rafmacalaba/datause-encoder-data huggingface dataset:
(1) (2) (3) Sign petition Attend demonstration Work on community problem

|Trust in police||-0.283***||
|---|---|---|---|
|||(0.0563)||
|Trust in parliament|0.0386<br>|0.110<br>||
||(0.0610)|(0.0652)||
|Trust in courts|0.0423||0.131**|
||(0.0581)||(0.0484)|
|Trust in elections|0.0956|0.0541|0.116*|
||(0.0549)|(0.0580)|(0.0466)|
|Trust in people|0.133*|0.133||
||(0.0639)|(0.0686)||
|Interested in politics|0.648***|0.814***|0.818***|
||(0.0473)|(0.0512)|(0.0410)|
|Free to join any organization|0.184***|0.155**|0.137**|
||(0.0467)|(0.0511)|(0.0420)|
|Free to speak|||0.102*|
||||(0.0416)|
|Has felt discriminated against|0.474***|0.584***|0.502***|
||(0.0515)|(0.0544)|(0.0448)|
|Has been victim of a crime|0.312***|0.334***|0.295***|
||(0.0463)|(0.0499)|(0.0403)|
|Has experienced food insecurity|-0.249***<br>(0.0552)|||
|Age|0.0104***|0.00551***|0.00854***|
||(0.00144)|(0.00157)|(0.00126)|
|Sex|||0.211***|
||||(0.0396)|
|Education level|0.144***|0.0954***|0.0783***|
||(0.0155)|(0.0169)|(0.0129)|
|Income level|0.0670***|0.0592***|0.0126|
||(0.0100)|(0.0106)|(0.00864)|
|Social class|0.110***<br>|||
||(0.0256)|||
|Receives subsidy|||0.100*|
||||(0.0429)|
|Employed|0.179***|0.206***|0.275***|
||(0.0487)|(0.0538)|(0.0435)|
|Owns a house|||0.152***|
||||(0.0431)|
|Has internet|0.156**|0.102||
||(0.0528)|(0.0582)||
|Has sewage||0.0882||
|||(0.0603)||
|_cons|-3.550***|-3.109***|-2.702***|
||(0.168)|(0.142)|(0.122)|
|N|14676|15232|15523|

Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
the EU in figures 9-11. The welfare results for preferential reduction of barriers in services with the Africa region, depicted in figure 6, are rather robust and somewhat skewed toward larger gains. A 95 percent confidence interval for equivalent variation as a percent of consumption is: .031 to .241 around a sample mean of .097. There is a negative value for equivalent variation in only two of the simulations.

The welfare results for a free trade agreement with the EU that includes services are depicted in figure 9. A 95 percent confidence interval for equivalent variation as a percent of consumption is: .217 to .877 around a sample mean of .399. There are no simulations with negative estimated welfare changes.

In figures 7 and 10, we show “box and whisper” diagrams for the sample distribution of the percentage change in output by sector. Sectors are on the horizontal axis and the percentage change in output is shown on the vertical axis. The bars in the box are the means of the distributions. Fifty percent confidence intervals are depicted by the boxes, while the vertical lines show 95 percent confidence intervals.

The means of the systematic sensitivity results show a similar pattern to the point estimates regarding the expansion of the services sectors. While the confidence intervals are rather tight for most sectors, they reveal a large range of uncertainty for several sectors. With respect to the EU agreement, while the sign of the direction of change does not change within the 95 percent confidence interval, the confidence intervals of expected output change are large for tourism, water transportation, cashew nuts. The sign does change, however, for the output effect on the coffee sector, suggesting considerable uncertainty for this result. Regarding negative output impacts, results appear rather robust within a 95 percent confidence interval. For the Africa agreement, most sectors have tight confidence intervals, but the insurance, water transportation and coffee sectors have a large range of uncertainty (including some sign changes on the direction of the impact), so the sensitivity analysis reveals that we can not a great deal of confidence in the sign of the impacts for these three sectors.

