{"id":205156,"date":"2019-05-08T19:38:55","date_gmt":"2019-05-08T19:38:55","guid":{"rendered":"https:\/\/watchdog.md\/?p=205156"},"modified":"2023-06-13T10:36:11","modified_gmt":"2023-06-13T07:36:11","slug":"irregularity-analysis-on-moldova-parliamentary-elections-february-24th-2019","status":"publish","type":"post","link":"https:\/\/dev.watchdog.md\/en\/analyzes\/205156\/irregularity-analysis-on-moldova-parliamentary-elections-february-24th-2019\/","title":{"rendered":"Irregularity analysis on Moldova parliamentary elections. February 24th, 2019."},"content":{"rendered":"

<\/p>\n

[et_pb_section bb_built=”1″][et_pb_row][et_pb_column type=”4_4″][et_pb_text _builder_version=”3.0.85″ background_layout=”light”]<\/p>\n

\u00a0<\/p>\n

\n
The study was developed as part of the project \u201cWatchdog.MD: Whistle-blowers in Energy and Public Policies in Moldova\u201d, implemented with the financial support of the European Endowment for Democracy (EED). The content does not necessarily represent the official opinion of the EED. Responsibility for information and expressed views belongs entirely to the authors.<\/em><\/h6>\n<\/blockquote>\n

\u00a0<\/p>\n

This analysis is devoted to discern some irregularities within the 24th February Moldova Parliamentary elections. Some of these problems are evident but most are hidden. Authors will use the different instruments of mathematical statistics to uncover such irregularities and electoral fraud like:<\/p>\n

–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 irregularities on polling stations;<\/p>\n

–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 tricks with electoral activity (turnout);<\/p>\n

–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 tricks with statistical indicators;<\/p>\n

–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 mysteries of the appearance\/disappearance of the DPM electorate;<\/p>\n

–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 disproportion between constituencies and territorial units.<\/p>\n

Authors will use Chi\u0219in\u0103u and areas both simultaneously and separately to compare and to identify the differences.<\/p>\n

Statistical instruments will be the same:<\/p>\n

–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 analysis of means and deviations;<\/p>\n

–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 analysis of pair and multiple correlation;<\/p>\n

–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 analysis of regression to the mean;<\/p>\n

–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 regression equations;<\/p>\n

–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 analysis of partial correlation.<\/p>\n

Finally, we will try to analyze \u201eshare\u201d and \u201evalue\u201d of each irregularity in the case under investigation.<\/p>\n

For the sake of simplicity, we shall begin our discussion of irregularities of February 24th<\/sup>, 2019 parliamentary elections with the most apparent and accessible data, successively addressing more complicated issues. As will be seen below, there is an abundance of facts to do this.<\/p>\n

First, we must consider polling stations which have evidently doubtful results.<\/p>\n

In contrast to 2014 elections, where there were no polling stations with such kind of\u00a0 \u00a0irregularities, in 2019 there have been a lot of it. This is itself evidence of nature of this elections. Let us consider only those constituencies, where share of votes for DPM and \u0218or Party has met the following inequality<\/a>\u00a0:<\/p>\n

Mi <\/sub>> M0 <\/sub>+ 3\u03ed,<\/p>\n

Where Mi<\/sub> is share of votes for the party in a particular polling station, M\u00ad0<\/sub> is its mean share of votes in a given locality (In the present case Chi\u0219in\u0103u, as all suburban areas are treated as separate localities), 3\u03ed is a double standard deviation.<\/p>\n

