rafmacalaba/datause-encoder-data huggingface dataset:
10 urban and rural , eas, but this was followed by a deterioration from<sup>1983/84</sup> to 1985/86. On balanice, the headcount index changed little over the period. Ab,solute numbers of poor in Bangladesh rose from 66 million to 72<sup>million in</sup> 1985/86.

(ii) The same basic pattern is found for the lower poverty line, though the decline **In** the number of poor, as estimated solely from the HES, is now even more dramatic: while 43 million people did not attain three-quarters of the BBS poverty line in 1981/82, this falls to only 14 million in 1985/86. The growth rate estimates from NA, on the other hand, indicate an<sup>increase to</sup> 46 million in 1985/86.

(iii) The poverty gap measures reported in Table 2 follow a similar pattern to the headcount indices. The consumption deficit of the urban poor normalized by the total urban population represented 22% of the poverty line in 1981/82; for rural areas the proportion is slightly higher, at 25%.7 Another way of interpreting these figures is to consider the magnitude of the poverty gap as a proportion of mean consumption of all households. The aggregate poverty gap of the urban sector represents 21% of that sector's mea. consumption in 1981/82; for rural areas it represented 28% of mean consumption. Roughly a one quarter increase in national consumption would be needed to eliminate poverty in Bangladesh, if that increase could be perfectly targeted at the poor. That is a big "if"; later I shall discuss the problem of reaching the poor when suchi perfect targeting is not feasible.

(iv) The distributionally sensitive measure of poverty (a=2) indicates a modest overall improvement during the period using the NA means, though, again, the rate of improvement is very considerably less than that implied by the unadjusted HES.
# **The SEC Rule Change in 2004—or Why International Coordination of Regulation Does Not Necessarily Lead to Improved Regulation**

The SEC rule change in 2004 that changed how the SEC figured the net capital requirements is now seen as a significant mistake. Some journalists have mistakenly called this deregulation.<sup>42</sup> What these journalists fail to note is that this rule change was the antithesis of deregulation; rather it was the imposition of internationally coordinated regulatory standards—rules known as the “Basel Rules.” The Basel Committee rules were the consequence of years of work by the central bankers of the world and are based on the belief that a common set of global banking standards would result in more efficient use of capital and a more stable global financial system. The Basel rules call for banks to have capital reserves of eight percent on a risk weighted basis. Commercial loans had a risk weight of 100, so had to be backed by 8 percent capital in reserve. But AAA rated securities (like the securitized mortgage pools), had a substantially lower risk weight of 20 percent, so banks only had to have 1.6 percent capital in reserve to back investments in AAA rated securities. Basel I rules were replaced with Basel II rules in 2007, which allow for more use of internal bank models in the assessment of risk, something which we would all question today; but rules for residential mortgages did not change.) As I have explained, all those responsible for assessing the risks earned large fees from underestimating the risk and the investment banks ended up leveraging themselves far too highly. Swiss authorities are now raising the minimum capital requirements above those allowed under the Basel Rules. The SEC rule change was one of the regulatory failures that contributed to the financial crisis. But rather than a deregulation problem, this is a cautionary tale against agreement to internationally coordinated regulatory standards. If they substitute for prudential regulation, they could be a lot worse.

# **V.  Lessons and Reforms**

# **1.  Eliminate the GSEs. As a second best solution, regulate the GSEs against excessive risk taking like normal banks.**

The raison d’etre of the GSEs was to lower mortgage interest rates. Studies by the Federal Reserve and the Congressional Budget Office have shown that they have failed in that objective.

> 42 See Stephen Labaton (2008), “Agency’s ’04 Rule Lets Banks Pile Up Debt,” New York Times, October 2.
**<u>Table 2: Changes in use of outputs and inputs as well as farm profitability, 2021–23</u>**

