rafmacalaba/datause-encoder-data huggingface dataset:
conducted from September to December 2013. Post-harvest activities were conducted from January to early March 2014. Crop-cutting was conducted at any point during this period when the maize was deemed ready for harvest by the respondent. Area measurement was conducted in the post-planting visit. The data collection for LASER was conducted in 3 zones of the Oromia region in Ethiopia. In total, 85 enumeration areas (EAs) were randomly selected using the Central Statistical Agency of Ethiopia’s Agricultural Sample Survey (AgSS) as the sampling frame. Within each EA, 12 households were randomly selected from the AgSS household listing completed September 2013. Partners in the study include the Central Statistical Agency of Ethiopia, the World Agroforestry Centre (ICRAF), and the World Bank. This study also used Garmin eTrex 30 units to collect GPS land area data.

The last batch of data used in this analysis comes from the Nigeria Area Measurement Validation Study. The primary focus of this study was validation of area measurements. Extraordinary differences between GPS and farmer self-reported areas were observed in the first wave of the Nigeria General Household Survey, invoking the need to validate the methodologies. This study was conducted on a subsample of the General Household Survey panel households. After the measurement conducted in the second wave of the national survey a special team was deployed to re-measure a subsample of plots using three area measurement methods. Fieldwork for the area measurement validation study ran from March to May 2013. Four states were selected for inclusion in the study based on safety, location, and previous performance of farmer self-reported area and GPS area. The plot selection was stratified on plot size to ensure a complete range of plot sizes included. In total, 211 households were selected, including 518 plots. The study was implemented by the National Bureau of Statistics, Nigeria, and the World Bank. The GPS Unit utilized for this study was the Garmin GPS Maps 62.

In each of the three studies, agricultural plots were measured first by farmer self-reported estimate, then by compass and rope, and finally by GPS. The order of measurements was deliberate and great attention was paid to this in the field. Farmer estimation must be recorded prior to any objective measurement so as not to influence the farmer. Enumerators were instructed in all studies _not_ to influence the farmer’s estimate.

Although in each of the three studies the training of methods was conducted in the same way, with the same LSMS staff present at each training, there were two differences in implementation worth noting. First, in each survey enumerators were required to repeat the compass and rope measurement if the closing error was 5% or more. However, in the Ethiopia experiment the closing error calculation was done on the spot by the enumerators, possible because of the use of computer-assisted personal interviewing. In Tanzania and Nigeria, the closing error was calculated by the supervisors (in some 13
# **1. Introduction**

Following both the 2008-2009 global financial crisis and the current COVID-19 outbreak, many subnational governments—states, regions, and municipalities—increased their indebtedness to compensate for the decline in central transfers and tax revenues.<sup>1</sup> This rise has been partly driven by central government guarantees which, even if implicit, created incentives for local governments to potentially reach for higher-than-optimal levels of debt. This increase in indebtedness may eventually lead to periods of fiscal consolidation to fulfill debt obligations.<sup>2</sup> Overall, the economic impact of these austerity episodes is ambiguous. On the one hand, Alesina, Favaro, and Giavazzi (2019a, 2019b) argue that fiscal consolidation can have a positive impact on output through higher business confidence and private investment. On the other hand, Fatás and Summers (2018) and House, Proebsting and Tesar (2020) argue that austerity can have a permanent negative impact on output through reduced potential growth.

In this paper, we analyze the economic effects of limits to public debt. For identification, we exploit the introduction of the Mexican “Law of Financial Discipline to States and Municipalities”, enacted in April 2016. Using the ratio of public debt to freely disposable income of subnational governments as its main indicator, the Financial Discipline (FD) Law established debt ceilings to rein in the rise of local governments’ indebtedness.<sup>3</sup> Prior to the implementation of the Law, the level of indebtedness of local governments varied significantly across states. For example, in the quarter preceding the implementation of the reform, the ratio of public debt to disposable income of Mexican states in the bottom and top quartiles was around 30 percent and 100 percent, respectively.