Some notes.<\/p>\n

    \n
  1. Normal (or Gaussian) distribution and normalized deviation is the simplest method to discern any irregularities without sophisticated mathematical analysis.<\/li>\n
  2. All those deviations have to have some comprehensive explanation, otherwise it could be considered like doubtful.<\/li>\n
  3. Rule of 3\u03ed is the simplest application of Gaussian \u2013 it means \u2013 68 \u2013 95.5 \u2013 99.7 probability that this value is confident (Table 1). It would be enough that we use 2\u03ed deviation, because we can obtain only 10 polling stations out of this interval. We have 26 within and some more just on border. At the first, we will take only 3\u03ed rule. We can have only 1 (one) polling station out of 3\u03ed interval, but there is lot of such cases now, and it\u2019s the first time in electoral history in Chi\u0219in\u0103u.<\/li>\n<\/ol>\n

    Statistics for DPM are the same. M0<\/sub> (average) \u2013 11.1, \u03ed (deviation) \u2013 2.0.<\/p>\n

    For \u201cSor Party\u201d M0<\/sub> (average) \u2013 6.0, \u03ed (deviation) \u2013 1.5.<\/p>\n

    For example, we will calculate normalized deviation for the very first case (23 constituency, 1 polling station). (14.7 \u2013 6.0)\/1.5 > 5.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    Constituency<\/td>\n\n

    Polling<\/p>\n

    station<\/p>\n<\/td>\n

    		Location<\/td>\nDPM<\/td>\n\u201cSor Party\u201d<\/td>\n\n

    Normalized<\/p>\n

    deviation<\/p>\n<\/td>\n

    		Peculiars<\/td>\n<\/tr>\n
    23<\/td>\n1<\/td>\nWoodworking factory<\/td>\n\u00a0<\/td>\n14.7<\/td>\n> 5<\/td>\nIsolated neighborhood<\/td>\n<\/tr>\n
    23<\/td>\n2<\/td>\nWoodworking factory<\/td>\n\u00a0<\/td>\n11.8<\/td>\n> 3<\/td>\nIsolated neighborhood<\/td>\n<\/tr>\n
    23<\/td>\n4<\/td>\nAirport<\/td>\n\u00a0<\/td>\n21.7<\/td>\n> 10<\/td>\nIsolated neighborhood<\/td>\n<\/tr>\n
    23<\/td>\n5<\/td>\nAirport<\/td>\n\u00a0<\/td>\n18.3<\/td>\n> 8<\/td>\nIsolated neighborhood<\/td>\n<\/tr>\n
    23<\/td>\n25<\/td>\nNeighborhood<\/td>\n30.7<\/td>\n\u00a0<\/td>\n> 9<\/td>\nHospital<\/td>\n<\/tr>\n
    25<\/td>\n10<\/td>\nCenter<\/td>\n19.8<\/td>\n\u00a0<\/td>\n> 4<\/td>\nIsolated neighborhood<\/td>\n<\/tr>\n
    25<\/td>\n23<\/td>\nNeighborhood<\/td>\n18.2<\/td>\n\u00a0<\/td>\n> 3<\/td>\nUniversity hostel<\/td>\n<\/tr>\n
    25<\/td>\n25<\/td>\nNeighborhood<\/td>\n17.6<\/td>\n\u00a0<\/td>\n> 3<\/td>\nUniversity hostel<\/td>\n<\/tr>\n
    25<\/td>\n30<\/td>\nNeighborhood<\/td>\n20.3<\/td>\n\u00a0<\/td>\n> 4<\/td>\nHospital<\/td>\n<\/tr>\n
    25<\/td>\n31<\/td>\nNeighborhood<\/td>\n25.8<\/td>\n\u00a0<\/td>\n> 7<\/td>\nHospital<\/td>\n<\/tr>\n
    26<\/td>\n18<\/td>\nCenter<\/td>\n23.9<\/td>\n\u00a0<\/td>\n> 6<\/td>\nHospital<\/td>\n<\/tr>\n
    27<\/td>\n2<\/td>\nCenter<\/td>\n\u00a0<\/td>\n15.3<\/td>\n> 6<\/td>\nNew far neighborhood<\/td>\n<\/tr>\n
    27<\/td>\n8<\/td>\nNeighborhood<\/td>\n27.5<\/td>\n\u00a0<\/td>\n> 7<\/td>\nIsolated neighborhood<\/td>\n<\/tr>\n
    27<\/td>\n24<\/td>\nNeighborhood<\/td>\n19.1<\/td>\n\u00a0<\/td>\n> 4<\/td>\nUniversity and worker hostel<\/td>\n<\/tr>\n
    28<\/td>\n3<\/td>\nNeighborhood<\/td>\n\u00a0<\/td>\n12.8<\/td>\n> 4<\/td>\nNew far neighborhood<\/td>\n<\/tr>\n
    29<\/td>\n11<\/td>\nCenter<\/td>\n26.4<\/td>\n\u00a0<\/td>\n> 7<\/td>\n\n

