rafmacalaba/datause-encoder-data huggingface dataset:
<u>Question 5: How uniform is farmers’ self-reported shock identification of covariate shocks?</u> In order to confidently attribute losses to disasters based on self-reports, it is necessary to know how reliable farmers’ shock identification is. If the main shocks in the data, adverse rainfalls, are really covariate, plots in vicinity should face the same shocks and self-reports should thus be relatively homogenous within small geographic aggregates.

In the context of attributing losses to disasters, this papers’ primary interest is for farmers’ shock identification in the face of a shock, i.e. the consistency with which self-reports identify a shock if a shock actually occurred. In the absence of a clear, objective measure of shock exposure, the analysis cannot compare self-reports to a “true value” but will rather study their internal consistency in “grey zone” cases where we cannot confidently rule out the possibility that a certain shock occurred. The analysis therefore excludes cases where a specific shock, for example a drought, was not identified on a single plot within the respective geographic unit of analysis (for example the village-level). This allows to focus on cases where it is conceivable that a shock occurred.

We study the consistency of farmers’ shock reports at the farm- and enumeration area (EA-)level<sup>49</sup> based on several shock proxies. Specifically, four (three in Malawi) different questions in the LSMS-ISA data for self-reported shock identification, with some differences in shock definition and reporting periods, can be considered (Table 11).

_Table 11_ : Shock identification questions in LSMS-ISA data.

|**Question**|**Level of ident.**|**Reporting period**|**Answer options**|
|---|---|---|---|
|**Q1:**Why was the area<br>harvested less than the area<br>planted?|Plot-crop level|Current agr. season|Up to two reasons from a<br>list including drought,<br>irregular rains, insects,<br>animals,floods(Malawi only)|
|**Q2:**What was the main cause of<br>the damage on [CROP]?|Plot-crop level|Current agr. season|Main reason from a list<br>including too little and too<br>much rain|
|**Q3:**During the last 12 months,<br>was your household affected by<br>[SHOCK]?|Household level|Last 12 months|Yes/No response to a<br>number of shocks including<br>drought|
|**Q4:**Please describe important<br>events that have taken place in<br>this community since two years<br>ago including any events that<br>have occurred this year.|Community level<br>(~ EA level)|This year and past<br>two years|Up to four adverse events (if<br>the same shock occurred<br>multiple times, each time<br>counts separately)|

Q 1 and Q2 are asked for each crop on each plot that recorded a planted area loss or plant productivity loss, respectively. They are thus the most suitable questions for this paper as they are

> 49 For sampling purposes, 10-12 agricultural households (rural areas in Ethiopia) or 16 households (baseline sample in Malawi) form an enumeration area. In both countries, the typical EA is roughly equivalent to the village or community.

34
- Mbelle, Amon V.Y. 2008. _The impact of reforms on the quality of primary education in Tanzania_ . Research Report 08.1. Dar es Salaam: Research on Poverty Alleviation (REPOA).

- McCrary, Justin. 2008. “Manipulation of the running variable in the regression discontinuity design: A density test.” _Journal of Econometrics_ 142(2): 698-714.

- Moshoeshoe, Ramaele, Cally Ardington, and Patrizio Piraino. 2019. “The effect of the free primary education policy on school enrolment and relative grade attainment in Lesotho.” _Journal of African Economie_ . 28(5): 511–32.

- Moussa, Wael, and Carina Omoeva. 2020. “The long-term effects of universal primary education: Evidence from Ethiopia, Malawi, and Uganda.” _Comparative Education Review_ 64(2): 179-206.

- Munene, Ishmael I., ed. 2016. _Achieving education for all: Dilemmas in system-wide reforms and learning outcomes in Africa_ . Lanham, Md.: Lexington Books.

- Ngugi, Margaret Catherine Mumiukha, Flora Fedha, and Beatrice Ndiga. 2015. “Universal primary education in Kenya: Advancement and challenges.” _Journal of Education and Practice_ 6(14): 87-95.

- Nishimura, Mikiko, Takashi Yamano, and Yuichi Sasaoka. 2008. “Impacts of the universal primary education policy on educational attainment and private costs in rural Uganda.” _International Journal of Educational Development_ 28(2): 161-175.

