rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A14. Impact of living in the GMA and, once in the GMA, of participating in the** **<u>VAG/CRB on the poor households</u>**

|||Treatment Regres<br>capita co|sion mod<br>nsumptio|els for the impact<br>nexpenditure of<br>Participating|on per<br>in|
|---|---|---|---|---|---|
|Variable|Variable description|LivinginGM|A<sup>_a_</sup>|<br>VAG/CRB|<br><sup>_b_</sup>|
|||(1)||(2)||
|park2|Kafue dummy variable|0.192||0.083||
|||(0.237)||(0.112)||
|park3|Lower Zambezi dummy variable|0.290|**|0.317|***|
|||(0.129)||(0.073)||
|park4|Luangwa dummy variable|0.255|**|0.459|***|
|||(0.123)||(0.128)||
|hage|Age of the household head|-0.001||-0.003||
|||(0.002)||(0.002)||
|fhhh|Female-headed dummy|-0.048||0.001||
|||(0.055)||(0.064)||
|maxedu|Education of the most educated member|0.023|**|0.015||
||(years)|(0.010)||(0.013)||
|c0to14|Number of children less than 15 years old|-0.193|***|-0.182|***|
|||(0.013)||(0.019)||
|f15to60|Number of female members 15-60 years|-0.163|***|-0.167|***|
|||(0.031)||(0.053)||
|m15to60|Number of male members 15-60 years|-0.168|***|-0.175|***|
|||(0.029)||(0.042)||
|m61plus|Number of members older than 60 years|-0.152|***|-0.090||
|||(0.048)||(0.067)||
|kroad|Distance to the nearest main road in km|-0.003||-0.005|*|
|||(0.003)||(0.003)||
|kbsch|Distance to the nearest basic school in km|0.002||0.005||
|||(0.004)||(0.007)||
|kheal|Distance to the nearest health centre in km|-0.005|**|-0.005|**|
|||(0.002)||(0.003)||
|vacast|Value of consumer durable assets in ZMK|5.114|***|4.291|**|
|||(1.705)||(1.993)||
|dcoop|Participation in cooperatives dummy|0.231|**|-0.066||
|||(0.095)||(0.103)||
|gma or|Participation dummy variable<sup>_a,b_</sup>|0.205||0.137||
|dvcrb||(0.436)||(0.308)||
|cons|Intercept|13.602|***|13.751|***|
|||(0.397)||(0.131)||
|Number of|observations|839||424||
|Log-likelih|ood value|-1209||-446||
|Goodness|of fit Chi-Square|329.82||229.86||
|Rho||-0.205||-0.05||

> _a_ Participation dummy variable refers to the GMA dummy, equal to 1 if the household is located in a GMA

> _b_ The participation dummy variable refers to the CRB/VAG dummy, equal to 1 if the household participates in the VAG/CRB. This model uses a sub-sample of households that are located in the GMA Significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

29
# **Table 10: Top 20 institutions using the 31 RePEc criteria based on research output and citations in all fields of economics**

## Rank

- 1 Department of Economics, Harvard University (53) 2 Department of Economics, University of Chicago (42) 3 World Bank Group (248) 4 Department of Economics, Massachusetts Institute of Technology (MIT) (26) 5 Department of Economics, Princeton University (43) 6 Department of Economics, University of California, Berkeley (40) 7 London School of Economics (LSE) (174) 8 Department of Economics, Oxford University (137) 9 Economics Research, World Bank (115)

- 10 Department of Economics, New York University (46) 11 Booth School of Business, University of Chicago (43) 12 International Monetary Fund (IMF) (215) 12 National Bureau of Economic Research (NBER) (376) 14 Department of Economics, Columbia University (50) 15 Department of Economics, Stanford University (44) 16 Kennedy School of Government, Harvard University (48) 17 Department of Economics, Boston University (54) 18 Wharton School of Business, University of Pennsylvania (56) 19 Toulouse School of Economics (TSE) (107) 20 Graduate School of Business, Columbia University <u>(40)</u>

Source: RePEc website: <u>http://ideas.repec.org/top/.</u> Numbers in parentheses are the numbers of registered authors in RePEc.

48
explore in more detail how changes in local employment patterns relate to changes in economic welfare, to better understand the types of jobs that reduce extreme poverty in the rural sector.

