rafmacalaba/datause-encoder-data huggingface dataset:
Enhancing the transparency of government borrowing plans. The move to market‐based mechanisms to issue domestic securities would require more transparent communication from the MOF. This would involve the publication of an annual borrowing plan for the aggregate amount of monthly local currency borrowing in the domestic market, divided between the wholesale and retail markets, and the publication of the borrowing plan for T‐bills one month in advance with issue dates and instruments. The terms and conditions, borrowing procedures and other modalities for accessing T‐bills (e.g., bid tender form) should be accessible on the MOF website consistent with the regulation.<sup>103</sup> More broadly, the MOF needs to improve its existing debt reporting arrangements. This would include the development of a medium‐term debt management strategy to guide the government’s annual borrowing plan (see Box 8) and the comprehensive reporting of debt developments, including from SOEs, in the quarterly public sector debt statistical bulletin and annual public debt report, including to inform the periodic review and update of the medium term debt strategy.<sup>104</sup>

# **Box 8.  Steps in Developing a Debt Management Strategy**<sup>**105**</sup>

A debt management strategy (DMS) should ideally guide the government’s annual plan for marketable debt financing. The following steps have been identified to develop a DMS:

1. Identify objectives for public debt management and scope of the debt management strategy

2. Identify the current debt management strategy, and cost and risk of the current portfolio (debt servicing cost, refinancing risk, interest rate risk, foreign exchange risk) 3. Identify and analyze potential funding sources based on relative cost‐risk and qualitative factors (multilateral loans, commercial loans, domestic bonds, Eurobonds) 4. Identify baseline projections and risk in fiscal, monetary, and market indicators (projections of exchange rate, capital account, international reserves, expectations of domestic and global liquidity conditions, market rates, and likely pricing of non‐market instruments) 5. Review longer‐term structural factors (commodity price vulnerability, access to concessional financing, trends in real effective exchange rate and inflation) 6. Assess and rank alternative debt management strategies based on cost‐risk trade‐off (change in cost and risk indicators, feasibility of the strategy, and success of the strategy in meeting the public debt management objectives) 7. Review the implications of candidate strategies with fiscal and monetary policy, and financial market development (interactions with fiscal and monetary indicators, debt sustainability indicators, and the debt market) 8. Propose a preferred DMS, secure approval for and publish the DMS.

> 103 Directive No. NBE/TRE/001/2011.

> 104 World Bank (2018). Ethiopia Growth and Competitiveness Programmatic Development Financing. World Bank Group, Washington D.C.

> 105 This box draws on World bank (2015). Issuance Plan for Government Securities: Guidance Note. World Bank Group, Washington D.C.

31
**Table 4. Second Stage Regressions: Linear Trend, Salaried Workers and Labor Market Indexes Depend Variable Cyclical RTS**

