rafmacalaba/datause-encoder-data huggingface dataset:
Recent events in North-African, Middle Eastern and some European and Latin American countries illustrate that conflicts over labor relations primarily arise in times of economic or political transformation: many of these societies maintained seemingly high levels of social stability for long periods of time, in spite of relatively large underlying differences in economic opportunities. However, these latent conflicts became politically ‘salient’ in the light of changing macro-economic and political outlooks.

Additionally, the extent and duration of conflicts in times of economic transformation can depend on the wider institutional and social context of a society. Economic theory suggests that societies with more consensual institutions will be better able to absorb conflicts between winners and losers of economic and political transformations (Fernandez and Rodrik 1991, Alesina and Drazan 1991). While empirical evidence on the specific link between national institutional contexts and labor-related conflict is still relatively scarce (see below), there is at least indirect empirical support for this hypothesis. Rodrik (1999) for instance, finds that societies with more consensual institutions generally experience fewer and shorter periods of economic crisis (see also Easterly et al. 2006). Other studies document more general associations between overall levels of social cohesion and slower moving institutional attributes of a country, including initial levels of economic inequality, ethnic fractionalization or the type of welfare state regime adopted by a society (Knack and Keefer 1997, Easterly 2001, Easterly and Levine 1997, Easterly et al 2006, Larsen 2007, Rothstein and Uslaner 2005).<sup>5</sup>

# **III. Existing evidence and gaps in the literature**

Even though the link between labor market policies and economic outcomes is relatively well documented in industrialized societies (for overviews see Lindbeck and Snower 2002, Freeman 2008), evidence for many parts of the developing world is less clear. Cross country comparisons that show that labor market regulations lead to increased inequalities in the workforce include Heckman and Pages (2000) and Botero et al. (2004). However, other studies find no or inconsistent linkages between worker protection and economic performance. For instance Forteza and Rama (2006) find no effect of Employment Protection Legislation (EPL) on the depth and durability of economic crisis in a sample of over 100 transition economies. But they show that the extent of labor organization deepens economic crisis and slows down the adjustment process. Freeman (2009) finds no consistent evidence between the level of labor regulation and growth rates (see also OECD 2011 and Calderon and Chong 2005, cited in Freeman 2009).

> 5 These studies variably measure social cohesion by national levels of self-reported trust, civic attitudes, social associations, political stability, civic liberties and violent conflict.

6
reaching commitments during their WTO accession negotiations. Lao PDR’s accession negotiations do not seem to have led to bindings which are as close to applied policies.

# **7.3 Comparing the regional and multilateral dimensions**

To bring all the dimensions together, ASEAN countries’ GATS commitments and Doha offers were far more restrictive than their AFAS commitments (with a gap of about 23 STRI points). Thus, there is no doubt that the countries – especially Myanmar - have displayed a far greater willingness to widen and deepen their legal bindings in the regional than the multilateral context. The two exceptions are again the recently acceding countries, Cambodia and Vietnam, which have made WTO commitments close to their AFAS commitments. But the ASEAN countries’ AFAS commitments themselves remained more restrictive than applied policies, as we saw above. And the gap between applied policy and Blueprint 2015 was still large (about 20 STRI points).

**Table 1: Restrictiveness of GATS commitments, Doha offers, AFAS commitments,** **<u>applied policy and Blueprint goals by country</u>**

|**Country**|**Restrictiveness**<br>**of GATS**<br>**Commitments**|**Restrictiveness**<br>**of Doha Offers**|**Restrictiveness of**<br>**AFAS**<br>**commitments**|**Restrictiveness**<br>**of applied**<br>**policy**|**Restrictiveness**<br>**of Blueprint**<br>**goals**|
|---|---|---|---|---|---|
|BRN|89.3|89.3|65.2|No data|19.1|
|IDN|78.2|74.0|49.5|48.3|19.1|
|KHM|24.1|23.4|18.5|10.0|19.1|
|LAO|76.0|75.3|55.3|44.6|19.1|
|MMR|100.0|100.0|42.8|26.4|19.1|
|MYS|76.0|73.1|54.2|42.3|19.1|
|PHL|78.7|78.7|55.0|48.6|19.1|
|SGP|60.4|59.8|30.5|10.8|19.1|
|THA|80.4|70.0|58.5|43.8|19.1|
|VNM|38.3|38.3|36.4|36.0|19.1|

