rafmacalaba/datause-encoder-data huggingface dataset:
Northern regions (especially Amhara, Tigray and Oromiya) the traditional Rist system meant that large land owning families cultivated at times vast areas, and reform meant an effective transfer for many. If anything, this would suggest that the areas studied in this paper have enjoyed historically relatively more secure tenure, and thus this sub-sample provides a tougher test of the impact of tenure security.

Mode of acquisition may provide some information on transfer rights, but there is by no means a simple direct mapping. We have a direct measure at the plot level whether the household perceives that the specific plot of land can be passed on to someone else (including via inheritance), although in this case only for the 1997 and 1999 round of data collection. Table 2 summarizes these data. A few interesting features emerge. First, households perceive that only about 60 percent of plots they cultivate could be transferred to others, including via inheritance, despite the fact that more than 80 percent of plots were either purchased or inherited. Indeed, even though farmers may have been allowed to keep land despite periods of land reform, they do not perceive that the usufruct rights will be perpetual. It is also striking that perceived transfer rights are statistically significantly lower in 1999 compared to 1997.

**Table 2: Descriptive Statistics – plot level variables - selected villages ERHS**

|Variable|1997|1999|
|---|---|---|
|Transfer rights (dummy=1 if yes)*<br>_transfer rights on inherited plots_<br>_transfer rights on purchased plots_<br>_transfer rights on allocated plots_<br>_transfer rights on sharecropped plots_|0.65<br>_0.71_<br>_0.83_<br>_0.69_<br>_0.00_|0.53<br>_0.57_<br>_0.50_<br>_0.53_<br>_0.25_|
|Share oftotal land (sumofplotsin ha) withtransfer rights**|0.66|0.48|
|Number ofplots|1212|939|

*The difference in transfer rights between 1997 and 1999 is statistically significant in all but one cases at the 99% level, the exception is for allocated plots, where the significance level is 95%. ** 66 percent of total land size (measured by the sum of the size of all the plots in ha) had perceived transfer rights in the 4<sup>th</sup> round, while this percentage declined to 48 percent in the 5<sup>th</sup> round.

The second data point is after the news on the new land reform in Amhara region will have filtered through, and even though the villages in the sample are outside this region, it surely will have affected people’s perceptions. Finally, transfer rights do not map directly into modes of acquisition. For example, people perceive transfer rights on allocated plots, and in 1999 even on 12
<!-- Start of picture text --> Sources of new management practices Reason for not implementing new management practices in 2017<br>Among firms that implemented new practices in 2017 8<br>2<br>ico}<br>Qo 2<br>E<br>© =<br>Oy<br>Q veo<br>= G<br>"7 =<br>5 a<br>eo58,<br>=<br>a<br>N o&& «.. s s a<<<br>Sssessee SS RS RS seSeS<br>~§oF§oFoF RS&Se&SeSe &SFeSFee &SseeSseeee 3-x-xx<br>© ° A & as<br>ConsultantsTrade assoc. or conferencesCustomers Suppliers Competitors New employees Other sources ~Sss<br><!-- End of picture text --> <!-- Start of picture text --> Reason for not implementing new management practices in 2017<br>8<br>ico}<br>Qo 2<br>E<br>=<br>Oy<br>veo<br>G<br>=<br>a<br>8,,<br>o&& «.. s s a<<<br>Sssessee SS RS RS seSeS<br>~§oF§oFoF RS&Se&SeSe &SFeSFee &SseeSseeee 3-x-xx<br>° A & as<br>~Sss<br><!-- End of picture text -->
# **Appendix H: Deviations from the Pre-Analysis Plan**

This study was preregistered at the EGAP registry (ID: [Redacted]) on [Redacted]. The registration took place prior to researcher access to outcome data. In this section, we discuss deviations from the pre-analysis plan.

First, the family outcomes governance, women empowerment, intra-village cohesion and inter village cohesion were pre-registered as secondary outcomes. In this manuscript, however, we present them together with the original main outcomes.

Second, a number of individual outcomes were originally preregistered but were not included. For the building quality dimensions of service provision related to the primary school and the hospital, electricity (EE38, ES34) and running water (EE32, ES33) were not included because of a lack of variation (almost no facility has running water or electricity). Related to governance’s participation dimension, we did not include the village chief’s opinions about the decision making process in the village (EC194-196), as these are particularly prone to social desirability biases. Related to governance’s transparency dimension, we excluded information related to bribes (Q107, Q123, Q136) because of a lack of variation (few people say they pay bribes). Finally, related to the women empowerment family, for sensitivity reasons, we excluded survey questions about respondents’ opinions related to rape (Q301-305). Including these individual measures, however, does not change this study’s findings.

