rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 2: Descriptive statistics for lease transactions</u>**

||<br> **Total**|**By ty**|**pe**|**With tena**|**nt char’s**|**w. registry**|
|---|---|---|---|---|---|---|
|||**Commercial**|**Personal**|**Commercial**|**Personal**|**Commercial**|
|**Parcel characteristics**|||||||
|Parcel area (ha)|2.08|2.27|1.55|2.34|1.52|2.31|
|Lease price ($/ha*a)|117.48|117.94|116.24|123.10|121.27|137.52|
|Median lease price ($/ha*a)|89.58|90.42|87.44|94.78|92.65|115.98|
|Post-July 2021|0.810|0.810|0.809|0.838|0.830|0.809|
|Tenant legal entity|0.948|0.949|0.945|1.000|1.000|0.975|
|Data on contract duration exists|0.931|0.931|0.929|0.939|0.937|0.943|
|if yes, 14-20 yr contract|0.175|0.183|0.151|0.182|0.151|0.213|
|<br>if yes, > 20 yr contract|0.108|0.108|0.108|0.114|0.108|0.137|
|<br>**Field characteristics**|||||||
|Parcel located within field|0.902|0.910|0.882|0.907|0.889|1.000|
|if yes, field area (ha)|63.02|68.29|48.59|69.34|48.17|95.04|
|field area share not mapped in cadaster|0.055|0.052|0.063|0.050|0.067|0.039|
|unregistered land in RRP||||||0.807|
|<br>if yes, field area share not registered||||||0.086|
|<1% unregistered area||||||0.162|
|1-10% unregistered area||||||0.465|
|<br>>10% unregistered area||||||0.179|
|Only owner registered in RRP||||||0.294|
|if yes, field area (%) with owner only||||||0.047|
|<br><1% only owner reg. area||||||0.062|
|1-10% only owner reg. area||||||0.200|
|>10% only owner reg. area||||||0.031|
|% of field used by tenant||||||0.694|
|field area share of largest user||||||0.795|
|Tenant is the largest farm||||||0.792|
|No. of registered users||||||2.435|
|<br>5 or more registered users||||||0.100|
|<br>**Tenant & village characteristics**|||||||
|Tenant's reg. area (ha)||||344.90|261.07|514.67|
|Tenant's cult. area (ha)||||296.42|226.98|428.15|
|Tenant's share of village land||||0.239|0.204|0.364|
|Tenant's sh. of v. land <33%||||0.621|0.629|0.410|
|Tenant's sh. of v. land 33-50%||||0.136|0.131|0.148|
|Tenant's sh. of v. land >50%||||0.177|0.132|0.367|
|No. of villages tenant operates in||||11.97|11.07|10.56|
|<br>Leases of comm. land in village|169.63|191.56|68.86|170.94|67.02|247.30|
|<br>Leases of comm. land in village|66.96|27.88|121.48|28.19|116.44|38.46|
|<br>**Time period of transaction**|||||||
|Year 2021|0.382|0.376|0.397|0.341|0.367|0.377|
|Year 2022|0.193|0.182|0.224|0.184|0.228|0.184|
|Year 2023|0.284|0.296|0.252|0.320|0.275|0.305|
|Year 2024|0.141|0.146|0.127|0.156|0.130|0.134|
|Numberofobservations|884,936|648,292|236,644|382,653|151,474|79,316|

_Source:_ Own computation based on land monitoring data reported by SGC, complemented by administrative and remotely sensed data as discussed in the text.