# **X. Results for Preferential Reduction of Barriers in Professional Services Liberalization**

48
# **Table A9: Share of households using fertilizer that could not access the desired amount.**

|**Burkina Fa**|Mean<br>**so**|Std. Err.|_N_|
|---|---|---|---|
|2023/01|45.7|3.8|397|
|2024/01|41.5|3.7|392|
|**Ethiopia**||||
|2023/01|49.8|3.4|369|
|2024/02|36.6|3.0|576|
|**Malawi **||||
|2023/04|68.8|2.7|705|
|2023/06<br>**Nigeria**|68.9|2.6|769|
|2023/06|47.8|2.3|1,096|
|**Tanzania**||||
|2023/11|47.3|3.9|367|
|**Uganda**||||
|2023/01|38.1|6.6|111|
|2023/10|14.2|3.3|184|

Note: Share of households using fertilizer that could not access the desired amount. All means in percent.

30
|Table 7: Cost Ef<br>|fectiveness Mul<br>|tipliers for<br>|Long-Term Out<br>|comes<br>|<br> <br>|
|---|---|---|---|---|---|
|Intervention|Outcome|Effect of<br>cash|Counterfactual|Effect of behavioral<br>intervention|Cost-effectiveness multiplier<br>=<br>ChaEaE EMCf bEhaECMaM ECEMEECECE<br>|
||||||<br> <br>EhaEaEEaEEaaECECEaMEaCh EeMEaMEC|
||MDAT<br>composite|.13*|.03|.09|<br><br>3.0|
|ML groups|Language<br>learning|.07|.02|.11*|5.5|
||Fine<br>motor<br>skills|.11|.03|-.01|-.33|
||Socialskills|.14*|.04|.09|2.25|
||MDAT<br>composite|.13*|.06|.08|1.33|
|ML<br>+<br>Affii|Language<br>learning|.07|.03|.10|3.33|
|rmaton|Fine<br>motor<br>skills|.11|.05|-.03|-.6|
||Social skills|.14*|.06|.11*|1.83|
||MDAT<br>composite|.13*|.06|.09|1.5|
|ML<br>+<br>Plan<br>|Language<br>learning|.07|.03|.12*|4|
|making|<br>Fine<br>motor<br>skills|.11|.05|-.03|-.6|
||Socialskills|.14*|.06|.09|1.5|

Notes: **1.** Amount of cash transfer: 120,000 ariary per year; additional cost of mother leader intervention: 30,000 ariary per year; additional cost of additional behavioral nudges: +24,000 ariary per year (so cost of ML+ nudges = 54,000 ariary) 26
countries, depending on whether the data are reported by the FAO or the United States Department of Agriculture.<sup>11</sup> Thus, for example, the differences in grain stock estimates vary by 13 percent in China, 32 percent in Indonesia, and 53 percent in Vietnam (similar to differences of over 40 percent in Brazil or Kazakhstan), according to AMIS (2012). With respect to safety nets—a critical instrument to mitigate the effects of high and volatile food prices and natural disasters—a recent World Bank report (World Bank 2011b) indicates that between 2008 and 2011, 80 out of the 137 countries analyzed had weak or nonexistent social welfare systems, and only 9 of these countries made a decisive effort to improve their systems. Of the nine countries, only one of them is in East Asia—Cambodia. Others, such as Thailand, Vietnam, and Malaysia, have not made major improvements. China already had strong capacity in this area and has continued to improve its social protection systems.

These arguments show the significance of the Asian contribution to the recent food crisis. Less cited, however, is the contribution that the region can make—and, in fact, is already making— toward being better prepared for future food crises. Despite the shortcomings mentioned above, several of the countries in Asia decided voluntarily to participate in AMIS. Recently, countries such as Pakistan and India have stepped up to fill the worldwide gap created by Thailand’s increased prices for rice exports. Also, the Asian region—along with Africa—leads the way in developing innovative agricultural production practices, which have come to be referred to as “smart climate agriculture.” The goal of this type of agriculture is to simultaneously offer increased agricultural productivity (thus reducing poverty and food insecurity); improved crop resistance to extreme weather conditions (adaptation); greater sequestration of carbon emissions; and curtailment of deforestation (mitigation). An illustrative—rather than exhaustive—number of examples of smart climate agriculture include programs for the restoration of mangrove 10
# **4.2 Results**

The Table 7 results show that market-based financial systems, a lower share of manufacturing, a higher degree of urbanization, less effective contract enforcement systems, and institutional development are associated with a higher share of household credit, even after controlling for an array of other country traits. Table 7 presents cross-country regression of the share of household credit in total credit on the different country characteristics discussed in the previous section. We first present regressions with the four separate groups of variables before testing their robustness in a horserace. Finally, we control for legal origin and religious composition, country traits that we showed are associated with the importance of enterprise and household credit relative to GDP. As these are simple OLS regressions, we do not imply any causality from our findings.