Finally, we originally suggested to conduct subgroup analysis across a wide set of different characteristics: improvements in service provision, gender parity, type of community and type of project. We also suggested to explore unintended consequences of the program, specifically whether the program increased prices in treatment communities. We did not include these additional analyses to avoid making the manuscript unwieldy.

17
# **Explaining Enterprise Performance in Developing Countries with Business Climate Survey Data**

**Jean-Jacques Dethier, Maximilian Hirn and Stéphane Straub**<sup>**1**</sup>

JEL Codes: L5, O4, O12 Keywords: Investment Climate. Growth and Productivity. Economic Development.

> 1   J.J. Dethier (jdethier@worldbank.org) and M. Hirn (mhirn@worldbank.org) are with DEC, The World Bank, Washington, D.C. and S. Straub (stephane.straub@univ-tlse1.fr) with Arqade, Toulouse School of Economics, Toulouse.
<!-- Start of picture text --> Bie =x. angie.pre= ioe RESys OESae eee “stoq ee me si%! eeepee " : % a 4<br>ad Ss ” Boone 3 poe a \ %. EF ;= re. ag Do | Y %<br>eS is ee Foe ae’ AS : a<br>ee Ms isORE Se »<br>weeret ? Pakrd Feé aed geEe SeNe +%a . Myanma -a<br>A ced ge fase ; g SS Thailand we<br>« = aE age? ae tae 4 (ae é ;<br>i 3 ne 3 : S Fie 5 L G “ae<br>t i: Vie ay x te Ja gek ee.<br>LR SR seme tae Va Be a<br>BS y fs i i a ~ 3s “4 pe<br>A Major cities rN ae 4 \ rn<br>Lb Ponts = \ ile 2 pene he ae ba ——¥<br>ve Existing rail ee ay, 2 eins eae, sd<br>/~/ Existinghighways (pre- re, ap Bangkok% = tte.“SNea creareei i<br>ce or a * Be<br>2S BRIunderrail, built or ce. : a yy Ys LeSeemiss<br>construction oP , 4 ". hae<br>*S* BRIpannedrail, > Yeo: gta ;<br>se BRI built highway,or under Fir. ibe a“a ;<br>construction om os<br>WZ Protected areas -a .<br>Forest on £3 fs NS<br>=z coverGlobalin forest2000 é me Weteysiax<br>a 3 &<br>[cast | Forest2000 - gain,2015 Be<br>sy Forest loss, Sods, =% 2 , £ . 2 : ‘ A<br>2000 - 2015 ~ ts e A B<br>© 75150 300KM ae 3 D Pm * Singapore J "<br>Looittipris oN oat<br><!-- End of picture text -->
<!-- Start of picture text --> Singapore<br>United Arab Emirates<br>Chile<br>Malaysia<br>Colombia<br>Kenya<br>Rwanda<br>Tanzania<br>0% 10% 20% 30% 40% 50% 60% 70% 80%<br>Mobile money account (%, age 15+) Credit card ownership (%, age 15+)<br><!-- End of picture text --> _Source:_ Findex 2017.

Mobile money services are playing a big role in promoting the digital economy in developing countries. Alibaba’s Alipay and Tencent’s WeChat Pay are underpinning the tremendous e-commerce market in China. M-Pesa, Kenya’s leading mobile payment service provider, has partnered with PayPal and Singapore’s TransferTo to increase access to the global marketplace for millions of Kenyan businesses. A vast majority of the unbanked population who were excluded from the traditional financial system are now able to access financial services, including payment. In Cambodia, through a network of 60,000 agents, including local franchises, street vendors, and cafés, Ascend Money is allowing more people to participate in digital transactions.

Firms benefit from a significantly reduced transaction cost through mobile money services. In contrast to traditional credit/debit payment model under which numerous players each peel off a layer of the processing fee, merchants now need only pay a low processing fee to the nonbank mobile money service providers. For example, merchants pay a transaction fee of 0.55 percent on the flip side for every purchase under Alipay, whereas they would be charged 2.75 percent under the credit/debit card model.<sup>74</sup> Reduced transaction costs, greater liquidity, and increased credit worthiness are associated with mobile money activities.<sup>75</sup> Regulations have not caught up with the rapid development of mobile money activities.<sup>76</sup> In the Arab Republic of Egypt, Bangladesh, and Kazakhstan, laws prohibit nonbank businesses from providing mobile money services on their own. The traditional financial institutions that lobby heavily to remain the gateway of the digital payment system, as well as the uncertainty about the financial risks posed by mobile money service providers, dissuade governments from giving the green light to the new type of payment service providers.