**Table 2: Restrictiveness of GATS commitments, Doha offers, AFAS commitments, applied** **<u>policies and Blueprint goals by sector</u>**

|**STRI by sector**|**Restrictiveness**<br>**of GATS**<br>**Commitments**|**Restrictiveness**<br>**of Doha Offers**|**Restrictiveness of**<br>**AFAS**<br>**commitments**|**Restrictiveness**<br>**of applied**<br>**policy**|**Restrictiveness of**<br>**Blueprint 2015**<br>**goals**|
|---|---|---|---|---|---|
|**Overall**|**70.1**|**68.2**|**46.6**|**31.1**|**19.0**|
|**Financial**|**60.0**|**60.0**|**51.3**|**26.4**|**19.5**|
|Banking|64.6|64.6|59.0|24.6|21.3|

24
little from trade liberalization, whereas poverty in the South as well as in the Center-West decreases quite substantially due to the importance of agricultural income for the poor in these regions.

_<u>Table 20: Poverty impact of trade, agricultural stayers by owning land</u>_

||2001 levels|<sup>BaU 2001-</sup><br>15 changes<br>Landowner|Doha % of<br>BaU<br>change<br>households|Full % of<br>BaU<br>change|2001 levels <br>N|<sup>BaU 2001-</sup><br>15 changes<br>o land owni|Doha % of<br>BaU<br>change<br>ng household|Full % of<br>BaU<br>change<br>s|
|---|---|---|---|---|---|---|---|---|
|P0|37.1|-10.5|101.7|108.0|48.5|-12.5|101.9|110.6|
|P1|16.6|-6.2|102.7|109.1|22.2|-7.4|102.3|108.4|
|P2|9.5|-3.9|102.7|109.4|13.1|-5.0|102.2|107.8|
|Population %|38.4||||61.6||||

Source: Authors’ calculations.

Table 20 shows the poverty changes for landowners and agricultural households who do not own land separately. The landowning households account for approximately 40 percent of the population in agricultural households. The differences between the two groups of agricultural households are quite striking. The poverty incidence among landowning households is much lower; the difference is more than 10 percentage points. Poverty decreases quite substantially for both groups in the BaU. Note that we only consider households who stay in agriculture. Poor households who do not own land benefit little from the Doha round, but full liberalization brings about an additional decrease of more than 1 percentage point in the headcount (affecting almost 100 000 individuals). The reason why they benefit more from both trade scenarios is that they are more specialized in agricultural income. This is not necessarily what one would expect, but it may well be that owning land provides the resources to set up a small nonagricultural business. Noteworthy is the finding that under both trade scenarios poverty gap as well as severity index decrease stronger than the headcount in terms of the BaU change. This again has to do with the specialization of households. The poorer landowning households derive a higher share of their income from agriculture whereas the richer households (at least among the poor) earn a higher share of income from nonagricultural activities.

49
10 in those of international organizations. They consistently underestimated the actual declines in GDP (Table 1). Of course, some of the discrepancy between the expectations and outcomes may be explained by political developments that few could predict. The collapse of the Soviet Union and the breakdown of the CMEA severely disrupted trade flows and led to large short-run output declines.”!

# Fact 2. Emergence of unemployment and increased poverty.

Higher social costs are reflected in two things: unemployment and increase in poverty. Indeed the two go to some extent together. The linkage between unemployment and poverty is stronger in countries where unemployment rates are high, and where, even more importantly, the share of the long-term unemployed (those unemployed for more than a year) is greater. This is because unemployment benefits usually run for one year,” and those who are unemployed for longer either lose the eligibility altogether or receive only a fraction of earlier benefits.” In Poland in 1993, for example, poverty of 36 percent of the poor could be related to unemployment. Poverty of half of those 36 percent was due to long-term unemployment.

"According to some estimates, up to a third of the output decline in the FSU may be attributable to trade disruption. For Eastern Europe, the estimates range from 8.5 percent of GDP for Czechoslovakia to 6.3 percent for Poland (see Rosati, 1993).

221n some countries (the Baltics) for even less—6 months; in Armenia, 3 months only. In Ukraine, unemployment benefits are (in 1995) to be cut from 3 months to 1.