||**_Panel_**|**_A: Current E_**|**_xports_**|||**_Panel A: Lagg_**|**_ed Exports_**||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Exports/Exports(-1))|0.181|0.153|0.178|0.144|0.255|0.257|0.247|0.240|
|PanelA / Panel B|_(0.084)**_|_(0.095)_|_(0.081)**_|_(0.093)_|_(0.108)**_|_(0.111)**_|_(0.095)**_|_(0.099)**_|
|Hours *(Exports/Exports(-1))|-0.002|-0.002|-0.002|-0.002|-0.002|-0.002|-0.002|-0.003|
|PanelA / Panel B|_(0.001)**_|_(0.001)***_|_(0.001)***_|_(0.001)***_|_(0.001)***_|_(0.001)***_|_(0.001)***_|_(0.001)***_|
|Redundancy*(Exports/Exports(-1))|-0.002|-0.002|-0.001|-0.001|-0.002|-0.002|-0.002|-0.002|
|PanelA / Panel B|_(0.001)***_|_(0.001)**_|_(0.001)***_|_(0.001)**_|_(0.001)***_|_(0.001)***_|_(0.001)***_|_(0.001)***_|
|Wmin/Wmed*(Exports/Exports(-1))|0.089|0.101|0.042|0.065|0.065|0.066|0.044|0.055|
|PanelA / Panel B|_(0.082)_|_(0.084)_|_(0.081)_|_(0.081)_|_(0.107)_|_(0.108)_|_(0.099)_|_(0.100)_|
|Ext. Financ|-0.206|-0.207|-0.252|-0.227|-0.192|-0.157|-0.224|-0.184|
||_(0.095)**_|_(0.101)**_|_(0.088)***_|_(0.094)**_|_(0.108)*_|_(0.110)_|_(0.103)**_|_(0.103)*_|
|CPI|0.007||0.014||0.010||0.017||
||_(0.004)**_||_(0.008)*_||_(0.003)***_||_(0.009)*_||
|CPI(-1)||0.005||0.010||0.008||0.014|
|||_(0.004)_||_(0.010)_||_(0.003)***_||_(0.011)_|
|Dom. Financ|||||||||
|Lending rate|-0.016|-0.015|||-0.010|-0.008|||
||_(0.026)_|_(0.029)_|||_(0.026)_|_(0.026)_|||
|Deposit rate|||0.004|0.002|||0.003|0.003|
||||_(0.002)_|_(0.003)_|||_(0.002)_|_(0.003)_|
|Obs|165|151|172|156|151|151|156|156|
|R-square|0.31|0.29|0.29|0.29|0.32|0.32|0.32|0.31|

All regressions include country and year dummy variables and no intercept Levels of Sig: 1% ***, 5%**, 10%* 27
Russia's trade performance dominates the totals for the 15 countries taken together; and its stagnating exports in 1997-1998 result primarily from weakness in prices of energy and raw materials , which account for the bulk of its exports. The Baltics and Russia were the countries which reoriented their trade the fastest. While other countries (Belarus, Tajikistan) sustained actual declines in their exports to the rest of the world, which were in absolute terms less at the end of the period than at the beginning (Table 1).

There were several reasons for the decline in intra-FSU trade during the early years of the transition. Probably the most important was the collapse of the payments system. Also, some trade, which was clearly uneconomical, collapsed from the introduction of foreign competition; and some declines resulted from conscious shifting of exports of raw materials, especially energy, away from countries in the FSU, which could not pay, and towards countries in the West which could. Except for the Baltics, the main policy response to the trade decline at the time was the establishment of a network of state trading agreements akin to the CMEA arrangements, as well as the establishment of a so called "free trade" area for the Comnmonwealth of Independent States (CIS). 4 Export controls were imposed on raw materials and energy for several reasons. First to implement a shift in the direction of trade; and secondly, in order to keep domestic prices of these inputs artificially low as a means of providing support to industrial users and consumers. But export controls were also used by powerful industrial and energy interests to generate huge rents in Russia, Ukraine and elsewhere (Aslund, 1999a and 1999b). On the import side, controls were few: tariffs were typically low. But protection was provided through the highly depreciated exchange rates, as well as through exchange controls.

As countries started to introduce their own currencies and stabilization programs started to take hold in 1994-1996, and as they also initiated broader market oriented reforms, the different trade regimes that are in place today started to emerge. The transition had several _4 Michalopoulos and Tarr, 1994 contains a detailed discussion of trade policies in these countries through 1994, based on seven country case studies._ 6
# **1 Introduction**

The prices of several goods, including intermediate inputs such as cement, broadband internet, steel reinforcement bars, and urea fertilizer, are highest on average in low-income economies, many (though not all) of which are in Africa (see Figure 1). This fact is important for at least two reasons. First, higher prices for intermediate goods can slow economic growth (Jones, 2011). Second, this evidence runs counter to the general tendency for prices to rise with national income (Balassa, 1964; Samuelson, 1964; Summers and Heston, 1991)—a cornerstone of modern international macroeconomics.

