rafmacalaba/datause-encoder-data huggingface dataset:
3. Offered 500 pesos (US$36) gift card that they would receive one month after completing the training, 4. Offered a free taxi ride to and from the course location, 5. Provided a video CD containing positive testimonials from people who had attended the course.

Treatments 1 and 2 enable us to examine whether individuals are more likely to attend the course as the benefits of attending the course increase. This helps get at the issue of whether individuals are making rational decisions by responding to changes in the net benefits, as well as to measure how the demand for training varies with these benefits. The comparison of treatments 2 and 3 enables us to see whether high discount rates or present bias is a reason for a lack of attendance – people might think the course has benefits, but because these benefits occurs in the future and attendance occurs today, keep putting the course off. If this was the issue, we would expect much greater response to Treatment 2 than to Treatment 3. Treatment 4 lowers the costs of attending the training, which enables examination of whether one important component of _c_ is the constraint. Treatment 5 aims to reduce informational constraints that may prevent people from attending the course if they are not sure if it will be helpful, thereby attempting to reduce the difference between _Ebt_ and _bt._ The incentive treatments were assigned through stratified randomization using the entire treatment group. We stratified by whether the respondent was screened through the branch vs. mail, online, or street survey, by income group, by whether the person was (i) a client of our partner financial institution, (ii) a client of another financial institution, (iii) neither, and by attendance status thus far. The attendance status categories where (i) attended, (ii) was scheduled, but didn't attend, (iii) was reached, but didn't want to be scheduled and, (iv) can't be reached. Individuals who had already attended the course did not receive the incentives, but they were included in the randomization to allow us to report post-incentive treatment take-up rates by incentive type for the complete treatment group.

# **5.3 Impacts of Experiments to Increase Attendance**

Figure 1 shows the overall percentage attending in each incentive treatment group (after the initial treatment offer and the booster treatments). Some participants were unreachable by phone, so we show results as a percentage of all participants assigned to a treatment, and of all who could be reached to actually offer them the treatment. Table 2 provides regression estimates of the impacts of the various treatment arms on attendance.

17
status change in the 18 Food and Agriculture Organization (FAO) Regions that span the world’s oceans? We will follow the fisheries science literature, where biological overfishing occurs when fishing pressure ( _F_ ) is higher than the fishing pressure that would produce the maximum sustainable yield ( _Fmsy_ ). Then, the relevant metric is the ratio _F/Fmsy_ , where _F/Fmsy >_ 1 indicates overfishing, _F/Fmsy_ = 1 indicates biologically sustainable fishing, and _F/Fmsy <_ 1 indicates fishing pressure could increase without threatening the long-term biological size of the stock. We refer to this ratio as overfishing status.

# **6.1 Calculating changes in overfishing status**

We calculate the change in overfishing status that would result from a reduction in Chinese distant water fuel subsidies. Specifically, we consider three policy changes that would reduce the subsidy by 10%, 30%, and 50%. We follow the same approach as McDermott et al. (2019), who calculated the changes in overfishing status that result from changes in fishing effort<sup>27</sup> using catch-weighted mean _F/Fmsy_ values by FAO Region. Our values of overfishing status are for 4,713 fish stocks and they come from Costello et al. (2016).<sup>28</sup> We take fishing hours observed during 2020, the most recent publicly available year of data, as the baseline level of fishing activity currently occurring in the world’s oceans. We then aggregate fishing hours by Chinese and all other vessels to the level of FAO Region and calculate the fraction attributable to Chinese distant water vessels (Figure A10). Since we only analyze the subsidies granted to China’s distant water fishing fleet, we assume that only Chinese fishing outside Chinese waters responds to changes in subsidies.<sup>29</sup> For each policy scenario, we use our estimate of the elasticity of fishing by Chinese distant water vessels with respect to fuel subsidies (Column 3 of Table 4) to calculate the predicted change in fishing by Chinese vessels in each FAO Region, and the corresponding

> 27McDermott et al. (2019) study the preemptive fishing effort that occurs following the announcement of a soon-to-be-established marine reserve.

