rafmacalaba/datause-encoder-data huggingface dataset:
# Table 6

Treatment Effects: Savings and Consumption

||||||_Depend_|_ent variable:_|||||
|---|---|---|---|---|---|---|---|---|---|---|
|||Saving|s and remitta|nces||||Consumption|||
||Has any<br>savings<br>(1)|Log total<br>savings<br>(2)|Log<br>formal<br>savings<br>(3)|Log<br>informal<br>savings<br>(4)|Log total<br>remit-<br>tances<br>(5)|Log total<br>consump-<br>tion<br>(6)|Log food<br>consump-<br>tion<br>(7)|Log<br>non-food<br>consumption<br>(8)|Bought<br>item _>_ Tk<br>1,000<br>(9)|Shocks<br>index<br>(10)|
||||||**Panel **|**A: endline**|||||
|Bank or mobile payroll|0.038**|0.508**|2.424***|-0.333|-0.196|-0.116*|0.009|-0.135**|-0.027*|-0.051**|
||(0.019)|(0.198)|(0.226)|(0.203)|(0.219)|(0.063)|(0.154)|(0.063)|(0.015)|(0.026)|
||[0.076]|[0.027]|[0.001]|[0.128]|[0.249]|[0.095]|[0.502]|[0.065]|[0.095]|[0.080]|
|Bank or mobile account|-0.005|0.074|0.389|-0.025|0.054|-0.092|-0.174|-0.078|-0.018|-0.049|
||(0.024)|(0.252)|(0.276)|(0.252)|(0.276)|(0.079)|(0.191)|(0.079)|(0.019)|(0.031)|
||[0.453]|[0.427]|[0.160]|[0.494]|[0.453]|[0.236]|[0.249]|[0.245]|[0.245]|[0.130]|
|Baseline controls|X|X|X|X|X|X|X|X|X|X|
|Test: payroll = account|0.036|0.041|0.000|0.155|0.297|0.747|0.268|0.439|0.561|0.918|
|Observations|2,376|2,376|2,376|2,376|2,376|2,376|2,376|2,376|2,376|2,002|
|R-squared|0.063|0.098|0.169|0.076|0.075|0.069|0.134|0.051|0.039|0.042|
|Mean control [endline]|0.815|7.519|2.736|6.259|7.395|8.819|5.974|8.466|0.101|0.169|
||||||**Panel B**|**: full panel**|||||
|Bank or mobile payroll|0.110***|0.885***|1.322***|-0.146|-0.081|-0.077**|0.066|-0.086**|-0.016*|-0.013*|
||(0.015)|(0.144)|(0.128)|(0.122)|(0.125)|(0.038)|(0.110)|(0.040)|(0.009)|(0.007)|
||[0.001]|[0.001]|[0.001]|[0.236]|[0.403]|[0.084]|[0.410]|[0.067]|[0.122]|[0.099]|
|Bank or mobile account|0.059***|0.576***|0.593***|0.182|-0.131|-0.043|0.110|-0.039|-0.017|-0.004|
||(0.019)|(0.185)|(0.159)|(0.170)|(0.183)|(0.055)|(0.155)|(0.056)|(0.014)|(0.008)|
||[0.006]|[0.006]|[0.001]|[0.280]|[0.397]|[0.397]|[0.397]|[0.397]|[0.236]|[0.463]|
|Respondent fxed efects|X|X|X|X|X|X|X|X|X|X|
|Test: payroll = account|0.007|0.084|0.000|0.047|0.780|0.555|0.777|0.415|0.966|0.282|
|Observations|16,286|16,286|16,286|16,286|13,415|13,436|13,436|13,435|13,429|8,467|
|R-squared|0.074|0.051|0.022|0.157|0.049|0.012|0.046|0.017|0.001|0.001|
|Mean control [follow-up]|0.440|4.304|2.533|2.158|4.510|8.812|6.773|8.292|0.068|0.020|

Notes: Treatment effects on savings and consumption. Panel A reports results based on the endline survey, Panel B reports results based on data from the midline follow-up and endline surveys. The omitted group in both panels consists of respondents in the control group who continued to be paid in cash and did not receive an account. Baseline controls include stratification dummies, marital status, number of dependents, dummies for savings, formal savings, years of work experience, and years with current employer. Robust standard errors in parentheses, sharpened q-values in brackets. * _p <_ 0.1, ** _p <_ 0.05, *** _p <_ 0.01.
# **5.  Debt dynamics in Africa: Analyzing the fiscal sustainability gap**