The explanation for this unexpected cross-country pattern in price levels is not certain. One view is that higher prices reflect higher costs of production. An alternative view is that higher prices might instead reflect a higher markup. To the extent that prices do reflect a higher markup, a variety of economic forces could lie underneath. Regulatory requirements (e.g., environmental standards) or disadvantageous market institutions (e.g., borrowing constraints, expropriation risk) can increase entry costs and reduce entry and competition. Given entry, collusive conduct (e.g., cartels) also increases markups. Higher markups could reflect a small market size that is unable to sustain more than a few competitors. The appropriate policy response, if any at all, critically hinges on quantifying the relative importance of these forces. Such a quantification exercise is complicated by a lack of consistent data on prices, quantities, and market structure across economies.

This paper distinguishes between these hypotheses in the case of Portland cement, an industry that is both of intrinsic relevance but also affords a number of methodological advantages. Cement is a critical input in the construction sector and thus in aggregate investment, with the average economy spending 1.3 percent of GDP on it in our data. We combine a recent panel of internationally comparable prices from the 2011 and 2017 rounds of the International Comparison Program (ICP) with a directory of cement plants in 96 economies on all continents. These data allow us to tease apart and quantify drivers of markups and costs in the global cement industry. We do so by estimating a model of a cross-section of markets in which identification of markups and costs is well understood.

Analysis of the model yields four main insights. First, in an industry characterized by a relatively homogeneous product, differences in marginal costs and markups contribute roughly an equal share to the average difference in prices across economies. Africa stands out because it had the highest average US dollar cement price of any continent in 2011, the highest average marginal cost, and the highest average markup, at about 50 percent.

Second, by 2017 the average cement price in Africa had fallen by one-third, due threefifths to a decline in marginal cost and two-fifths to a decline in the markup. These de2
there is one additional margin to test for the impact of the program: households with larger extended families may be more affected by SR.

They use the Colombian Household Surveys 1986-2005 (before and after reform); it is a bi-yearly data from 1986 to 1996 and yearly from 1997 to 2005. It is representative of 10 cities and provides repeat cross-sections. They find a 2-5% increase in informality post-SR. The effects of the SR are strongest in households with no vulnerable members and attenuated in household with vulnerable members and also in household without potential extended beneficiaries. They also estimate their results in a panel of poor individuals matched twice with similar results.

Identification of these effects of SR comes from time variation across cities as to when a means test of eligibility for SR was implemented. In their panel estimates, the variation is also across eligibility to SR or not. However, with their data (biannual data for household surveys or two waves for the panel of poor individuals), it appears there is only one round of data difference in the introduction of the program across cities. Thus, most of the identification comes from pre and post comparisons. This makes the results particularly susceptible of being influenced by other changes affecting labor markets that were occurring at the same time, e.g. a broader package of labor market reforms were introduced to make labor markets more flexible and there were external shocks that affected labor demand. The simultaneity of these changes makes it difficult to ascribe all of the effects to the SR program.

The papers reviewed, both in Mexico and outside of Mexico, suggest that social programs that put incentives on informal work can have an effect on the level of informality. However, the papers are not conclusive and there is room to improve the measurement of the effects. The next section describes the data and the identification strategy proposed for this task.

# **4. Expected Effects of** **_SP_**

_SP_ provides health benefits to households provided that the individuals are not in formal jobs or covered by SS through someone else in the household. In that regard, the introduction of _SP_ can affect various margins: 11
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Mengistae, T. and Honorati, M. (2007) “Constraints to productivity growth and to job creation in the private sector”, _Chapter 2 in Ethiopia Investment Climate Assessment 2007_ Washington DC: World Bank. Processed.

- Pages, P., Aterido, R. and Hallward-Driemeier, M. (2007)."Investment Climate and Employment Growth: The Impact of Access to Finance, Corruption and Regulations Across Firms," _RES Working Papers_ 1066, Inter-American Development Bank, Research Department.

- Ranis, G. and Stewart, F. (1993) “Rural nonagricultural activities in development: theory and application” _Journal of Development Economics_ 40: 75-101.