> 28These stocks represent 78% of global reported fish catch. Table A4 reports the number of stocks in each FAO Region.

> 29Chinese waters here refer to China’s internationally-recognized Exclusive Economic Zone and its claims in the South China Sea. Though the latter claims are contested by other countries, Chinese vessels fishing in the South China Sea are unlikely to receive distant water fuel subsidies (since China considers those waters domestic).

34
**Figure 7: Trends in the absolute concentration index of MDG indicators**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>-.1 0 .1 .2 -.1 0 .1 .2 -.1 0 .1 .2 -.1 0 .1 .2<br>Absolute concentration index Absolute concentration index Absolute concentration index Absolute concentration index<br>Full immunization Measles ANC4+ SBA<br>-.1 0 .1 .2 -.1 0 .1 .2 -.1 0 .1 .2 -.1 0 .1 .2<br>Absolute concentration index Absolute concentration index Absolute concentration index Absolute concentration index<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>-.1 0 .1 .2 -.1 0 .1 .2 -.1 0 .1 .2 -.1 0 .1 .2<br>Absolute concentration index Absolute concentration index Absolute concentration index Absolute concentration index<br>Earliest survey Latest survey<br>.6 .2 .25<br>.5<br>.5 .2<br>.4 .15<br>.4 .3 .15<br>.1<br>.3 .2 .1<br>Population average Population average Population average Population average<br>.2 .1 .05 .05<br>.1 0 0 0<br>1 1 1 1<br>.8 .8 .8<br>.8<br>.6 .6 .6<br>.6<br>.4 .4 .4<br>Population average Population average Population average Population average<br>.4<br>.2 .2 .2<br>0 .2 0 0<br>.8 .25 .6 .8<br>.2<br>.6 .6<br>.4<br>.15<br>.4 .4<br>.1<br>Population average Population average Population average .2 Population average<br>.2 .05 .2<br>0 0 0 0<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. . The population rate is as a fraction, so e.g. 0.2 means a population average of 20%. This is true also for IMR and U5MR which are typically expressed per 1,000.

32
<!-- Start of picture text --> Log earning post baseline (compared to workers with <5 schooling)<br>8<br>48<br>6<br>Pa<br>c<br>I<br>oO<br>i=)<br>Se 4 7<br>O7ap4 |+ .¢<br>O11<br>i]<br>@ Pre-covid cohort<br>®@ Covid cohort<br>-<br>5-7 years 8~9 years 10~11 years >12 years *<br><!-- End of picture text -->
<mark>Muller, K and C Schwarz (2020a), “Fanning the Flames of Hate: Social Media and Hate Crime”,</mark> _<mark>Journal of the European Economic Association</mark>_ <mark>.</mark> <mark>Muller, K and C Schwarz (2020b), “From Hashtag to Hate Crime: Twitter and Anti-Minority Sentiment”, SSRN Working Paper.</mark> <mark>Ortony, A, G L Clore and A Collins (1990),</mark> _<mark>The cognitive structure of emotions</mark>_ <mark>, Cambridge University Press.</mark> Paltoglou, G and M Thelwall (2012), “Twitter, MySpace, Digg: Unsupervised Sentiment Analysis on Social Media”, _ACM Transactions on Intelligent Systems and Technology_ 3(4).

<mark>Prati, A, M G Onorato and C Papageorgiou (2013), “Which reforms work and under what institutional environment? Evidence from a new data set on structural reforms”,</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>95(3): 946-968.</mark> Rodrik, D (2010). “Diagnostics before Prescription.” _Journal of Economic Perspectives_ 24(3):33-44 Tetlock, P (2007), “Giving Content to Investor Sentiment: The Role of Media in the Stock Market”, _Journal of Finance_ 62(3): pp 1139-1168.

Turney, P D (2002), “Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews”, arXiv preprint cs/0212032.

World Bank (2017), _World Development Paper 2017: Governance and the Law_ , Washington, DC: World Bank.

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Zhuravskaya, E, M Petrova and R Enikolopov (2020), “Political Effects of the Internet and Social Media”, _Annual Review of Economics_ 12: 415-438.

