rafmacalaba/datause-encoder-data huggingface dataset:
14 2) The second model assumes that the variable is stationary with a change in the mean after 12/16/94. This is suggested by inspection of the plot of MEXDIS. The variable can be described by a decreasing trend with a change in the constant after the December crisis. We estimated the following model, where the dummy _dl_ takes value one after 12/16/94 and zero before that date: _4 MEXDIS = c + a t + dl + E±_ 7 _. MEXDIS(-i) i=_ (2) The results are: _LS // Dependent Variable is MEXDIS Sample: 6/15/90 - 7/12/96 Included observations: 318_

|Variable<br>Coefficient|Std.|Error|T-Statistic|Prob.|
|---|---|---|---|---|
|C<br>78.52782|19.|52125|4.022684|0.0001|
|T<br>-0.253105|0.07|5648|-3.345822|0.0009|
|DI<br>72.04601|18.|44906|3.905132|0.0001|
|MEXDIS(-1) 1.186698|0.05|6149|21.13472|0.0000|
|MEXDIS(-2) -0.204627|0.08|5975|-2.380075|0.0179|
|MEXDIS(-3) -0.185268|0.08|5210|-2.174256|0.0304|
|MEXDIS(-4) 0.118871|0.05|4331|2.187901|0.0294|
|R-squared<br>0.966|303|Mean|dependent var|657.8306|
|Adjusted R-squared 0.965|653|S.D. d|ependent var|282.6279|
|S.E. of regression<br>52.37|935|Akaik|e info criterion|7.938791|
|Sum squared resid<br>8532|58.4|Schwa|rtz criterion|8.021604|
|Log likelihood<br>-1706|.490|F-stati|stic|1486.385|
|Durbin-Watson stat<br>1.97|1090|Prob(F|-statistic)|0.000000|

The drift, the trend and the dummy _dl_ are very significant. An ADF based on this model suggests the rejection of the null of unit root. **_15_**

# _ii) POLDIS_ (sample: 7/29/94 to 7/12/96).

I) The first model is a simple random walk, that is an AR(]) with one unit root. The evidence for unit root is very strong.16 The value of the Dickey-Fuller Test Statistic is -1.264866, while the 10% critical value is -1.6174.

> **15** In order to perform a proper test, we split the sample as before: 4/06/90 to 12/16/94 and 12/23/94 to 7/12/96. For the first sub-period the drift and the trend are not significant and there is strong evidence of unit root. For the second period, drift and trend are significant and we can reject the null of unit root at the 5% critical level.

> **16** The evidence of unit root is maintained if we split the sanple at 12/16/94 as for MEXDIS.
(Meng _et al._ , 2013; Banerjee _et al._ , 2015; <mark>Asher and Novosad, 2020;</mark> Bahal, 2020; Millan _et al._ , 2020; Chaurey and Le, 2022), but not much evidence is available for Viet Nam.

Viet Nam presents an interesting, if not unique, case study. The country has achieved one of the fastest poverty reduction rates in the world. Following its economic reforms in 1986, its (headcount) poverty rate sharply decreased by about one-third from 58% in 1993 to 37% in 1998, and another two-thirds to 14% in 2010 and 12% in 2012 before reaching a remarkable low of 4% in 2020 (World Bank, 2022). While the country has implemented a number of poverty reduction programs based on a strong pro-poor development strategy, there is, however, limited evidence linking these interventions to its successes in poverty reduction. More worrisome, recent evidence suggests that poverty is increasingly segregated in certain provinces and there is rising within-province inequality in Viet Nam (Benjamin _et al._ , 2017; Lanjouw _et al._ , 2017; Dang _et al._ , 2023). By offering a multi-component poverty reduction program supporting various aspects of the economy, ranging from employment support, investment in infrastructure, improvement with government personnel policies to education support, the country aims to sustainably reduce its poverty.

Yet, the few existing studies exploring the impact of other large-scale poverty reduction programs in Viet Nam offer mixed evidence. Nguyen _et al._ (2015) show positive impact of Program 135 during 2007-2012, which specifically targeted ethnic minority communes, on the living standards of households within the program's targeted areas. However, Phan _et al._ (2016) do not find significant effects of national targeted programs for poverty reduction during 2002-2010. At the same time, the 1993-1998 period had no national targeted poverty alleviation programs but still witnessed a significant poverty decline. From 2000 onwards, Viet Nam has implemented various poverty alleviation programs, with nearly VND 560 trillion (approximately US$25 billion) invested in all national targeting programs 4
**Table 7: Multivariate analysis of the determinants of the measurement residuals**

