rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 5: Income effects of closing the gap to the reference group**

**Figure 6: Income effects of closing the gap to the reference group – direct effects and through the mean**

(percentage points increase in growth rates) (percentage points increase in growth rates) <!-- Start of picture text --> Life expectancy Life expectancy<br>School enrollment Connectivity<br>Connectivity School enrollment<br>Inflation variation Democratic accountability<br>Inflation Inflation variation<br>Democratic accountability Inflation<br>Credit/ GDP Credit/ GDP<br>-2 0 2 4 6 -2 0 2 4 6 8 10 12<br>Bottom 20 Bottom 40 Bottom 20 Bottom 40<br><!-- End of picture text --> _Source:_ the authors’ calculations As mentioned above, our specification is consistent with the standard Solow growth model. In such a specification, innovations (Δ) in X, the enabling environment, have an effect on the subsequent growth spell, but that effect is not permanent but only transitory. The persistence parameter (ρ+1) being relatively high, this transitory effect will, however, take some time to fade away.

As a final thought exercise, therefore, we tried to capture this dynamic and simulated the effects of closing the gaps with the reference group for some of our variables. The simulated divergence paths from the baseline scenario of the income growth of the two poorest quintiles are presented in Appendix F. One notices that the effect of an innovation on income growth, while temporary, lingers on almost two decades.

As a result, while the immediate effect could be relatively small, cumulatively improvements in some variables could bring us a long way towards the goal of a 3 percent poverty rate. Closing the gap in life expectancy, for instance, would leave the two poorest quintiles about 40 percent richer than otherwise, with about an additional 2 percent annual income growth on average over two decades. Similarly, closing the gap in school enrollment or connectivity would be associated with an additional annual average income growth for the bottom 40 of about 0.7 percent and 0.6 percent, respectively.

# **V. Conclusion**

Haiti, as many other low-income economies, has a vision to become an emerging economy in the foreseeable future. Even when experiencing periods of economic growth, the country has not been able to share equally the proceeds of this better performance among its population. Meanwhile, the standard view that overall economic growth would sooner or later benefit every segment of the

-18-
4 adjustment, European bank leverage and reliance on wholesale funding remain relatively high compared to other regions where deleveraging also took place but started from lower levels. For many global European banks, the L/D ratios were above 100 percent and median asset values stood at almost 19 times equity, compared to around 10 times for their U.S. counterparts at end2010 (figure 2).<sup>2</sup>

**Figure 3. Foreign Claims of European Banks on Emerging and Developing Countries**

<!-- Start of picture text --> 1,600<br>Asia & Pacific Mar 2005–Dec 2011<br>1,400 Europe<br>Latin America/Caribbean<br>1,200 Middle East<br>Africa<br>1,000<br>800<br>600<br>400<br>200<br>-<br>US$billion<br>Mar.2005 Jun.2005 Sep.2005 Dec.2005 Mar.2006 Jun.2006 Sep.2006 Dec.2006 Mar.2007 Jun.2007 Sep.2007 Dec.2007 Mar.2008 Jun.2008 Sep.2008 Dec.2008 Mar.2009 Jun.2009 Sep.2009 Dec.2009 Mar.2010 Jun.2010 Sep.2010 Dec.2010 Mar.2011 Jun.2011 Sep.2011 Dec.2011<br><!-- End of picture text --> Source: Bank for International Settlements (BIS), Consolidated Banking Statistics, table 9D.

<!-- Start of picture text --> Figure 4. European Consolidated Banking System Figure 5. European Consolidated Banking<br>Leverage, 2007–11 System Loan to Deposit Ratios, 2007–10<br>28 27.9 13 200<br>181<br>175<br>171<br>162<br>26 152<br>25.0 12 150 147<br>138 139<br>24<br>11<br>100<br>22 21.1<br>20.9<br>20.2<br>19.8 19.7 10<br>20 19.0 19.1 18.7 50<br>18 9<br>2007 2008 2009 2010 2011<br>0<br>Total All Banks Large Banks<br>2007 2008 2009 2010<br>Assets/Equity: All Banks Large Banks All Banks Large Banks<br>Percent<br>Total assets/Equity<br>Risk-weighted Assets/Tier 1 Captal (%)<br><!-- End of picture text --> Source: ECB Consolidated Banking Data.

