rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Diagnostic tests for parsimonious inflation models, 1999:10-2008:11

||AR 1-7|ARCH 1-7|Heterosced.|Normality|RESET|DW|R<sup>~~2~~</sup>|
|---|---|---|---|---|---|---|---|
|Model 1:|F(7,90) =|F(7,83) = 1.70|F(17,79) = 1.31|χ2 (2) = 4.76|F(1,100) =|2.24|0.85|
|Cereals|1.80 [0.10]|[0.12]|[0.21]|<br>[0.09]|1.81 [0.18]|2.04|0.85|
|Model 2:|F(7,91) =|F(7,84) =|F(15,82) =|χ2 (2) =|F(1,97) =|||
|Food|<br>0.45 [0.87]|<br>0.92 [0.49]|<br>0.83 [0.64]|<br>2.82 [0.24]|<br>0.04 [0.84]|||
|Model 3:|F(7,95) =|F(7,88) =|F(12,89) =|χ2 (2) =|F(1,101) =|1.96|0.33|
|Non-food|<br>1.11 [0.36]|<br>0.57 [0.77]|<br>1.41 [0.17]|<br>1.42 [0.49]|<br>1.27 [0.26]|||
|Model 4:|F(7,90) =|F(7,83) =|F(16,80) =|χ2 (2)=|F(1,96) =|1.76|0.81|
|CPI|<br>1.01 [0.42]|<br>0.48 [0.84]|<br>0.50 [0.93]|<br>7.59 [0.02]*|<br> <br>3.90 [0.05]|||

_Note:_ * indicates significance at 95% level. AR is a test for serial autocorrelation for up to seven lags, ARCH is a test autoregressive heteroscedasticity, RESET is a test for non-linearity and DW is the Durbin-Watson test. Probability values in brackets. See Doornik and Hendry (2007) for details.

increase in world prices raises the domestic price level by 1 percent in the long run, given the exchange rate. When there is disequilibrium in the external sector for food, about 9 percent of the disequilibrium is removed every month by changes domestic prices, again assuming the exchange rate is constant.

The agricultural output gap is also important. Its coefficient is -0.18 (t-value = -6.13). It explains most of the swings in cereal prices away from long-run equilibrium. The impact is quite large. A hypothetical shift from no output gap to a serious drought, such as in 2003, would raise cereal price inflation by up to 3.5 percentage points per month, calculated as the coefficient on _ag_ , -0.18, times the minimum value of _ag_ during the drought, -0.20. This implies that such a drought would increase annual inflation from zero to up to 40 percent within a year if all the other explanatory variables have zero impact. Since the impact of the drought is temporary, inflation would then decline.

For illustrative purposes, Model 1 is reported with the error correction term for the monetary sector, which was removed by Autometrics. It is clearly insignificant (tvalue 1.14). This strengthens the evidence in favor the external sector as the main determinant of cereal prices in the long run. However, money seems to matter in the short run: money growth lagged two months enters significantly with a coefficient of 0.64. As mentioned earlier, conditioning inflation on contemporaneous growth rates can affect the results. In this case, money growth at time _t_ would replace lagged money growth. Yet, it seems unlikely that changes in money supply growth would have an almost instantaneous effect on cereal price inflation. On the other hand, an increase in prices might increase demand for nominal money quickly, making it reasonable to 28
# **3. Data**

The Ministry of Education (MOE) in Guatemala has been constructing a comprehensive database of its students since 2009 (Adelman _et al_ ., 2018). Currently, all schools are required to provide complete lists of all students who are enrolled in each grade and the MOE assigns each an individual identifier to track them over time. Thus, these records are a longitudinal census of all students in the country for as long as they remain in the education system.

For the purposes of this study, these records enable the identification of students who drop out and the year in which they leave school. The data also include student-level demographics (gender, age, location); academic information (level, grade, final results of their academic year: promoted, not promoted, or withdrew); as well as school attributes, such as sector (public; private; cooperative; municipal), teaching modality (bilingual; monolingual), and school identifiers. We also have information on the municipality and department where the student attends school. There are available data for two pre-COVID-19 years (2018 and 2019) and two post-pandemic years (2020 and 2021), which corresponds to the most recent and complete school year data available from the Guatemalan MOE. We believe these records are well-suited to estimate the effects of differential municipal exposure to COVID-19 infections on educational outcomes for all students.