Exploiting the introduction of the Law along with the ex-ante variation in public debt ratios across states, we estimate the impact of public debt ceilings using a difference-in-difference

> 1 From 2011 to 2018 the share of public debt of local governments worldwide increased from 14 to 22 percent (IMF, 2018). Similarly, the Fiscal Monitor of the IMF (2020) details a large increase in local government debt following the COVID-19 outbreak.

> 2 Regarding implicit guarantees, for example, Danish municipalities receive specific financial help from the central government if they get into financial difficulties, and are put under administrative control (Mau, 2015). In Germany, the constitutional court ruled that the federal government had to bail out two Länder (states) in financial distress. The Spanish financial crisis in 2012 was in large part driven by the excessive indebtedness of local regions with the _Cajas_ (Santos, 2017).

> 3 When promulgating the FD Law, President Peña-Nieto noted that “[T]he priority of the law is to ensure the stability of the country's public finances by establishing the requirements and conditions for the government to grant federal approval of the debt contracted by states and municipalities.”

2
Barros, Ricardo P. and Rosane Mendonça, 1996. Flexibilidade do mercado de trabalho brasileiro: uma avaliação empírica, in Camargo, J. M. (ed.), Flexibilidade do mercado de trabalho no Brasil. Rio de Janeiro: FGV Editora, p 157-201.

Bartelsman, Eric, Haltiwanger, J. and Stefano Scarpetta, 2004. Microeconomic evidence of creative destruction in industrial and developing countries. World Bank Policy Research Working Paper 3464.

Besley, Timothy and Robin Burgess, 2004. Can Labor Regulation Hinder Economic Performance? Evidence from India. Quarterly Journal of Economics, CXIX, 91-134.

Blanchard, Olivier and Justin Wolfers, 2000. The role of shocks and institutions in the rise of european unemployment: aggregate evidence. Economic Journal , CX, 1-33.

Boeri, Tito and Pietro Garibaldi, 2006. Shadow Sorting. CEPR Discussion Papers 5487, C.E.P.R. Discussion Papers.

Boeri, Tito and Juan Jimeno, 2005. The Effects of Employment Protection: Learning from Variable Enforcement , European Economic Review , XLIX, 2057-2077.

Bosch, Mariano and William F. Maloney, 2006. Gross Worker Flows in the Presence of Informal Labor Markets: The Mexican Experience 1987-2002.

Bosch, Mariano, Gori, Edwin and William F. Maloney, 2007. The Determinants of Rising Informality in Brazil: Evidence from Gross Worker Flows. IZA Discussion Paper 2970.

Botero, Juan, Djankov, Simeon, La-Porta, Rafael , Lopez-de-Silanes, Florencio and Andrei Shleifer, 2004. The Regulation of Labor. Quarterly Journal of Economics, CXIX, 1339 - 1382.

Bourguignon, Francois, 1990. Growth and inequality in the dual model of development: the role of demand factors. Review of Economic Studies, 57, pp. 215–28.

Bulow, Jeremy and Lawrence H. Summers, 1986. A Theory of Dual Labor Markets with Application to Industrial Policy, Discrimination, and Keynesian Unemployment. Journal of Labor Economics, Vol. 4, No. 3, Part 1., pp. 376-414.

Caballero, Ricardo J., Cowan, Kevin N., Engel, Eduardo, and Alejandro Micco, 2006. Effective Labor Regulation and Microeconomic Flexibility. Cowles Foundation Discussion Paper 1480.

Caballero, Ricardo J., Engel, Eduardo, and Alejandro Micco, 2004. Microeconomic Flexibility in Latin America **.** Economic Growth Center, Yale University Discussion Paper 884.