This section examines the evolution of public debt dynamics by assessing the fiscal sustainability gap—e.g. see Blanchard (1993), Ley (2009), and Cotarelli and Escolano (2014). This summary indicator compared the country’s actual primary balance with its debt-stabilizing balance. Under certain macroeconomic and financial scenarios, the debt-stabilizing primary balance captures the long-term and cumulative impact of sustained fiscal deficit on public debt stocks (World Bank 2017).

We describe the evolution of the fiscal sustainability gap from 2003 to 2016 for Sub-Saharan African countries. Specifically, we conduct two types of comparisons: (a) an international comparison, where the region is benchmarked to other developing regions, and (b) within-region benchmarking for countries classified by their extent of natural resource abundance and access to markets. The analysis of fiscal sustainability gaps not only entails movements in fiscal balances and public debt stocks but also country fundamentals that influence the long-term debt stabilizing ratio (e.g. growth prospects and the future profile of interest rates).

## **5.1.  Measuring fiscal sustainability gaps**<sup>**11**</sup>

The fiscal sustainability gap aims to capture the emerging pressures from the accumulation of widening fiscal deficits over time to unsustainable debt stocks—even if the level of public indebtedness was low. This gap signals the amount of fiscal adjustment required to reach a debt target under different macroeconomic scenarios (Kose et al. 2017, pp. 5).

The primary sustainability gap ( _psg_ ) for country _i_ in year _t_ is defined as follows (Kose et al. 2017): where _pb_ is the primary balance (as percentage of GDP), _r_ is the nominal interest rate, _g_ is nominal GDP growth, and �̅ is the target of the debt-to-GDP ratio. In this paper, the gaps are computed based on current growth rates and interest rates—see Kose et al. (2017) for a more detailed description. The debt stabilization level considered in this paper for developing countries (including SSA) and advanced countries (AEs) is the peer-group median for both emerging market and developing countries (EMDEs) and advanced economies (AEs) correspondingly.<sup>12</sup>

> 11 This section draws heavily from Kose et al. (2017).

> 12 Kose et al. (2017) calculate the primary sustainability gap under 5 different assumptions for growth rates, interest rates, and the targeted debt ratio; namely: (i) country-specific medians for GDP growth and interest rates over the 1990-2006 period, (ii) GDP growth and interest rates at their current levels, (iii) the nominal interest rate is computed as a country-specific standard deviation over the country-specific median, and the nominal GDP growth as a countryspecific standard deviation below the country-specific median, (iv) country-specific minimum nominal interest rates and maximum nominal GDP growth rates, and (v) country-specific medians for nominal interest rates, nominal GDP growth, and the target debt ratio.

19
Michelson, Melissa R. 2003. "Getting Out the Latino Vote: How Door-to-Door Canvassing Influences Voter Turnout in Rural Central California." _Political Behavior_ 25(3): 247-63.

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Milbrath, Lester W. 1965. Political Participation: How and Why Do People Get Involved in Politics? Chicago: Rand McNally.

Moulton, Brent R. 1986. ̳ ̳Random Group Effects and the Precision of Regression Estimates,‘‘ _Journal of Econometrics_ 32: 385–397.

Mullainathan, S. and E. Washington. 2009. ―Sticking with Your Vote: Cognitive Dissonance and Political Attitudes‖ _American Economic Journal: Applied Economics_ , 1, 1, 86-111.

Nickerson, David W. 2006. ―Volunteer Phone Calls Can Increase Turnout: Evidence from Eight Field Experiments.‖ American Politics Research 34 (May): 271–92.

Nickerson, David W. "Friends Don't Make Friends Vote: Selection and Reputation in Voter Mobilization" Paper presented at the annual meeting of the American Political Science Association, Hyatt Regency Chicago and the Sheraton Chicago Hotel and Towers, Chicago, IL, Aug 30, 2007.

Nickerson, David W. 2008. ―Is Voting Contagious? Evidence from Two Field Experiments.‖ American Political Science Review, 102(1): 49–57.