    Include prisons<\/p>\n<\/td>\n<\/tr>\n

    30<\/td>\n23<\/td>\nNeighborhood<\/td>\n17.4<\/td>\n\u00a0<\/td>\n> 3<\/td>\nNew far neighborhood<\/td>\n<\/tr>\n
    30<\/td>\n25<\/td>\nNeighborhood<\/td>\n23.3<\/td>\n\u00a0<\/td>\n> 5<\/td>\nNew far isolated neighborhood<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    Table 1. Irregularities on polling stations, Chi\u0219in\u0103u, 2019 Parliamentary elections.<\/strong><\/h6>\n
      \n
    1. There are a total of 18 \u201csuspicious\u201d polling stations which makes up more than 7% of the total. Next 26 polling stations (more than 10%) with DPM and \u201cSor party\u201d results stay near the \u201cborder\u201d of 2\u03ed. It\u2019s incredible amount, keeping in mind that one doesn\u2019t need any sophisticated methods to observe these deviations; they are quite apparent.<\/li>\n
    2. Deviations of \u201c\u0218or party\u201d are simultaneously larger and localized, while in the case of DPM, they are more spatially distributed. Largest ones in the latter case are connected with special and unusual polling stations most amenable for influence peddling, such as hospitals, hostels and prisons.<\/li>\n
    3. There have been irregularities in almost all constituencies. To the least they have took place in the most \u201cleftist\u201d constituencies (i.e. \u211624 and \u211628), and \u211626. The latter case is special, because of the most intensive struggle between Acum and PSRM candidates which resulted in difference just in 100 votes in this single member constituency.<\/li>\n<\/ol>\n

      Some preliminary conclusion:<\/p>\n

        \n
      1. Electoral fraud have certainly taken place. However, to quantify them one needs more sophisticated statistical methods.<\/li>\n
      2. These methods will be used at the same time to explore qualitative (which are connected to electoral activity) and quantitative (which are connected to internal correlations of the DPM electorate) indicators.<\/li>\n
      3. Therefore, it would be possible to determine quantitatively amount of falsifications both in Chi\u0219in\u0103u and in Moldova.<\/li>\n<\/ol>\n

        Tricks with electoral activity.<\/strong><\/p>\n

        The correlation of dynamics (i.e. changes during the period under review) of electoral activity and results of major parties is considered as one of the most important indicators of presence or absence of electoral fraud<\/a>.<\/p>\n

        Some notes.<\/p>\n

        1.Such shifts are quite usual for some cases. Indeed, ethnic minorities and their parties (representatives) are generally considered to be more passive during electoral process.<\/p>\n

        2.For example, there is a lot of literature concerning passivity of Afro-Americans in the US elections<\/a> . In the Baltic countries Russians are usually much more passive than native population. In the recent Ukraine presidential elections, Zakarpattia, Odessa and Chernivtsi regions which are the regions with the maximal concentration of ethnic minorities, have all shown the least electoral turnout<\/a>.<\/p>\n

        3.Moldova usually serves as a counterexample to this consistent pattern although some fluctuations occur. To adjust them, changes in activity are considered instead of activity itself. It helps to avoid bias and clarify the whole picture.<\/p>\n