**Figure 5: Poverty rate by employment sector Figure 6: Share of population by employment sector**

<!-- Start of picture text --> 90 83.6 100%<br>80 73.8 90%<br>80% 35.4<br>70 48.5<br>70%<br>60 70.4<br>50.4 60% 79.8<br>50<br>50%<br>40<br>29.8 40%<br>30 30% 64.6<br>19.8 17.1 51.5<br>20 20%<br>11.8<br>9.1 29.6<br>10 4.6 10% 20.2<br>0%<br>0<br>Agriculture Non‐Agriculture Total Extreme Moderate Non‐poor Total<br>poor poor population<br>Extreme poor Moderate poor Non poor Agriculture Non‐Agriculture<br>Figure 7: Share of workers in agricultural sector by urban/rural<br>100%<br>23.8<br>80% 39.8<br>60%<br>40% 76.2<br>60.2<br>20% 32.5 36.3 46.1<br>17.0<br>0% 4.0 5.5<br>Extreme Moderate Non‐poor All urban Extreme Moderate Non‐poor All rural<br>poor poor poor poor<br>Urban Rural<br>Agriculture Non‐agriculture<br><!-- End of picture text -->

**Figure 7: Share of workers in agricultural sector by urban/rural**

# **3.2.The poor tend to be young**

Over one in five children under 15 lives in an extremely poor household, and children under 15 make up 44% of all extreme poor (Figures 8 and 9). Extreme poverty rates are 8.2 percentage points higher for children 0 to 14 than those for young adults 15 to 24, and over 13 percentage points higher than adults aged 65 and above. This variation in poverty rates across age groups is 13
# **II.V – Tajikistan L2T Baseline Survey for 2015**

Although the Tajikistan HBS survey is made available to the World Bank and other development partners, the sampling design used is not well-suited to the purposes of poverty mapping because the geographic coverage of the survey is limited in comparison to other options. Geographic coverage is crucial in the context of poverty mapping, and as an alternative, a survey with a more widely distributed sample was used. The baseline for the L2T Survey was selected because it: i) used the 2010 national census sample frame for the L2T baseline survey conducted by the national statistical agency, and ii) used a widely distributed traditional stratified two-stage clustered sample design. In the first stage, 150 clusters were selected, with a probability of selection proportional to size. In the second stage, 3,000 households were selected to participate in the survey.

The sample was designed to be nationally representative for consumption and expenditure. The survey was conducted over a two-month period beginning in March 2015. CAPI systems were used and all data were collected in person. As with the L2CU study, continuous data collection was conducted following the baseline survey, however, no comprehensive consumption or expenditure model was included in follow-up rounds. The interviews were implemented under the supervision of the Ministry of Health and Social Protection of Tajikistan and the World Bank.

# **II.VI – Satellite and Administrative Data**

The main source of satellite and administrative data for this study is the AidData project at the college of William and Mary.<sup>3</sup> The spatially aggregated data available for Kazakhstan from this source include:

- 1) World Bank project locations

- 2) Yearly VIIRS day night band nighttime lights data (without stray light correction)

- 3) Version 4 DMSP-OLS Nighttime Lights composites. The lights from cities, towns, and other sites with persistent lighting, including gas flares. Ephemeral events, such as fires have been discarded. Calibrated across sensors and years using Elvidge 2014 coefficients

- 4) Average precipitation per year, created using UDel Precipitation data set (v4.01)

- 5) Average air temperature per year, created using UDel Air Temperature dataset (v4.01)

- 6) Global slope (in degrees) derived from Shuttle Radar Topography Mission (SRTM) data set (v4.1) at 500m resolution

- 7) Global elevation (in meters) from Shuttle Radar Topography Mission (SRTM) data set (v4.1) at 500m resolution

- 8) Binary indicating locations with deposits of known on-shore oil and gas deposits

- 9) Standard MODIS land cover type data product (MCD12Q1) in the IGBP Land Cover Type Classification

- 10) Yearly value for Normalized Difference Vegetation Index (NDVI). Created using the NASA Long Term Data Record (v4) AVHRR data

> 3 Data available at <u>http://geo.aiddata.org/query/#!/</u>

20
necessary ingredient for generating consensus on reforms (Isham et al. 2002, Rodrik 1999a).

We contribute to this strand of the literature by analyzing the relationship between natural resource abundance and micro economic reforms. The analysis is done on a sample of 133 countries, with data on reforms from the World Bank’s Doing Business project. Previous studies have shown these reforms to expand growth (for example, Klapper, Laeven and Rajan (2006) and Barseghyan (2008)).