- Nudzor, Hope Pius. 2013. “Exploring the policy implementation paradox: using the free compulsory universal basic education (fCUBE) policy in Ghana as an exemplar.” _International Journal of Qualitative Studies in Education_ 26(8): 933-952.

- Oketch, Moses O., and Caine M. Rolleston. 2007. “Policies on free primary and secondary education in East Africa: A review of the literature.” _Review of Research on Education_ 31:131–158

- Okonkwo Osili, Una, and Bridget Terry Long. 2008. “Does female schooling reduce fertility? Evidence from Nigeria.” _Journal of Development Economics_ 87(1): 57-75.

- Republic of Madagascar, 2012. Plan intérimaire pour l’éducation [Interim Education Sector <u>Plan] 2013–2015.</u>

- Sifuna, Daniel N. 2016. “Universal primary education and the challenge of quality in SubSaharan Africa” in Munene, Ishmael I., ed. _Achieving education for all: Dilemmas in system-wide reforms and learning outcomes in Africa_ . Lanham, Md.: Lexington Books.

- Sifun <mark>a, Daniel N. 2007. “The challenge of increasing access and improving quality: An analysis of universal primary education interventions in Kenya and Tanzania since the 1970s.”</mark> _<mark>International Review of Education</mark>_ <mark>53(5-6): 687-699.</mark>

- Somerset, Anthony. 2009. “Universalising primary education in Kenya: the elusive goal.” _Comparative Education_ 45(2): 233-250.

20
# **2 Empirical Strategy and Data**

## **2.1 Data**

Our main data sources include multiple rounds of the Economic Censuses in India, namely the 3rd (1990), 4th (1998), 5th (2005) and 6th (2013) rounds. The census covers all economic enterprises in the country, except those engaged in crop production and plantations, and provides information on the number of workers hired by each enterprise, number of enterprises, as well as ownership (male/female) of enterprises. We aggregate this information at the district-level. However, after 1990 several new districts were created in India. As a result, the administrative boundaries of many districts changed between the various Economic Census rounds. Therefore, we reclassify the newly formed districts to their original district administrative boundaries in 1990. In total therefore, we have 433 districts in our data.

We also use National Sample Survey Consumer Expenditure (NSS CES) rounds 55 (199900), 61 (2004-05) and 68 (2011-12), for household expenditure data. To explore linkages between the tradable and non-traded services sectors, we use the Indian Input Output Transactions (IOT) Table from 2006-2007. Lastly, the trade data for world import demand for the services sector comes from the WIOD database, as described in Timmer et al. (2015).

## **2.2 Classifying Tradability**

Our discussion of tradable and non-tradable services first requires a classification. To classify sectors into tradable and non-tradable, a popular approach is to analyze the geographic dispersion of industries, following Jensen and Kletzer (2006). However, Gervais and Jensen (2019) have recently improved upon this approach by constructing a classification based on implied trade costs.

Due to data limitations, we cannot estimate trade costs in the same way as Gervais and Jensen (2019). Instead, we follow the approach proposed by Head and Ries (2001) and then adapted by Chen and Novy (2011) using data from the World Input-Output Database (WIOD).

In this sense, implied bilateral trade costs can be expressed as a ratio of intra-national to international trade flows: 6
taxes collected at the border between 5‐10%) with little variability; very few and limited special import regimes; moderate use of anti‐dumping regulations (between 2 and 5 new measures in the year); few restrictions on service trade (e.g., Services Trade Restrictiveness Index between 10 and 25); trade data beyond regular statistics is made available for policy analysis.

“b. Clear and consistent trade facilitation strategies at national and agency levels. Formal processes for consultation and cooperation with stakeholders. Performance standards established and monitored and data on clearance times publicly available. Information on trade‐ clearing procedures and formalities is published and accessible via the Internet. The hierarchy of regulatory framework and the links are clear. Appeal mechanisms are established and accessible. Few instances of corruption in border management agencies. Risk management used extensively (e.g., < 20 % Customs physical inspection). Speedy and predictable processing of goods (e.g., some facilitated procedures for traders with good compliance records). IT system used by Customs and other key border management agencies, with some interconnectivity. Largely open access to foreign transport service providers (e.g., transit transport services are fully liberalized within an economic grouping and transit routes are not specified on the permit).” The 1999 and 2015 criteria differ in that (a) the 2015 criteria are more detailed and longer, (b) the 2015 criteria have been modernized by, for instance, frequent references to the internet which was not a factor in 1999, and (c) the 2015 criteria refer to indexes which were unavailable in 1999, such as the Services Trade Restrictiveness Index.

