rafmacalaba/datause-encoder-data huggingface dataset:
As in a majority of countries, the unemployment rate is negatively related to the level of education of individuals (see Appendix Table 4). On average, members of the labor force with higher education face an approximately 2 times lower unemployment risk than their counterparts with upper secondary education. This degree of difference in unemployment rates amongst education groups is similar to that found in Lithuania, but somewhat lower than those measured for other Central European countries (see Appendix Figure 5).

**Ethnic considerations.** Unemployment is higher amongst non-Latvians than among ethnic Latvians. Until 2001, the unemployment rates were about 1.8 times larger for ethnic minorities in comparison with Latvians. In 2002, however, this ratio fell to 1.5 as a result of a decline in unemployment among nonLatvians. Evidence from the 2000 Population Census (see Appendix Table 4) suggests that the ethnic gap in unemployment rates is caused mainly, but not entirely, by a lack of Latvian language skills. Another factor which contributes to the difference in unemployment rates between Latvians and ethnic minorities is that the Latgale region, with the highest unemployment rates in the country, is predominantly populated by non-Latvians.

**Figure 12.  Unemployment Rates by Ethnicity: Ethnic Minorities vs. Majority Population. Estonia and Latvia, 1997-2002**

<!-- Start of picture text --> 25 2.0<br>1.8<br>Estonians<br>20 1.6<br>1.4 Non-Estonians<br>15 1.2 Latvians<br>1.0 Non-Latvians<br>10 0.8 EE ratio<br>0.6<br>LV ratio<br>5 0.4<br>0.2<br>0 0.0<br>1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> _Source:_ Calculation based on LFS data.

**Unemployment duration.** As in other transition countries, long-term unemployment is one of the most acute problems facing the Latvian labor market. Long-term unemployment generally refers to the unemployment spells which exceed one year. Appendix Table 5 shows the distribution of unemployed persons by education and duration of unemployment. Appendix Figure 6 shows that between 2000 and 2002 Latvia performed much better than other countries of Central and Eastern Europe in reducing longterm unemployment. In the second quarter of 2002 incidence of long-term unemployment in Latvia, 42 percent, was at the same level as in the EU-15. This was the first year since 1996 in which less than half of the unemployed (annual average 46.4%) were unable to find a job within the year. In 2003 incidence of long-term unemployment decreased to 43.8%.

Appendix Table 6 describes how long-term unemployment has evolved in the Baltic countries since 1997. A decrease of this indicator by 12 percentage points between 2001 and 2002 is cause for optimism in Latvia. Still, the design of policies for fighting long-term unemployment requires a deeper insight into the following issues: (i.) Identifying individuals who are most likely to experience unemployment, and those who are most likely to experience long-term unemployment conditional on job loss; (ii.) Determining 16
<!-- Start of picture text --> Dhaka - Employment (1000s)<br>by sector<br>10000<br>8000<br>5368<br>6000 4707<br>4000<br>— 874<br>2000 3364 3287<br>0<br>Scenario A Scenario C<br>HE Manutacturing HE Tradable Services<br>HER, (Non-Tradable Services<br><!-- End of picture text --> <!-- Start of picture text --> East-Dhaka - Employment (1000s)<br>by sector<br>2000<br>1500<br>630<br>1000500 1008w |<br>my<br>0 171<br>Scenario A Scenario C<br>HE Manufacturing HE Tradable Services<br>HERs(Non-Tradable Servicees<br><!-- End of picture text -->