Reardon, T., J.E. Taylor, K.Stamoulis, P.Lanjouw and Balisacan, A. (2000) “Effects of Non-Farm Employment on Rural Income Inequality in Developing Countries: An Investment Perspective” _Journal of Agricultural Economics,_ 51(2):266-288.

Romer, P. (1987) "Growth Based on Increasing Returns due to Specialization" _American Economic Review_ 77: 57-62.

- Sleuwaegen, L. and Goedhuys, M. (2002) "Growth of firms in developing countries, evidence from Cote d'Ivoire" _Journal of Development Economics_ 68(1): 117-135.

- Sőderbom, M. and Teal, F. (2004) "Size and Efficiency in African Manufacturing Firms:  Evidence from Firm-Level Panel Data” _Journal of Development Economics_ 73: 369-394.

- Teal, F. (2007), “A Note on Using Firm Surveys for Policy Analysis in Low Income Countries” _Mimeo._ Oxford University.

Tybout, J.R. (2000) "Manufacturing Firms in Developing Countries: How Well Do They Do, and Why?" _Journal of Economic Literature_ 38: 11-44

- Venables, A. (2008) “New Economic Geography” in _The New Palgrave Dictionary of Economics,_ 2nd Edition.

- World Bank (2004) _Sri Lanka: Improving the Rural and Urban Investment Climate_ World Bank, Washington DC.

-29-
||(1)*(2)|0.0149|-0.158|0.196*|0.225*|0.0579|0.108|0.201|
|---|---|---|---|---|---|---|---|---|
|||(0.160)|(0.136)|(0.114)|(0.116)|(0.156)|(0.130)|(0.172)|
||Observations|41,200|41,200|41,200|41,200|41,200|41,200|41,200|
||R-squared|0.548|0.548|0.548|0.548|0.548|0.548|0.548|
||(1) GVC|0.589***|0.421***|0.459***|0.433***|0.424***|0.618***|0.677***|
|||(0.102)|(0.116)|(0.111)|(0.0964)|(0.109)|(0.115)|(0.144)|
||(2) Obstacle|-0.550**|-0.267|-0.0307|-0.212|-0.392|0.585***|-0.784***|
|GVC4||(0.244)|(0.224)|(0.207)|(0.234)|(0.248)|(0.210)|(0.277)|
||(1)*(2)|-0.972**|0.0159|-0.168|-0.0473|0.00781|-0.895**|-1.373**|
|||(0.437)|(0.393)|(0.497)|(0.453)|(0.553)|(0.397)|(0.582)|
||Observations|41,200|41,200|41,200|41,200|41,200|41,200|41,200|
||R-squared|0.548|0.548|0.548|0.548|0.548|0.549|0.549|
||||(d) Fem|ale Production|Workers||||
||(1) GVC|0.339***|0.529***|0.298***|0.366***|0.379***|0.310***|0.377***|
|||(0.0530)|(0.113)|(0.0730)|(0.0523)|(0.0923)|(0.0748)|(0.0673)|
||(2) Obstacle|0.257|0.537***|-0.295|0.412*|-0.117|0.151|0.602***|
|GVC1||(0.206)|(0.192)|(0.220)|(0.236)|(0.220)|(0.197)|(0.230)|
||(1)*(2)|-0.0835|-0.784**|0.0974|-0.186|-0.304|0.0499|-0.287|
|||(0.220)|(0.336)|(0.339)|(0.261)|(0.352)|(0.318)|(0.234)|
||Observations|41,205|41,205|41,205|41,205|41,205|41,205|41,205|
||R-squared|0.498|0.499|0.498|0.499|0.498|0.498|0.499|
||(1) GVC|0.411***|0.390***|0.453***|0.437***|0.371***|0.453***|0.592***|
|||(0.0921)|(0.102)|(0.0850)|(0.0858)|(0.0914)|(0.104)|(0.0969)|
||(2) Obstacle|0.211|0.274|-0.243|0.381|-0.239|0.160|0.585**|
|GVC4||(0.206)|(0.220)|(0.232)|(0.258)|(0.249)|(0.174)|(0.227)|
||(1)*(2)|-0.0681|0.0491|-0.245|-0.213|0.189|-0.210|-1.005**|
|||(0.451)|(0.413)|(0.299)|(0.329)|(0.395)|(0.391)|(0.451)|
||Observations|41,205|41,205|41,205|41,205|41,205|41,205|41,205|
||R-squared|0.495|0.495|0.495|0.495|0.495|0.495|0.495|