Table 2 shows descriptive statistics before and after the pandemic began. We have over 16 million observations across these four years. The table confirms the outcome trends shown in Figure 1, with higher dropout, promotion, and switching from private to public schools after the pandemic. Dropout is defined as between-year dropout: a student is observed in one school year but does not appear in the records the following academic year. Effective promotion is a binary indicator that is equal to one if a student is observed today in a higher grade than they were in the previous year and zero otherwise. School switching is a binary indicator equal to one if a student 10
4 provides descriptive statistics on marital status and marriage trajectories. We examine selection issues in Section 5 and associations between marital status and women’s welfare in Section 6. A final section concludes.

# **2. Marriage dissolution in Senegal**

Colonial legislators attempted to minimize social tensions by establishing a variety of coexisting legal statuses with respect to family law, and more generally the civil code (decree of November 10, 1903). The Senegalese people could either comply with the rules of a general statute (similar to French law), or elect to fall under the prescriptions of Islamic, Animist or Christian statutes according to their beliefs (Brossier 2004; Yade 2007).<sup>8</sup> Nonetheless, a modern-day divorce law that gave women the right to secure a divorce was also introduced at the time. This was a major step forward for women who, until then, depended on their husband’s agreement to be freed from matrimonial ties (Yade 2007). However, a wife seeking a divorce could be asked by the judge to reimburse what she and her family had received from the husband at marriage.

In 1972, more than 10 years after independence, a new set of family laws was approved. It aimed to unify the various statutes present under colonial rule. Two sources of differentiation remain that are of primary interest for our purposes as they have consequences for divorcees and widows. In addition to providing dispositions for civil marriages, the law allows for Islamic and customary marriages performed in front of witnesses to be registered ex-post in the civil register. Furthermore, the Family Code contains two chapters dedicated to inheritance rights, one for the general case and one specific to the Muslim population, which constitutes 95% of Senegal’s population.

Marriages recorded in the civil register can be ended by divorce. Divorce is a mandatory step for women who wish to remarry. The judge decides on the custody of children according to their best interest. In principle, the husband can be required to provide for the subsistence of his ex-wife.

Nevertheless, a majority of religious or customary marriages are never officially registered, and in such cases, customary practices apply in the case of repudiation or divorce,

> 8 Note that Islam was already the religion of the majority of the population at the end of the 19th century and that animism is perfectly compatible with both monotheist religions present in the country. The type of Islam practiced in Senegal is in fact tinted by animist traditions.

5
The 1985-1994 Global Real Estate Cycle. Page 29 savings rate higher in the financial systems of Continental Europe and Japan (CEJ countries) than in those of Anglo-Saxon countries (AS countries). Figure 11 provides a partial picture of the debt-carrying capacity within the household sector. It shows that there was during the 1980s a general trend toward rising levels of aggregate personal debt compared to income. The competitive forces of financial liberalization and deregulation have led to the rapid development of retail banking and an apparent decline in credit rationing. There were rising levels of debt to income ratios, and to a lesser extent debt to asset ratios. Between 1981 and 1990, personal indebtedness rose particularly sharply in the US, Britain and Japan. Within the sample, Italy is very much **the** odd man out with a very low level of initial indebtedness, and little change during the decade.

**Figure 11: Household Debt as Percent of Household Disposable Income in Selected Countries**

<!-- Start of picture text --> Houeuhold debt as % of household disposable income 110 and 19g1<br>120-<br>100- f f<br>go<br>40-<br>Japant SrttaSn Unftd ladsa France Weal Ilaly<br>State C1980 *1991 Gennany<br><!-- End of picture text --> Source: IMF; Salomon Brothers; OECD; BIS.

In addition to large country differences in overall indebtedness, there are major differences in debt, wealth and income across the population of the same country. Since 1980, there has been a rising levels of defaults in most countries, particularly during recessions. High rates of default have affected significant segments of the population in economies like the LTK, Australia, and The Nordic Countries where volatility has been extreme and variable rate loans used. Price deflation **and rising real interest** rates have accentuated debtor problems in most other countries. The ability to rebuilt household balancesheets rapidly has weakened as many OECD countries are experiencing a decline in their personal savings rate. This is especially the case in the US where the personal savings rate hit a historical low of 4.3% for the 12-month period ending in August 1994.