23
**Table 3.  Change in Food Consumption Scores Following the 2017 IPC Announcement**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|VARIABLES|ln (FCS)|ln (FCS)|ln (FCS)|ln (FCS)|ln (FCS)|
|Post Announcement Indicator|0.045***|0.035**|-|0.038***|0.037***|
||[0.008]|[0.017]||[0.012]|[0.012]|
|Quarter Indicators||||||
|____________________<br>Q2 2017 Indicator|-||-0.035**|-|-|
||||[0.013]|||
|Q3 2017 Indicator|-||0.001<br>|-|-|
||||[0.014]|||
|Q4 2017 Indicator|-||-0.022*|-|-|
||||[0.011]|||
|Q1 2018 Indicator|-||0.016|-|-|
||||[0.010]|||
|Q2 2018 Indicator|-||0.247***<br>|-|-|
||||[0.012]|||
|Q3 2018 Indicator|-||0.185***|-|-|
||||[0.016]|||
|Interaction Between IPC Status and the Post<br>Announcement Indicator||||||
|____________________<br>Post Announcement Indicator*IPC 4 Indicator|-||-|-0.007|-|
|||||[0.034]||
|Interactions Between the Post Announcement<br>Indicator and Individual Governorates in IPC 4<br>Regions||||||
|____________________<br>Post Announcment Indicator*Abyan Indicator|-||-|-|-0.021*|
||||||[0.012]|
|Post Announcment Indicator*Al Hudaydah Indicator|-||-|-|-0.072*|
||||||[0.035]|
|Post Announcment Indicator*Hajjah Indicator|-||-|-|-0.007|
||||||[0.012]|
|Post Announcment Indicator*Lahj Indicator|-||-|-|0.030**|
||||||[0.011]|
|Post Announcment Indicator*Sa'ada Indicator|-||-|-|-0.053***|
||||||[0.018]|
|Post Announcment Indicator*Shabwah Indicator|-||-|-|0.049***|
||||||[0.012]|
|Post Announcment Indicator*Taizz Indicator|-||-|-|0.039**|
||||||[0.015]|
|Observations|357|357|357|357|357|
|Controls and Governorate Fixed Effects|N|Y|Y|Y|Y|
|Note: This table estimates how the average Food Cons<br>March 2017. Column (1) estimates a sparse specificatio<br>an indicator equaling one if the survey was conducted<br>variables; column (3) estimates an identical specificatio<br>survey happened during that quarter-year time period;<br>severe IPC classification in the 2017 IPC announcement<br>governorate indicators for each governorate that was cl<br>number of people killed in the month before the surve<br>governorate and reported in parentheses. * denotes si<br>level; and***denotes significance at the 1 percent lev|umption Scor<br>n regressing t<br>after March 20<br>n but replaces<br>column (4) in<br>(IPC 4); and co<br>assified as IPC<br>y, and four lag<br>gnificance at t<br>el.|e changed follo<br>he average sha<br>17; column (2)<br>the post indic<br>teracts the pos<br>lumn (5) furth<br>4 2017 IPC an<br>s of the same v<br>he 1 percent le|wing the annou<br>re of responde<br>adds governor<br>ator with indica<br>t indicator with<br>er interacts the<br>nouncement. C<br>ariable. Stand<br>vel; ** denotes|ncement of th<br>nts in each gov<br>ate fixed effect<br>tors equaling<br>an indicator f<br>post indicator<br>ontrol variabl<br>ard errors are<br>significance at|e 2017 IPC in<br>ernorate on<br>s and control<br>one if the<br>or the most<br>with<br>es include the<br>clustered by<br>the 5 percent|

23
<!-- Start of picture text --> 16<br>Q A Q<br>4A<br>ray<br>1.5<br>2<br>©<br>g 14<br>oO A A<br>s ° $<br>1.3<br>a<br>1.2 °<br>. 2 eo $ S Re<br>gPa Qe& SeRS< ©sf& cy& ~SF&<br><!-- End of picture text -->
at the ISIC Rev. 3 level for 30 countries in our dataset. We compute the ratio of male to female wages as our control measure of relative wages at the sector level in a country. Another important modification to the econometric model is the change in the dependent variable. Rather than estimating the female labor share (see equation 2), the regressions in this section estimate the female wage share (see equation 1).