That said, governments can take a few steps to avoid handicapping innovation or discouraging new entrants from entering the digital payment system, while ensuring that customers are protected. Standardizing the licensing of payment service providers ensures quality control of players in the market and helps streamline the government’s supervision and oversight of their activities. Security measures should be in place for all types of payment service providers. Most countries covered by the Digital Business Indicators project except Armenia, Honduras, Korea, and Pakistan require payment service providers to establish at least one 20
Since Proposition 2 shows a necessary condition under which poverty rates estimated from inequalities based on different poverty lines are comparable, it can be used to evaluate the comparability of poverty rates estimated in NISR (2015).

III.2.2 Comparability of the official poverty trend between EICV3 and EICV4 Here we explore the comparability of the poverty rates estimated in NISR (2015). The three conditions of proposition 2 need to be satisfied. Since both EICV3 and EICV4 use the same approach for constructing nominal household expenditures and adopt the same adult equivalent scale, condition (i) holds. Condition (ii) – comparability of COLIs – and condition (iii) – the update of poverty lines, however, _may not_ hold.

In the case of condition (ii), there are three main differences. First, the base months to which the two COLIs are normalized are different. For EICV3, the food COLI uses January 2001 as the base month, while EICV4 uses January 2014 as the base month.<sup>9</sup> Second, the weights of the COLIs differ. For EICV3, the weights for the food COLI are derived from EICV1 (2001) data, while those of the nonfood COLI come from EICV3 data. In contrast, for EICV4, the weights of both food and nonfood COLIs are derived from EICV4 data. Third, the price data used for constructing the COLIs are different. For EICV3, NISR (2015) uses MINAGRI data to estimate the food COLI, and the official CPI data to estimate the non-food COLI. For EICV4, NISR (2015) uses CPI price data for both food and non-food COLIs. It is therefore difficult to guarantee that condition (ii) is satisfied.

<mark>As for (iii), the poverty line used for estimating poverty rates in EICV3 is based on EICV1 data and valued at January 2001 prices (RWF 64,000), while the poverty line used for estimating poverty rates in EICV4 is based on EICV4 data and valued at January 2014 prices (RWF 159,375). Thus, condition (iii) will hold if the two poverty lines satisfy the following equation:</mark> To check whether this is the case, we need an estimate of the inflation rate between January 2001 and ����� January 2014, i.e. π����� . This is not directly available from the data. We therefore split this into two components; the inflation rate between January 2001 and January 2011 and the inflation rate between January 2011 and January 2014, i.e.

����� There are two sources of data available for each of these inflation rates. In the case of π����� , we can either use the COLI estimated in NISR (2015) or the WDI (World Development Indicators) 2018 data. In case of the former, we take a population weighted average of the COLIs across all regions for January 2011, and ����� ������ � approximate π����� � �π����� ����. Using EICV3 data, this comes out to be 2.05 (see Appendix table A1). Interestingly, this number is the same as the inflation rate from WDI.

> 9 Strictly speaking, the non-food COLI for EICV3 is first estimated for January 2011 as the base month and then adjusted to January 2001 as the base month when aggregating it with the food COLI (NISR 2012).

12
development. Second, “plumbing” factors, such as quality of information about the country, the legal and regulatory framework, settlement and registration facilities, and the quality of the local financial markets, critically affect the investability assessment, especially for small and mediumsized economies. Table 2 provides a summary of selected “housekeeping” and “plumbing” factors influencing the investability decisions. Finally, the investment process has become more rigorous, explicit, and consequently more data intensive over the past 10 years. The trend has been to quantify subjective indicators that were previously incorporated in a “touchy-feely” way. As investors have demanded more information, countries, multilateral institutions and independent vendors have supplied more, and the asset allocation process has changed.

Future research in this area could expand the universe of investors (specifically to include investors outside the main financial centers and local to the emerging market regions, as well as a wider variety of equity investors) to further refine the analysis in order to differentiate the investability criteria and process across investor types. Institutional investor databases containing portfolio flow information across countries could be exploited to empirically test the relative importance of the factors identified in this paper in relation to the binary investability decision.