2 The limited decline in loan to deposit ratios may also be due to declining deposits as a result of flight to quality, as experienced by Greece, Ireland, Italy, and Spain.
provisions. With the exception of services and investment, coverage of more than 75 percent of essential provisions is rare and happens in less than 15 percent of the mapped agreements.

One important caveat regarding this data set is that it does not cover all of the trade agreements that have been in force during the period under study. Specifically, our information on provisions is limited to agreements that are in effect in present day, i.e., excluding any agreements that are no longer in effect. For this reason, we drop observations associated with agreements no longer in effect. This means that the effects of newer agreements are identified by changes in trade relative to when that pair did not have any agreement rather than relative to pre-existing agreements. The majority of the observations that are dropped are due to pre-accession agreements that new European Union (EU) members sign before joining the EU. Thus, to use one of these cases as an example, Italy-Croatia is included in our data for years 1992-2000 (after Croatian independence and before the initial EU-Croatia PTA in 2001) and for year 2016 (after Croatia joins the EU in 2013). The EU is treated differently in our analysis for this reason, as we discuss further in Section 4. To identify agreements no longer in effect, we consult the NSF-Kellogg database created by Jeff Bergstrand and Scott Baier crosschecked with data from the WTO. The EU and the earlier European Community are treated as the same agreement for these purposes, though it is allowed to evolve as new provisions are added.

Table 2: Coverage of essential <u>provisions</u> by <u>policy</u> area

||Share of|agreements c|overing:|
|---|---|---|---|
|Policy Area|0 to 25%|25% to 75%|over 75%|
|Anti-dumping and Countervailing Duties|99%|1%|0%|
|Competition Policy|48%|47%|5%|
|Environmental Laws|88%|12%|0%|
|Export Taxes|41%|59%|0%|
|Intellectual Property Rights|76%|23%|1%|
|Investment|6%|64%|30%|
|Labor Market Regulations|68%|17%|15%|
|Movement of Capital|44%|42%|13%|
|Public Procurement|53%|40%|7%|
|Rules of Origin|7%|93%|0%|
|Sanitary and Phytosanitary Measures|87%|13%|0%|
|Services|6%|62%|33%|
|State-Owned Enterprises|45%|54%|1%|
|Subsidies|59%|41%|0%|
|Technical Barriers to Trade|93%|7%|0%|
|Trade Facilitation and Customs|21%|78%|0%|
|Visa and Asylum|27%|70%|3%|

Note: Coverage ratio refers to the share of essential provisions for a policy area contained in a given agreement relative to the maximum number of essential provisions in that policy area. Source: Mattoo, Rocha and Ruta (2020) 7
|||(0.0433)||(0.043)||(0.043)||(0.043)|
|---|---|---|---|---|---|---|---|---|
|lsize|-0.0111|-0.0166|-0.017|-0.020|-0.016|-0.019|-0.013|-0.018|
||(0.0377)|(0.0364)|(0.037)|(0.036)|(0.037)|(0.036)|(0.037)|(0.036)|
|lage|0.1548***|0.1585***|0.159***|0.161***|0.158***|0.161***|0.157***|0.160***|
||(0.0585)|(0.0568)|(0.058)|(0.057)|(0.058)|(0.057)|(0.058)|(0.057)|
|SOE|-0.1702|-0.1337|-0.066|-0.058|-0.028|-0.025|-0.047|-0.037|
||(0.1724)|(0.1702)|(0.174)|(0.175)|(0.187)|(0.187)|(0.188)|(0.185)|
|FEM|0.0983|0.1055|0.116|0.119|0.113|0.116|0.106|0.111|
||(0.1080)|(0.1075)|(0.108)|(0.108)|(0.108)|(0.108)|(0.108)|(0.108)|
|Constant|-0.3848**|0.2719|-0.377*|0.104|-0.379**|0.132|-0.379**|0.210|
||(0.1931)|(0.3815)|(0.193)|(0.376)|(0.193)|(0.375)|(0.193)|(0.377)|
|Observations|11,594|11,594|11,594|11,594|11,594|11,594|11,594|11,594|
|R-squared|0.0862|0.0919|0.095|0.098|0.094|0.097|0.090|0.095|