**Table 3: Female labor share premium, trading vs. non‐trading firms, 2010‐2013 vs. 2014‐2017**

|Dependent<br>variable:||**_2010_**|**_‐2013_**|||**_2014_**|**_‐2017_**||
|---|---|---|---|---|---|---|---|---|
|_fem_shisrt_|(1)<br>**_exp_**|(2)<br>**_imp_**|(3)<br>**_gvc_**|(4)<br>**_fdi_**|(5)<br>**_exp_**|(6)<br>**_imp_**|(7)<br>**_gvc_**|(8)<br>**_fdi_**|
|ln_Yisrt_|0.1107|0.3933***|0.2095|0.4750***|0.4244***|0.6738***|0.5355***|0.5661***|
||(0.455)|(0.007)|(0.155)|(0.001)|(0.006)|(0.001)|(0.000)|(0.000)|
|ln_kisrt_|‐0.2386|‐0.1816|‐0.2114|‐0.1185|‐0.1212|‐0.0064|‐0.1043|‐0.0960|
||(0.198)|(0.329)|(0.254)|(0.524)|(0.523)|(0.979)|(0.583)|(0.614)|
|ln_tfpisrt_|‐1.4652|‐1.3942|‐1.5487*|‐1.5008|‐1.4273*|‐2.5379***|‐1.5001**|‐1.5296**|
||(0.122)|(0.131)|(0.097)|(0.103)|(0.060)|(0.009)|(0.048)|(0.044)|
|**_tradeisrt /_**|**5.4433*****|**2.3297*****|**5.1306*****|**1.7190***|**2.4588*****|**0.9192**|**1.3171**|**1.1118**|
|**_globalisrt_**|**(0.000)**|**(0.000)**|**(0.000)**|**(0.066)**|**(0.001)**|**(0.276)**|**(0.203)**|**(0.378**)|
|_constant_|72.2688***<br>(0.000)|10.8937<br>(0.120)|70.8940***<br>(0.000)|66.9885***<br>(0.000)|<br>58.4506***<br>(0.000)|0.7596<br>(0.892)|57.1632***<br>(0.000)|55.5684***<br>(0.000)|
|Obs.|8,540|8,498|8,540|8,531|5,945|4,103|5,945|5,945|
|R‐squared|0.54|0.54|0.54|0.54|0.51|0.50|0.51|0.51|

Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). All regressions include country‐sector, subnational region and year fixed effects.

**Figure 10: Female labor share premium (percentage points), trading vs. non‐trading firms excluding apparel and computing, by worker type**

<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>Exporting vs. non‐exporting Importing vs. non‐importing GVC participants vs. non‐ FDI vs. non‐FDI firms<br>firms firms participants<br>Total workers Production workers Non‐production workers<br><!-- End of picture text --> Note: The graph shows mean differences in the female labor share between trading and non‐trading firms for total, production and non‐production workers. All regressions exclude wearing apparel (ISIC 18) and office, accounting and computing (ISIC 30) and control for output, capital intensity, TFP (all in natural logarithms) as well as country‐sector, subnational region and year fixed effects. All results shown are significant at the 10% level. For the underlying regression results, see Appendix 5 and 6.

The female wage share premia for trading relative to non‐trading firms are shown in Tables 4 and 5.

The findings suggest that higher relative wages between male and female workers – or a higher gender wage gap – are associated with a lower female wage share which is in line with expectations (see section 20
central bank’s balance sheet: where _uir_ , _udr_ and _uer_ stand for the innovations in the reference interest rate, the discount rate, and the selected exchange rate measure, respectively.

Equation (13) puts forward the behaviour of the current account. It states that innovations in current account flows depend positively on the exchange rate and a foreigncurrency supply disturbance operating through the current account. The exchange rate response is justified by the behaviour of the trade balance in goods and services.

Equation (14) relates innovations in the capital and financial account (as defined above) to innovations in the reference interest rate, the exchange rate, and an autonomous shock. While the domestic interest rate is expected to affect positively the capital and financial account, the sign of the effect of the exchange rate is less clear. On the one hand, an unexpected depreciation could make the country more attractive for foreign direct investors. On the other hand, an unexpected depreciation might lead to a contraction in the demand for domestic currency, with the corresponding flight to quality. The latter effect probably dominates the former one, for it seems reasonable to think that FDI decisions are based on longer-term conditions rather than on monthly forecast errors.

Equation (15) captures the behaviour of the central bank regarding the external sector of the economy, indicating how the monetary authority responds to the developments of the market for foreign currency. According to (15), the central bank observes and responds to the contemporaneous foreign currency supply (or demand if negative) shocks.<sup>6</sup> The strength of the response is given by the coefficients _φca_ and _φka_ . For example, under a currency board regime, the central bank provides all the currency (foreign or domestic, depending on the case) that the external sector needs at every point in time at the fixed exchange rate. This amounts to the identifying assumption that _φca_ = _φka_ = 1.

Equation (16) is the currency demand function of the public. It states that interest rate and exchange rate innovations affect negatively the change in the public’s holdings of domestic currency. The expected relation with the exchange rate addresses the aforementioned aspect regarding the confidence in the domestic currency in the event of an unexpected depreciation.

