopia 2018/19) to 71% of harvest (Niger 2011). Disaster losses have also become more common over time (Table 3). In the 11 years from 2008 to 2019 that our dataset spans, the estimated likelihood of a plot incurring a disaster loss increased by close to 10 percentage points. This is seemingly driven by a higher prevalence of small shocks as the estimated share of harvest lost on plots with any loss decreased by on average 1.1 percentage points with every year studied.

In aggregate, crop losses due to adverse climatic events reduce the total national crop production by between 3% in Nigeria 2018-19 and 81% in Niger in 2011. A total of 29% of potential harvest value is lost across the countries and agricultural seasons observed in our dataset (Figure 1, Panel C and Table 4]).

3
_Table 15: Total estimated value of crop production lost (LSMS-ISA) and total aggregate economic losses (EM-DAT) due to droughts_

|Country|Year|Total aggregate losses<br>due to drought (in 2022<br>dollars) in LSMS-ISA|Total aggregate losses<br>due to drought (in 2022<br>dollars) in EM-DAT|
|---|---|---|---|
||2013 - 2014|589 million<br>[ 281 ; 896 ]|_missing_|
|Ethiopia|2015 - 2016|2,980 million<br>[ 2000 ; 3960 ]|2,134 million|
||2018 - 2019|1,354 million<br>[ 229 ; 2479 ]|_missing_|
|Malawi|2018 - 2019|41 million<br>[ 23 ; 60 ]|_missing_|
|Mali|2014|70 million<br>[ 55 ; 85 ]|_missing_|
||2017|165 million<br>[ 136 ; 194 ]|_missing_|
|Nir|2011|1.583 million<br>[ 1294 ; 1871 ]|_missing_|
|ge|2014|185 million<br>[ 129 ; 241 ]|_missing_|
|Nigeria|2019|80 million<br>[ 29 ; 131 ]|_missing_|

_Note: missing entries correspond to cases where either no event was reported or information on the population affected was missing in the EM-DAT. 95% confidence intervals for estimated totals are calculated with LSMS-ISA microdata_ 36
_Note_ : Tourism-reliant countries are those with inbound tourism expenditure as a share of GDP during 2015-19 above the 3rd percentile of the share in all EMDEs, based on UN World Tourism Organization data. Commodity exporter economies are those when, on average in 2017-19, either (1) total commodities exports accounted for 30 percent or more of total exports or (2) exports of any single commodity accounted for 20 percent or more of total exports. Economies for which these thresholds were met as a result of re-exports were excluded. Micro states are those with a population of less than 200,000. Risk of overall debt distress ratings reflect the latest published ratings based on the IMF-World Bank low-income debt sustainability analysis framework. Dates of latest published results vary across countries. Grant-reliant countries are those with grants as a share of GDP during 2015-2021 above the 90th percentile of all EMDEs. FCS = fragile and conflict-affected situations; IBRD = International Bank for Reconstruction and Development; IDA = International Development Association.

24
in some cases female migrants do not converge completely to the employment outcomes of similar natives even after a decade. On the other hand, gender differences for skilled individuals have consistently been found to be smaller (Lee et al. 2020; Bratsberg et al. 2014). It is an empirical question whether our analysis will predict faster integration for refugee women than for men—given that women traditionally have larger employment gaps at arrival and several factors may affect their subsequent participation in the labor market—, but we expect to observe similar convergence speed for skilled refugees irrespective of 17. The Global Attitudes Survey was carried out by the Pew Research Center in Spring 2018. https://www.pewresearch. org/fact-tank/2019/08/09/people-around-the-world-express-more-support-for-taking-in-refugees-than-immigrants/ 9