||_Dep. var: m_|_esresWB-CCI_|_Dep. var. m_|_esresTI-CPI_|
|---|---|---|---|---|
|Regressors|All observations|Excluding|All observations|Excluding|
|||10 outliers||10 outliers|
|_Asia & Pacific_|-0.4102|-0.2714|-0.0992|0.1070|
||(0.3213)|(0.2886)|(0.3243)|(0.2972)|
|_America & Caribbean_|-0.9691**|-0.6412*|-0.8116**|-0.4874|
||(0.4018)|(0.3569)|(0.3778)|(0.3353)|
|_Africa & Middle East_|0.3450|0.4022|0.4826|0.4655|
||(0.3502)|(0.3281)|(0.3624)|(0.3453)|
|_Per capita GDP. PPP_|11.7404|8.8195|21.7674*|11.8142|
||(10.0708)|(9.5360)|(11.189)|(8.4577)|
|_Pop_|-0.0063|0.0052|-0.0008*|-0.0010**|
||(0.0006)|(0.0006)|(0.0004)|(0.0004)|
|_R_g/gdp_|-0.0102***|-0.0097***|-0.0075*|-0.0010**|
||(0.0035)|(0.0027)|(0.0041)|(0.0004)|
|_Free_press_|-0.0036|-0.0008|0.0127|0.0068|
||(0.0120)|(0.0116)|(0.0113)|(0.0109)|
|_Free_speech_|-0.1566|-0.0559|-0.0909|-0.0357|
||(0.2883)|(0.280)|(0.266)|(0.24089)|
|_Voice_acc_|0.4953|0.467|0.1817|0.2087|
||(0.3785)|(0.361)|(0.3578)|(0.3176)|
|_Emp_right_|0.0734|0.0114|0.1048|0.0539|
||(0.0777)|(0.0663)|(0.0790)|(0.0682)|
|_Democ_|-0.0440**|-0.0234|-0.03245*|-0.0247|
||(0.0194)|(0.0176)|(0.0178)|(0.0164)|
|_Checks_|-0.0436|-0.0642|-0.0332|-0.0228|
||(0.0457)|(0.0420)|(0.0404)|(0.0346)|
|_Stability_|0.1302|0.1582|0.1605|0.1560|
||(0.1705)|(0.1491)|(0.1592)|(0.1467)|
|Observations|105|95|105|96|
|R-squared|0.328|0.295|0.354|0.337|

_Notes_ . OLS estimates. Robust standard errors are between parentheses. * * p-value < 0.1; ** p-value < 0.05; *** p-value < 0.001. For a description of the variables, see the note at the bottom of Table 6 and Appendix B. Countries for which _PACI_ z = 0 and Pr zero_ cases zPACI_ > 0.015 and countries for which _PACI_ z > 10000 have been excluded.

23
recorded in the custom’s data, the exporter is not registered with the government and does not show up in our other two data sources. As we only know a firm’s sector when it has a taxpayer identification number (TIN), with the tax authority for business income tax or for payroll tax purposes, we omit exporters and importers without a TIN. Because most of the small exports are by entities without a TIN, we omitted them entirely from our calculations as the few included observations with a TIN would not provide a faithful representation of the universe of small-scale trade transactions. Moreover, we also imposed a 10,000 USD per year threshold, because firms exporting less than even this minimal amount cannot really be considered to be participating in an international value chain in a substantive way.

In Figure 6 we show the evolution for the distribution of exporters from the manufacturing sector separately for the three exporter-size categories. A few patterns are worth mentioning. The decline towards the end of the period, mentioned earlier, is strongest for small firms. Even though small-scale manufacturing firms are more likely to be registered with the government than small-scale agricultural firms, this is unlikely to be the primary reason for the larger number of small manufacturing exporters. The growth in number of manufacturing exporters is even more pronounced for firms exporting at least 100 000 USD per year, which makes it likely that the different evolution from agriculture is not merely a registration issue. Even agricultural firms exporting this much are highly likely to be formally registered.

Manufacturing seems to be a sector where exporters of all three sizes seem to be viable. While small mining firms only appeared later in the period, the reverse is true for manufacturing. In 2008-2010 there eleven small exporters, but only two large ones. All categories expanded by 2014 and at relatively similar rates. By 2014 there were ten large manufacturing exporters, which is consistent with a more natural evolution of successful firms thriving and gradually expanding. Also, manufacturing is the only sector where the firm frequency varies inversely with firm size. This ordering does not hold strictly in every year, but hold in average. In 2013 and 2015 there were more medium-sized than small manufacturing exporters, but this was caused by greater volatility in the number of small exporters, which itself is quite natural.