We start with a core group of variables that measure different demand and supply factors that capture the economic and financial structure. We find that more market-based financial systems, a higher share of manufacturing in GDP and a higher share of urban population are positively and significantly at the 5% level associated with a higher share of household credit in total credit (Column 1). A higher degree of informality in the economy, on the other hand, is associated with a lower share of household credit in total credit, although this result is significant only at the 10% level.

The Column 2 results suggest that countries with less effective contract enforcement have a higher share of household credit in total credit, while there is a positive association with the overall level of institutional development. Neither creditor rights, nor credit information sharing nor inflation, however, enter significantly in this regression. These findings suggest that while the importance of credit to households, as opposed to firms, increases as countries develop their overall institutional framework, effective contract enforcement is more relevant to firm credit. There are several explanations one could think of. In the case of small consumer loans, credit to household might rely on the law of large numbers and less on the contractual system in the first place. On the other hand, consumer loans for cars or for housing might be better secured than firm credit and therefore require less reliance on the formal contractual framework. The existence and efficiency of credit registries are not related to a higher share of household credit. In unreported regressions, we also considered the separate effect of private and public credit registries but could not find any significant relationship either. Macroeconomic stability – while 16
<!-- Start of picture text --> 200<br>150<br>100<br>50<br>0<br>0 20 40 60 80 100<br>Percentile of intensity of online sales<br>Wave 1 Wave 2 Wave 3<br>(May,2020-Oct,2020) (Nov,2020-Apr,2021) (May,2021-Nov,2021)<br>Value of online sales / Monthly sales 2019<br><!-- End of picture text --> Figure 12: Distribution of share of online sales across firms and waves Figure 13 also shows how, in relative terms, the differences in the share of online sales across firms in different sectors have closed. The relative mean deviation measures how disperse is the share of online sales is within each sector. An indicator closer to 1 shows high variance (i.e. some firms with high participation in online markets and many with low or no participation), and an indicator closer to 0 shows that all firms have the same share of online sales. The figure shows that even in sectors such as agriculture or manufacturing where at the start of the pandemic there was a large variance in terms of participation of online channels, the latest round of data shows a decrease in the relative mean. The largest drop comes from firms in the hospitality sectors. This result is consistent with the magnitude of the shock for these firms and how their recovery has relied on digital channels.

16
While such impediments are both explicitly discriminatory and non-discriminatory in character, those targeted specifically at foreign investors raise the cost of entry and limit the competitiveness of foreign invested firms once established. Such ‘trade costs’ are arguably more significant, and potentially more FDI-inhibiting, than the non-discriminatory ‘doing business’ impediments stemming from undue red tape or unduly onerous regulatory requirements that weigh equally on established foreign and domestic firms.

**Figure 16. Restrictiveness of services trade policies by sector and modes of supply, 2018**

<!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Mode 1 Mode 3 Mode 4 Overall<br><!-- End of picture text --> Source: OECD services trade restrictive index (STRI) 2018 It bears noting that the scope and focus of the concept for trade facilitation in goods embedded in the TFA has a very different context from the discussion now emerging for trade facilitation in services and investment. While the process of liberalization of trade in goods started with GATT more than half a century ago, the process of promoting the full transparency and liberalization of measures affecting international trade in services is of more recent vintage. Furthermore, 25 years after the negotiation of the General Agreement on Trade in Services (GATS), the level of market-opening progress reached on this front has been - and remains - very tepid. The question naturally arises of why the level of ambition of any proposed negotiations on trade facilitation in services should be so narrowly drawn and limited.

Further, even if governments insisted for political reasons on not undertaking any commitment to reduce the trade costs imposed by _discriminatory_ trade barriers, policy makers could at least consider undertaking the commitment of _status quo_ bindings regarding discriminatory conduct. If governments are genuinely committed to facilitating trade in services, they should at least consider not worsening the already restrictive level of discriminatory barriers obtaining in many services sectors and countries. From a domestic political perspective, undertaking such a commitment should prove viable since it would not require any parliamentary approval, and no interest groups - except those keen on further obstructing trade and FDI - should be affected as no departure from current practice would occur. Yet such a step 33
> <sup>19</sup>