        We\u2019ll take into account some details which are specific to Moldova.<\/p>\n

        Some Moldova details.<\/p>\n

          \n
        1. As a whole, since the first \u201crelatively free\u201d elections in 1989 and completely free 1990 elections, electorate in the city of Chi\u0219in\u0103u is divided between nominal \u201cRussian-speaking\u201d (left-wing) and \u201cMoldovan\u201d (right-wing) blocks. It\u2019s enough to say that all elections with second tour voting in Chi\u0219in\u0103u since 2011 (mayor elections in 2015 and 2018(cancelled), presidential elections in 2016) have ended absolutely the same result. The \u201cleft-wing\u201d candidate has got a bit more than 50%, while the \u201cright-wing\u201d a bit less than 50%. The only example of some \u201cthird force\u201d getting more than 10% was Silvia Radu with 16.7% in the first tour of 2018 mayor elections.<\/li>\n
        2. 2.\u201dNew edition\u201d DPM (after 2009) had begun from a good result in Chi\u0219in\u0103u. Then it worsened a lot. DPM got 2.2% and 2.7% of votes in 2015 mayor and municipal council elections respectively, while in 2016 presidential elections there was no DPM candidate at all.<\/li>\n
        3. Hence, the 2016 statistics is inapplicable, while 2015 statistics is applicable to a limited extent. So, we\u2019ll use 2014 as a reference. It should be noted that 2014 elections have been one of the most competitive during all the history of independent Moldova, consequently minimizing electoral fraud.<\/li>\n<\/ol>\n

          Let\u2019s consider again some statistical indicators, such as correlations. Some of them can be very large, for example, correlation between share of votes for one or another party and of electorate cluster.<\/p>\n

          Indeed, one can find factors ruling level of support of a particular party which are highly dependent of territory. For example, Poroshenko has won with a landslide presidential elections in Lvov, while gaining less than 25% of votes in the country as a whole. This implies, there is some distinct category of electorate (let\u2019s call it for convenience\u2019 sake \u201cGalician relatively high-income urban electorate\u201d which has given more than two thirds of their votes for Poroshenko. In comparison, in Russian-speaking regions of Eastern and Southern Ukraine, Poroshenko has got less than 5% of votes<\/a> . However, even this huge difference isn\u2019t maximal; moreover, both in Ukraine, Romania, Moldova and so on there are cities, districts and regions which are even more different in terms of electoral results.<\/p>\n

          Nevertheless, when we consider electoral turnout differences, we\u2019ll see them being much smaller. Even 10-15% is a huge deviation. It has never been observed during the whole history of independent Moldova. 30% decrease of turnout is an upper bound even in the case of districts in the USA where Afro-Americans live in high concentrations (some researchers regard this case as an absolute limit). As usual turnout would be approximately 60%, 30% decrease means a twofold decrease. There are very few such examples in the political practice of the modern Europe that occur only as a result of severe discrimination or extremely strained interethnic relations (Kosovo might serve as an example)<\/a>. \u00a0History and present of Moldova has nothing to do with this situation.<\/p>\n

          However, 2019 statistics suggest otherwise.<\/p>\n\n\n\n\n\n
          \u0421orrelation coefficient \u2013 change of electoral turnout (%) and 2019 elections DPM votes (%)<\/td>\nYear to year<\/td>\nCoefficient<\/td>\n<\/tr>\n
          2019 to 2014<\/td>\n0.34<\/td>\n<\/tr>\n
          2019 to 2018<\/td>\n0.45<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
          Table 2. Correlation coefficients based on change of electoral turnout (%) and 2019 elections DPM votes (%)<\/strong><\/h6>\n

          Both indicators show moderate correlation, though in the light of above, it is an extremely high value. By constructing and solving a regression model, we obtain a very peculiar result. Each percent of turnout increase results in 0.95% increase of DPM support. I.e. almost all \u201cadditional\u201d voters have voted for DPM (the rest of them have voted for \u0218or Party). The results, summarized in Table 2 just cannot be higher than they are; otherwise 100% growth in electoral activity.<\/p>\n