**Figure 6:  Number of manufacturing exporters by size**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>No. of firms exporting > 10 000 USD & < 100 000 USD / year<br>No. of firms exporting > 100 000 USD & < 1 million USD / year<br>No. of firms exporting > 1 million USD / year<br><!-- End of picture text --> 12
We next examine spending on the three main categories in greater detail. <u>Computer software and databases</u> is the smallest category, but it is the fastest growing, having risen from 0.4 percent of GDP in 2000 to 1 percent in 2008. Further, as shown in Figure 4, this trend is driven by a near tripling of expenditures on software.

<!-- Start of picture text --> Figure 4: Expenditures on Computerized Information (% of GDP)<br>1.0%<br>0.8%<br>0.6%<br>0.4%<br>0.2%<br>0.0%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Computer software Computerized databases<br><!-- End of picture text --> Figure 5 breaks down expenditures on <u>innovative property</u> into its components. The two major constituents of innovative property expenditure by Brazilian firms are new product development in the financial industry, and science and engineering R&D. It may come as a surprise that Brazil‘s private sector spends considerably more on new product development in financial industry than on in-house R&D. However, this could in part be an artifact of the CHS methodology, which assumes that 20 percent of intermediate expenses in the financial services industry go towards new product development. As discussed in Section 2.2, there is no research that can guide us towards finding a more appropriate ratio for Brazil, and we have decided to use 20 percent in order to maintain comparability with CHS.

20
- 14 -

completed any educational cycle.<sup>3</sup> / In rural areas, 4% of women and 10% of men have completed primary education. Less than 2X of men have completed a higher cycle. In Abidjan, about 1/4 of men have a primary education degree, 3% have completed secondary education, and 2.2% have a university degree. Women hold fewer certificates and diplomas than men, and the relative gap is largest at the upper secondary level. However, since the gap is lower for university degrees, it suggests that relatively more women than men who obtain a baccalaureate are also successful at obtaining a university diploma.

Vocational and technical education (VTE) can be obtained in CMte d'Ivoire both through formal and informal channels. The formal VTE system is extremely complex, as it consists of many different types of public and private institutes. The public institutes depend administratively and budgetarily from no less than 18 different ministries.4/ Thev can be grouped as follows (for the meaning of abbreviations, see Figure 1):

- 3/ This table as well as all following tables pertain to the population aged 5 or above.

- 4/ Detailed descriptions of the VTE system can be found in Guirassi (1986) and ONFP (1986). Two general studies of the VTE sector in C8te d'Ivoire are Achio (1983) and ONFP (1984).
Returning to the differences between Columns 1-3 and 4-6, we suspect that three factors are behind the weaker response on total activity compared to entry. First, our post-upgrades data come from 2005 and 2007, which is just at the end of the GQ upgrades that began in 2001. It takes time for activity to shift spatially, especially if there are agglomeration forces or similar with existing industry bases, and we are likely under-estimating the ultimate changes that may occur in the spatial distribution of Indian manufacturing as a consequence. By contrast, the entry margin—where location choices are being made at present—adjust much faster to the changing attractiveness of regions, and thus register sharper effects in the short- to medium-run. A second reason why total shifts in activity may be dampened in districts on the GQ network is that entrants may be displacing incumbent establishments from the districts. In fact, this competition is a key reason cited by proponents for infrastructure investments. Unfortunately, our data do not allow us to study the exit margin with sufficient detail to make accurate assessments.

Table 2b presents evidence on a third rationale that partly overlaps with the other two. Prior to the GQ project, there existed some infrastructure linking these cities. In a minority of cases, the existing roads did not even comprise the beginning of a highway network, and so the GQ project built highways where none existed before. In other cases, however, a basic highway existed that could be upgraded. Of the 70 districts lying near the GQ network, new highway stretches comprised some or all of the construction for 33 districts, while 37 districts experienced purely upgrade work.

In Table 2b, we split the 0-10 km interaction variable for these two types of interventions. The results are very interesting. Columns 4-8 show mostly similar entry and productivity consequences regardless of the initial roadwork’s condition. Columns 1-3, however, show distinct effects regarding total activity. Places that completely lacked a highway before GQ exhibit increases in aggregate activity. In these cases, the entry has enough aggregate consequences to register during the time period of our study. On the other hand, upgrades of existing facilities display null effects. Because of the earlier two limitations noted, it could be that upgraded portions will also demonstrate increases in aggregate activity in the long-run.