# **Debt deflation and Rising Negative Equity in Housing**

Tax policies have often induced households to assume excessive amounts of debt during **the** boom. In the Nordic countries in particular, high marginal income tax rates were coupled with very generous tax deduction making the real **cost of borrowing for housing very low and typically negative for**
government guarantee. The Romanian case is curious: borrowing from commercial banks in 1989 was US$248 million, of which US$247 million was short term. Merchandise imports in 1989, however, were US$3.41 billion. According to IIF statistics, reserves increased that year by overUS$1 billion. Obviously,Romaniadoesnotappearto have financed its imports through short term credits, although bankers always say that the only business they did (and still do) with Romania is short term and trade related. Poland imported a total of about US$7 billion of merchandise in 1989, and borrowed a little over US$1 billion in short term commercial loans that year.

By 1990 (Fig. 15) the share of short tenn commercial debtwassharplyreducedforHungaryandCSFR,whilefor Poland it remained roughly constant. (The figure for Bulgaria reflects the moratorium and three month rollovers, in particular arrears and penalty interest which is recorded as ST credits).

In 1991 the same figures for CSFR and Hungary fall even further, with the trend expected to continue in 1992 or at best remain unchanged.

# D. Maturity Profile of Total External Debt

Given tde discussion in the preceding sections of the relative importance of commercial debt, and the maturity profile of this debt, it may be useful to contemplate the _maturity profile of overall obligations to hard currency_ creditors in the region. Table 3 below shows relative shares of short term and long term total financial flows for different countries: Hungary and Poland have remarkably small portions of ST debt outstanding relative to their long term obligations. ForCSFR,medium andlong term debtisgrowing while short term debtis falling. ForRomaniaboth categories aregrowing dramatically. The figures for Bulgaria mainly reflect the effects of the moratorium.

Table 4 looks _atflows of short term debt from private sources in the period 1989-1991,_ with commercial banks' projections for 1992: As indicated earlier, private short term flows from nonbank sources, while modest, will be more important than from commercial banks.

Source: Staff estimates based on Institute of Intemational Finance Country Reports.

_Table 3:_ Breakdown of Total External Debt by Maturity (in US$ million)

||1|98|1|99|1|99|19|9(f)|
|---|---|---|---|---|---|---|---|---|
||MLT|SI||MLT<br>a||MLT<br>a|hMJ|a|
|Hungary|17,230|3,307|18,541|2,940|19,090|3,055|18,267|3,382|
|CSFR|4,596|3,587|5,180|2,905|6,808|2,443|7,444|2,383|
|Bulgaria|7,520|2,704|7,402|3,272|7,864|3,125|8,415|3,054|
|Poland|37,061|1,295|46,625|1,339|47,676|1,384|46,891|1,302|
|Romania|269|313|167|352|1,166|985|2,785|1,009|

_Table 4:_ Net Short-term Flows to the Region'

||1|289|1|2|1|291|12|
|---|---|---|---|---|---|---|---|
||Comnmercial|Other<br> <br>Private|Commercial|Other<br> <br>Privait|Commnercia|Other<br>l Private|Other<br>Conunercial Private|
|Hungary|-56|42|-1,200|555|0|110|180<br>200|
|CSFR|182|-71|-423|-70|-150|50|0<br>40|
|Bulgaria|388|93|374|-12|0|-8|0<br>3|
|Romania|149|-11|-5|-23|180|50|100<br>50|

(') This information was not available for Poland 8
cereal deficits are a result of negative climatic and environmental factors (such as crickets), they also occur because of weak agricultural performance. If we consider the 30 year period between 1981 and 2010, millet yields in Niger peaked at less than 0.5 ton per hectare, while yields varied between 0.5 and 0.8 ton per hectare in Mali and Burkina Faso, two neighboring countries with a similar agro-climatic situation.

Data from the _<mark>Enquête Nationale sur les Conditions de Vie des Ménages et l’Agriculture</mark>_ (ECVMA) in Niger are utilized for the analysis. The ECVMA is a national, LSMS-ISA type survey of household living conditions and agriculture. It was conducted in 2011 by the Niger _Institut National de la Statistique_ (INS), with technical and financial assistance from the World Bank. The survey covers a sample of 3,968 households with 1,538 in urban areas and 2,430 in rural areas. The sample was drawn using a stratified twostage sampling, and to cover urban areas (Niamey, Other urban) in two strata and all rural agro-ecological zones (Agricultural, Agro-pastoral, Pastoral) in three strata. In the first stage of the sampling, 270 enumeration areas (EA) were drawn among the nearly 10,000 EAs and at the second stage, 12 or 18 households were drawn from each EA respectively in urban and rural areas. Data collection was organized in two visits, a post-planting visit from mid-July to mid-September 2011 and a post-harvest visit in November and December 2011. Three questionnaires were designed to collect a range of information on households, their farms and the communities in which they live. For the household questionnaire, the data collected concerned the household roster, health, education, employment, non-farm enterprises, housing, non-labor income and food and non-food consumption. The community questionnaire is dedicated to information on access to services and market prices. As for the agriculture questionnaire, it is designed to collect data on access to land, inputs used (seeds, fertilizers, pesticides, etc.), labor (household and hired labor), equipment, production, marketing and farm income, and extensive data on livestock.