Robust standard errors clustered by country and year in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Notes: i) Each regression controls for firms’ age, city of operation and share of government ownership ii) All the regressions include country, year, sector, and size fixed effects.

iii) Country-year-sector averages are used to reduce the risk of endogeneity between the business environment and firm-level.

iv) Each column for each GVC definition represents a separate regression. v) All the variables are in log.

vi) The intercept is included.

20
# **References**

- Alencar, L. & Ponticelli, J. (2016). Court enforcement, bank loans and firm investment: Evidence from a bankruptcy reform in brazil. _Quarterly Journal of Economics_ , 131(3), 1365–1413. 1, 2, 4, 30, 31

- Barro, R. (1991). Economic growth in a cross section of countries. _Quarterly Journal of Economics_ , 106(2), 407–443. 4

- Bosio, E., Djankov, S., Glaeser, E. L., & Shleifer, A. (2020). Public procurement in law and practice. _National Bureau of Economic Research_ . 3

- Cameron, A. C. & Miller, D. L. (2015). A practitioner’s guide to cluster-robust inference. _Journal of Human Resources_ , 50(2), 317–372. 13, 15

- Chang, T. & Schoar, A. (2007). Judge specific differences in chapter 11 and firm outcomes. _AFA Chicago Meetings Paper_ . 2

- Chemin, M. (2009a). Do judiciaries matter for development? evidence from india. _Journal of Comparative Economics_ , 37(2), 230–250. 2

- Chemin, M. (2009b). The impact of the judiciary on entrepreneurship: Evaluation of pakistan’s access to justice programme. _Journal of Public Economics_ , 93(1-2), 114–125. 2

- Chemin, M. (2018). Judicial efficiency and firm productivity: Evidence from a world database of judicial reforms. _Review of Economics and Statistics (forthcoming)_ . 1, 3, 30

- Coviello, D., Ichino, A., & Persico, N. (2014). Time allocation and task juggling. _American Economic Review_ , 104(2), 609–623. 21

- Coviello, D., Ichino, A., & Persico, N. (2015). The inefficiency of worker time use. _Journal of the European Economic Association_ , 13(5), 906–994. 2

32
# Table 1: **Balance Covariates and Treatment Cell Size**

|||_Panel A1: _|_Waves I, II, and _|_III Balance of Co_|_variates_|||
|---|---|---|---|---|---|---|---|
||Full<br>Sample<br>(1)|Moral<br>Incentive<br>(2)|Simple<br>Reminder<br>(3)|Religious<br>Placebo<br>(4)|Credit<br>Reputation<br>(5)|Control<br>Group<br>(6)|_p_-value<br>(7)|
|Age|42.06|42.39|42.12|41.76|42.02|42.06|0.632|
||[9.077]|[9.325]|[8.781]|[8.722]|[9.099]|[9.201]||
|Female|0.40|0.40|0.41|0.41|0.39|0.40|0.914|
||[0.489]|[0.490]|[0.491]|[0.491]|[0.488]|[0.489]||
|Muslim|0.92|0.92|0.91|0.91|0.92|0.92|0.427|
||[0.273]|[0.271]|[0.286]|[0.289]|[0.271]|[0.264]||
|Income|151|135|185|134|176|132|0.424|
|(Millions Rp)|[837]|[175]|[1240]|[188]|[1370]|[201]||
|Credit Limit|13.50|13.90|13.20|13.80|13.30|13.50|0.418|
|(Millions Rp)|[9.312]|[9.708]|[8.651]|[9.440]|[9.258]|[9.386]||
|||_Panel A2: _|_Waves I, II, and_|_III Treatment C_|_ell Size_|||
|Wave I|2871|400|400|400|800|871||
|Wave II|2985|400|400|400|800|985||
|Wave III|1965|200|200|200|400|965||
|Total|7821|1000|1000|1000|2000|2821||