||<br>**2021**|<br>**2022**|<br>**2023**|**Δ21-22**|**Δ21-23**|
|---|---|---|---|---|---|
|**Panel A: Area cultivated & profitability**||||||
|Area cultivated (ha)|216.8|215.1|226.5|-0.01|0.04|
|Rapeseed yield (t/ha)|2.71|2.55|2.68|-0.06|-0.01|
|Maize yield (t/ha)|7.05|5.58|6.72|-0.21|-0.05|
|Sunflower yield (t/ha)|2.36|1.86|2.23|-0.21|-0.06|
|Soybeans yield (t/ha)|2.45|2.05|2.42|-0.16|-0.01|
|Wheat yield (t/ha)|4.25|3.45|4.03|-0.19|-0.05|
|Barley yield (t/ha)|3.69|3.09|3.59|-0.16|-0.03|
|Any sales|0.78|0.68|0.77|-0.13|-0.01|
|Share of output sold|0.82|0.60|0.62|-0.27|-0.24|
|Revenue/ha 6 main crops (US$)|1,088|714|615|-0.34|-0.44|
|Inputs/ha 6 main crops (US$)|305|385|197|0.26|-0.36|
|Inputs/ha 6 main crops (US$)|778|313|418|-0.60|-0.46|
|**Panel B: Other factors**||||||
|War damage to land/structures (%)||0.08||||
|Members working full time on farm|1.53|1.53|1.99|0.00|0.30|
|Members working part-time on farm|0.68|0.72|0.89|0.06|0.24|
|Permanent employees|5.31|5.22|5.14|-0.02|-0.02|
|Rating of personal situation (1-10)|7.37|3.68|4.42|-0.50|0.20|
|Received public support (%)|0.32|0.40|0.36|0.25|-0.10|
|if yes, amount received|310.53|308.96|321.06|-0.01|0.04|
|Lease price paid for land (US$/ha)<br>||115.28|112.88||-0.02|
|Interested in leasing more land<sup>1</sup>||0.85|0.79||-0.07|
|Interested in buying land||0.79|0.74||-0.06|
|Max. price to be paid (US$/ha)||1,540|1,473||-0.04|
|Received credit last 12 months||0.25|0.22||-0.12|
|if yes, amount received||84,689|70,755||-0.16|
|if yes, interest paid||4.57|8.83||0.93|
|Number of farms|1,206|1,206|1,206|||

_Source:_ Own analysis of balanced household survey panel data as described in the text.

_Note:_ Profit is the value of output net of purchased inputs, that is, it includes remuneration for fixed factors and family labor. The six main crops are wheat, barley, rapeseed, soybean, maize, and sunflower. ha = hectare.

18
<!-- Start of picture text --> 65<br>60<br>55<br>- oT eee<br>5 aan aie<br>s 50 may<br>a ~<br>~<br>45 ‘Y<br>ny<br>‘,<br>40<br>35<br>1993 1995 1997 1999 2001 2003 2005<br>— private consumption share in GDP ——— wage share in GDP<br><!-- End of picture text -->
28

|**MDG**|**Policy Objectives**|**What to do?**|**Link with the Budget**|
|---|---|---|---|
|**_Goal 3. Promote gender eq_**<br>**Target 4.**Eliminate gender|**_uality and empower women_**<br>disparityinprimary and secondary edu|cation, preferably by2005 (andinall le|vels ofeducation nolaterthan 2015)|
|_Indicator 9._Ratio of girls to boys<br>in primary, secondary (and<br>tertiary) education|•<br>Prevent poor teenage girls from<br>dropping out of school in secondary<br>and higher education|•<br>Inform the families, reduce the<br>parents’ opportunity cost, and<br>strengthen ad-hoc institutions|•<br>Finance awareness-rising campaigns to<br>encourage demand for girls’ schooling<br>•<br>Budget adequate subsidies for<br>children, notably girls in secondary<br>school, coming from the<br>disadvantaged environments<br>•<br>Establish an inter-ministry<br>arrangement for coordinating the<br>_National Strategy for the Promotion of_<br>_Women_|
|**_Goal 4. Reduce child mort_**|**_ality_**|||
|**Target 5.**Reduce by two-thi|rds, between 1990 and2015, the unde|r-fivemortalityrate||
|_Indicator 13._Under-five mortality<br>rate(per 1,000)<br>_Indicator 14._ Infant mortality rate<br>(per 1,000)|•<br>Provide access to health and nutrition<br>services to infants in the poorest<br>quintile of the population|•<br>Provide access to health and nutrition<br>services to children and their mothers,<br>in particular in rural areas|•<br>Increase budget allocations to finance<br>effectively the implementation of the<br>_National Health, Nutrition and_<br>_Sanitation Policy_<br>•<br>Construct, furnish, stock up and staff<br>90 new health unit; make operational<br>17 mobile medical teams at the<br>regional level; establish 5 inter-<br>regional medical centers, and ensure<br>adequate stock of medicines<br>•<br>Make the planned_Relief and Nutrition_<br>_Centers_operational, to coordinate food<br>deliveryin remote areas|
|**_Goal 5. Improve maternal_**|**_health_**|||
|**Target 6.**Reduce by three-q|uarters, between 1990 and2015, them|aternal mortalityratio||
|_Indicators 16._Maternal mortality<br>ratio (per 100,000 live births)|•<br>Improve the geographical coverage<br>and the quality of medical assistance|•<br>Organize information campaigns and<br>increase access to assisted-child-births<br>in rural areas|•<br>Finance awareness campaigns to<br>prevent pregnancy under unfavorable<br>condition<br>•<br>Set up a treatment system for obstetric<br>urgencies and neonatal infections, by<br>making operational 17 mobile medical<br>teams at the regional level<br>•<br>Put in place an institutional framework<br>to ensure that obstetrical urgencies and<br>post-natal infection are adequately<br>addressed,especiallyin rural areas|
Glass Barriers Kraay & Murrell (2013) have designed a new methodology that uses randomization but does not assume that reticence decreases—contrary to Warner (1965), for instance. Besides the “conventional question” (CQ), i.e., a sensitive question (set in binary terms) that the respondent is asked to answer directly, their approach includes a set of “random-response questions” (RRQs), for each of which the respondent privately tosses a coin (we used playing cards instead). She is instructed to answer “Yes” whatever the true answer to the sensitive question if the coin comes up heads and to answer the question otherwise. The RRQs are also sensitive—a crucial assumption is that the proportion of respondents who have done the sensitive action, which Kraay and Murrel call the “rate of guilt,” is the same across the CQ and RRQs— and far from assuming that randomization elicits more truthful answers, the known probability of a “Yes” is used to estimate the incidence of reticence in the sample thanks to the generalized method of moments (GMM). Reticenceadjusted rates of tax evasion are presented in Table 2.