Our data are not subject to the criticisms of other measures of economic institutions, which suffer from significant endogeneity issues, and measure outcomes rather than institutions (for example, Glaeser et al. 2004). The Doing Business data are focused on specific regulatory or legal reforms, and as such they are a better indicator of underlying institutions. At the same time, they are sufficiently micro-measured so as to avoid the possibility of such reforms affecting the usage of natural resources.

We find robust evidence for the negative link between natural resource abundance and growth-enhancing reforms. The estimates imply that a move from the most natural resource rich country (Swaziland) to the poorest (Japan) increases the probability of reform by 44.7 percentage points, a large effect given that the probability of reform in the full sample is only 57.1%. A move from the 75<sup>th</sup> percentile to the 25<sup>th</sup> percentile on resource abundance equals 10.9 percentage points more reform.

Using alternative measures of natural resource abundance, we find that the likelihood of growth enhancing reforms increases by a high of 50.7 and a low of 23.9 percentage points when we move from the most to the least natural resource abundant country. These results are robust to the inclusion of various proxies for the initial 3
Private sector firms interviewed in mid-2006 unanimously concurred that the economic liberalization starting in 1993 has been a major stimulus to higher efficiency and productivity (Box 2). Microeconomic evidence from Investment Climate Assessment 2007 corroborates this anecdotal evidence. It confirms significant growth in total factor productivity during the period 2002-2006 after stagnation during the 1990s.<sup>25</sup> Box 2: Vignettes from Interviews with Private Sector Firms, July 2006

- Firms noted that reductions in import tariffs had spurred exports because it had lowered their costs, exactly as Lerner symmetry would predict.   One firm described this as decisive in doubling its capacity in 2003, even more important than the fall in interest rates.

- All firms mentioned pressures to increase efficiency and productivity by reducing waste and technological upgrading because of greater competition and falling profit margins as a consequence of lower import barriers. Many mentioned adopting Gemba Kaizen techniques to reduce waste, get workers more involved in preventive maintenance and doing things better on the shop floor.<sup>a/</sup> One multinational said it had reduced manufacturing unit costs by 30-40 percent in spite of rising labor and energy costs.

- Kenyan labor was typically described as well-educated and the best in the region.

- While the increased competition has not so far led to bankruptcies, the expectation was voiced that loss-making parastatals will eventually close.

In short: competition and hard budget constraints are forcing firms to become more efficient and innovative, while the increased incentive for exports is spurring capacity expansion.

a/ Gemba Kaizen in Japanese means change for the better (kaizen) in the workplace (gemba). It was originated by Masaaki Imai, a Japanese management consultant, who established the Kaizen Institute in 1986.

A point worth making is that in spite of the cut in VAT, income tax and import tariff rates, which contributed to the decline in revenues since the mid-1990s till 2001/02, the revenueto-GDP ratio at around 21 percent is still high in Kenya compared to other countries of a similar income level. Kenya collects at least 4-7 percentage points of GDP more in revenue than its EAC partners, Tanzania and Uganda, and is a positive outlier among developing countries.

> 25 Based on a firm-level production function, Soderbom (2004) reported that average firm TFP increased by 7percent (but was not statistically significantly different from zero) over the entire 1999-2002 period. Using a similar methodology, ICA (World Bank 2007) provides preliminary evidence that TFP increased in manufacturing firms by a statistically significant 15 percent over 2002-2006.

19
Pr(GTi = 1|X) = Φ( ß0 + ß1 GEND i + ß2 EXPE i + ß3 ASSO i + ß4 FORM i +

where _GTi_ takes the value of 1 if the trader participates in group trade and 0 otherwise, _GEND i_ is a dummy variable that take the value of 1 if the trader is a women, _EXPE i_ is a dummy variable that takes the value of 1 if the trader has at least 5 years of trading experience, _ASSO i_ is a dummy variable that takes the value of 1 if the trader is a member of a trader association, _FORM i_ is a dummy variable that takes the value of 1 if the trader has a registered business, _FUEL i_ is a dummy variable that take the value of 1 if the trader uses a fuel-powered vehicle for transport, _TIME i_ is a dummy variable that takes the value of 1 if the trader requires no more than 3 hours to reach the border, _PERI i_ is a dummy variable that takes the value of 1 if the trader carries products that are classified as perishable, _PROT i_ is the applied tariff for the products that the trader carries,<sup>9</sup> and DGAT, DGOM, DKAV, DKIL, DNYA, DRUB, and DRUM are dummy variables that represent the different border locations and thus account for location-specific effects. Φ _(.)_ is the cumulative normal distribution function.