29
# **Table A24: Cost of Corruption**

||**Tax yield per**<br>**declaration**<br>**(average)**|**% Counterfac-**<br>**tual increase**<br>**without**<br>**corruption**|**Tax yield per**<br>**declaration**<br>**(average)**|**% Counterfac-**<br>**tual increase**<br>**without**<br>**corruption**|
|---|---|---|---|---|
|**A. Before Delegated Rand**|**omization of Ins**|**pector Assignment**|||
||Declarations wit<br>excess inte|h signifcant<br>raction|All declar|ations|
|Actual tax yield|$11,423||$10,446||
|Counterfactual tax yield without corruption, based on:|||||
|internal reference price|$940|8.2%|$97|0.9%|
|internal reference price & measured weight|$1,468|12.8%|$152|1.5%|
|external reference price|$2,281|20.0%|$236|2.3%|
|external reference price & measured weight|$2,962|25.9%|$306|2.9%|
|third party valuation advice|$1,102|9.6%|$114|1.1%|
|**B. During Delegated Rand**|**omization of Ins**|**pector Assignment**|||
||Declarations wit<br>excess interactio<br>withheld from ra|h signifcant<br>n that were<br>ndomization|All declar|ations|
|Actual tax revenue|$8,645||$10,749||
|Additional counterfactual tax yield without corruption, based on:|||||
|internal reference price|$3,745|43.3%|$95|0.9%|
|external reference price|$11,223|129.8%|$281|2.6%|
|third party valuation advice|$1,198|13.9%|$34|0.3%|

_Notes:_ Tax losses are calculated as the difference between the counterfactual tax yield collected in the absence of significant excess interaction and the actual tax yield. The counterfactual additional tax yield is calculated using measures of tax revenue losses based on different sets of reference prices (see Sections 3 and 7 for details).

88
Coefficients on PFM Quality from Table 1 can help shed light on the degree to which the upward trend in use of country PFM systems over time, as reported in OECD (2011a), may be attributed to any upward trend in quality. For 31 countries with data for 2005 and 2010, on both PFM Quality and use of PFM systems, the latter averaged 32.6% in the initial year, increasing to 37.5%. by 2010. Mean PFM Quality increased only from 3.56 to 3.61. Based on the coefficient of 7.5 in equation 1.3, improvements in PFM Quality account for an increase of only about 0.4 percentage points in use of PFM systems, less than one-tenth of the actual increase. Even based on the larger coefficient in equation 1.2, improvements in quality statistically explain only about one-fifth of the observed increase in use. Performing the same exercise for the 49 countries with the necessary data available in the shorter time span 2007 to 2010, the small average improvement in PFM Quality (from 3.35 to 3.39) is even less effective in accounting for the 4.4 percentage point increase in use of PFM systems (from 30.0% to 34.4%). Civil Liberties is even less effective in accounting for the observed improvement in use of PFM systems, as the mean value for Civil Liberties actually worsens slightly over time in these samples. The overall increase in donors’ use of country PFM systems, for a given set of countries and controlling for other factors, suggests that the PD campaign may have succeeded in encouraging donors to accept more (short-term) risk under the premise that bypassing country systems undermines them, thereby slowing development in the long run.

The remainder of this section reports robustness tests, mostly designed to address three potential problems with the basic results reported in Table 1. First, those estimates are potentially affected by sample selection bias. Second, there may be omitted variables correlated with quality and use of country systems, despite the inclusion in 1.3 and 1.4 of various control variables and donor-year fixed effects. Third, coefficients for PFM Quality may be biased 16
��������������� �, (2) where _WTP_ is stated willingness to pay to register a transaction for a parcel that was acquired but not yet registered and **_X_** , **_Y_** , and **_Z_** are vectors of household, parcel, and community characteristics, _β, γ_ , and _δ_ are vectors of coefficients to be estimated, and � is a random error term.

Although they will be only suggestive, results for specifications with province fixed effects for all parcels (column 1) and only those located in the cell of residence (column 2) in table 9 provide three insights. First, WTP increases with parcel size (elasticity of 0.11) but is lower for parcels allocated by government. Second, distance from the DLOs but not the share of certificates in the cell reduces WTP, either because of higher transport costs or lack of information. Finally, location in a sectors where a SLM had already been posted at the time of the survey significantly increases WTP for registering land in the cell of residence by some 60%, possibly as a result of greater awareness, or lower transport costs.