|||_Table A-_|_12: Firms that i_|_nvest and gover_|_nment-owned b_|_anks_|||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|INV|INV|INV|INV|INV|INV|INV|INV|
|SOB|0.1652**|0.1808**|0.167|0.127|0.150|0.109|0.234|0.195|
||(0.0707)|(0.0733)|(0.176)|(0.176)|(0.176)|(0.175)|(0.183)|(0.181)|
|INTER|||-0.004|0.133|0.036|0.179|-0.190|-0.039|
||||(0.333)|(0.345)|(0.346)|(0.355)|(0.380)|(0.377)|
|ROA||0.0296||0.031||0.032||0.029|
|||(0.0209)||(0.021)||(0.021)||(0.021)|
|LTA||-0.0419||-0.045||-0.045||-0.042|
|||(0.0314)||(0.033)||(0.032)||(0.032)|
|lsize|-0.1336***|-0.1368***|-0.134***|-0.136***|-0.133***|-0.136***|-0.134***|-0.137***|
||(0.0248)|(0.0246)|(0.024)|(0.024)|(0.024)|(0.024)|(0.025)|(0.025)|
|lage|0.0371|0.0392|0.037|0.039|0.037|0.039|0.037|0.039|
||(0.0416)|(0.0416)|(0.042)|(0.042)|(0.042)|(0.042)|(0.042)|(0.042)|
|SOE|0.0120|0.0428|0.012|0.032|0.007|0.022|0.030|0.046|
||(0.1094)|(0.1102)|(0.106)|(0.107)|(0.108)|(0.109)|(0.108)|(0.109)|
|FEM|-0.1228*|-0.1183*|-0.123*|-0.120*|-0.123*|-0.120*|-0.122*|-0.118*|
||(0.0688)|(0.0687)|(0.070)|(0.070)|(0.069)|(0.069)|(0.069)|(0.069)|
|Constant|2.0132***|2.3387***|2.013***|2.363***|2.013***|2.366***|2.014***|2.336***|
||(0.1228)|(0.2761)|(0.122)|(0.291)|(0.122)|(0.288)|(0.122)|(0.279)|
|Observations|11,594|11,594|11,594|11,594|11,594|11,594|11,594|11,594|
|R-squared|0.0704|0.0733|0.070|0.073|0.070|0.074|0.071|0.073|

|||_Table A- 1_<br>|_3: Firms that in_<br>|_novate and gove_<br>|_rnment-owned_<br>|_banks_<br>|||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|INNOV|INNOV|INNOV|INNOV|INNOV|INNOV|INNOV|INNOV|
|SOB|-0.1309|-0.0976|0.080|0.094|0.150|0.165|0.084|0.113|
||(0.0817)|(0.0696)|(0.214)|(0.223)|(0.225)|(0.233)|(0.239)|(0.240)|
|INTER|||-0.497|-0.462|-0.673|-0.642|-0.587|-0.580|
||||(0.448)|(0.491)|(0.488)|(0.529)|(0.568)|(0.594)|
|ROA||-0.0335||-0.041*||-0.043*||-0.039|
|||(0.0259)||(0.025)||(0.025)||(0.025)|
|LTA||-0.0468||-0.035||-0.033||-0.042|
|||(0.0332)||(0.039)||(0.038)||(0.035)|
|lsize|0.0554|0.0532|0.053|0.052|0.053|0.052|0.055|0.053|

41
effects are the significant uptick in public lower-secondary education, and a fall in uppersecondary education, between 2009 and 2010. In no case is there a significant change in enrollment rates between 2007, 2008, and 2009. We conclude that the recession did not have a measurable effect on the level of enrollments.