Plutzer, Eric. 2002. ―Becoming a Habitual Voter: Inertia, Resources, and Growth in Young Adulthood.‖ American Political Science Review 96(2): 41–56.

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Thomas, Duncan, 1990, Intra-Household Resource Allocation: An Inferential Approach The Journal of Human Resources, Vol. 25, No. 4: pp. 635-664 Vicente, Pedro C. 2007. ― _Is_ Vote Buying Effective? Evidence from a Field Experiment in West Africa.‖ Oxford University and BREAD, Working Paper.

Vicente, Pedro C. and Leonard Wantchekon, 2009, Clientelism and vote buying: lessons from field experiments in African elections, Oxford Review of Economic Policy, Volume 25, Number 2, 2009, pp.292–305 28
**TABLE 16. Employment by Worker Type**

||Man|agers|Skilled|Workers|Unskilled|Workers|
|---|---|---|---|---|---|---|
||FE|FE‐2SLS|FE|FE‐2SLS|FE|FE‐2SLS|
||(1)|(2)|(3)|(4)|(5)|(6)|
|ICT|0.032***|0.20*|0.066***|0.33**|0.043***|0.27**|
||(0.011)|(0.10)|(0.012)|(0.15)|(0.010)|(0.12)|
|ICT|0.010|0.076|0.055***|0.29*|0.022|0.24**|
||(0.015)|(0.10)|(0.015)|(0.15)|(0.015)|(0.12)|
|ICT * Growth|0.041**|0.096***|0.021|0.083***|0.038|0.10**|
||(0.017)|(0.028)|(0.019)|(0.031)|(0.024)|(0.047)|
|Observations|2,432|2,432|2,420|2,420|3,463|3,463|

Dependent variable: change in log employment of managers (columns 1 and 2), skilled workers (columns 3 and 4) and unskilled workers (columns 5 and 6). Regressors: ICT investment dummy, and ICT investment dummy interacted with a firm‐level indicator variable defined as in Table 15. Instruments defined as in Table 3. Robust standard errors in parenthesis.

40
14

**Figure 7: Change in per capita exports to EC and Structural change in exports**

<!-- Start of picture text --> 2.00<br>1.80 * CR&SR<br>1.60 * Bulgaria<br>CD a Poland<br>c1 A0 China<br>. 1.20 - CEEC * Hungary<br>ia 1.00<br>; 0.80 0 RoiWOsu<br>L- 0.60<br>0.40<br>0.20<br>0.001 I I<br>-100 0 100 200 300<br>Change in per Capita Exports to EC. 1989 - 1993<br><!-- End of picture text --> restructuring is happenning through product differentiation- Although aggregate measures of relative specialization have not changed very much for the Czech Republic, there have been significant changes within the broad product groups that were already exported to the EU. A similar conclusion holds for Hungary and Poland, but Hungary's aggregate performance has been lagging.

It is, of course not possible to prove beyond all doubt cause and effect of industrial restructuring, revealed by these remarkable changes in export structure. But the data are, nevertheiess, suggestive. Among the CEECs, all countries have liberalized prices of manufactured goods early on in the refonn process (between 1989 and 1991). Rapid changes in export structure and performance generally started immediately following price reforrn (see Appendix, Table 2), for example, in Hungary and Poland in 1990, in former Czechoslovakia in 1991. and in most of the FSU in 1993.

One would thus expect that the earliest reformers would have seen the largest change in trade structure during the four year period 198993. This is indeed the case, if one compares the CEEC average with the FSU average. But this is not the case in Central and Eastern Europe. The Czech and Slovak Republics show much more change and growth than Hungary and Poland. This could be either attributed to factor endowments, macro-economic policy, or different privatization strategies. All three probable played a role: Hungary had the most open economy before the reforms, and can thus be expected to show somewhat less change. It has also not used exchange rate adjustment as extensively as the other countries to increase export competitiveness. But it possibly does not matter all that much as wages are only 10% (Poland), 11% (Czech and Slovak Republics) or 15 % (Hungary) of German wages, just across the border.
# **<u>Table A.1 Baseline Balance (Weighted)</u>**