# **Dynamic Specifications**

Table 3a presents a dynamic version of the pre-post estimations using the shorter spatial horizon that measures effects relative to districts 50+ km from the GQ network. In this specification, we interact the indicator variables for district distance bands with indicator variables for the years 2000, 2005, and 2007. By separately estimating effects for each year, we can observe whether the growth patterns appear to 15
# **Table 6:** Two-Stage Least Squares Estimation

Dependent Variable: STANDARD DEVIATION OF CONSUMPTION GROWTH (Annual Rates)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_BANKt- 1_|-6.23**<br>(3.45)|...|...|...|
|_LLYt-1_|...|1.78<br>(5.16)|...|...<br>_|
|_PRIVATEt-1_|||_3.41**<br>(1.56)|...|
|_PRIVYt- 1_|...|...|...|-2.25*<br>(1.24)|
|_ACCOUNTt-_<br>_1_|-. 049<br>(.029)|-. 079<br>(.060)|-. 076*<br>(.024)|-. 097*<br>(.022)|
|_ENFORCEt-_<br>_1_|-. 334<br>(.582)|-. 474<br>(.356)|.073<br>(.368)|- .174<br>(.303)|
|_CORRUPTt- 1_|.395*<br>(.195)|.500*<br>(.230)|.434*<br>(.123)|.386*<br>(.140)|
|_STRUCTUREt-_<br>_1_|.442<br>(.313)|- .105<br>(.430)|.530*<br>(.241)|.738*<br>(.359)|
|_FXVOLt_|2.80<br>(5.46)|7.74*<br>(3.41)|7.71*<br>(3.35)|9.27*<br>(2.81)|
|_No. of obs._<br>|27<br>|25|26|26|
|R________________|_.91_|.96|.97|.97|

Note: Country-specific and time-specific fixed effects estimate. Heteroskedasticity-corrected standard errors are in parentheses. *, ** respectively denote significance at the 5 percent and 10 percent levels. The variables _MEAN, GROWTH, INFMEAN, INFSTDEV, GOVMEAN, GOVSTDEV, POLITY, OPEN,_ and _OPEN * FXVOL_ included but not shown.
De Loecker (2011). That is, they control for market structure and demand shocks and take into account the dynamic effect of the liberalization on investment and exit decisions, and, as a result, on future productivity. As a general problem, there are several methodological issues which make it difficult to pin down the effect of services liberalization on productivity in manufacturing. First, although it is less likely, theoretically, services sectors reforms could be induced by increased manufacturing productivity, rather than causality running from services sector reform to manufacturing productivity increases (the so called ―endogeneity‖ problem). The authors argue, however, that the liberalization of services in Ukraine was exogenously imposed by its trading partners as part of Ukraine‘s WTO accession package. If this is valid, the authors do not have an endogeneity problem. The data cited above suggests that countries in Eastern Europe and Central Asia that have acceded to the WTO or the EU are more liberal than those who have not done an accession to either the WTO or the EU. This provides some credibility to the authors‘ claim. Second, for many countries, it is hard to disentangle the effect of services liberalization from the effect of other factors, because services liberalization rarely comes alone, without labor market deregulation, trade liberalization and other economic reforms. The authors maintain, however, that during the time period of their sample, the reform package in Ukraine was limited to services liberalization. The study by Copenhagen Economics _et al._ (2005) provides some credibility to this claim, as it finds that the principal changes that Ukraine made as part of its WTO accession were the changes to liberalize the services sectors. The conclusion in the study -- the liberalization of FDI in services would be the major contributor to welfare gains and would stimulate development of the manufacturing sectors (machinery and equipment would expand by 4.7 percent) – is well in agreement with results of Shepotylo and Vakhitov. To strengthen the conclusion and to respond to remaining concerns about the 10
DISCRIMINATION AGAINST SEXUAL MINORITIES IN EDUCATION AND HOUSING: EVIDENCE FROM TWO FIELD EXPERIMENTS IN SERBIA