On the whole, however, it appears that the overall direction of the criteria was rather similar in 2015 to what it was in 1999. For instance, in 1999 to get a ‘2’, the customs authorities “make discriminatory or <u>ad hoc</u> exemptions and valuations”, while in 2015 to get a ‘2’, the customs authorities run a “very discretionary and discriminatory trade regime with widespread and discretionary use of highly trade‐restrictive NTBs ... with discretionary and nontransparent exemptions [and] haphazard application of special import regimes”.

34
standards and regulatory frameworks, as well as differences<sup>in the developmentof</sup> financial and capital markets and macroeconomic conditions.

The purpose of this paper is to explore the relative importance<sup>ofcountry</sup> characteristics and the effect of different types of creditor rights<sup>that can helpexplain the</sup> relative use of bankruptcy. For this, we collect from various<sup>governmentandprivate</sup> sources a unique dataset of the number of commercial bankruptcy<sup>filings in35countries.</sup> As shown in Appendix 2, almost all countries in our sample have laws protecting<sup>secured</sup> creditor rights and have bankruptcy laws permitting both liquidation<sup>and restructuringof</sup> distressed firms. There is considerable variation, however, in<sup>howfrequentlytheselaws</sup> are resorted to through formal bankruptcy filings. The data on<sup>actual bankruptciesallows</sup> us to investigate which legal design features and macro, financial,<sup>and othercountry</sup> characteristics affect the likelihood that creditors use formal<sup>bankruptcy proceduresasa</sup> means of resolving corporate financial distress. To our knowledge,<sup>this paperis the first</sup> attempt to identify empirically reasons for the use of bankruptcy<sup>across countries.</sup> We find, correcting for overall financial development and<sup>macroeconomic</sup> shocks, that bankruptcies are higher in common-law countries<sup>andinmarket-oriented</sup> financial systems. We also find that greater judicial efficiency<sup>isassociatedwithmore</sup> use of bankruptcy, but that the combination of stronger creditor<sup>rights-both aggregated</sup> and evaluated separately by specific features - and greater judicial<sup>efficiency leads to less</sup> use of bankruptcy. Interestingly, we find that the presence of<sup>a "stay on assets"leadsto</sup> fewer bankruptcies independently of the efficiency of the judicial system.<sup>These findings</sup> suggest that there are important ex-ante incentive effects of<sup>insolvencysystems,</sup> including encouraging less risky behavior and more out-of-court<sup>settlements.Butour</sup> _5_
Table 3 shows coefficient estimates for the emigration variable and its interaction with economic sector fixed effects, using various measures of emigration and different specifications. We distinguish the construction sector from the rest of industry, given its importance in the Moldovan economy. The results indicate that the impact of emigration is, in general, significantly higher in the construction and service sectors. In these sectors, the implied wage elasticity is at least double those of agriculture and industry.

These results may be explained by recent changes in labor demand in each sector, as previously suggested in Section II. Although real GDP grew at an average of around 7 percent per annum between 2003 and 2006, growth in labor demand was much more muted. As with many other transition economies, increases in labor productivity resulted from shedding excess labor. Indeed, over this period, employment generation in Moldova was stagnant with practically no net job creation. Looking across various sectors, however, the lack of job creation was largely the result of significant job loss in the agricultural sector, stagnant job creation in industry (excluding construction), rapid job growth in construction, and modest job growth in the service sectors, led by the wholesale and retail trade and finance sectors. More generally, as experienced in other transition economies, Moldova’s enterprises increasingly face market pressures, leading them to shed the excess labor inherited from central planning; in the agriculture sector, labor shedding has been taking place since the end of the 1990s.<sup>18</sup> The lack of job creation in the agricultural sector and the expansion of the construction industry and service sectors are thus possible explanations for the differences in the impact of the emigration variable.