**Table 6.  Measuring the  Impact of the 2008-09 Recession on School Enrollment Rates**

|||Enrollment|Change in age-|specific enrollm|ent rate comp|ared to|
|---|---|---|---|---|---|---|
|||rate (%)|2007:Q1||||
|Pre-school|Total|34.5||4.1|3.3|3.7|
||||||_0.97_|_0.95_|
||Private|8.8||0.3|-0.2|0.0|
||||||_0.82_|_0.89_|
||Public|25.7||3.9|3.5|3.7|
||||||_0.90_|_0.97_|
|Primary school|Total|99.3|0.0|0.0|0.0|-0.1|
|||||_0.82_|_0.88_|_0.74_|
||Private|16.5|-1.4|-1.4|-1.4|-3.7|
|||||_0.65_|_0.99_|_0.00_|
||Public|82.8|1.4|1.3|1.4|3.7|
|||||_0.70_|_0.99_|_0.00_|
|Lower secondary|Total|97.2|0.3|-0.2|-0.1|0.3|
|||||_0.42_|_0.81_|_0.48_|
||Private|10.8|-0.7|0.0|0.2|-1.0|
|||||_0.53_|_0.77_|_0.13_|
||Public|86.4|1.0|-0.2|-0.3|**1.3**|
|||||_0.25_|_0.90_|_0.05_|
|Upper secondary|Total|81.4|-0.7|-1.6|-1.2|0.0|
|||||_0.51_|_0.79_|_0.46_|
||Private|8.9|1.9|1.3|1.8|**0.1**|
|||||_0.87_|_0.43_|_0.01_|
||Public|72.5|-2.6|-2.9|-3.0|-0.1|
|||||_0.56_|_0.97_|_0.09_|
|Higher education|Total|37.9|-6.2|-5.1|-4.4|-5.2|
|||||_0.89_|_0.83_|_0.80_|
||Private|6.8|-4.8|-0.4|-0.4|-0.3|
|||||_0.58_|_0.99_|_0.97_|
||Public|31.1|-4.8|-4.7|-4.1|-4.9|
|||||_0.67_|_0.81_|_0.76_|

Note: Note: The coefficients measure the time fixed effects relative to 2007:Q1. The figures in italics are p- values for a test for whether there is a difference between the fixed effects between that year and the previous year. Boldfaced numbers are statistically different from the previous year at the 5% level; yellow cells indicate lower enrollment rates in 2009 than in 2008.

A common household response to a drop in income is to cut purchases of durable goods sharply in order to be able to maintain consumption of non-durables, including food. The results in Table 7 are designed to determine whether this is what occurred in 2009. We use the same approach as employed earlier, testing whether there are differences in the time fixed effects between one year and the next, for major spending headings.

Losers and Winners in Recession: Thailand 2008-09 22
**Table 2: Discoveries by Country**