3.2 Long-term viability of group trade

> 9 If a trader carries several products that are subject to differing tariffs, the highest level of protection for the respective products is used.

15
# **Figure 4: Impact of stock market liquidity on the comovement between local and US stock market returns**

Figure shows result from regressing the comovement between local and US stock returns on the stock market turnover ratio across countries.

<!-- Start of picture text --> 2.5<br>CYP<br>2<br>CZE<br>KOR<br>VNM GRC<br>POL<br>1.5 QAT ZAFEGY IND FIN<br>AUT TUR<br>RUSIRL DEU<br>BGR<br>HRV HUN FRA SWE ITA<br>MEX BELHKG AUS<br>LUX ROM BRA SGP TWNNLD ESP<br>NOR<br>NZL<br>1 IDN CANDNK GBR<br>KENCOL ISR<br>JPN<br>LVA PRT<br>MUS KWT ISLCHN CHE<br>ARG KAZSRB LTU MYS THA<br>NAMPER EST<br>MLTSVNMNGTUN LBN JOR<br>0.5 CRI PHL<br>SVKBWAJAMUKRBHRVENLKACHLOMN BGDMAR ARE<br>NGA<br>0 BMU<br>0 20 40 60 80 100 120 140 160 180 200<br>ECU<br>-0.5<br>Stock market turnover (%)<br>Comovement between local and US stock market returns (Period pre-Lehman)<br><!-- End of picture text --> 37
area, countries that have a federal constitution show less primacy—a smaller share of the urban population in the largest city of a country. Federalism helps level the playing field across cities, giving smaller hinterland cities greater freedom to finance and provide their own public services and to have regional banks. Similarly, improvements in the democratic environment lead to decreased primacy. Democratization allows elected candidates to represent hinterland region interests in a national legislature.

Figure 3 shows how a more general measure of urban concentration is lower in more democratized states and declines as democratization proceeds. Of course, institutions are endogenous, so causal inference is tough. Shocks that affect urbanization may also affect democratization. For example, Bruckner and Ciccone (2011) argue that rainfall shocks in SubSaharan Africa affect trade and income, which in turn affect the opportunity costs of conflict and hence democratization. But rainfall directly affects urban-rural incomes and urbanization.

**Figure 3. The spatial Gini and political structures**

(a) 1970 degree of democracy versus 2000 Gini (b) Changes in democratization and the Gini, 1970–2000 <!-- Start of picture text --> residual of Gini(2000) vs. residual of Democracy(1970), (2)<br><!-- End of picture text --> <!-- Start of picture text --> residual of gini00 (2) Fitted values<br><!-- End of picture text --> <!-- Start of picture text --> residual of d_Gini vs. residual of d_Democracy, 2000-1970<br><!-- End of picture text --> <!-- Start of picture text --> residual of d_Gini vs. residual of d_Democracy, 2000-1970<br><!-- End of picture text --> <!-- Start of picture text --> residual of d_Gini vs. residual of d_Democracy, 2000-1970<br><!-- End of picture text --> <!-- Start of picture text --> residual of d_Gini(2000-1970) Fitted values<br><!-- End of picture text --> _Source:_ Henderson and Wang 2007.

_Note:_ The graphs look at the partial relationship between democratization and a measure of urban concentration, the spatial Gini, after factoring out national population, national income, and planned economic and federal structure influences. The spatial Gini is like an income Gini. Rank all cities in a country from smallest to largest. The spatial Lorenz curve is the cumulative share of the national urban population as we move from the smallest city to the largest. The Gini is the area between the Lorenz curve and the 45-degree line. On the 45-degree line, all cities would be of equal size in a graph of size rank against cumulative share. Democratization is measured by the polity index. Graph (a) shows the relationship between 1970 democratization and 2000 spatial concentration. Graph (b) shows the relationship between changes in democracy and changes in spatial concentration.

# **_Why do we care about big-city bias?_**

Favoring big cities in national markets and public service provision makes them attractive for firms—and hence for workers looking for jobs. Favored cities thus face increased in-migration, 15
while our focus on one remedy-imposing country does obviously impose a limit on the extent to which one can extrapolate from our results to lessons for other remedy-imposing countries; nevertheless, the United States’ trade remedy laws and procedures in particular serve as a model that many other WTO members emulate as they construct their own provisions. Thus lessons learned from the U.S. experience and interaction with WTO dispute settlement may arguably have implications for other international users of trade remedies as well.