||**Control**<br>**(Mean)**|**Arm 1**<br>**(Mean)**|**Arm 2**<br>**(Mean)**|**Arm 3**<br>**(Mean)**||**Nor**|**malized Mea**|**n Differen**|**ces**||
|---|---|---|---|---|---|---|---|---|---|---|
|Number of Gram Panchayats (GPs)|80|60|60|60|**Control-**|**Control-**|**Control-**|**Arm 1-**|**Arm 1-**|**Arm 2-**|
|Number of households|1,872|1,406|1,394|1,395|**Arm 1**|**Arm 2**|**Arm 3**|**Arm 2**|**Arm 3**|**Arm 3**|
|**A. Household characteristics**|||||||||||
|**Number of members in the household**|||||||||||
|All|5.31|5.236|5.298|5.19|.0256|.004|.0432|-.0219|.0172|.0397|
|Children less than 5 years|.6453|.5897|.6826|.6663|.0522|-.0336|-.0191|-.0861|-.0718|.0147|
|Children between 6–14 years|1.065|1.054|1.011|1.02|.0074|.0361|.0303|.0289|.0229|-.0064|
|**Caste/social category of the household**|||||||||||
|General|.3286|.2913|.3443|.3143|.057|-.0235|.0217|-.0805|-.0353|.0451|
|Scheduled caste/scheduled tribe (SC/ST)|.5039|.5282|.4641|.5459|-.0344|.0564|-.0595|.0909|-.0251|-.1161|
|**Religion of the head of household**|||||||||||
|Hindu|.2392|.2287|.2244|.2495|.0174|.0248|-.017|.0074|-.0345|-.0418|
|Sikh|.7084|.7195|.727|.6986|-.0174|-.0291|.0152|-.0118|.0326|.0444|
|**Economic category**|||||||||||
|Below poverty line (BPL)|.5042|.5166|.5499|.5136|-.0175|-.0647|-.0132|-.0472|.0043|.0515|
|Above poverty line (APL)|.3404|.2677|.2444|.2489|.1122|.1501|.1427|.0378|.0304|-.0073|
|**Landholding by category**|||||||||||
|Small and marginal farmer|.2273|.2183|.2876|.2557|.0153|-.0978|-.0471|-.1132|-.0624|.0507|
|No land|.7032|.711|.638|.6895|-.0122|.0983|.021|.1105|.0332|-.0772|
|**Asset ownership**|||||||||||
|Own house|.9782|.9779|.974|.9735|.0014|.0194|.0213|.0179|.0199|.002|
|Radio|.03606|.02933|.05012|.03702|.0268|-.049|-.0036|-.0754|-.0304|.0454|
|Color television|.9089|.8407|.8948|.8807|.1466|.0337|.0651|-.1133|-.0819|.0314|
|Gas stove|.8787|.8176|.8504|.8709|.1208|.0585|.0166|-.0625|-.1043|-.0418|
|Refrigerator|.8375|.7866|.8446|.8221|.0922|-.0138|.0289|-.1061|-.0633|.0428|

40
3

# Inteoduction

In the economlc literature, the relationship between real intereat and private investment in LDCs has received considerable attention. Until the early 1970s, the economists assumed that low interest rates would promote investment spending and economic growth in accordance with Keynesi&n and neoclassical theories. McKinnon (1973) and Shaw (1973) were the first to challenge seriously this conventional wisdom. These two authors suggested that higher real interest rates would raise savings, increase the volume of domestic credits extended by the financial system and hence the equilibrium rate of investment. In order to assess tho McKinnon-Shaw hypothesis, an impressive number of empirical studies have been carried out during recent year2/. In the present state of research, the tendency is to admit the validity of this hypothesis. As a matter of fact, establishing high real interest rates has become a standard part of the policy advice given to LDC's by external experts, ranging from the visiting academic economist via the World Bank to emissaries of the IH(see Van Wijnbergen (1983) or Polak (1989)).

The most favored justification for a high interest rates policy in LDCs is derived from the presence of liquidity constraints on private investment decisions. Policies that impose artificially low interest rate ceilings tend to constrain the supply of capital and lead to an excess demand for capital relative to what would happen if the deposit interest rate were allowed to find its market-clearing level. Because the principal constraint on investment is the quantity, rather than the cost, of financial resources, a rise in interest rates will increase the supply of credit to finance private investment. "Any effect exerted by the rate of interest on private investment is not direct within thls rationing framework but, rather, occurs via the channel of financial savinsl (Blejer an Khan (1984), p.386). Even if a clear consensus has emerged in recent years that a significant fraction of the population in developing countries is affected by liquidity constraints3/, the problematic issue is how far to go. We argue, by focusing on the demand for capital goods, that a number of factors might influence the relationship between real interest rates, the supply of domestic credits and private investment.