||**Discovery Count**<br>**HS 4-Digit**|**Percent**<br>|**Discovery Count**<br>**HS 6-Digit**|**Percent**<br>|
|---|---|---|---|---|
|**Argentina**|<br>5|2%|<br>32|2%|
|**Australia**|5|2%|22|1%|
|**Burundi**|0|0%|0|0%|
|**Bolivia**|7|2%|15|1%|
|**Brazil**|10|3%|44|3%|
|**Central African Rep.**|0|0%|1|0%|
|**Canada**<br>|2<br>|1%<br>|19<br>|1%<br>|
|**Switzerland**|2|1%|19|1%|
|**Chile**|8|2%|31|2%|
|**China**|8|2%|39|2%|
|**Colombia**<br>|17<br>|5%<br>|43<br>|3%<br>|
|**Cyprus**|4|1%|5|0%|
|**Czech Republic**|17|5%|58|3%|
|**Germany**|0|0%|17|1%|
|**Denmark**|1|0%|10|1%|
|**Ecuador**|11|3%|30|2%|
|**Spain**|1|0%|24|1%|
|**Finland**|5|2%|30|2%|
|**United Kingdom**|4|1%|24|1%|
|**Greece**|2|1%|18|1%|
|**Guatemala**|4|1%|9|1%|
|**Hong Kong, China**|2|1%|7|0%|
|**Croatia**<br>|4<br>|1%<br>|11<br>|1%<br>|
|**Hungary**|5|2%|92|5%|
|**Indonesia**|29|9%|160|9%|
|**India**<br>|8<br>|2%<br>|53<br>|3%<br>|
|**Ireland**|1|0%|19|1%|
|**Iceland**|3|1%|5|0%|
|**Japan**|1|0%|4|0%|
|**Korea, Rep.**|9|3%|51|3%|
|<br>**Macao**|0|0%|5|0%|
|**Morocco**|10|3%|19|1%|
|**Mexico**|10|3%|66|4%|
|**Mauritius**|1|0%|4|0%|
|**Malaysia**|2|1%|42|2%|
|**Nicaragua**|8|2%|12|1%|
|**Netherlands**|2|1%|19|1%|
|**Norway**|1|0%|13|1%|
|**New Zealand**|4|1%|10|1%|
|**Oman**<br>|15<br>|5%<br>|47<br>|3%<br>|
|**Peru**|20|6%|66|4%|
|**Portugal**|6|2%|25|1%|
|**Paraguay**|7|2%|9|1%|
|**Romania**|26|8%|102|6%|
|**Saudi Arabia**|3|1%|18|1%|
|**Singapore**|1|0%|11|1%|
|**Sweden**|0|0%|24|1%|
|**Thailand**|8|2%|63|4%|
|**Trinidad and Tobago**|5|2%|28|2%|
|**Turkey**|13|4%|135|8%|
|**Taiwan, China**|4|1%|58|3%|
|**United States**|0|0%|3|0%|
|**South Africa**|11|3%|39|2%|
|**Total**|332|100%|1710|100%|

Source: Author’s calculations The instances of economic discovery in the 1990s were not evenly spread across all countries.

However, the distribution does not seem to be random. There is a pattern between discovery 18
# Appendix B: Application to the World Bank’s global poverty and inequality measures

The method we developed lends itself to being used in the World Bank’s global poverty measures for the countries that do not have any survey data. For the purpose of calculating regional and global poverty headcounts, countries without a household survey at any point in time, or countries without national accounts data needed for year-to-year extrapolations, are currently assigned the population-weighted regional average poverty rate. This affects 55 economies (Table B.1).<sup>5</sup> A few countries, such as Afghanistan, Saudi Arabia, the República Bolivariana de Venezuela, and the Democratic People’s Republic of Korea, make up the largest part of the population without data, adding up to more than 200 million people across all the missing economies (Table B.2).

**Table B.1: Economies without household survey data at any point in time in PIP**

||Cuba|||
|---|---|---|---|
|Afghanistan|Curaçao|Kosovo|San Marino|
|American Samoa|Dominica|Kuwait|Saudi Arabia|
|Andorra|Equatorial Guinea|Libya|Singapore|
|Antigua and Barbuda|Eritrea|Liechtenstein|Sint Maarten (Dutch part)|
|Aruba|Faeroe Islands|Macao SAR, China|Somalia|
|Bahamas, The|French Polynesia|Monaco|South Sudan|
|Bahrain|Gibraltar|Nauru|St. Kitts and Nevis|
|Barbados|Greenland|New Caledonia|St. Martin (French part)|
|Bermuda|Grenada|New Zealand|St. Vincent and the Grenadines|
|British Virgin Islands|Guam|Northern Mariana Islands|Timor-Leste|
|Brunei Darussalam|Hong Kong SAR, China|Oman|Turks and Caicos Islands|
|Cambodia|Isle of Man|Palau|Venezuela, RB|
|Cayman Islands|Korea, Dem. People’s|Puerto Rico|Virgin Islands (U.S.)|
|Channel Islands|Rep.|Qatar||