          As it can be seen of Picture 1, such difference in electoral activity is a very recent phenomenon. Indeed, it hadn\u2019t manifested so acutely before 2018. May be in some regions situation was different (it will be discussed separately), but until 2018 (for example, in 2014-15 elections) in Chi\u0219in\u0103u there were no turnout irregularities.<\/p>\n

          \u00a0<\/p>\n

          \u00a0<\/p>\n

          \u00a0<\/p>\n

          [\/et_pb_text][et_pb_text _builder_version=”3.0.85″ background_layout=”light”]<\/p>\n

          \"\"<\/a><\/p>\n

          Picture 1. Electoral turnout for different Moldova political segments.<\/strong><\/h6>\n

          Tricks with activity: some conclusion.<\/p>\n

          1.\u201dLeft\u201d and \u201cRight\u201d electorate demonstrate symmetrical turnout behavior, hence proving social roots of electoral activity. There has never been any ethnicity-related turnout shift in Chi\u0219in\u0103u during the period under review.<\/p>\n

          2.At the same time, DPM electorate is increasing its electoral activity since 2018, making it unbelievably high in 2019. It\u2019s apparent that \u201cDPM electorate\u201d in this case is just a fake; however, there are no doubts that this fake was exactly what helped DPM gain 11% in Chi\u0219in\u0103u during 2019 elections.<\/p>\n

          3.Below we\u2019ll try to identify what this \u201cDPM electorate\u201d can be and what it cannot be. Based on the data present in Picture 1 and Table 2 it is possible to make first quantitative conclusion: roughly half of votes for DPM are falsified, so their actual share is less than 6%. Furthermore, there are doubts that this 6% represent real votes too.<\/p>\n

          For \u201cSor party\u201d it would little bit less \u2013 near 20-25% from all votes accepted in Chi\u0219in\u0103u. But this votes were decisive for \u201cSor party\u201d to overcome threshold and to enter Parliament.<\/p>\n

          To be continued…<\/strong><\/em><\/p>\n

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          <\/p>","protected":false},"excerpt":{"rendered":"

          [et_pb_section bb_built=”1″][et_pb_row][et_pb_column type=”4_4″][et_pb_text _builder_version=”3.0.85″ background_layout=”light”] \u00a0 The study was developed as part of the project \u201cWatchdog.MD: Whistle-blowers in Energy and Public Policies in Moldova\u201d, implemented with the financial support of […]<\/p>\n","protected":false},"author":2,"featured_media":205157,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"off","_et_pb_old_content":"","_et_gb_content_width":"","inline_featured_image":false,"footnotes":""},"categories":[43],"tags":[],"class_list":["post-205156","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analyzes"],"_links":{"self":[{"href":"https:\/\/dev.watchdog.md\/en\/wp-json\/wp\/v2\/posts\/205156","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dev.watchdog.md\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dev.watchdog.md\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dev.watchdog.md\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dev.watchdog.md\/en\/wp-json\/wp\/v2\/comments?post=205156"}],"version-history":[{"count":12,"href":"https:\/\/dev.watchdog.md\/en\/wp-json\/wp\/v2\/posts\/205156\/revisions"}],"predecessor-version":[{"id":206238,"href":"https:\/\/dev.watchdog.md\/en\/wp-json\/wp\/v2\/posts\/205156\/revisions\/206238"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dev.watchdog.md\/en\/wp-json\/wp\/v2\/media\/205157"}],"wp:attachment":[{"href":"https:\/\/dev.watchdog.md\/en\/wp-json\/wp\/v2\/media?parent=205156"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dev.watchdog.md\/en\/wp-json\/wp\/v2\/categories?post=205156"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dev.watchdog.md\/en\/wp-json\/wp\/v2\/tags?post=205156"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}