<!-- Start of picture text --> me‘R505: EO<br>15 rH<br>Dhanat 3302<br>3<br>Savat ay = 20000<br>0<br>bo -20000<br>y an leas<br>R620)<br>204 (zeo0g) OO<br>Sreenagar Munshig 4<br>R620 eeNeots WAS<br><!-- End of picture text --> <!-- Start of picture text --> a‘R5OS-<br>15 rH<br>Dhanat 3302<br>3<br>Savat ay = 20000<br>0<br>i bo -20000<br>‘<br>y an bea f<br>R620)<br>204 (zeo0g) OO<br>Sreenagar Munshig 4<br>R620 eeNeots Ws<br><!-- End of picture text --> <!-- Start of picture text --> nme ‘R505:<br>15 a<br>Dhanart 3302<br>3<br>Savat ay = 20000<br>0<br>i bo -20000<br>><br>y aa Leas<br>RE)<br>204 (zeo03) CO<br>Sreenagar Munshig 4<br>RB20 ewaNeots WAS<br><!-- End of picture text -->
7 a-id found Colombia to be financially semi-open. Edwards and Khan (1985) extends the approach and develops a general model of interest rate determination---by combining elements of a completely financially open economy, and a completel, financialiy closed economy. They test the appiicability of the model for two countries, Colombia (1968-82) and Singapore (1976-83), because the countries are at different stages of relative financial openness---Singapore being a highly open economy, while Colombia has onl; a partially free domestic financial sector with restrictions on capital movements. Evidence is shown that, as expected, Singapore interest rates are entirely determined by world inter-st rates (covered interest parity term has a coefficient equal to about unity), while in Colombia's case, both domestic and foreign factors are important. Frankel and MacArthur (1988) test for the factors underlying real interest differentials across 24 countries, including seven 'developing countries". The real interest differential is decomposed into a covered interest differential, and an exchange premium. Evidence is show1 i that in 3 relatively open developing countries (i.e. Hong Kong, Singapore and Malaysia), the covered interest differential is very low, and lower than even in the group -f European industrial countries; but high in the other 4 developing countries (i.e. Bahrain, Greece, Mexico and South Africa). Blejer (1982) performed tests for uncovered interest rate parity in Argentina for the period 191/7- 81 and could not disprove the hypothesis that UIP applied. Lizondo (1983) found evidence for large and persistent covered interest parity differentials for Mexico during 1979- 80. McNelis and Schmidt-Hebbel (1991) in a study of financial liberalization in Chile for the period 1975- 82 find that nominal intere,t rates became dominantly influenced by foreign interest rates (covered interest rate parity), as financial liberalization proceeded, and much lcss by domestic monetary conditions (as represented by time-varying coefficient of domestic credit). Ahmed and Kapur (1990) in a study of Indonesia that follows the framework of Edwards and Khan (1985), find that domestic interest rates between 1984-87 are largely explained by three main factors: (a) domestic monetary factors; (b) lagged world interest rates adjusted for the central bank forward exchange (i.e. the swap) rate---a test of covered interest parity; and (c) expected real exchange rate change as proxied by the price of oil exports'.

**The** most significant coefficients for domestic interest rate determination **are** found to be: (a) the central bank discount **rates** and the dummy for domestic mnonetary policy shock; **and** (b) the lagged covere4 interest parity **term.**
the World Bank. As a result we believe that our measures represent a reasonable approximation of the reality on the ground. To limit the risk of spurious time-series variation, we use a single base year only (2003).

One important advantage of our road distance mapping approach is that it enables us to produce detailed measures of bilateral road quality that take full account of transit effects. We can consider road quality in third countries, not just the exporter and importer (cf. Nordås and Piermartini 2004). We can also use actual transit distances to weight road quality in each country along the route, rather than using an approximation such as a count of the number of border crossings (cf. Coulibaly and Fontagné 2006).

As in Buys et al. (2006), we construct two measures of road quality. The first is a distance weighted average ( _paved_ave_ ). We construct it using the percent of paved roads in the exporting and importing countries, as well as in all transit countries along the set of minimum distance routes used to calculate international distances as set out above. The paved roads data are weighted in each case by the proportion of the total distance traveled in each country. Our second measure ( _paved_min_ ) is calculated using the same information, but taking the minimum percent of paved roads observed across the exporting, importing and transit countries. The purpose of this measure is to help identify bottlenecks and assess the potential for cross-country infrastructure spillovers. An incidental benefit of our approach is that our quality measures are unlikely to suffer unduly from endogeneity to bilateral trade flows, since they depend also on policy decisions by countries not involved in a given bilateral relationship.

Interestingly, we find strong geographical concentration (65%) of _paved_min_ scores in just three countries: Albania, Hungary and Romania. To the extent that _paved_min_ is found to be a significant determinant of bilateral trade, this finding suggests that improved road quality in

- 8 -
Indonesia’s vanilla, _sM_ a market-share parameter, and ε _Ms_ the elasticity of supply of Madagascar’s vanilla, It is easily checked that _θM_ is increasing in the elasticity of supply and in the number of traders, as raising either of those parameters reduces their monopsonistic power.

The counterfactual experiment described above yields simulated values for _θM_ , _pMp_ and _XM_ which we then combine with household-survey data to generate a simulated income distribution. Finally, we recalculate the poverty headcount and the poverty gap, with results shown in the last columns of Table 7 (poverty gap and headcount and Gini coefficient) and in Figure 7 (actual and counterfactual kernel density estimates of the distribution of income in the vanilla region).