27
# **5. Conclusions**

Using for the first time micro data from household surveys in an unbalanced panel framework covering 26 transition economies over a 16-year period, the paper has investigated the correlates of inequality increase in post-Communist countries. Another feature the paper has been the use, not of a single inequality index like a Gini coefficient, but decile shares which give a much more detailed picture of changes in the entire distribution. While, for example, the Gini coefficient can remain unchanged with increases in income shares among both the rich and the poor (and a corresponding decline in the middle); the share-based analysis captures these changes well. Using the method of seemingly-unrelated regressions, run for each decile, and fixed (country effect) specification, we find that reforms, as measured by the average EBRD index for a given country/year, have had a robust negative effect on income shares of the bottom four deciles, and positive on income shares of the top two deciles.

The intuitive feeling that reforms in post-Communist countries were anti-poor (at least in the distributional sense) is confirmed. Breaking down the reform index into its nine EBRD-defined types of reforms, we find that the negative effect on income shares of the bottom is associated mostly with infrastructural reforms which included introduction (or increases) of fees for services, and privatization of electricity, railways, roads, water provision etc. On the other hand, small scale privatization is associated with the opposite (pro-poor) effect. Among the other relevant variables, the most important and significant is the role of democracy which raises the incomes shares of the bottom and middle deciles. Not surprisingly, we find inflation to be anti-poor: highly significant for the income shares of both the bottom and the top. Growth as such has, on the other hand, been disequalizing. However, this effect is sufficiently small so that growth overall is associated with an increase in real income of the bottom deciles (including the lowest)—that is, even if the bottom decile’s income share is reduced. In other words, growth was anti-poor in relative, but not in an absolute, sense. Finally, once we control for country-effects, we find absence of association between government expenditures as a 25
Table 6: Wald test results

|Variable|_χ_<sup>2</sup>|_p_-value|
|---|---|---|
|Ym|0.117|0.733|
|Yx|0.551|0.458|
|dGDPpc|0.821|0.365|
|GDPsim|23.625|0.000|
|dist|6.997|0.008|
|comlang|0.044|0.834|
|border|0.037|0.847|
|colony|3.051|0.081|
|PTA|4.588|0.032|

The results of Table 5 and Table 6 indicate that the coefficient on the PTA dummy is significantly larger for preferential trade rather than total trade. This result is consistent with the expectations expressed in the beginning of this section and shows that the estimated effects of preferential liberalization on bilateral imports are diluted by the inclusion of low tariff lines where the margin of preference is close to zero. However, the magnitude of change in the PTA coefficient suggests that the extent of this dilution is much smaller than expected. Section 3.2 has shown that, using a 3 percent MFN threshold, trade in tariff lines where preferences are likely to matter is less than half of the total trade between PTA partners. If the only effect of PTAs is to liberalize trade in tariff lines above 3 percent MFN, the expected change in bilateral trade flows implied by the PTA coefficient should be at least twice as large in the preferential specification as in the total trade specification (since the change in the level of imports should be the same across specifications, but the denominator is more than two times larger when total imports are used as the dependent variable). Instead, we observe that the value of the PTA coefficient increases by just over 13 percent, and the implied semi-elasticity is 137 percent instead of 114 percent.

There are two potential explanations for this finding. On the one hand, it is possible that product exclusions, sensitive lists, phase-in periods, and implementation problems limit the domain of PTAs to a much smaller subset of trade than what is captured by our 3 percent threshold. In this case, our coefficient estimate continues to represent the diluted effect of preferential liberalization percent MFN. In this case, the coefficients on GDP similarity and the PTA dummy are significantly different across specifications at the one percent level, while the coefficients on distance and colonial links are significantly different at two and five percent, respectively.

37
k in year t, denoted as t,, were calculated as the ratio of collected duties to customs value. In addition, MF'N tariffs, tROw were calculated as the ratio of collected duties to customs values from all exporters to the US excluding beneficiaries of preference programs (such as GSP, NAFTA, CBI, AGOA). Note that both MFN tariffs and US prices vary over products and years but not over the beneficiary countries.