Equation (17), in turn, is the liquidity demand by commercial banks, expressed in innovation form. As in Bernanke and Mihov [6], we will relate this variable to the reference interest rate and the discount rate. I expect the change in liquidity to depend

> 6In Argentina, all foreign-exchange transactions are made through the central bank, and so the assumption that it observes these shocks is straightforwardly sustained in the example to be carried out below.

8
Reinhart, Carmen M. and Christoph Trebesch (2016) Sovereign Debt Relief and its Aftermath, _Journal of the European Economic Association_ **,** Vol. 14(1), February 2016, 215-251.

Rojas-Suarez, Liliana (2002). “Rating Banks in Emerging Markets: What Rating Agencies Should Learn from Financial Indicators,” in _Ratings, Rating Agencies, and the Financial System,_ Richard Levich, Giovanni Majnoni,and Carmen M. Reinhart (eds.), (New York: Springer).

Schularick, Moritz, and Alan M. Taylor, (2012). “Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises.” _American Economic Review_ Vol. 102(2): 1029–61.

Trebesch, C. (2019). “Resolving Sovereign Debt Crises: The Role of Political Risk” _Oxford Economic Papers,_ Vol. 71(2), <mark>April 2019, 421–444.</mark> Schumacher, J., Trebesch, C., Enderlein H. (2021). “Sovereign Defaults in Court,” _Journal of International Economics,_ <mark>forthcoming.</mark> Velasco, Andres, (1987) “Financial crises and balance of payments crises: A simple model of the southern cone experience _,” Journal of Development Economics_ , Vol. 27(1-2), October, 263-283.

<mark>World Bank (2020a).</mark> _<mark>Poverty and Shared Prosperity 2020: Reversals of Fortune</mark>_ <mark>. (Washington DC: The World Bank).</mark> <mark>World Bank (2020b). Covid-19 Finance Sector Related Policy Responses</mark> <u><mark>https://datacatalog.worldbank.org/dataset/covid-19-finance-sector-related-policy-responses.</mark></u> World Bank (2020c). Covid-19 Debt Service Suspension Initiative, https://www.worldbank.org/en/topic/debt/brief/covid-19-debt-service-suspension-initiative 37
month now are statistically significant. The coefficient is more significant with larger magnitude for larger common social media thresholds, and largest at the 30% threshold (see column [5]).<sup>4</sup> The coefficient of 0.047 implies that a one standard deviation increase in foreign protests, which is 3.1 more protests per million persons, leads to 1.15 more domestic protests per million persons. This is 37% of the standard deviation of a country’s monthly protests. To put this in perspective, at the onset of the Arab Spring, protests in Tunisia jumped from zero in November 2010 to close to 15 protests per million persons in January 2011, which is 5 times the standard deviation of protests in our sample.

Social media is found to play a significant role in driving spillover of protests using the newsbased measure. News-based protest about neighboring countries in the previous month has statistically significant effect on current month domestic news-based protest (see Column [1] of Table 2), but global protest using inverse distance-weighted in previous month has statistically insignificant effect (Column [2]). We observe large and consistent news-based protest spillovers for countries with strong media penetrations. This is true for all three thresholds of social media (columns [3] to [5]). The coefficient of 0.912 in column [5] implies that one standard deviation increase in previous month’s foreign news-based protests, which is 0.632 percentage point increase, leads to a 1.78 percentage points increase in the contemporaneous month of its own newsbased protests. This is equivalent to about 3 times the standard deviation of news-based protests.<sup>5,6</sup> Our results are consistent with the social media being a catalyst for both push and pull factors behind protests, i.e. social media makes it much easier for protesters in one country to raise their voice, and for sympathizers in other countries to read, learn and emulate.

> 4 When we run the regressions with distance alone on the samples in columns [3],[4],[5], the coefficients of foreign protests (per million persons) are not significant (Appendix Table A3, Panel A). Appendix Table A4 shows that our main results are robust to using an earlier cross-section of data for Facebook penetration for the year 2010 for 61 countries for both actual protests (Panel A) and news-based protests (Panel B).

> 5 Note that changes in sample size are not driving the significance of the role of social media. Indeed, Appendix Table A3 Panel B shows that coefficients associated with distance-weighted foreign protests remain insignificant and are smaller when using the samples presented in Columns [3] to [5] in Table 1.