# **1.2. Results**

## **Feminine Boys Have More Limited Access to Primary Schools**

<mark>Feminine boys were three times more likely (14 percent) to be refused enrollment in public primary schools than non‐feminine boys (4 percent). Most feminine boys did not face direct refusal; of the refusals, 78.6 percent (11 percent of all boys) came after some delay or hesitation on the part of the school. None of the non‐feminine boys were confronted with a refusal preceded by hesitation; they were refused without hesitation and for strictly technical reasons (schools at full capacity). Amongst the boys who were accepted, feminine boys faced significantly more hesitation in the decision than the non‐feminine boys. Out of the accepted feminine boys, 51 percent were accepted with hesitation compared to only 25 percent of non‐feminine boys. Enrollment of non‐feminine boys was only met with hesitation when the school perceived the boy’s academic performance and discipline to be a potential problem. The difference in the kinds of refusals and acceptances faced by the two different groups of boys reinforces the conclusion that femininity is the reason for discrimination.</mark> <!-- Start of picture text --> Figure 1: Acceptance and refusal of feminine and non‐feminine boys<br>FEMININE BOYS<br>NON‐FEMININE BOYS<br>3% 4%<br>6%<br>11%<br>27%<br>8%<br>25%<br>65%<br>51%<br>Accepted without hesitation<br>Accepted without hesitation<br>Accepted with hesitation<br>Accepted with hesitation<br>Not clear<br>Not clear<br>Refused with hesitation<br>Refused with hesitation<br>Refused without hesitation Refused without hesitation<br>Database: all schools, n=184<br><!-- End of picture text --> Omitting the 7 percent of unclear cases (8 percent for feminine boys and 6 percent for non‐ feminine boys) and combining all acceptances and all refusals clearly shows the discrimination — against feminine boys their chances of being refused enrollment are three times higher (figure 2).<sup>xxi</sup> 6
|Maternal and Child Nutrition and Health Results Project|Samuel Lantei Mills|
|---|---|
|Maternal and Child Nutrition and Health Results Project|Samuel Lantei Mills|
|Maternal and Child Nutrition and Health Results Project|Samuel Lantei Mills|
|Benin Multisectoral Food Health Nutrition Project|Menno Mulder-Sibanda|
|Benin Multisectoral Food Health Nutrition Project|Menno Mulder-Sibanda|
|Benin Multisectoral Food Health Nutrition Project|Menno Mulder-Sibanda|
|Benin Multisectoral Food Health Nutrition Project|Menno Mulder-Sibanda|
|Benin Multisectoral Food Health Nutrition Project|Menno Mulder-Sibanda|
|Benin Multisectoral Food Health Nutrition Project|Menno Mulder-Sibanda|
|Benin Multisectoral Food Health Nutrition Project|Menno Mulder-Sibanda|
|Benin Multisectoral Food Health Nutrition Project|Menno Mulder-Sibanda|
|Benin Multisectoral Food Health Nutrition Project|Menno Mulder-Sibanda|
|Benin Multisectoral Food Health Nutrition Project|Menno Mulder-Sibanda|
|Benin Multisectoral Food Health Nutrition Project|Menno Mulder-Sibanda|
|Competitive Industries Project for Khyber Pakhtunkhwa|Kiran Afzal|
|Competitive Industries Project for Khyber Pakhtunkhwa|Kiran Afzal|
|Competitive Industries Project for Khyber Pakhtunkhwa|Kiran Afzal|
|Competitive Industries Project for Khyber Pakhtunkhwa|Kiran Afzal|
|Competitive Industries Project for Khyber Pakhtunkhwa|Kiran Afzal|
|Competitive Industries Project for Khyber Pakhtunkhwa|Kiran Afzal|
|Clean and Efficient EnergyProject|Manaf Touati|
|Clean and Efficient EnergyProject|Manaf Touati|
|Clean and Efficient EnergyProject|Manaf Touati|
|Clean and Efficient EnergyProject|Manaf Touati|
|Clean and Efficient EnergyProject|Manaf Touati|
|Clean and Efficient EnergyProject|Manaf Touati|
|Clean and Efficient EnergyProject|Manaf Touati|
|Clean and Efficient EnergyProject|Manaf Touati|
|Clean and Efficient EnergyProject|Manaf Touati|
|LB - Improve Capacityfor Env. Compliance|Alaa Ahmed Sarhan|
|LB - Improve Capacityfor Env. Compliance|Alaa Ahmed Sarhan|
|SME Revitalization and Employment Pilot Project|Nabila Assaf|
|SME Revitalization and Employment Pilot Project|Nabila Assaf|
|SME Revitalization and Employment Pilot Project|Nabila Assaf|
|SME Revitalization and Employment Pilot Project|Nabila Assaf|
|SME Revitalization and Employment Pilot Project|Nabila Assaf|
|Open Data Platform for MonitoringPovertyReduction Programs|Maria Loreto Padua|
|Second Tamil Nadu Road Sector Project|James Markland|
|Second Tamil Nadu Road Sector Project|James Markland|
|Second Tamil Nadu Road Sector Project|James Markland|
|Second Tamil Nadu Road Sector Project|James Markland|
|Second Tamil Nadu Road Sector Project|James Markland|
|Second Tamil Nadu Road Sector Project|James Markland|
|Second Tamil Nadu Road Sector Project|James Markland|
|Second Tamil Nadu Road Sector Project|James Markland|
|Second Tamil Nadu Road Sector Project|James Markland|
|Second Tamil Nadu Road Sector Project|James Markland|