access to MPLADS during this period: MPs elected in 1991 who faced elections again in 1996; MPs elected in 1996 who faced elections in 1998; and MPs elected in 1998 who faced elections in 1999. It is not possible to analyze the influence of specific legislator characteristics on MPLADS spending over the period 1993-1999 because we cannot disaggregate constituency spending between the 3 cohorts. However, the fourth cohort of MPs with access to MPLADS was elected in 1999; its term in office lasted until the next elections of April 2004, or four fiscal years. For this cohort we have constituency-level data on spending incurred by individual MPs over their term in office from 1999-2004. We therefore analyze the determinants of variation in MPLADS spending by this 1999-elected cohort of legislators across 483 electoral districts.<sup>18</sup> We first estimate the following basic specification to examine the role of political parties in determining legislator effort to bring public works to their constituencies: _UtilizationMPLADS d s_   1 * _PartyStronghold ds_   2 * _CandidateStronghold ds_   3 * _Reserved ds_   4 * _MarginVictoryds_   5 * _1993_  _99Spending ds_ (1)  _d_   _s_   _ds_ The left-hand side variable, _UtilizationMPLADS_ , is the actual spending incurred by the MP in constituency _d_ in state _s_ as a percentage of what the MP was entitled to spend on public works in his/her constituency. The entitlement includes the legacy of unspent procedures at that time. This was rectified in 1999 under the new implementation guidelines.

18 The total number of national electoral districts in India is 543. We drop 39 districts from our analysis because the Election Commission of India does not provide 1999 electoral data for these districts. We also omit 20 districts which held by-elections between 1999 and 2004, usually due to the death of the 1999-elected incumbent, thereby changing the identity of the politician in the middle of the term in office. One last district was dropped because of an apparent data error in which total MPLADS spending was reported as negative.
**- - -** **<u>Table 4: Estimated effects on built up structures from hand labelled pre and post intervention satellite imagery</u>**

||<br>**All struc**<br>|<br>**tures in squa**<br>|<br>**re meters**<br>|<br>**Finished st**<br>|<br>**ructures in sq**<br>|<br>**uare meters**<br>|
|---|---|---|---|---|---|---|
||**All**|**1000m**<br>**band**|**500m band**|**All**|**1000m**<br>**band**|**500m band**|
|Treatment*Post|-4.905***|-3.081**|-2.674|-5.085***|-1.293|-0.0620|
||(0.953)|(1.300)|(1.869)|(0.983)|(1.362)|(1.958)|
|Post|18.93***|17.96***|20.61***|21.38***|20.30***|21.69***|
||(0.830)|(0.977)|(1.421)|(0.856)|(1.023)|(1.489)|
|Constant|108.2***|97.91***|94.64***|101.5***|89.78***|86.84***|
||(0.289)|(0.456)|(0.653)|(0.298)|(0.477)|(0.684)|
|Observations|40,684|16,236|8,578|40,684|16,236|8,578|
|R-squared|0.0650|0.0730|0.0909|0.0796|0.0939|0.105|
|Mean dep. Var.|115.8|106.0|104.2|110.2|99.57|97.67|
|Mean dep. var. control 2013|68.59|71.51|76.06|61.34|64.80|70.44|
|Mean dep. var. treated 2013|134.8|133.2|126.1|130.6|128.1|120.4|

_Source:_ Own computation based on LAA’s land administration database and digitized records as well as hand-digitized plot size from 2013 and 2016 aerial photography as described in the text.

24
Profiling Green Jobs and Workers in South Africa: An Occupational Tasks Approach Jacqueline Mosomi<sup>1</sup> and Wendy Cunningham<sup>2 3</sup> Keywords: green jobs, green occupations, South Africa, task-based approach, labor market structure JEL Classification: J21, 055, Q01, Q50 1 Southern Africa Labour and Development Research Unit (SALDRU), UCT 2 The World Bank 3 We are grateful to Josefina Posadas, Abla Safir, Samantha De Martino, Gibson Mudiriza, Javier E. Baez, German Caruso and Elizabeth Ninan for detailed comments and to Debbie Budlender and the participants at a SALDRU-UCT seminar and the Inequality, Work and Nature (IWN) conference in Cape Town.
17 greater vigor. In the Cold War confrontation, victory for the democratic side could not be assured. But following the collapse of the Soviet Union, there was no real military challenge to the self-confidence of the democracies. Apart from military strength, they could take heart from the Freedom House statistic that free countries control 89% of the world’s GDP, and could sustain either an arms race or a prolonged war against any opponent in sight.