Table 4 uses the U.S. data on trade remedies to illustrate a number of comparative results that our formal econometric investigation formally seeks to confirm. First, and perhaps not surprisingly, a U.S. trade remedy that leads to the loss of a large value of imports in the U.S. market is more likely to result in a measure being challenged than one resulting in the loss of a small value of imports. This is consistent with the first row of table 4, which shows that the mean level of lost imports in WTO-challenged measures is $49.9 million, relative to $3.2 million for nonchallenged U.S. remedies. Second, we also find that the capacity for the foreign country to retaliate over U.S. exports should it “win” the case is also associated with a higher probability of it bringing forward a dispute in the first place. This is illustrated by a simple comparison made in table 4, where the mean share of U.S. exports sent to countries that challenge trade remedies is 16.9%, whereas the U.S. is less reliant on the average country that does not formally challenge a U.S. remedy at the WTO, as that country receives only 6.5% of U.S. exports.

We also investigate and provide evidence of a second avenue through which the capacity for retaliation threats matters, and we find that this avenue could be serving as a _substitute_ to formal WTO dispute settlement proceedings. The data suggests a strong _negative_ relationship between a WTO dispute filing and the foreign industry’s capacity to directly retaliate against the U.S. industry through a reciprocal _antidumping_ investigation and measure of its own. Consider the destination of the production of the U.S. industry that receives the protection from the initial U.S. trade remedy. Table 4 illustrates that the share of the value of that production that is _exported_ to the affected foreign country is _higher_ (0.9% versus 0.7%) in the average case in which the foreign country does _not_ file a WTO complaint. This evidence is consistent with the hypothesis that WTO dispute settlement procedures and the foreign country’s own antidumping law can serve as substitute policy instruments. Thus some exporting countries that are affected by U.S. trade remedies may be choosing “vigilante justice” and directly targeting their U.S. competitors with antidumping actions, in lieu of convincing their government to confront the U.S. with a formal WTO dispute. Finally, we also note the relationship between this phenomenon and the idea first proposed by Prusa (1992), that antidumping law may inadvertently serve as a device that facilitates _collusion_ between foreign and 4
market it can be safely assumed that a household who does not desire more work on NREGS is not rationed in the private market.

# **4. Performance evaluation**

By comparing the distributions of unadjusted consumption Ci with adjusted consumption Ci∗ we are able to quantify the impact of allowing for the disutility of work on assessments of the program’s performance. There are two aspects of performance to focus on. The first is targeting <u>performance. Here we analyze the relationship between the participation rates in NREGS and the</u> position along the distribution of consumption per capita, for both the raw data and using locally weighted regressions.<sup>10</sup> Using the two different welfare measurements, Ci and Ci∗, we estimate f and f<sup>∗</sup> defined as non-parametric relationships between the participation rate ( _PR_ ) and the position along the distribution of these two measurements: and similarly for the adjusted consumption, for which the non-parametric function is denoted f<sup>∗</sup> . Thus PR(x) is the participation rate observed for a given level of consumption x and FC(. ) is the cumulative distribution function (CDF) of consumption and _f_ is a smooth non-parametric function to be determined empirically.<sup>11</sup> If the program is indeed well targeted in terms of adjusted consumption C<sup>∗</sup> then we should observe that the function f<sup>∗</sup> is decreasing for all _x_ .

Second, we study the impact of our adjustment on the gains from the scheme. For this purpose we use household-specific estimates of forgone income for men and women from the Bihar Survey. These are answers to the questions: _“If you were doing some other work instead of this during these days, how many days do you think you would have worked?”_ and _“If you were doing some other work instead of this during these days, what wage would you might have earned per day?”_<sup>12</sup> (The NSS does not contain these questions, which were developed for the Bihar survey.) The post-NREGS distribution of consumption is that observed in the data.

> 10 We used the method of running-line least-squares smoothing using Cleveland's (1979) tricube weighting function, as programmed in  Stata (as the “lowess” command). We use a smoothing parameter of 0.5. We could have used the cross-validated bandwidth instead, but for the essentially graphical purposes of this paper we followed Deaton (1989) in selecting a bandwidth such that gives enough smoothness without losing detail, thus also avoiding the computational burden implied by cross-validated bandwidth selection.

> 11 Alternatively we can focus directly on the relationship between _PR_ and _x_ (rather than _F_ ( _x_ )). However, using _F_ ( _x_ ) for the horizontal axis assures an even spread of data points, giving an incidence graph that is less prone to outliers at the extremes.

> 12

> For further discussion see Dutta et al. (2012b).

9