19
Surface runoff ( _Rs_ ) is described in terms of the storage state, _z_ , and the effective precipitation, Peff.  Epsilon (ε), a calibration parameter, allows surface runoff to vary non-linearly with storage (Yates 1996).

Sub-surface runoff (Rss) is a function of the relative storage state, multiplied by a coefficient α _._ Total runoff for each time step is the sum of the two runoff components: More details of the model theory are presented in Yates (1996).

# **_2.2 Data requirements_**

The modeling process is highly data intensive because of the number of cells (ca. 16,000) across Africa. The data types required for this study can be broadly divided into four types: geographical data, climate data for model input, physiological data and model calibration data. This section describes the data sources and, where necessary, the steps taken to prepare them for use in this study. A summary of exact data sources is provided in Table 2.

10
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Barattieri, A., M. Cacciatore, and F. Ghironi. 2021. “Protectionism and the Business Cycle.” _Journal of International Economics_ 129, 103417.

Biljanovska, N., F. Grigoli, and M. Hengge. 2017. “Fear Thy Neighbor: Spillovers from Economic Policy Uncertainty.” IMF Working Paper, WP/17/240, International Monetary Fund, Washington, DC.

Bonadio, B., and A. A. Levchenko. 2018. “The economics and politics of revoking NAFTA.” mimeo, https://www.imf.org/en/News/Seminars/Conferences/2018/02/08/~/media/D161FF9ABB174 1C6874C8A8E4F938762.ashx.

Corcose, G., M. del Gatto, G. Mion, and G. Ottaviano. 2012. “Productivity and Firm Selection: Quantifying the New Gains from Trade.” _Economic Journal_ 122: 754-798.

Corsetti, G., A. Meier and G. J. Müller. 2012. “What determines government spending multipliers?” _Economic Policy_ 27(72): 521-565, October.

Dix-Carneiro, R., and B. K. Kovak. 2017. “Trade Liberalization and Regional Dynamics.” _American Economic Review_ 107(10): 2908-46.

Dornbusch, R. 1974. “Tariffs and Nontraded Goods.” _Journal of International Economics_ 4(2): 177-85.

31
<!-- Start of picture text --> Qatar: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>30<br>20<br>10<br>0<br>-10<br>-20<br><!-- End of picture text -->

# **B- Oil importers**

<!-- Start of picture text --> Jordon: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>30<br>20<br>10<br>0<br>-10<br><!-- End of picture text --> <!-- Start of picture text --> Lebanon: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>50<br>0<br>-50<br><!-- End of picture text --> <!-- Start of picture text --> Morocco: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>10<br>0<br>-10<br><!-- End of picture text --> <!-- Start of picture text --> Tunisia: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>20<br>10<br>0<br>-10<br><!-- End of picture text --> 41
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- Dessy, S., F. Marchetta, R. Pongou, and L. Tiberti. 2020. “Climate Shocks and Teenage Fertility.” _GLO Discussion Paper 490_ .

- Dessy, S., L. Tiberti, and D. Zoundi. 2023. “The gender education gap in developing countries: Roles of income shocks and culture.” _Journal of Comparative Economics_ 51(1): 160-180.

- Dessy, S., L. Tiberti, M. Tiberti, and D. Zoundi. 2021. “Polygyny and Farm Households' Resilience to Climate Shocks.” _World Bank Policy Research Working Paper_ 9663. Washington, DC: World Bank.

- Deuchert, E., and C. Felfe. 2015. “The tempest: Short-and long-term consequences of a natural disaster for children’s development.” _European Economic Review_ 80: 280-294.

- Diakakis, M., et al. 2015. "Hurricane Sandy mortality in the Caribbean and continental North America." _Disaster Prevention and Management_ 24(1): 132-148.

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- Dillon, A., and E. Quinones. 2010. "Gender-differentiated asset dynamics in Northern Nigeria: Background paper prepared for the state of food and agriculture 2010–11." _ESA Working Paper_ 11-06. Rome: FAO.