In Romania, for example, the long-term unemployed receive, at best, only an income support equal to about 11 percent of the average wage, while the minimum unemployment benefit is about 25 percent of the average wage (see Subbarao and Mehra, 1995, p3). In Poland, (flat) unemployment benefits are 35 percent of the average wage; the maximum amount of social assistance is 28 percent of the average wage. Even in Russia where the position of the unemployed is notoriously bad, an unemployed worker who was earning average wage before being laid off will receive more during the 15 months while unemployment benefits last than after provided rate of inflation remains less than about 400 percent per year. (This is because unemployment benefits are normally unindexed. Social assistance after expiry of the benefits is equal to the minimum wage which is, in the second half of 1994, was about 8 percent of the average wage. Calculated from Layard (1994, p.102).
As a conclusion, the finding that a substantial proportion of HIV infected couples are discordant female appears robust to alternative explanations, with the potential exception of Ghana, an to a lesser extent Tanzania, where HIV infection before marriage and bias due to covering of HIV testing might be concurring – but not alternative - explanations. It therefore seems that this result is difficult to explain without infidelity among married women, even if, as illustrated in table 4, very few women report sexual activity outside their union. This suggests either that extra-marital sex by women in union is more common than reported or that this is a very risky activity. De Walque (2006) reports that married women who engage in extra-marital sex are less likely to use a condom than single women or married men. In both cases, this implies that extramarital sexual activity among cohabiting women, whatever its causes, is a substantial source of vulnerability to HIV that should be, as much as male infidelity, targeted by prevention efforts.

The discrepancy between the substantial fraction of HIV infected couple where only the female is HIV positive and the very low levels of self-reported infidelity among married women also suggests that self-reported sexual behaviors might be particularly prone to bias and that this bias might vary in direction or magnitude according to the gender of the respondent. When an HIV test is taken, it is not possible to lie about one’s HIV status. It is much easier to be less accurate about one’s sexual life. The next section investigates further how couples diverge in reporting their behaviors.

# **V Discordance in reported behaviors**

Gersovitz (2005) discusses the issue of self-reporting sexual behaviors in the Demographic and Health Surveys and shows several inconsistencies, in particular regarding the age at first sexual intercourse and virginity. This section addresses the bias in self-reported behaviors from two angles.

First, I compare the way male and female partners in the same union report sexual behaviors. For some behaviors, like infidelity (table 4) and voluntary counseling and testing (table 3) discordant reports by husband and wife are possible: one partner can be 16
**Table A.17:** Dynamic Effects on Public Goods Provision

||2015-2020|2015-2017|2018-2020|2021-22 (placebo)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|New village head|0.503<sup>_∗_</sup>|0.902<sup>_∗_</sup>|0.440|-0.364|
||(0.263)|(0.529)|(0.340)|(0.549)|
|Observations|378|122|256|134|
|Control mean|0.23|0.33|0.15|0.45|
|Robust p-value|0.053|0.063|0.165|0.615|
|MSE-opt. bandwidth|18.7|18.4|19.0|22.2|
|Effective obs.|161|52|109|80|

_Notes_ : This table reports RD estimates of _γ_ in equation (1). The dependent variable is the index of local public service provision constructed using the 2021 Podes data. We restrict the sample to villages that conducted their most recent election between 2015-2020 (column 1); between 2015 and 2017 (column 2) or between 2018 and 2020 (column 3). In column 4, we restrict the sample to villages that conducted their most recent election in 2021 or 2022, namely after data collection for the 2021 Podes survey. Thus, these regressions can be interpreted as placebo checks.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors clustered by village in parentheses.

**Table A.18:** Effects on Citizens’ Perceptions of Service Provision: Index Components

||**In**|**dex**|Wa|ter|Gar|bage|Ro|ads|Elect|ricity|Kinder|garten|Sch|ools|Hea|lth|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|(16)|
|New village head|0.073<sup>_∗∗_</sup>|0.208<sup>_∗∗_</sup>|0.035|0.057|0.208<sup>_∗∗_</sup>|0.364<sup>_∗∗_</sup>|0.149<sup>_∗∗_</sup>|0.351<sup>_∗∗_</sup>|0.025|0.164|0.003|0.047|0.009|0.037|0.030|0.157|
||(0.043)|(0.102)|(0.102)|(0.211)|(0.097)|(0.170)|(0.077)|(0.186)|(0.023)|(0.136)|(0.078)|(0.145)|(0.080)|(0.182)|(0.047)|(0.150)|
|Observations|8848|8846|8797|8771|8783|8817|8842|8836|8839|8837|8828|8741|8834|8794|8833|8798|
|Control mean|0.78|-0.028|0.72|0.027|0.37|-0.16|0.91|0.073|0.99|-0.058|0.79|-0.016|0.76|-0.043|0.93|-0.023|
|Robust p-value|0.039|0.014|0.624|0.630|0.017|0.022|0.017|0.021|0.231|0.117|0.917|0.630|0.863|0.730|0.347|0.163|
|MSE-opt. bandwidth|15.2|14.9|17.7|16.0|23.0|26.5|12.4|13.3|28.8|23.9|21.2|29.0|22.9|20.7|21.0|18.7|
|Effective obs.|3479|3427|3897|3533|4575|5140|2898|3165|5536|4801|4403|5465|4579|4309|4349|4057|