The initial sample consisted of 6,695 plots, however a number of them have been lost or removed from the analysis for various reasons. First for 136 plots, there was no activity during the season. For 783 plots, the entire harvest was lost due to drought<sup>1</sup> . We also have 628 plots eliminated due to the absence of information on one of the two visits and 134 other plots missing other important information (areas cultivated, identity of the manager, etc.). Finally during data processing, we noted that some plots were unreasonably small and others unreasonably large. We have attempted to attenuate any bias caused by the plot size outliers by removing from the sample 200 plots which represent the smallest and largest 2 percent of plots. The final sample consists of 4,814 plots.

> 1 We include these plots in an alternative specification.

5
Policy Research Working Paper 9451

# **Abstract**

Entrepreneurs in Myanmar face many challenges to starting and operating a business. As is the experience globally, women often experience these challenges to a greater extent and face additional sociocultural barriers, limiting their equal participation in the economy. To develop a better understanding of the dynamics holding back private sector development, especially for women, this paper uses data from the first-of-a-kind, firm-level data set available in Myanmar. The analysis explores the variance of experience female-owned micro, small, and medium-size enterprises face compared with their male-owned counterparts. The paper assesses the barriers imposed on entrepreneurs and their businesses and identifies firm-level characteristics leading to the use of good business practices. Further, the analysis investigates the adoption of gender and family-friendly policies, as an outcome and as a determinant of business success. The purpose of the study is to gain a better understanding of the barriers to gender-inclusive private sector development in Myanmar and provide tangible recommendations to private- and government-level actors. Overall, the analysis finds the major constraints for women entrepreneurs are access to finance and sociocultural factors, such as family responsibilities and household work.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fhayati@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The next panel in Table 6 focuses on applicants’ expectations about the future. For this purpose, we use five questions in the survey. The first of these asked respondents what they thought their chances were of “providing a good home for themselves and their families in the future”. Responses were coded from “very unlikely” (coded as =1) to “very likely” (coded as =5); the second question asked respondents about the desired age of marriage; the third question asked about the desired age of their first child.<sup>26</sup> Finally, we analyzed applicant reports on their subjective well-being, using an adaptation of the “MacArthur ladders”.<sup>27</sup> Applicants were shown a picture of a ladder with 10 rungs, and were told that higher rungs corresponded to better socioeconomic status. They were asked to place themselves on the ladder in relation to everyone in their communities, at the time of the survey, as well as where they expected to stand “in the future, when they have their own family”.

As before, the first column of Table 6 shows that, among children who were turned down for CSP scholarships, those who have completed more schooling thought it more likely that they would be able to provide a good home; an additional year of schooling is associated with higher desired age of marriage (0.9 years) and higher desired age of first childbirth (1.4 years); on average, children with one more year of schooling place themselves about 0.1 rungs higher on the MacArthur ladder at the time of the survey, and expect to be about 0.3 rungs higher when they have their own family. The second column of the table shows, however, that there is no evidence of significant CSP program effects on any of these outcomes.

The last panel of Table 6 focuses on the associations between education levels, the CSP program, and adolescent mental health. For this purpose, we use two separate scales. The first, which we call “Mental Health Scale 1”, is based on a scale used to estimate mental health among survivors of Hurricane Katrina in the United States, and includes 12 questions;28 individual responses are coded from “strongly agree” (coded as =5) to “strongly disagree” (coded as =1). The second scale, which we call “Mental

> (rather than simply the number of correct responses). The CSP program effects are similar when we use this variable instead.

> 26 Of the sample of 2273 applicants, 21 were already married and 10 already had a child.  These are excluded from the analysis of desired age for marriage and first child.

> 27 For a description and bibliography of papers that use MacArthur ladders, see the MacArthur Foundation’s Network on SES and Health website: http://www.macses.ucsf.edu/Research/Psychosocial/notebook/subjective.html. 28 We are very grateful to Christina Paxson and Cecilia Rouse for making this scale available to us.

23