- Dillon, A., V. Mueller, and S. Salau. 2011. “Migratory responses to agricultural risk in northern Nigeria.” _American Journal of Agricultural Economics_ 93(4): 1048-1061.

- Dinkelman, T. 2017. “Long-run Health Repercussions of Drought Shocks: Evidence from South African Homelands.” _Economic Journal_ 127 (604): 1906–39.

- DiPietro, J. A., and K. M. Voegtline. 2017. "The gestational foundation of sex differences in development and vulnerability." _Neuroscience_ 342: 4-20.

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- Dupre, M. E., and S. O. Meadows. 2007. "Disaggregating the effects of marital trajectories on health." _Journal of Family Issues_ 28(5): 623-652.

- Eastin, J. 2018. “Climate change and gender equality in developing states.” _World Development_ 107: 289-305.

22
Table 7: The effect of collective actions by SHGs on the number of issues addressed by the WM

||Numbe|r of issues ad|dressed by|the WM|
|---|---|---|---|---|
||O|LS|2S|LS|
||(1)|(2)|(3)|(4)|
|SHG active|0.89<sup>_∗∗∗_</sup>|0.91<sup>_∗∗∗_</sup>|1.48<sup>_∗∗∗_</sup>|1.54<sup>_∗∗∗_</sup>|
||(0.16)|(0.16)|(0.42)|(0.42)|
|Male||-0.29<sup>_∗∗_</sup>||-0.33<sup>_∗∗∗_</sup>|
|||(0.12)||(0.13)|
|Education level (years)||0.03||0.03|
|||(0.02)||(0.02)|
|Land (acres)||-0.04<sup>_∗∗∗_</sup>||-0.04<sup>_∗∗∗_</sup>|
|||(0.01)||(0.01)|
|Number of children||-0.02||-0.01|
|||(0.03)||(0.03)|
|Age||0.05<sup>_∗_</sup>||0.05<sup>_∗_</sup>|
|||(0.03)||(0.03)|
|Squared age||-0.00<sup>_∗_</sup>||-0.00|
|||(0.00)||(0.00)|
|Caste category: SC||-0.16||-0.12|
|||(0.21)||(0.23)|
|Caste category: OBC/FC||0.02||0.06|
|||(0.16)||(0.17)|
|Ward fxed effects|yes|yes|yes|yes|
|Election year dummies|yes|yes|yes|yes|
|_F_-stat frst stage|||31.6|29.1|
|Observations|441|441|441|441|

An observation in the regression model is a WM. The dependent variable is the total number of different issues that the WM deals with during his mandate. The variable “SHG active” is a dummy that takes value one if a SHG started undertaking collective actions in ward _j_ before or during the mandate of WM _i_ . The first two columns provide the results using standard linear regression techniques. In the last two columns we instrument for “SHG active”, using the timing of SHG creation in the ward. The uneven columns do not include WM characteristics, while the even columns do so. Standard errors clustered at the ward level are given in parentheses. *** significant at 1 percent, ** significant at 5 percent, * significant at 10 percent _Source_ : Authors’ analysis based on data described in the text.

40
## **A couple with two young children**

We assume that both children are unemployed (i.e. lack any means of subsistence), and that they are between 15 and 25 years old. Then, the MLS for the household is SK8,410, and child allowances amount to SKI,680 (if HH income under 1.36 * MLS = SKI1,437) or SKI,100 (if HH income between 1.36 and 1.99 times MSL = SKI 1,437 to SK16,735).

# A. Unemployed spouse

|Individualand HHincome|UI<br>First3months<br>**(RR**50%)|**UI**<br>Afterfirst3months<br>**(RR**45%)|SA(Objective)|
|---|---|---|---|
|Individualincome(AW)|5,400<br>(0.49)|4,927<br>(0.45)|3,230<br>(0.29)|
|**HHincome(AW)**|10,090<br>(0.80)|10,090<br>(0.80)|10,090<br>(0.80)|
|Individualincome (MW)|3,230<br>(0.90)|3,230<br>(0.90)|3,230<br>(0.90)|
|**HH**income (MW)|10,090<br>(1.00)|10,090<br>(1.00)|10,090<br>(1.00)|