# **5. Conclusion and implications for future research**

Our analysis shows that Rwanda has made remarkable progress in a very short period of time and points towards a number of steps to make this accomplishment sustainable and fully realize the benefits from it.

Enormous differences in the rates with which subsequent transactions are registered point towards large differences in benefits or relative cost of land registration across the population. Building on a tradition of piloting in Rwanda’s land sector, exploring impacts of targeted fee reductions, with or without campaigns to increase male and female land owners’ or SLMs’ awareness/knowledge could be a promising and low cost way to quickly identify options that could help bring the country closer to full registration of all transfers, helping to ensure the sustainability of the large investment made under LTR. Using LAIS, together with databases maintained by other institutions, to regularly and in real time monitor the indicators reported here, among others, could help RNRA to document the benefits already generated by LTR. Without maintenance it will be difficult to sustain these benefits, and manage the process of doing so.

Establishing a monitoring system that builds on planned real-time integration of the Land Administration and Information System (LAIS) with data on court cases or disputes by Ministry of Justice, land lease fees by the Revenue Authority, and land use by the Ministry of Agriculture promises to enhance these benefits. Regular monitoring and publication of key statistics at high levels of spatial disaggregation will allow the Rwanda National Resource Authority (RNRA) to measure progress in addressing remaining challenges, manage the process of doing so and, if data (at more aggregate levels to protect confidentiality of the underlying information) are made public, significantly improve the basis of information (e.g., on land prices) for private sector decision-making.

14
and that there is no substitution into other food items. We subsequently reflect the loss of purchase power by reducing household consumption expenditure by the extra costs they would incur to buy the same amount of cereals at higher prices. For self-produced cereals we calculate the value of the reduction in cereal production following the drought and adjust total consumption expenditure accordingly. We then assess the effect these changes in consumption expenditure have on food poverty rates across the country.

In 2010 the total number of these food poor people was 3.2 million (22% of the population), of which a majority lived in Sikasso, Mopti and Segou. Following the 2011 drop in cereal production (estimated at 25% at end December 2011) due to localized drought, the number of the food poor is estimated to have increased by 363,000 people. If we also take into account the rise in cereal prices and the consequent drop in purchase power, the total amount rises by another 174,000 people, _increasing the number of foodpoor Malians from 22% to 26% of the population or an additional 610.000 people._ The largest absolute increase is in Mopti-Segou where 227,000 more people become food poor as consequence of the combined shocks. The largest relative increases are witnessed in northern regions of Mali where the proportion of people living in food poor households increases by 41% (in total an additional 58,000 households) (See Table 8). This implies that even before the March 2012 coup, the northern regions of Mali was already witnessing the largest relative increase in the number of food poor households in the country, following the price and production shocks of 2011.

**Table** **<u>8. Number of people (in ‘000) per region under the fod poverty line without and with shocks</u>**

||**No of**<br>**existing**<br>**foodpoor**|**Food poor**<br>**if cereal**<br>**prod**<br>**reduces by**<br>**25%(a)**|**Food poor if**<br>**cereal prices**<br>**rise by 25%**<br>**(b)**|**Both**<br>**shocks (a)**<br>**+(b)**|**Number of**<br>**additional**<br>**food poor**<br>**people**|**%**<br>**increas**<br>**e in**<br>**food**<br>**poor**<br>**people**|
|---|---|---|---|---|---|---|
|Kayes-Koulikoro|578|674|636|744|166|29%|
|Sikasso|1504|1619|1543|1653|149|10%|
|Mopti-Ségou|977|1122|1013|1204|227|23%|
|Tombouctou-Gao-Kidal|141|159|173|199|58|41%|
|Bamako|51|47|66|66|14|28%|
|Total|3210|3574|3385|3821|610|19%|
|Additional (no)||363|174|610|||
|Additional (% of total||2.5%|||||
|population)|||1.2%|4.2%|||

_Source: Authors’ calculations using ELIM –MICS 2010._ Those who have become food poor following both shocks are overwhelmingly located in rural areas. However, the rise in cereal prices also increases the number of the food poor 11
interest rates, with rates rising by 66 basis points (bps) over the course of 2014, and continuing to increase gradually over the next two years, so that cumulatively there is a 166 bps by the end of 2016. Short-term rates are also assumed to begin normalization during this period, rising by a cumulative 170 bps over the three years. Additional unobserved effects of QE—as captured by the QE episode indicator—are assumed to gradually decline and taper off fully by 2016.