# **V. Conclusion**

This study is the first to look at the impact of emigration on wages in a country outside North America. Moldova offers an ideal case to examine the impact of emigrationinduced labor supply shock on wages as emigrants represent close a third of the total labor force. Consistent with the literature, we find a positive impact of emigration labor

> 18 World Bank (2005).

15
Table 35

|Dependent variable: Nee|d of fiscal|adjustment|based on d|ebt/revenu|e||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|0.051***|0.037**|0.058***|0.042**|0.173***|0.132*|-0.121**|0.070|
|structural as % GDP|(0.014)|(0.015)|(0.018)|(0.019)|(0.034)|(0.069)|(0.054)|(0.050)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-7.299***||-9.008***|-8.035***|-8.914***|-8.732***|-8.978***|
|||(1.166)||(1.526)|(1.523)|(1.529)|(1.527)|(1.526)|
|Inflation (t-1)||0.044||1.358**|1.234*|1.289**|1.191*|1.370**|
|||(0.400)||(0.644)|(0.631)|(0.644)|(0.633)|(0.644)|
|U.S. Interest rate||-0.032||-0.067*|-0.088**|-0.073*|-0.086**|-0.067*|
|||(0.030)||(0.039)|(0.039)|(0.039)|(0.039)|(0.039)|
|**Political factors**|||||||||
|Gini|||0.029**|0.029**|0.027**|0.042***|0.030**|0.029**|
||||(0.012)|(0.013)|(0.013)|(0.016)|(0.013)|(0.013)|
|Rule of law|||0.062|0.178|0.153|0.163|-0.161|0.180|
||||(0.108)|(0.121)|(0.122)|(0.122)|(0.160)|(0.121)|
|Margin of Majority|||-0.714*|-0.633|-0.572|-0.660|-0.597|-0.337|
||||(0.406)|(0.419)|(0.418)|(0.419)|(0.419)|(0.639)|
|Election's year|||-0.043|-0.036|-0.026|-0.032|-0.029|-0.038|
||||(0.105)|(0.107)|(0.108)|(0.107)|(0.107)|(0.107)|
|**Interactions**|||||||||
|EMEs * Deviation|||||-0.210***<br>(0.043)||||
|Gini * Deviation||||||-0.002<br>(0.002)|||
|Rule of law * Deviation|||||||0.040***<br>(0.012)||
|Majority * Deviation||||||||-0.048<br>(0.078)|
|Observations|2,798|2,738|1,949|1,918|1,918|1,918|1,918|1,918|
|Number of ifscode|137|137|98|98|98|98|98|98|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|r2_p<br>Standard errors in parent<br>*** p<0.01, ** p<0.05, *|0.00444<br>heses<br>p<0.1|0.0209|0.00939|0.0338|0.0460|0.0347|0.0389|0.0340|

- 64 -
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- <u>Panel C: Regulatory and supervisory capacity for microfinance (0 4)</u>

|||<br>D<br>Version 1(Ful samp|<br>epvar: Average loan si<br>les)|ze /per capita G<br>Version 2|<br>NI<br>(DroppingRural b|<br>ank samples)|
|---|---|---|---|---|---|---|
||OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Regulatory and supervisory capacity for microfinance (0-4)|0.0206|-0.0089|0.01|0.0212|-0.0092|0.014|
||[0.611]|[0.297]|[0.604]|[0.615]|[0.245]|[0.500]|
|NBFI dummy * Regulatory and supervisory capacity for microfinance (0-4)|0.0341|0.0345***|-0.0004|0.0472|0.0321***|0.0021|
||[0.584]|[0.006]|[0.990]|[0.468]|[0.005]|[0.945]|
|Combined group dummy * Regulatory and supervisory capacity for microfinance (0-4)|0.06|0.0092|-0.003|0.1166|0.021|0.0092|
||[0.524]|[0.497]|[0.917]|[0.437]|[0.162]|[0.806]|
|NBFI dummy|-0.0502|-0.015|0.115|-0.0604|-0.0152|0.1065|
||[0.771]|[0.614]|[0.244]|[0.725]|[0.580]|[0.274]|
|Combined group dummy|0.1439|0.1162***|0.1747**|0.1098|0.0485|0.1596|
||[0.489]|[0.000]|[0.041]|[0.729]|[0.164]|[0.122]|
|Observations|641|641|616|583|583|560|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.266|0.565|0.406|0.275|0.605|0.429|
|Adjusted R-squared|0.244|0.552|0.387|0.25|0.591|0.408|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|0.0547|0.0255**|0.00964|0.0685|0.0229**|0.0161|
||[0.307]|[0.016]|[0.761]|[0.222]|[0.019]|[0.609]|
|Combined group dummy|0.0806|0.000227|0.00699|0.138|0.0118|0.0232|
||[0.337]|[0.985]|[0.829]|[0.309]|[0.395]|[0.593]|