**Table B.2: Missing economies ranked by population in 2019**

|Economy|Population 2019(in millions)|
|---|---|
|Afghanistan|38|
|Saudi Arabia|36|
|Venezuela, RB|29|
|Korea, Dem. People’s||
|Rep.|26|
|Cambodia|16|
|Somalia|16|
|Cuba|11|
|South Sudan|10|
|Hong Kong SAR,||
|China|8|
|Libya|7|
|Other Economies|40|
|Total Missing|205|

5 Table B.1 includes economies that are missing in all years (e.g., the Democratic People’s Republic of Korea), but also economies that are missing only for some years (e.g., the República Bolivariana de Venezuela). The latter occurs when the national accounts data needed for the extrapolations is unavailable in some years.

34
Table 3: FAT-PET Estimates

||(1)<br>Full Sample|(2)<br>Digital|(3)<br>Energy|(4)<br>Transport|(5)<br>cross-sectoral|
|---|---|---|---|---|---|
|1/SE|0.0163|0.0135|-0.0004|0.0230|0.1624***|
||(0.012)|(0.013)|(0.001)|(0.014)|(0.021)|
|Constant|3.9578***|3.3843***|3.1852***|3.9941**|-0.1170|
||(0.934)|(1.163)|(0.365)|(1.633)|(1.042)|
|Observations|1,074|230|219|393|232|
|R-squared|0.087|0.130|0.000|0.061|0.566|

Notes: FAT-PET estimates at the sector level. Robust standard errors, clustered at the article level, in parentheses: *** p<0.01, ** p<0.05, * p<0.1 Table 4: PEESE Estimates

||(1)<br>Full Sample|(2)<br>Digital|(3)<br>Energy|(4)<br>Transport|(5)<br>cross-sectoral|
|---|---|---|---|---|---|
|1/SE|0.0199|0.0153|0.0065**|0.0311**|0.1613***|
||(0.012)|(0.013)|(0.003)|(0.013)|(0.021)|
|SE|5.2852**|0.9091|8.0849**|7.9020***|4.4989*|
||(2.208)|(0.905)|(3.794)|(2.381)|(2.262)|
|Observations|1,074|230|219|393|232|
|R-squared|0.132|0.171|0.182|0.126|0.618|

Notes: PEESE estimates at the sector level. Robust standard errors, clustered at the article level, in parentheses: *** p<0.01, ** p<0.05, * p<0.1 32
28

# **Table 6**

Autoregressive distributed lag representation for _kpr_

||(stand|ard errors in bracket|s)||
|---|---|---|---|---|
|Variable||Lag||Sum|
||0|1|2||
|_kpr_|-1|0.980|-0.347|-0.367|
||(-)|(0.213)|(0.161)|(0.089)|
|_kg_|-0.328|-0.121|0.142|-0.308|
||(0.138)|(0.202)|(0.111)|(0.117)|
|z|-0.151|0.292|0.100|0.242|
||(0.144)|(0.209)|(0.175)|(0.103)|
|_crpr_|0.039|0.014|0.006|0.059|
||(0.025)|(0.028)|(0.021)|(0.030)|
|_Pk_|-0.059|-0.025|0.006|-0.078|
||(0.036)|(0.040)|(0.034)|(0.055)|
|_time_|0.017|||0.017|
||(0.004)|||(0.004)|
|_constant_|2.678|||2.678|
||(0.692)|||(0.692)|
|R=0.999||SEE = 0.44%|Sample: 19|62/63-1993/94|

## Misspecification tests

|Alternative hypothesis|Null distribution|Statistic|p-value|
|---|---|---|---|
|Istorderautocorrelation|F(1,_15)_|0.034|0.813|
|2nd order autocorrelation|F (2, 14)|0.159|0.857|
|ARCH|F (1, 14)|0.051|0.822|
|Non-normality|X (2)|3.851|0.146|
|Functional misspecification<br>(RESET)|F (1,_15)_|0.015|0.904|
some future time, offer competing services. For example, although two companies applied for licenses to provide paging services, MPTC refused to issue a license to one firm and issued a license but failed to allocate spectrum space to the other (Article 19, 1998, p. 9).