Figure 7 Kernel density estimates of income distribution, vanilla region <!-- Start of picture text --> Actual Counterfactual<br>6.0e-06<br>4.0e-06<br>2.0e-06<br>0<br>0 100000 200000 300000 400000<br>realfulldep<br>Counterfactual<br><!-- End of picture text --> Source: EPM, authors calculations.

Detailed results are shown in Annex Table A2. Note that the benchmark number of Malagasy traders consistent with observed markups under the Cournot 0 assumption ( _nM_ ) ranges between four and five, an estimate that is just consistent with evidence given in Blarel and Dolinsky (1995, p. 274) that five packers 29
Table A.3: Logit analysis, 1995‐2015, Frequency of ERRs in ICRs as dependent variable (Model 1)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Pres/Nopre|sPres/Nopre|sPres/Nopre|sPres/Nopre|sPres/Nopre|sPres/Nopre|sPres/Nopres|
|CPIA rating|0.319***<br>(3.45)|0.209**<br>(2.21)|0.362***<br>(3.87)|0.341***<br>(3.21)|0.329***<br>(3.54)|0.297***<br>(2.70)|0.271***<br>(2.77)|
|CPIA scale dummy|‐0.512***<br>(‐2.59)|‐0.523***<br>(‐2.62)|‐0.580***<br>(‐2.91)|‐0.428*<br>(‐1.94)|‐0.488**<br>(‐2.46)|‐0.496**<br>(‐2.20)|‐0.562***<br>(‐2.79)|
|Exit year sequence|‐0.950***|‐0.954***|‐0.955***|‐0.930***|‐0.854***|‐0.848***|‐0.922***|
||(‐7.74)|(‐7.70)|(‐7.76)|(‐6.52)|(‐6.74)|(‐5.66)|(‐7.02)|
|Quadratic form of exit year sequence|0.0152***|0.0152***|0.0152***|0.0148***|0.0135***|0.0133***|0.0145***|
||(7.93)|(7.91)|(7.92)|(6.61)|(6.83)|(5.63)|(7.12)|
|East Asia and Pacific|‐0.202|‐0.174|‐0.230|‐0.121|‐0.190|‐0.118|‐0.170|
||(‐1.36)|(‐1.16)|(‐1.55)|(‐0.70)|(‐1.28)|(‐0.67)|(‐1.11)|
|Africa|‐0.854***|‐0.603***|‐0.919***|‐0.770***|‐0.869***|‐0.598***|‐0.676***|
||(‐6.03)|(‐4.13)|(‐6.41)|(‐4.59)|(‐6.10)|(‐3.43)|(‐4.57)|
|East Europe and Central Asia|‐0.524***|‐0.217|‐0.532***|‐0.473***|‐0.521***|‐0.181|‐0.230|
||(‐3.45)|(‐1.38)|(‐3.50)|(‐2.68)|(‐3.43)|(‐0.99)|(‐1.46)|
|Latin America & Caribbean|‐0.575***<br>(‐3.91)|‐0.412***<br>(‐2.75)|‐0.584***<br>(‐3.96)|‐0.521***<br>(‐3.04)|‐0.586***<br>(‐3.97)|‐0.387**<br>(‐2.21)|‐0.417***<br>(‐2.76)|
|Middle East & North Africa|‐0.364**|‐0.208|‐0.436**|‐0.263|‐0.356*|‐0.157|‐0.256|
||(‐1.97)|(‐1.11)|(‐2.34)|(‐1.22)|(‐1.92)|(‐0.71)|(‐1.35)|
|Agriculture|2.359***<br>(20.40)|2.387***<br>(20.36)|2.359***<br>(20.35)|2.447***<br>(18.27)|2.366***<br>(20.40)|2.478***<br>(18.17)|2.377***<br>(20.13)|
|Energy & mining|2.743***|2.625***|2.749***|2.807***|2.727***|2.688***|2.624***|
||(19.83)|(18.70)|(19.84)|(17.36)|(19.69)|(16.34)|(18.62)|
|Transport|3.289***<br>(24.14)|3.105***<br>(22.48)|3.282***<br>(24.05)|3.326***<br>(21.23)|3.279***<br>(24.01)|3.143***<br>(19.69)|3.106***<br>(22.24)|
|Water|2.482***|2.412***|2.461***|2.500***|2.467***|2.400***|2.376***|
||(15.84)|(15.24)|(15.67)|(13.90)|(15.71)|(13.17)|(14.97)|
|Urban Development|1.485***<br>(8.73)|1.424***<br>(8.27)|1.500***<br>(8.79)|1.582***<br>(8.04)|1.482***<br>(8.70)|1.532***<br>(7.65)|1.430***<br>(8.28)|
|IEG evaluation method (PPAR=1)||0.317***||||0.307***|0.300***|
|||(8.49)||||(7.03)|(7.42)|
|Log net commit. (m$)|||0.0811***|||0.0848***|0.0650***|
||||(3.62)|||(3.19)|(2.78)|
|Project length||||0.132**||0.102*||
|||||(2.38)||(1.80)||
|TTL performance|||||0.00925***|0.00837**|0.00568*|
||||||(3.15)|(2.35)|(1.86)|
|Closing extension (months)|||||0.00925***|0.00837**|0.00568*|
||||||(3.15)|(2.35)|(1.86)|
|Observations|4020|4020|4020|3065|4009|3057|4008|
|Pseudo R‐squared|0.259|0.274|0.262|0.266|0.261|0.284|0.278|
|t statistics in parentheses|*p<0.10,**|p<0.05,***|p<0.01|Estimation|method: Log|it||