The purpose of this paper is to confront the HcKinnon-Shaw hypothesis with a simple model of investment behavior. The resulting model is esetimted and simulated for Argentina over the 1° .-82 period, giventhat this country has been affected by various inte.est rates 2/For a review of this literature, see Fry (1988).

3/Note that the rationing in LDCs is justified as a disequilibrium phenomenon caused by legal ceilings on interest rates. By contrast, for developed countries, the argument is based on modern theories of imperfect information.
|**Table 1: Objective and subjectiv**<br>|**e indicators**<br>|**on land service**<br>|**delivery in Afri**<br>|**can regions**<br>|||
|---|---|---|---|---|---|---|
|**Variable**|**Central**<br>**Africa**|**East**<br>**Africa**|**North**<br>**Africa**|**South**<br>**Africa**|**West**<br>**Africa**|**Total**|
|**Panel A: Land registry inform**|**ation**||||||
|Registration cost (%)|9.3|5.3|4.7|6.9|7.2|7.02|
|# of ctries w. digital registry|1|1|0|1|0|3|
|# of ctries dig. Cadaster|1|1|1|0|1|4|
|Reg&Cad. digital & linked|1|1|0|0|0|2|
|Main city in register|1|0|0|4|1|6|
|Main city in cadaster|1|0|1|3|1|6|
|Court decision in < 1 year|1|1|0|1|1|4|
|No. of countries|12|7|6|12|16|53|
|**Panel B: Survey information**|||||||
|Can get land information|0.197|0.208|0.093|0.210|0.189|0.189|
|Rich people can defraud|0.754|0.656|0.538|0.603|0.598|0.613|
|Poor people can defraud|0.158|0.120|0.167|0.194|0.161|0.167|
|Support women’s rights|0.778|0.731|0.470|0.795|0.714|0.723|
|No. ofcountries|3|3|3|10|15|34|

_Note:_ In panel A, row 1 reports cost of registering a property transaction (without a survey) as percentage of property value while rows 2-6 report the number of countries with a digital registry, a digital cadaster (not just scanned survey plans), or a digital registry and cadaster that are linked, most of the properties in the main city in the register or the cadaster, and the ability to obtain a first instance court decision for a typical land dispute in less than a year. Registration cost is as a share of property value. All information is based on 2020 World Bank ‘Registering Property’ Data.

For panel B, ‘ _Can get land information’_ is the share of those responding ‘very likely’ to the question ‘If you went to the local lands office to find out who owns a piece of land in your community, how likely is it that you could get this information?’ _Fraudulent registration (ordinary and rich)_ tabulates the share of respondents who think it is very likely for an ordinary or a rich person, respectively, to ‘...pay a bribe or use personal connections to get away with registering land that does not belong to them’ _Women’s rights_ is the share who ‘agree or strongly agree’ with the statement ‘Women should have the same rights as men to own and inherit land’. All data are based on the Afrobarometer Survey Round 7.

32
23 that in a global welfare sense the world moved three-fifths of the way towards global free trade in goods over that quarter century.

Second, developing countries benefited proportionately more than high-income economies (1.0 percent compared with 0.7 percent of national income) from those past policy reforms, and would gain nearly twice as much as high-income countries by completing that reform process (an average increase of 0.9 percent compared with 0.5 percent for highincome countries). Of those prospective welfare gains from global liberalization, 70 percent would come from agriculture and food policy reform. This is a striking result given that the shares of agriculture and food in global GDP and global merchandise trade are only 3 and 6 percent, respectively. The contribution of farm and food policy reform to the prospective welfare gain for just developing countries is slightly greater, at 72 percent.

Third, the developing countries’ share of the world’s primary agricultural exports rose from 43 to 55 percent, and its farm output share from 58 to 62 percent, because of those reforms, with rises in nearly all agricultural industries except rice and sugar. Removing remaining goods market distortions would boost their export and output shares to 64 and 65 percent, respectively.