In the television sector, where MPTC owned the non-operational TV Malawi, MultiChoice Malawi, which was originally owned by Lonrho but later was sold to Malawian and Zimbabwean investors, applied for a license to re-broadcast several international channels (e.g., BBC World Service and a South African Sports Channel) to paying consumers and a free channel to all consumers in 1994. After agreeing to broadcast one free channel in addition to three channels for subscribers, MMC gained the support for the Ministry of Information and applied for a license. In May 1998, the Ministry of Information gave MMC permission to start broadcasting. However, MPTC failed to issue MMC a license and, consequently, MMC started operating without a license. By allowing firms to start operating without issuing formal licenses, some entry was possible. However, since the firms' legal positions remained uncertain, this policy probably inhibited investment and promoted short time horizons.

11.2 Access.

In 1985, MPTC had 0.27 lines **0.7** ____________________________________ for every 100 people, slightly lower than 0.6 _ _J_ the average for Sub-Saharan Africa **0.5** which was around 0.31 lines per 100 **0.4 0.3** inhabitants. However, the difference 0.2 was not large, especially given that 0.1 , , , , , , , , , , , , , , Malawi was more rural than the average a, co, 0, a, a, co a,7 co on ,, cnF,a,ar for Sub-Saharan Africa - in 1985, 90 -c-Malawi -{Sub-Saharanfrca percent of the Malawian population lived in rural areas compared to 75 Figure 1: Fixed Lines per 100 inhabitants. Source: Intemational Telecommunication Umon (2001) However, over the next decade, Malawi's relative performance declined and by 1998, service penetration was low even by regional standards. In 1998, Malawi had 0.34 main lines per 100 inhabitants (see Figure 1), 8
_Announcement and Training._ Treatment status is chosen in each phase using a lottery, where ID numbers are drawn by the research team to assign treatment status. This helps promote feelings of fairness, and also makes it clear that Olam, the employer, is in no way making the decision of who receives accounts. Drawing ID numbers (rather than names) enables workers to know if they themselves are chosen, while maintaining the privacy of selected workers.

To ensure workers understand how the blocked accounts work, we undertake training sessions within the factory with all workers assigned to the Private or Non-private conditions. Workers attend the sessions in small groups of about 5 workers each. These sessions are attended by a bank staff member to answer questions, and led by a moderator from the research team. The sessions explain the rules of the accounts, including choosing thresholds, and work through examples. At the end of the session, each worker takes a quiz to verify comprehension of the account rules. If the worker scores below 80% on the quiz, they are retrained one-on-one by a moderator. The gap between when workers learn their treatment status and when accounts take effect for most workers is 3-4 weeks: 1-2 weeks of pure announcement period, plus about 2 weeks to get the paperwork in place.

_Privacy of deposits._ Workers who enroll in the blocked accounts continue to be paid the take-home portion of their earnings (i.e., any amount earned less than the threshold) in cash, in the same way as before. Any amount earned above the threshold is directly deposited by the factory to the bank, and this amount is not discussed when payments are distributed to help maintain privacy. Instead, workers enrolled in the accounts are given a small receipt discreetly at a different time that verifies how much was deposited into their savings account. Consequently, our protocols ensure the privacy of savings deposits—while leaving the process of obtaining payment of the cash component of earnings on paydays unchanged. These payment protocols were the same for both the Private and Non-private treatments.

_Enrollment and roll-out._ In Phase 1, workers were enrolled in the experiment in three staggered waves, with data collection for Phase 1 ending in March 2019. Phase 2 was then conducted from April 2019-July 2019.

# **6 Empirical strategy**

## **6.1 Data sources**

Our primary data source is Olam’s detailed daily administrative data at the worker level. This includes individual daily attendance, output (the quantity of nuts processed), and the internal validity of our estimates, since treatment assignment is stratified within a given phase.

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