_Panel B1: Wave IV Balance of Covariates_

||Full<br>Sample|Moral<br>Incentive|Non-Religious<br>Moral Incentive|Implicit<br>Moral Incentive|Cash<br>Rebate|Control<br>Group|_p_-value|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Age|42.27|41.85|42.01|42.73|42.33|42.43|0.762|
||[9.500]|[9.177]|[9.145]|[9.423]|[9.201]|[10.497]||
|Female|0.39|0.42|0.38|0.38|0.37|0.40|0.687|
||[0.488]|[0.494]|[0.487]|[0.486]|[0.482]|[0.591]||
|Muslim|0.92|0.93|0.90|0.91|0.93|0.92|0.518|
||[0.271]|[0.253]|[0.302]|[0.281]|[0.253]|[0.265]||
|Income|135|122|138|132|152|128|0.340|
|(Millions Rp)|[190]|[154]|[192]|[187]|[233]|[172]||
|Credit Limit|13.50|13.10|14.20|13.10|13.80|13.40|0.568|
|(Millions Rp)|[9.836]|[9.361]|[10.600]|[9.535]|[9.849]|[9.815]||
|||_Pan_|_el B2: Wave IV _|_Treatment Cell Size_||||
|Wave IV|1686|336|336|336|336|342||

_Notes_ : Panel A1 reports summary statistics for the sample and presents a test of random assignment for waves I, II, and III. Column (1) reports the mean level of each variable, with standard deviations in brackets, for the full sample. Columns (2) to (6) report the mean level of each variable, with standard deviations in brackets, for all the experimental conditions. Column (7) reports the _p_ -value of a test that means are the same in all the five experimental conditions. Panel A2 reports treatment cell sizes by month. Panels B1 and B2 replicate for wave IV.

26
Table B10: Summary of absolute variance contributions, alternative choices of _π_ , never enrolled children only

|||Ke|nya<br>|Tan|zania<br>|Uga|nda<br>|
|---|---|---|---|---|---|---|---|
||π|σ.|(s.e.)|σ.|(s.e.)|σ.|(s.e.)|
|Individual|0|0.10|(0.001)|0.06|(0.002)|0.17|(0.002)|
||0.5|0.10|(0.001)|0.06|(0.002)|0.17|(0.002)|
||1|0.10|(0.001)|0.05|(0.002)|0.17|(0.002)|
|Household|0|0.92|(0.012)|0.76|(0.012)|0.59|(0.011)|
||0.5|0.64|(0.008)|0.57|(0.009)|0.47|(0.009)|
||1|0.51|(0.007)|0.46|(0.007)|0.40|(0.008)|
|School|0|0.20|(0.003)|0.16|(0.003)|0.15|(0.003)|
||0.5|0.44|(0.007)|0.27|(0.005)|0.20|(0.004)|
||1|0.80|(0.012)|0.51|(0.009)|0.36|(0.008)|
|Sorting|0|0.11|(0.001)|-0.15|(0.003)|0.03|(0.001)|
||0.5|0.49|(0.007)|0.35|(0.006)|0.25|(0.005)|
||1|0.32|(0.004)|0.30|(0.005)|0.23|(0.005)|
|Residual|.|1.21|(0.017)|0.82|(0.014)|0.83|(0.017)|
|Total|.|1.55|(0.013)|1.11|(0.011)|1.03|(0.012)|

Note: the table sets out the absolute variance contribution (in standard deviation units) attributable to each component of the test score, where ‘sorting’ is the contribution of the between-factor covariance; different initializations of the partitioned iterative algorithm are indicated by column _π_ ; standard errors are reported in parentheses, calculated using the asymptotic approximation due to Ahn and Fessler (2003). Source: own calculations.

55