# B. Spouse employed at average wage

|**Individual**and**HR**income|**_Ut_**<br>First3months<br>**(RR50%)**|UI<br>After first3months<br>**(RR45%)**|SA(Objective)|
|---|---|---|---|
|Individualincome(AW)|5,400<br>(0.49)|4,927<br>(0.45)|0|
|**HRincome(AW)**|17,450<br>(0.80)|16,347<br>(0.75)|12,630<br>(0.58)|
|**Individualincome(MW)**|1,800<br>(0.50)|1,620<br>(0.45)|**0**|
|**HHincome(MW)**<br>__|13,850<br> <br>(0.96)|13,670<br>(0.95)|12,630<br>(0.87)|

# C. Spouse employed at minimum wage

|Individual and**HH**income|UI<br>First3months<br>**(RR**50%)|**Ut**<br>Afterfirst3months<br>**(RR45%)**|SA(Objective)|
|---|---|---|---|
|**Individualincome(AW)**|5,400<br>(0.49)|4,927<br>(0.45)|0|
|**RHincome(AW)**<br>._________<br>___<br>_|10,680<br>____<br>(0.74)|10,207<br>(0.71)|10,090<br>(0.70)|
|Individualincome(MW)|3,230<br>(0.90)|3,230<br>(0.90)|3,230<br>(0.90)|
|**HHincome(MW)**|10,090<br>(1.20)|10,090<br>(1.20)|10,090<br>(1.20)|

42
years, declined to 10% from 12% between 1996 and 2004. Following international standards on anti-money laundering and combat for terrorism financing, banks in Zambia have adopted guidelines that prevent them from providing services to customers that cannot be identified or cannot demonstrate their source of income. Although Zambians have national identification documents, many people can not proof their source of income.

A similar situation affects private sector firms in Zambia, in particular small and medium enterprises. There are no official statistics on the number of SMEs in Zambia, but it is believed that many of them are not registered, do not pay taxes, and do not have audited accounts. As a result, they can not access financial services – either savings, deposits, cash management, lending, guarantees, etc—offered by banks.

An additional challenge faced by Zambia is related to the HIV/AIDS problem. Principally, HIV/AIDS threatens the country’s capacity building efforts because it strikes the educated and skilled as well as the uneducated. Consequently it reverses and impedes the country’s capacity by impacting human productivity and life expectancy. The long periods of illness of the skilled personnel in employment has translated into severe loss in economic productivity, which leads to considerable loss to the employer in terms of manhours. Nowadays, life expectancy in Zambia is only 36 years, largely due to the HIV/AIDS problem.

During the past years, banks in Zambia have operated in an environment characterized not only by low economic growth and falling per capita incomes, but also by high inflation, a large fiscal deficit, exchange rate volatility, and an external debt burden.

|**Table 3. Zambia.**<br>|**Select Macro**<br>|**economic I**<br>|**ndicators, 19**<br>|**97-2005**<br>||
|---|---|---|---|---|---|
|**Indicators**|**1997-2001**|**2002**|**2003**|**2004**|**2005**|
|RealGDPGrowth|2.4%|3.3%|5.1%|5.4%|5.1%|
|Realpercapita GDPgrowth|0.2%|0.9%|2.7%|2.9%|2.6%|
|Real PerCapita GDP*|$313|$325|$334|$343|$352|
|Consumerprices**|24.7%|22.2%|21.4%|18.0%|18.3%|
|Overall fiscal balance, including<br>grants (inpercent ofGDP)|-4.4%|-5.1%|-6.0%|-3.0%|-2.3%|
|Trade balance (inpercent ofGDP)|-5.0%|-6.9%|-7.2%|1.5%|0.8%|
|External debt to official creditors<br>(inpercent ofGDP)|181.6%|135.4%|107.9%|77.8%|54.3%|
|Nominal effective exchange rates<br>(Index,2000=100)|127.0|85.4|70.8|69.0|78.6|

*In US$ Dollars, at 2000 prices, using 2000 exchange rates **Annual average percent change

Between 1997 and 2001, inflation in Zambia averaged 24.3%, which was largely caused by the fiscal deficit, the high growth of money supply and depreciating Kwacha, and occasional shocks such as the effects of drought. A significant fall in inflation requires, among other things, a reduction in the government’s budget deficit, which will reduce its need to borrow from domestic markets to finance the deficit.