Fourth, for developing countries as a group, net farm income (value added in agriculture) is estimated to be 4.9 percent higher than it would have been without the reforms of the past quarter century, which is more than ten times the proportional gain for nonagriculture. If policies remaining in 2004 were removed, net farm incomes in developing countries would rise a further 5.6 percent, compared with just 1.9 percent for non-agricultural value added. As well, returns to unskilled workers in developing countries – the majority of whom work on farms – would rise more than returns to other productive factors from that liberalization.

# **Why Does This Matter? Where to from Here?**

The degree of distortions to K/S/V mattered in the late 1980s because policies of many developing countries at that time were harming their economies and especially their farmers. Since farm households were much poorer on average than non-farm households, these policies were not only national welfare-reducing but also contributing to inequality and poverty. The above comparison of K/S/V results and those of the new World Bank study
Table D.2: Summary Statistics for Endline Variables

|Variable|# obs|Mean|Std dev.|10<sup>th</sup> pctile|90<sup>th</sup> pctile|
|---|---|---|---|---|---|
|_Panel A: Labor Market Measures_||||||
|Employed|6607|0.323|0.468|||
|Earnings|2112|623|1183|2|1500|
|Hours worked|2121|28.5|21.6|4.0|56.0|
|Hourly wage|2097|33.1|72.3|0.1|77.8|
|Wage employment|2102|0.885|0.319|||
|Self employment|2102|0.114|0.318|||
|_Panel B: Job Search Measures_||||||
|Any search|6608|0.692|0.462|||
|Applications submitted<sup>a</sup>|6577|12.8|21.5|1.0|27.0|
|Hours searched|6601|9.9|14.2|0.0|25.0|
|Search cost|6599|116|167|0|300|
|Responses<sup>a</sup>|6593|0.861|2.147|0.000|2.000|
|Ofers<sup>a</sup>|6592|0.207|0.680|0.000|1.000|
|_Panel C: Belief Measures_||||||
|Fraction of assessments overconfdent|6607|0.345|0.237|||
|Fraction of assessments underconfdent|6607|0.176|0.166|||
|Targeted search|6891|0.175|0.380|||
|Planned applications<sup>a</sup>|6591|16.1|29.7|3.0|30.0|
|Expected ofers<sup>a</sup>|6531|4.49|5.70|1.00|10.00|

Table shows summary statistics for selected endline variables. Percentiles are omitted for binary variables. All monetary figures are reported in South Africa Rands. 1 Rand _≈_ USD 0.167 in purchasing power parity terms. Intensive-margin labor market measures (e.g. earnings) are set to missing for non-workers. Intensive-margin search measures (e.g. search cost) are set to zero for non-searchers. Missing values reflect item non-response, mostly due to respondents reporting that they don’t know the answer. All period-specific outcomes use a 7-day recall/forecast period unless marked with<sup>a</sup> (30-day recall/forecast period).

Table D.3: Comparison between the Workseeker Sample and External Populations

||(1)<br>South Africa|(2)<br>Gauteng<br>province|(3)<br>Gauteng<br>age 18-29|(4)<br>Study sample<br>of workseekers|
|---|---|---|---|---|
|Age|28.914|31.461|23.776|23.646|
||(19.606)|(19.099)|(3.326)|(3.299)|
|Male|0.489|0.504|0.512|0.382|
|Currently Employed|0.290|0.375|0.346|0.378|
|Currently Searching|0.102|0.151|0.302|0.984|
|_<_ complete secondary school|0.737|0.612|0.429|0.011|
|Complete secondary school|0.184|0.255|0.449|0.610|
|_>_ complete secondary school|0.072|0.120|0.116|0.379|

Table compares the sample of workseekers in this study (column 4) to several external benchmarks: the country (column 1), the province of Gauteng where the study takes (column 2), and people in Gauteng in the eligible age range for the study (column 3). National and provincial statistics are calculated from the Quarterly Labour Force Surveys (QLFS), averaging over all 2016 waves and using post-stratification weights supplied by Statistics South Africa. QLFS data are not available by city but the greater Johannesburg metropolitan area where the study is conducted accounts for over half the population of the Gauteng province. Standard deviations are shown in parentheses for all continuous variables.

50