Note: t statistics in parentheses, * p<0.10, ** p<0.05, *** p<0.01 29
**33**

among tradable crops (often export crops) and non-tradables (most often food staple crops) to avoid falling price effects.<sup>30</sup> In Asia on the other hand, where the landless account for an important share of the poor and the labor markets are much better developed, technological change for maximum employment creation (i.e. land saving and not labor saving technologies) is advised for maximal poverty reduction. Benefits from agricultural technological change come mostly from linkage effects through the rest of the economy in Latin America, leading to the possibility that technological changes in the fields of large farmers can be more beneficial to the poor farmers than the direct effects derived from technological change on their own farm.

# **7 Concluding Remarks**

To analyze the role of agriculture in poverty reduction, the paper developed a simple conceptual framework in which the relative contribution of a sector to poverty reduction is shown to depend on four factors: its direct (1) and indirect (2) growth effects as well as its elasticity of total poverty to sectoral growth (3) and the sector’s share in the overall economy (4) which together determine the sector’s participation effect. Reviewing the evidence on the growth contributions of agriculture and the non-agricultural sector, it emerges that while the agricultural sector on average has grown more slowly than non-agriculture, this appears to be largely due to a migration out of agriculture into non-agriculture. The latter could simply be due to equilibrating labor movements out of agriculture into non-agriculture in response to more productive opportunities in nonagriculture (industrial pull). Yet, the more rapid increase in agricultural TFP historically observed in both developed and developing countries lends support to the view of agriculture as a dynamic sector with substantial growth potential whereby productivity increases in agriculture induce labor to move out of agriculture (agricultural push).

In addition, both the micro and the cross-country evidence indicate that the indirect or growth linkage effects from agriculture to non-agriculture appear quantitatively large and at least as

> 30  See World Bank (2005e) for a detailed discussion of this argument in Ethiopia.
-34-