At least three recent wars can be interpreted in this perspective. The Kosovo War in 1999 was undertaken against the authoritarian government of Serbia with the stated purpose of preventing human rights violations in Kosovo. The outcome of the war is somewhat ambiguous as far as the long-term situation in Kosovo is concerned, but it was interpreted as an unqualified success in the sense that the Milosevic regime collapsed shortly after its defeat in the war. In 2001 the US (with support from the UN) unseated the Taliban regime in Afghanistan in retaliation for its support for terrorism. In 2003 regime change again seems to have played a role in the decision to go war against Iraq, although other issues (weapons of mass destruction, Iraqi support for terrorism) were touted more prominently in the hope of obtaining a better legal basis and greater international support for the intervention. In the public debate, the possibility of intervention to force regime change has also been discussed in relation to Iran, Syria, and North Korea. Such a strategy has not been suggested in the case of China. This could be interpreted as implying that the winds of internal change are sufficiently strong in China that a democratic transition will eventually happen without external intervention. But the reluctance to take on the world’s largest authoritarian country can also be interpreted in a democratic peace perspective: As Reiter & Stam (2002) argue, democracies tend to win the wars they participate in because they are better than autocracies at calculating their chances, and thus avoid taking on opponents that they cannot be sure to defeat.

But can democratic interventionism be documented beyond reference to individual wars, where stated and unstated goals can easily be contested, particularly in the absence of declassified records of the decisionmaking? In the following we shall first summarize some existing research on this topic, before reporting results from our own empirical study. We focus on four main issues: Do democracies engage in frequent interventionary behavior? Do they target mainly authoritarian states? Do these interventions tend to promote democratization? And what kind of democratization is promoted?
_Table 9: Heterogeneity in disaster exposure by plot manager characteristics_

||Poo|led|Ethi|opia|Mal|awi|M|ali|Ni|ger|Nig|eria|Tan|zania|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Dependent variable:**<br>Any disaster loss on<br>|(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|(7)<br>|(8)<br>|(9)<br>|(10)<br>|(11)<br>|(12)<br>|(13)<br>|(14)<br>|
|plot|No control|Control|No control|Control|No control|Control|No control|Control|No control|Control|No control|Control|No control|Control|
|Female plot manager|0.0222**|0.0267***|0.00350|0.0126|0.0529***|0.0272**|0.0843***|0.0614***|0.0503*|0.0475*|0.0386*|0.00467|0.0138|0.0355*|
|Age of plot manager|(0.00939)|(0.00904)|(0.0171)|(0.0169)|(0.0137)|(0.0123)|(0.0237)|(0.0215)|(0.0293)|(0.0280)|(0.0207)|(0.0165)|(0.0201)|(0.0186)|
|(decades)|0.00727***|0.00708***|0.00829*|0.00750*|0.00112|0.00407|0.0124***|0.0124***|-0.0137***|-0.00896*|0.00774|0.00564|0.0119**|0.00878*|
||(0.00249)|(0.00237)|(0.00457)|(0.00418)|(0.00446)|(0.00416)|(0.00443)|(0.00429)|(0.00525)|(0.00517)|(0.00542)|(0.00533)|(0.00523)|(0.00481)|
|Plot manager has<br>primary educ|-0.0140|-0.0138|-0.0176|-0.0258|-0.0467***|-0.00906|-0.0386*|-0.0178|-0.0609|-0.0594|0.0186|-0.00118|-0.0216|-0.00847|
||(0.0125)|(0.0124)|(0.0300)|(0.0285)|(0.0145)|(0.0140)|(0.0224)|(0.0216)|(0.0480)|(0.0473)|(0.0155)|(0.0179)|(0.0314)|(0.0336)|
|Observations|115,041|115,041|39,289|39,289|17,123|17,123|31,019|31,019|8,911|8,911|6,591|6,591|12,107|12,107|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Crop FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Country FE|YES|YES|NO|NO|NO|NO|NO|NO|NO|NO|NO|NO|NO|NO|

Note: Average marginal effects from multivariate logit regressions. Controls include dummy variables for input use (any hired labor, any inorganic fertilizer, any organic fertilizer), plot characteristics (plot area, irrigration, intercropping, plot ownership, elevation, and a topographic wetness index), household characteristics (urban/rural residence, livestock farming, and electricity access) as well as main crop and country fixed effects. The estimates are weighted to be nationally representative. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 30