The RRQs included in the IDI questionnaire are presented in Appendix A. Some of the questions come from Kraay & Murrell’s battery, which was used in Cambodia among other developing countries to estimate the share of the population who had been in a situation where a bribe was expected in the past year. The others were developed based on preliminary observations and qualitative interviews of SSCBTers. They were then fielded along with the other sections of the survey during pilots in all three checkpoints.

_Table 2: Reticence Estimation and Adjustment of Answers to Sensitive Questions Following Kraay & Murrell’s (2013) Methodology_

||Pooled|Bavet|Poipet|Vangtao|Men|Women|Brokers|Traders|
|---|---|---|---|---|---|---|---|---|
|Guilt|.189***|.121*|.183*|.270**|.165**|.221***|.202*|.109**|
|Reticence|.796***|.712***|.779***|1.02***|.749***|.871***|.708***|.756***|
|Probability reticent person<br>answers question reticently|.476***|.596***|.439***|.361***|.508***|.432***|.540***|.508***|
|Effective reticent|.379***|.424***|.342***|.369***|.381***|.376***|.382***|.384***|
|Number of observations|157|55|54|48|65|91|45|63|
|Naïve guilt rate estimated<br>directlyfrom surveyresponses|.117***|.070**|0.120*|.170***|.102**|.138***|.125*|.113***|

_Source:_ Authors’ calculations. The Stata code for estimating these parameters was graciously shared with us by Peter Murrell.

_*p_ <.1 ** _p_ <.05 *** _p_ <.01 Cambodia and Lao PDR 26
12 of future primary surpluses which, of course, they don't know. The most they may know is the value of current and lagged primary surpluses, the proxy which in fact we used as regressor. The degree of debt indexation, in turn, was measured by the ratio of indexed to non-indexed debt. Indexed debt comprised both inflation and dollar-indexed debt instruments. The dummy for the debt agreement took value one after the deal announcement in July 1989.

The hypotheses described above are supported by the econometric eviderce reported in Annex 1. The additional tightening of fiscal policy since 1988, captured by increases in real primary fiscal su: -. uses, led to lower explicit debt repudiation risk premia. Increases (reductions) in the level of debt indexation were associated with increases (reductions) of debt repudiation risk premia. Finally, as expected, the 1989 foreign debt agreement (captured by a dummy) reduced risk premia.