Rati: V15 LkerTable S

||CottPer C|eeltaprodyat|is"|
|---|---|---|---|
|1970<br> <br>|175|16|,|
|Argenwt<br>0.60|0.60|0.40|0.36|
|OrallI<br>0.21|0.17|0.10|0.14|
|Colombia<br>0.16|0.16|0.17|0.10|
|Chi1e<br>0.16|0.11|0.10|0.17|
|Usule<br>0.6<br>|0.17|0.10|0.37|
|Per.<br>o<br>.<br>JJ<br>o9l|**JJs-**|||
|o0.n|0.1|0.24|0'f1|
|3d<br>(0.18)|(0.16)|(0.10)|(0.05)|
|Kaayp<br>O.51|0.441|0.4"|0.47|
|Mreeee<br>0.4t|0.54|0.40|O.5|
|Umlami<br>0.17|0.16|0.14|0.15|
|NigerIa<br>0.10|0.11|0.11|0.10|
|Teacania<br>0.40|0.U I|0.42|0.40w|
|Zeab1<br>0.36|0.24|0.21|0.20|
|31-^^E<br>0.14|0.15|0.16|**0.16**|
|U<br>(0.15)|(0.18)|(0.14)|(0.10)|
|India<br>O."|0.70|0.90|0.56|
|Pinkib<br>e<br>0.10|0.14|0.16|0.14|
|SriLak.<br>_AN_|J,Al<br>OUM|J4AMI||
|**sn**<br>0.41|<br>0.4n|0.48|0.18|
|3d<br>(0.22)|(0.14)|(0.11)|(0.21)|
|oW"ng<br>K..<br>0.44<br>|0.42|0.40|o.n7|
|Kores<br>0.20|0.17|0.22|0.1n|
|Singpore<br>0.14|0.22|0.16|0.11|
|**£**<br>0.21|0.22|0.16|0.28|
|SI<br>(0.18)|(0.11)|(0.11)|(0.05)|
|@r.ee<br>_0.84<br>|0.41|0.40|0.5U|
|Portugal<br>O.|0.87|AM|0.4|
|3<br>0.16|0.54|0.50|0.51|
|3d<br>(0.02)|(0.16)|(0.01)|(0.06)|
|Avetrim<br>64.0*|0.76|0.31|0.75|
|Goemny<br>62.70|0.07|0.70|O."|
|Spin<br>sO.?t|0."|0.57|0.46|
|<br>awed<br>.<br>71.70|O.S|0.7n|O."|
|United Kingdo"<br>5.1|0.62|0.61|0.51|
|<br>USA<br>U-.1|0.51|0.51|0.47|
|caned.<br>40.00|0."|O."|0.60|
|x<br>**0."**|0.01|0.00|O.5|
|d<br>(0.11)|(0.10)|(0.14)|(0.12)|

Save e Table 2 a*d UMnutctrlng Doaa S4e (610), The World Sbe". for OeMrGe o O? yonre lnd.ated with *, the source In World Tablo. 1597. **x:** Arlt&bmti averae; Sd **a** Stand"rd deviation. 1 Oata e uanstat4ri,e predwlefti correspod to 167. 2 Dat on manufacturing productieo cerre pend to 1964.
[21] [22] ,, _China's Energy Science and Technology Development Outlook for the 21st Century_ , _Electric Power Supervision Annual Report 2010_<sup>[23]</sup> , and other literature on technical parameters. and economic analysis of power production.

# **B3. End-use energy demand module**

The demand for end-use energy services in our TIMES model is classified into four major final demand sectors: buildings, industrial, transportation and agricultural. End-use energy demands are projected based on the relationships between energy demand and corresponding drivers. The relationships are established based on statistical analysis (e.g., regression analysis) using historical data. The end-use demand forecasts differ across the final demand sectors. The transportation sector is divided into different modes: road, rail, air and water. Each mode has two types of transportation services: passenger and freight. The industrial sector is divided into sub-sectors and technologies. The building sector is divided into: urban residential buildings, rural residential buildings, general public buildings and large public buildings. In each type of building, end-use energy demand is divided into heating, refrigerating, lighting, cooking, hot water and electronic equipment.

In the base year (2015), the model is calibrated with detailed data. The data for sectoral and sub-sectoral energy consumption are compiled from Energy Balance and the Industrial Statistical Yearbook. Table B3 shows data used in the base year by industrial sector category.

**Table B3** Energy consumption of each sub-sector of the industrial sector in the base year

|(Mtce)|
|---|

||Total|Coal|Oil|Gas|Electricity|Heat|
|---|---|---|---|---|---|---|
|Chemical|229.23|93.86|47.60|28.00|38.65|<br>21.12|
|Non‐metal|228.38|162.96|26.95|7.92|30.09|<br>0.45|
|Non‐ferrous metal|65.03|15.27|5.64|2.36|38.46|<br>3.31|
|Paper|28.47|15.72|0.89|0.23|6.58|<br>5.06|
|Textile|36.66|11.97|1.45|0.37|15.69|<br>7.18|
|Black metal|473.39|76.92|2.69|43.34|56.68|<br>8.44|
|Others|498.14|116.30|119.58|33.59|161.85|<br>27.13|
|Total|1559.30|493.00|204.80|115.80|348.00|<br>72.70|

Transport energy demands are derived based on vehicle type, fuel type, technical efficiency and fuel economy and different emission standards. In the base year, transport energy demands are as shown in Table B4.