22
|Rural Access and MobilityProject(RAMP)|Olatunji Ahmed|
|---|---|
|Rural Access and MobilityProject(RAMP)|Olatunji Ahmed|
|Rural Access and MobilityProject(RAMP)|Olatunji Ahmed|
|Rural Access and MobilityProject(RAMP)|Olatunji Ahmed|
|Rural Access and MobilityProject(RAMP)|Olatunji Ahmed|
|Rural Access and MobilityProject(RAMP)|Olatunji Ahmed|
|Rural Access and MobilityProject(RAMP)|Olatunji Ahmed|
|Fundamental SchoolQualityProject|JerryG. Strudwick|
|Fundamental SchoolQualityProject|JerryG. Strudwick|
|Fundamental SchoolQualityProject|JerryG. Strudwick|
|Fundamental SchoolQualityProject|JerryG. Strudwick|
|Fundamental SchoolQualityProject|JerryG. Strudwick|
|Fundamental SchoolQualityProject|JerryG. Strudwick|
|Second Health Sector Rehabilitation and Development Project|TimothyA. Johnston|
|Second Health Sector Rehabilitation and Development Project|TimothyA. Johnston|
|Second Health Sector Rehabilitation and Development Project|TimothyA. Johnston|
|Second Health Sector Rehabilitation and Development Project|TimothyA. Johnston|
|Second Health Sector Rehabilitation and Development Project|TimothyA. Johnston|
|Second Health Sector Rehabilitation and Development Project|TimothyA. Johnston|
|Second Health Sector Rehabilitation and Development Project|TimothyA. Johnston|
|Second Health Sector Rehabilitation and Development Project|TimothyA. Johnston|
|Second Health Sector Rehabilitation and Development Project|TimothyA. Johnston|
|Second Small Enterprises Project|Enrique Aldaz-Carroll|
|Second Small Enterprises Project|Enrique Aldaz-Carroll|
|Second Small Enterprises Project|Enrique Aldaz-Carroll|
|Second Small Enterprises Project|Enrique Aldaz-Carroll|
|Second Small Enterprises Project|Enrique Aldaz-Carroll|
|Second Small Enterprises Project|Enrique Aldaz-Carroll|
|Second Small Enterprises Project|Enrique Aldaz-Carroll|
|Second Small Enterprises Project|Enrique Aldaz-Carroll|
|Heat Reform and BuildingEnergyEfficiencyProject|Emmanuel Py|
|Heat Reform and BuildingEnergyEfficiencyProject|Emmanuel Py|
|Heat Reform and BuildingEnergyEfficiencyProject|Emmanuel Py|
|Heat Reform and BuildingEnergyEfficiencyProject|Emmanuel Py|
|Heat Reform and BuildingEnergyEfficiencyProject|Emmanuel Py|
|Heat Reform and BuildingEnergyEfficiencyProject|Emmanuel Py|
|Heat Reform and BuildingEnergyEfficiencyProject|Emmanuel Py|
|Heat Reform and BuildingEnergyEfficiencyProject|Emmanuel Py|
|Heat Reform and BuildingEnergyEfficiencyProject|Emmanuel Py|
|Heat Reform and BuildingEnergyEfficiencyProject|Emmanuel Py|
|Heat Reform and BuildingEnergyEfficiencyProject|Emmanuel Py|
|Heat Reform and BuildingEnergyEfficiencyProject|Emmanuel Py|
|Structural Adjustment Credit Project(03)|Christina A. Wood|
|Structural Adjustment Credit Project(03)|Christina A. Wood|
|Structural Adjustment Credit Project(03)|Christina A. Wood|
|Structural Adjustment Credit Project(03)|Christina A. Wood|
|CommunityDevelopment Fund Project|Caroline Mascarell|