> 6 The samples used in Columns [3] to [5] in Table 1 only include countries above the social media thresholds—weights are not defined otherwise. Results remain robust when we impose undefined weights as zeros when countries having relatively low social media penetration are included in the samples (Appendix Table A5).

10
Given returns to school resources and teachers’ human capital (measured by national achievement test scores), the government can determine the optimal allocation of the above education assets across schools. To motivate the analysis, variations of these resources and teachers’ human capital are characterized in two years, 2005 and 2010. Moreover, the distributions are decomposed into between and within school divisions (similar to provinces), so as to identify what dimension of the distribution is more important for policy making. The combination of the two sources of information—returns to school resources and — teachers’ human capital and actual distributions (variations) between and within divisions (in two separate years) enables us to identify the magnitude of possible sub-optimality of the education resources.

The national school database is used to explore potential inequality of school resources across regions and provinces and its dynamic changes in the period of 2005 to 2010, and their implications for students’ achievements. In particular, the analysis focuses on pupil classroom and teacher ratios (PCR and PTR, respectively) and per-pupil teachers’ salaries (which increase with principals’ and teachers’ ranks).<sup>7</sup> PCR represents the amount of school physical facilities (classrooms), while PTR and per-pupil teachers’ salary capture teachers’ human resources (quantities and qualities, respectively).

Variations of the above mentioned school and teacher resources are decomposed into two dimensions: (i) between divisions and (ii) within divisions. The analysis shows that the within-division variations are larger than the between-division variations, which directly means that inequity in school and teacher resources is largely related to the allocation decision in each division. Different divisions look rather similar if the averages are simply compared. A major portion of the variations is attributed to the withindivision resource allocations. This is particularly true in non-city divisions.

> 7 Teachers’ quality increases with on-the-job and off-the-job training. On-the-job training is closely related to accumulation of actual teaching experience in schools, while off-the-job training requires direct and opportunity costs incurred for knowledge transfer, e.g., attending a workshop and college. Their salary is a function of rank and position, which reflect their performance and accumulated experience.

3
Strongly relative lines are no less problematic in cross-section comparisons, such as between countries or urban and rural areas. For example, OECD (2008, Chapter 5) reports the same poverty rate for the US as for Mexico. In another example, the urban poverty line proposed by Osberg and Xu (2008) for China (set at half the median) is 2.4 times their rural line, or 1.7 times when deflated by the differential in absolute lines, anchored to cost-of-living differences facing the poor, as used by Ravallion and Chen (2007). The Osberg-Xu method suggests little difference in poverty incidence between urban and rural China, while the Ravallion-Chen method indicates far higher poverty measures in rural China.

There are other possible methods of setting explicitly relative lines that do not have the strong-relativity property of being proportional to the overall mean. An example is found in the debates on the official poverty line for the US. It will be recalled that this is an absolute line, which is only updated over time for inflation and that this has been questioned by many observers (as reviewed by Blank, 2008). A number of proposals have been made for new lines that build in concerns about relativity, by (implicitly or explicitly) adjusting the line for rising overall living standards. The most developed proposal was made by an expert panel of the National Research Council (Citro and Michael, 1995). The panel recommended that US poverty lines should be anchored to the current median of expenditures on food, clothing and shelter. This would clearly generate poverty lines with a positive elasticity to the mean, but the elasticity will be less than unity given that these goods tend to be necessities. However, one concern with this proposal is that it is unclear why concerns about relative poverty would apply only to necessities; it would seem more natural to assume that the income gradient in a poverty line stems from social inclusion needs that go beyond necessities in a country such as the US.

# **_Weakly relative poverty lines_**

Ravallion and Chen (2010) propose a concept of weakly relative poverty in which the elasticity of the poverty line to the mean is positive above some critical value, rises with higher mean consumption, but only reaches unity as mean consumption goes to infinity. This places a natural upper bound on the weight attached to relative deprivation, namely that it cannot matter so much that measured poverty does not fall when all incomes increase by the same proportion.

The Ravallion-Chen proposal generalizes the synthesis of absolute and relative lines proposed by Atkinson and Bourguignon (2002) who postulated two key capabilities: physical survival and social inclusion. The former is the capability of being adequately nourished and 18