**Table B4** Transportation sector data for the base year

|Road gasoline/Mt|99.47|
|---|---|
|Highway diesel/Mt|115.3|
|Railwaydiesel/Mt|9.79|

43
<!-- Start of picture text --> GCC ASEAN SAR MENA AFR LAC OECD ECA<br> Financial Telecom Retailing<br> Transportation Professional<br>Note1: Comparing regional STRIs based on 2008 data; ASEAN region has 6 countries<br>Note2: EAP is excluded, as it has only CHN and MGL<br>Note: The services trade restrictions index (STRI) at the regional level is calculated as a simple average of individual country’s<br>STRIs. The STRI in the cross-border air passenger transport subsector is excluded. Regional abbreviations: GCC – Gulf<br>Cooperation Council, ASEAN- ASEAN Member countries, SAR – South Asia Region, MENA – Middle East and Northern<br>Africa, AFR – Sub-Saharan Africa, LAC – Latin America and Caribbean, OECD – High income OECD, ECA – Europe and<br>Central Asia. The financial STRI includes scores for retail banking mode 1 and mode 3, auto mobile, life, and reinsurance mode<br>1 and mode 3 respectively. Telecom STRI includes scores for fixed line and mobile. Retailing STRI includes scores for retailing<br>mode 3. The transport STRI includes STRIs for air passenger international mode 3, maritime international mode 1 and mode 3,<br>road freight mode 3, and rail freight mode 3. Professional services STRI includes scores for accounting, auditing, legal advisory<br>on domestic law and foreign law in mode 1, mode 3, and mode 4. For comparability, the STRI scores for education, medical<br>services, and some other professional services subsectors are excluded.<br>Figure 2: STRIs by industry for ASEAN member countries, 2012<br>ASEAN Member States: Industry STRI, 2012<br>IDN PHL THA LAO MYS VNM MMR KHM SGP<br> Financial Telecom Retailing<br> Transportation Professional<br>Note: Sectors are comparable with 2008 sectors<br>100<br>80<br>60<br>40<br>20<br>Services trade restrictiveness index<br>0<br>100<br>80<br>60<br>40<br>20<br>Services trade restrictiveness index<br>0<br><!-- End of picture text --> Note: The services trade restrictions index (STRI) at the regional level is calculated as a simple average of individual country’s STRIs. The STRI in the cross-border air passenger transport subsector is excluded. Regional abbreviations: GCC – Gulf Cooperation Council, ASEAN- ASEAN Member countries, SAR – South Asia Region, MENA – Middle East and Northern Africa, AFR – Sub-Saharan Africa, LAC – Latin America and Caribbean, OECD – High income OECD, ECA – Europe and Central Asia. The financial STRI includes scores for retail banking mode 1 and mode 3, auto mobile, life, and reinsurance mode 1 and mode 3 respectively. Telecom STRI includes scores for fixed line and mobile. Retailing STRI includes scores for retailing mode 3. The transport STRI includes STRIs for air passenger international mode 3, maritime international mode 1 and mode 3, road freight mode 3, and rail freight mode 3. Professional services STRI includes scores for accounting, auditing, legal advisory on domestic law and foreign law in mode 1, mode 3, and mode 4. For comparability, the STRI scores for education, medical services, and some other professional services subsectors are excluded.

**Figure 2: STRIs by industry for ASEAN member countries, 2012**

10
<!-- Start of picture text --> iQNRPOS QNTMD iQNWS<br>NN N<br>©©o<br>2oO 2 2<br>> oO> oO><br>or or or<br>& € =<br>fe) fey fe)<br>£ £ £<br>fo)ov fo)al alfo)<br>D DD DD<br>g ~ g UN |<br>\ \ NYLON w-~NSY \<br>° - ° L/S \_ °<br>2019m1 2020m1 2021m1 2022m1 2023m1 2019m1 2020m1 2021m1 2022m1 2023m1 2019m1 2020m1 2021m1 2022m1 2023m1<br><!-- End of picture text -->

DiD coef.: 0.216* (0.117) N treated=14, N control=369 <!-- Start of picture text --> DiD coef.: 1.814*** (0.222) N treated=9, N control=369<br><!-- End of picture text --> DiD coef.: 0.582** (0.295) N treated=7, N control=369 <!-- Start of picture text --> Ws<br>*<br>3 ted<br>‘o<br>><br>2<br>£<br>om<br>L#]<br>Ss<br>o<br>mo<br>Le]<br>=<br>o<br>2019m1 2020m1 2021m1 2022m1 2023m1<br>DID coef.:0.517" (0.311) N treated=6, N control=219<br><!-- End of picture text -->