27
access to the courts is probably too low and courts too slow, biased, or unpredictable.<sup>17</sup> Access may be hampered by financial reasons or because there may be little understanding of the formal system. Courts may be “too slow” because of the incentives provided to various participants or lack of resources/capacity. Governments, and citizens broadly agree that in these countries, constituting much of the developing world, reforms which increase efficiency in terms of speed, make courts fairer and more accessible, are necessary to make the formal legal and judicial system relevant for most of the people, and are therefore highly desirable. Such reforms can change what is often called the “culture” of the business community. Businesses respond to incentives: clear definition and enforcement of property rights provides incentives to act that are different from a situation in which these two factors do not exist.

The need for reform of judicial systems becomes even clearer when we look at what citizens think of their systems. While it is a difficult matter to evaluate how efficient<sup>18</sup> judicial systems really are, indices have been constructed which reflect people’s perceptions of their judiciary’s performance. As the charts below show for a sample of countries, high income countries have judicial systems that are perceived to be much more efficient, to enforce contracts better, to give more access to people, and to be less corrupt than those of the lower and middle income countries.<sup>19</sup> The differences between the latter two groups are small except for access to justice.<sup>2021</sup>

> 17 By unpredictable I mean that there is little faith in the ability of the justice system to render consistent solutions to disputes. An unbiased or fair system is meant to be one where the law is applied as written and people perceive the adjudication processes to be generally fair.

> 18 I am using the term “efficient” in a broad sense to cover the concepts of cost, speed and fairness.

> 19 Note that the corruption indices refer to overall perceptions of corruption in the country and not just in the judiciary.

> 20 Sometimes perceptions of overall corruption may be different from perceptions of how corrupt the judiciary is.

12
-9-

the investment guarantees provided by the shareholders of the insurance companies is a proportion of the premium; and (b) brokerage fees tend to be a proportion of the value of the asset that changes hand - in this case one - insurance premium because the benefits obtained from the broker's effort are proportional to the value of the asset.

-In conclusion, the evidence suggests that administrative charges are closer. to being, a fixed cost per affiliate than being a multiple of local wages levels. This does not deny that significant components of total costs are a function of other variables such as pension assets.

# 3. Charges and services in Chile

This section discusses the level of charges and the types of services obtained by Chilean contributors in exchange in the new pension system that started in 1981. We present first the charges during active life and then move on to present c;xarges during retirement. In this discussion it should be noted that there are two major classes of providers of pension services in Chile: Pension Fund Managers (in Spanish, Administradoras de Fondos de Pensiones, AFPs) and Life Insurance Companies. AFPs provide services to all active workers and to a part of retirees. Life insurance companies provide annuities to part of the retirees.

Payments by different types of active contributors- :o the pension system, including commissions, are the following: Table **1** _Payments to the Chilean Pension System while Active_ Type of Registered Person (% of covered wage) Payments to Pension System Active Dependent Worker 12.94% Active Independent Worker: a) Contributor 12.94% of declared income b) Non contributor zero Out of the formal labor force, but not retired: zero Source: SAFP Bulletin. Data for September 1992. Total payments is the sum of 10% explicit contribution plus the average of the so called "additional" fee. which provides insurance for invalidity and survivorship and includes most of AFP's commission income. The other fees are not deducted from salary.