<!-- Start of picture text --> Public Bond Structure.<br>(thous. rrU. pesos of 1987)<br>35<br>30-<br>Is,<br>Am 1187 1988 AU 1989.A 1990<br><!-- End of picture text -->

**Figure** 10

# 4.2 Factors Behind Devaluation Expectations/Exchange Rate Uncertainty.

We now turn to factors underlying policy credibility as measured by unscheduled devaluation expectations/exchange rate uncertainty. Unscheduled devaluation expectations/exchange rate uncertainty were extremely high in 1988, declined in 1989, particularly during the months following the announcement of the debt deal, and then fell again substantially after the first quarter of 1990 (Figure 8). Factors governing unscheduled devaluation expectations are difficult to identify. However, we know that if the government is to resist a speculative attack on the currency, whether originating from private misperceptions or not, at least the stock of base money must be backed by foreign exchange reserves. In the sase of Mexico, though, where the government had frequent and high funding requlirements, e.g., domestic public debt, equivalent to approximately 20% of GDP, had an average maturity of less than a month, non-monetary government obligations also mattered due to their potential impact on base money growth. To amortize
Power Constraints and Firm-Level Total Factor Productivity in Developing Countries<sup>*</sup> Ablam Estel Apeti<sup>†</sup> and Alpha Ly<sup>‡</sup>

**JEL Classification** : L94, D24, O10

**Keywords** : Power constraints, Productivity, Developing countries

> *We thank Raja Chakir (INRAE), Jean-Louis Combes (LEO-UCA), Anna Creti (Paris Dauphine), Carolyn Fischer (The World Bank - DECSI), Megan Elizabeth Lang (The World Bank - DECSI), and Alexandru Minea (LEO-UCA) for their helpful comments and suggestions. We are grateful to the WBG Africa Fellowship Program organizing team.

> †Ablam Estel Apeti: University of G ̈ottingen, G ̈ottingen, Germany, Universit ́e Clermont Auvergne, Universit ́e d’Orl ́eans, LEO, The World Bank Group, E-mail: ablam ~~e~~ stel.apeti@uca.fr.

> ‡Alpha Ly (Corresponding Author): Paris Dauphine University, Climate Economics Chair, Chaire EIEA (Mines Paris - PSL & UM6P), Energy and Prosperity Chair, The World Bank - DECSI, E-mail: aly6@worldbank.org / alpha.ly@dauphine.eu
Two definitions of the poverty line were considered - total poverty and 75% of this line:

- For each year, the number of people whose consumption falls below the consumption poverty line is plotted in figure A2.1. As the definition of poverty is reduced (for example from 100% to 90%) fewer people will fall below the poverty line.

- The historical data suggests an average chronic poverty level of 8 million if there is no crop failure in a given year, when the poverty line is defined as 75% of its full value. This estimate of chronic poverty approximately aligns with the core caseload of the PSNP. To focus on transitory poverty, which is defined as poverty related to insufficient rainfall, a threshold of 75% of the total poverty line was assumed in this analysis.

# **Isolating Transitory Poverty**

In order to define the hypothetical contingent liability a simplifying assumption was made that the population in chronic poverty is covered by the core PSNP caseload. The excess population not covered by the core PSNP caseload must be covered by the scalability mechanism, and the mechanism:

- Defines someone as chronically poor if they would be in poverty in a year with sufficient rainfall (WRSI=100%). Chronically poor are assumed to be protected by the regular caseload of PSNP.

- Defines someone as transitorily poor if they would be in poverty in a year because WRSI<100%. Transitorily poor are assumed protected by the temporary in-season scaling of PSNP.

The base case results use the 75% poverty line, as the resulting estimate of the number of chronically poor is similar to the core caseload of the PSNP. Subtracting the chronic poverty estimate of 8 million from the total poverty estimates (both based on a poverty line of 75%) gives a per year estimate for those in transitory poverty for each of the 15 data points (2001 to 2015).

# **Defining a Contingent Liability Distribution**

A range of transitory poverty estimates is required for the analysis, so that the impact of risk financing strategies in both the average scenario and more extreme scenarios can be determined. A distribution assumption is required in order to consider return periods and likelihoods of occurrence, as well as to consider potential scale-up costs more extreme than those observed in recent history.

A Pareto distribution was fitted to the historic data for total population in poverty to extrapolate a set of scale-up payments for the hypothetical contingent liability. There were many options for a suitable distribution function and the Pareto was selected as a pragmatic choice, because it is a heavily skewed distribution. The historical data is plotted against the extrapolated data in the figure below.