_Notes_ : This table reports RD estimates on the indices of service access and quality used in Table 7, columns 2 and 3, and individual components of the indices. Odd-numbered columns report effects on perceived access and even-numbered columns report effects on perceived quality. p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors clustered by village in parentheses.
29

**Table 7. Global transport energy consumption, 1985**

|Region|Transport Energy<br>in Quads'|Regional<br>Share(%)|
|---|---|---|
|United States|20.0|35.4|
|Canada and Western Europe|13.0|23.0|
|Eastern Europe (including USSR)|8.4|14.9|
|Japan, Australia, New Zealand|4.4|7.8|
|Latin America and Caribbean|4.1|7.3|
|South and East Asia|2.5|4.4|
|Africa|2.0|3.5|
|Middle East|1.0|1.8|
|China|0.9|1.6|
|Global Total|**_56.3_**|**100.0**|
|_- Industrialized_|_44.6_|_79.6_|
|_- Developing_|_11.4_|_20.4_|

1 Quad is equivalent to 1 trillion Btu of energy Source: [Parson 1989]

**Table 8. Share of transport in regional energy consumption, 1985**

<!-- Start of picture text --> Region Transport Share of Regional<br>Energy Consumption<br>(percent)<br>United States 35.9<br>Canada & Western Europe 31.4<br>Eastern Europe (including USSR) 17.8<br>Japan, Australia, and New Zealand 36.4<br>Latin America and Caribbean 33.3<br>South and East Asia 24.1<br>Africa 39.6<br>Middle East 16.9<br>China 6.7<br>Global Total 27.5<br><!-- End of picture text --> Source: [Parson 1989]
Table 8: Impacts of decentralizing demonstration are fully explained by demonstration and connections to demonstrators

|||O<br>po|ne year<br>st-demo||Two years<br>post-demo|
|---|---|---|---|---|---|
||Pro|moted cr|ops|Placebo crops|Promoted crops|
||||Imp<br>|roved seed<br>||
||(1)|(2)|(3)|(4)|(5)|
|Intercept|0.032|0.029||0.004|0.023|
||(0.014)<br>[0.023]|(0.017)<br>[0.093]||(0.002)<br>[0.045]|(0.010)<br>[0.024]|
|Any demo in village|0.025|0.024||0.031|0.020|
||(0.026)|(0.025)||(0.013)|(0.014)|
||[0.334]|[0.335]||[0.015]|[0.166]|
|Shared demo in village|0.000|0.000||-0.011|0.012|
||(0.028)|(0.028)||(0.016)|(0.016)|
||[0.994]|[0.996]||[0.508]|[0.455]|
|Decentralized demo in village|0.008|0.008||-0.016|-0.018|
||(0.031)|(0.031)||(0.017)|(0.017)|
||[0.804]|[0.792]||[0.330]|[0.285]|
|Demo|0.127|0.128|0.102|0.017|0.049|
||(0.032)|(0.032)|(0.043)|(0.012)|(0.018)|
||[0.000]|[0.000]|[0.020]|[0.152]|[0.007]|
|# of connections to demo|0.017|0.017|0.026|0.000|0.001|
||(0.007)|(0.007)|(0.009)|(0.003)|(0.003)|
||[0.013]|[0.015]|[0.006]|[0.901]|[0.753]|
|Veteran cultivator||0.029||||
|||(0.016)<br>[0.079]||||
|# of connections||0.000<br>(0.002)<br>[0.956]||||
|Farmer FE|||X|||
|Pre-demo year sample only|X|X|X|X|X|
|# of observations (Farmer-crop-wave)|2,223|2,223|2,223|3,442|2,199|
|# of clusters (Farmer group)|70|70|70|70|70|

_Notes:_ Regression coefficients from Equation 10 are presented above in Column 1, with variants on Equation 10 in Columns 2 through 5, and with included control variables listed below. Robust standard errors clustered at the farmer group level are in parentheses, and p-values in brackets.

agriculture. Foster and Rosenzweig (1995) estimate the effect of others’ experiences in one’s village on profitability and initial adoption of improved seeds is between 1.2 and 2.7 times as large as the effect of own experience; these estimates imply between 47
dies are more strongly affected than other households, and the effect declines somewhat when broader categories of relatives are added. In view of the results, this would also suggest that living in a household in which a non-relative dies has less of an impact on consumption changes. Although not reported, this is confirmed in the data. Again, female deaths dominate the results and male deaths do not appear to have a significant impact.