The analysis is conducted for the 8 largest exporters of apparel to the US from the Caribbean and Central American regions, as we mentioned earlier. These are Costa Rica, Dominican Republic, El Salvador, Guatemala, Haiti, Honduras, Jamaica, and Nicaragua. Out of 24 eligible countries under the CBI, only 14 actually exported apparel to the US during 1989-2002. Our dataset of the 8 countries covers 99% of the $82.3 bn worth of apparel imports into the US from all eligible CBI countries during this period. The data has 211 8-digit categories which are grouped into 32 4-digit categories.

We should note that the product dummies are at the 4-digit level, rather than the 8-digit level!!. Most 8-digit categories within a 4-digit category are very similar and we believe the 4-digit product dummies capture most of the effects (quality, margin effects, demand/supply shocks) we are targeting while 8-digit dummies unnecessarily reduce our degrees of freedom. Despite the highly disaggregated data we use, it is possible that fixed effects are not enough to capture systematic differences between products. In particular, heteroskedasticity in residuals is a concern. For example,there may exist heteroskedasticity across panels i.e. the variance of the error may be different for each panel (the product k) This may be due to variation of scale in imports of different products. It could also be because of specific features of a product that systematically affect the error. To correct for heteroskedasticity, we adopt a two-step Feasible Generalized Least Squares estimation procedure. In the first step, we estimate 6 using OLS and this is used to calculate the residuals. These, in turn, are used to construct a consistent estimator for the variance matrix. We reweigh each of the variables by the inverse of the commodity-specific residual standard deviations from the variance matrix and estimate Say 9. The fixed effects GLS estimator allows the estimation of product, country and year-specific <u>weight or amount of fabric used, as this is the</u> way some apparel data are collected and reported. '18-digit categories are extremely narrow and detailed. For example, 6105 is Men’s or boys’ knitted shirts while 61052020 is Men’s or boys’ cotton knitted shirts.

11
<!-- Start of picture text --> Existence of recovery plan COVID plan approved<br>88<br>¢<br>8<br>8<br>2 Re<br>&<br>&<br>2<br>°o °<br>ME Yes EE No ME Yes<br>GEE in progress HEE in progress<br><!-- End of picture text --> <!-- Start of picture text --> Existence of recovery plan<br>High income Low income<br>Es<br>8<br>e<br>o<br>N<br>° o<br>Pd<br>Lower middle income Upper middle income<br>8<br>8<br>2<br>&<br>o<br>Ey: EE. ME i progress<br>Graphsby inc_group<br><!-- End of picture text --> <!-- Start of picture text --> COVID plan approved<br>High income Lower middle income<br>8<br>2<br>8<br>#<br>gi<br>° o<br>4<br>Upper middle income<br>8<br>8<br>8<br>a<br>&<br>o<br>EE ys EE progress<br>Graphsby inc_group<br><!-- End of picture text -->
**Table 1: Enrollment by Grade, Children ages (6-10 years old), 2012**

||Public|Private|Other|total|Percent|
|---|---|---|---|---|---|
|Population 6-10 years||||7416|100%|
|Never attended||||3550|47.9%|
|Attended & unenrolled||||171|2.3%|
|Enrolled|3360|249|86|3695|49.8%|
|Currently enrolled:||||||
|_Katchi_* & Grade1|894|56|21|971|13.1%|
|Grade2|1079|63|29|1171|15.8%|
|Grade3|630|55|22|707|9.5%|
|Grade4|365|34|9|408|5.5%|
|Grade5|231|22|3|256|3.5%|
|_Katchi_* – Grade 5|3199|230|84|3513|47.4%|
|Grade 6 and above|161|19|2|182|2.5%|

_<mark>Source:</mark>_ <mark>Data from Sindh Baseline Household Survey, 2012.</mark> _<mark>Note: Katchi</mark>_ <mark>is the pre-primary grade in which children are admitted in a public primary school.</mark> Learning outcomes are still poor for enrolled children. At Grade 3, only 24 percent of students can read words while only 32 percent can perform basic subtraction. At Grade 5, only 19 percent of students can read full sentences while 35 percent can perform division (ASER, 2015). Gender disparities in learning are large and similar for both English and Math; boys outperform girls by 6 percentage points (ibid).<sup>5</sup> SMCs were established at each school in Sindh in the 2000s as part of a broader agenda of decentralization and community participation in public services. These early SMCs included the head teacher and four elected community members, two of which were parents and two nonparents (village elders). However, these early SMCs had little formal control over school decision-

> 5 In this study’s baseline survey, in learning assessments designed to capture students’ achievement at Grade 1-5 levels, tested children (7-13 years) scored an average of only 27 percent across subjects.

12