The extrapolated data was based on 5,000 random samples (simulations) taken from the fitted Pareto distribution. A larger sample did not produce a fit that was any closer to the historical data. The greater drought induced poverty estimates sampled from the Pareto distribution (compared to historical data) are a result of the skewness of the Pareto distribution.

20
# **7.3 Do perceptions of non-state actors and donors have consequences for deference to the police and to the courts?**

Findings suggest that perceptions of non-state actors and donors have a positive effect on citizens’ willingness to defer to the tax department. Do perceptions of non-state actors and donors also have an effect on citizens’ willingness to defer to the police and to the courts? The Afrobarometer asked respondents whether they agree or disagree with the following: “The police always have the right to make people obey the law” and “The courts have the right to make decisions that people always have to abide by”.

I estimate two additional models to test whether perceptions of donor and non-state actor service provision have consequences for citizens’ willingness to defer to the courts and to the police. I include the same control variables as model 1. I also include the same measures of procedural justice, government performance, and enforcement. I replace the indicator of respondents’ perceptions of corruption among tax officials with an indicator of perceptions of corruption among the police and an indicator of perceptions of corruption among judges and magistrates. I also replace the measure of beliefs about who is responsible for collecting taxes with a measure of beliefs about who is responsible for maintaining law and order in their community.

Regression results suggest that perceptions of government performance, administrative competence and honesty, and procedural justice are positively associated with the willingness to defer to the police and to the courts.<sup>14</sup> As illustrated in Figure 6 and Figure 8, the effect sizes of procedural justice, government performance and administrative competence and honesty on the willingness to defer to the courts and to the police are smaller than the effect sizes of these measures on the willingness to defer to the tax department. The results point to a positive association between some measures of donor and non-state service provision and deference to police and to courts. Citizens who believe that donors and non-state actors exert too much influence, rather than too little influence, are more likely to be willing to defer to the courts and the police. Yet, I do not find any relationship between perceptions of the helpfulness of donors and non-state actors and the willingness to defer to the courts and to the police.

> 14Regression results for each of the different model specifications are available upon request.

25
was an increase of 5.2 percentage points. The underestimation of poverty changes is in line with the underestimation of income losses presented in Figure 10.

**Table 3. Change in poverty between 2019 and 2020. In percentage points**

||US$ 2.25/day|US$ 3.65/day|US$ 6.85/day|
|---|---|---|---|
|Quarter 1|0.82|0.56|0.76|
|Quarter 2|7.02|8.49|10.81|
|Quarter 3|6.80|8.52|10.75|
|Quarter 4|5.06|6.87|8.58|
|Average|4.92|6.11|7.72|
|Simulation|2.67|3.99|5.23|

Source: PNAD-C 2019 and 2020.

# **5 Conclusions**

<mark>In this study we estimated the distributional impacts of the pandemic in five developing countries: Brazil, Sri Lanka, the Philippines, South Africa, and Türkiye. We used a macro-micro simulation approach to capture the impacts of the pandemic via three channels – job loss, labor income declines, and changes in remittances – on total household per capita income/consumption and on</mark> the shares of poor, vulnerable, and middle-class populations in each country during 2020.

<mark>We analyzed the accuracy of using employment projections based on GDP-employment elasticities for 15 developing countries by comparing the projected employment levels with observed 2020 values. We also validated our microsimulation estimations for Brazil using 2019</mark> and 2020 microdata from the PNAD-Contínua.

<mark>Our findings showed, first, that employment estimates for 2020 based on elasticities were reasonably accurate in 11 of the 15 countries. However, in the remaining four countries the projections considerably overestimated employment levels and therefore underestimated job loss due to the crisis. This bias tended to be larger for labor markets that were more disrupted by the pandemic according to the stringency of social distancing measures, changes in workplace mobility, and a measure of scale-up of cash transfer programs. We also projected employment levels using elasticity estimations from a previous recession and found that they generate smaller biases in the projection of employment levels. However, depending on the method used for</mark> estimating elasticities, the biases do not differ much from using a non-period crisis.

<mark>Second, microsimulations for five countries across different regions of the globe showed declines in the per capita household income or consumption across the entire income/consumption distribution. Overall, the 2020 pandemic resulted in a decline of the middle class and an increase in poverty in all five countries. However, the pattern of impacts across the distribution varied across countries, with no clear pattern. When using employment projections based on elasticities</mark> instead of actual employment data, the projected income declines were smaller.

27