# **Conclusions**

More than 20 years after the AIDS epidemic surfaced in Africa, it is perhaps surprising that there are significant gaps in empirical evidence of the impact of HIV/AIDS in regards to the income of surviving household members. This paper uses unique longitudinal data designed to measure this impact. In the broader context, these results can be placed in the context of macroeconomic studies which draw competing conclusions about the implications of the AIDS epidemic for national levels of income per capita. We provide evidence that adult mortality shocks, especially of females, have a large impact on the growth in consumption of surviving household members in a region of Tanzania that has been seriously afflicted by the HIV/AIDS epidemic In our sample, 22% of the households experienced an adult death between 1991 and 2004. We find significant and robust evidence that the impact of a prime-age death results in a 7% drop in consumption in the first five years after the death. After five years the effect remains negative, but becomes smaller and more imprecise. Statistically, the effects are not significant, so there is no evidence of a persistent impact of shocks after five years, suggesting a strong recovery. Nevertheless, we should be cautious with this interpretation: Is there really no persistence to these serious shocks? The regressions are not be able to control for all other shocks and events, moving households up and down the consumption distribution, creating noise and 21
Our first set of controls includes dummy variables that indicate the country where the firm is located (country fixed effects). Thus, differences in overall economic development, financial development, and quality of macro-level institutions are purged from our estimation results.

Next, we control for firm-size (number of workers, logs). A greater allocative efficiency for larger firms and the presence of fixed costs that lead to economies of scale would imply a higher productivity for relatively larger firms (Tybout 2000; Bartelsman et al. 2013). However, the opposite case of diminishing labor productivity with firm-size is also possible due to decreasing returns to scale or diminishing returns to labor. Firm-size is also considered an important proxy measure for several firm attributes that could potentially affect labor productivity such as access to finance, access to raw materials and product markets, tendency to innovate, exporting activity, firm-efficiency and growth (see for example, Acs and Audretsch 1988; Cohen and Klepper 1996; Pagano and Schivardi 2003; Söderbom and Teal 2004; Diaz-Mayans and Sanchez 2008). Specific to the informal sector, Amin and Islam (2015) use Enterprise Surveys data on informal firms and find that labor productivity among informal firms declines sharply with firm-size. Informal firms are typically smaller compared to formal firms. Thus, the possibility of omitted variable bias resulting from the failure to control for firm-size cannot be ruled out.

The link between productivity and age of the firm has been extensively investigated. Age related effects among surviving firms can be explained by a range of factors, including scale economies gained from expansion over time, vintage effects due to younger firms employing new and improved technology or equipment, selection effects which weed out inefficient firms and imply higher productivity for the surviving older firms, and passive learning or learning by doing (see for example, Jovanovic 1982; Zimmerman 1982; Bahk and Gort 1993; Jensen et al. 2001; 12
# Table 12. Nomna Protection Afforded by "Tarflied" Nontariff Barriers Facing OECD Agriulural Imports.

||||Nomi|nal equivalent of ta|riffied nontariff barr|iers(X)|||
|---|---|---|---|---|---|---|---|---|
|Importing Country|Rice|Wheat|Coarse<br>Grains|Sugar|Beef and<br>Veal|Pork|Poultry|Dairy|
|Austnklia|0.0|0.0|0.4|52.4|0.0|0.0|0.0|6.7|
|Canada|0.9|57.7|34.7|34.7|38.0|0.0|226.0|288.4|
|United States|_5.0_|6.0|8.0|197.0|31.0|0.0|7.0|144.0|
|European Union|360.5|155.6|134.4|297.0|125.4|51.7|44.5|288.5|
|Japan|_500.0_|239.6|233.1|126.1|38.5|87.3|14.0|489.4|
|<br>New Zealand|0.0|0.0|7.2|2.9|0.0|20.0|28.5|19.6|
|Austria|0.0|400.0|241.0|178.0|239.0|178.0|38.0|463.0|
|Finland|10.0|352.0|204.0|493.0|394.0|320.0|264.0|389.0|
|Norway|454.0|495.0|394.0|na|405.0|428.0|379.0|435.0|
|Switzerland|67.0|179.0|242.0|273.0|479.0|227.0|767.0|795.0|
|Turkey|_50.0_|200.0|200.0|_150.0_|250.0|250.0|30.0|200.0|

'Delayed tariffication on this product. The figure shown is the estimated pre-Uruguay Round level of support.

Source: Merlinda Ingco, Agricultural Trade Liberalization in the Urucuav Round: One Steo Forward. One Sten Back?, (Washington: Supplementary Paper for the Conference on the Uruguay Round and Developing Economies, Washington, January 26-27, 1995).

**Un** **_4._**