Table 7: Deviation in cumulative changes in gross financial inflows, by normalization scenario relative to _status quo_ benchmark, 2014–2016<sup>_†_</sup>

||**History**||**Baselin**|**e**||
|---|---|---|---|---|---|
||2012|2013|2014|2015|2016|
|Developing GDP growth|5.0|5.4|5.5|5.8|5.9|
|High-income GDP growth|1.4|1.1|2.2|2.4|2.4|
|High-income yield curve|1.7|2.1|2.5|2.6|2.3|
|High-income 10Y rates|2.2|2.4|2.9|3.2|3.6|
|High-income 3M rates|0.4|0.2|0.3|0.6|1.2|
|VIX index|18.0|15.0|16.9|18.2|18.9|
||_Devia_|_tion of infow_|_s from_ status|quo _baseline_||
|Share of fows|||-3.7|-7.4|-10.0|
|Share of developing GDP|||-0.2|-0.4|-0.6|

> _†_ Upper panel: all variables, with the exception of the VIX, are given in percentage points; the VIX is given as an index. GDP growth rates are annualized rates for the respective quarter. Lower panel: all values are in percentage deviations relative to _status quo_ baseline, and are computed from gross inflows measured in constant 2010 U.S. dollars.

The results are reported in the lower panel of Table 7, with numbers computed as deviations relative to the _status quo_ benchmark. The model suggests that gross capital flows to developing countries would fall by some 4 percentage points in constant dollar terms in 2014, and cumulatively by 10 percent by 2016, compared to the _status quo_ with no change in unconventional monetary policies. In terms of developing-country GDP, gross capital flows would be lower by about 0.6 percent, again relative to the _status quo_ .<sup>42</sup><sup>_,_43</sup> The results of the panel analysis provide some sense of the potential impacts of changes in finan-

> 42Since the VAR model includes inflows as an input, it also produces alternative paths for gross inflows, which we can use as a consistency check. As it turns out, the VAR generates a very comparable outcome: a decline of 0.5 percent of GDP over the projection horizon.

> 43We also considered the possibility of a more rapid normalization outcome, where long-rates rise by around 100 basis points in 2014 itself, and QE effects taper off fully by end of 2014. In this case, the decline in gross inflows is front-loaded—falling by 7.5 percent in 2014—but the final decrease of about 10 percent by the end of 2016 is comparable across the two scenarios. Full results for this alternative scenario are available on request.

30
this probability of falling below the poverty line are considered part of the middle class. The estimated model allows associating these probabilities to consumption thresholds. In this way we can identify, along the consumption distribution, the percentage of individuals above or below these values.

As mentioned in section 2, the upper threshold for middle class is not defined. Clearly not all those below the 10 percent threshold are just middle class, they might be even richer. Yet, it is very difficult to define in a non-arbitrary way those belonging to the middle class and those richer, among those above 10 percent. The use of consumption data doesn’t provide accurate information on the richest percentiles of the Nigerian population. Therefore, it is likely that a number of households in the survey’s top percentiles would be considered, actually, as part of the middle class.

The three logit specifications and the full model with GME are all in the same range indicating limited variation. The threshold ranges between 348-389 Naira for the considered specifications. In dollars PPP the threshold is around $2.70-$3.02, which is close to the threshold indicated by the African Development Bank to define the lower middle class. Figures 1 and 2 plot the cumulative distribution of consumption per capita at national level and by zones and shows the 10 percent threshold. The full model-GME logit specification is used with the 10 percent yielding 378.39 Naira per capita per day.<sup>10</sup> A number of findings are relevant. First, as shown by Figure 1, the percentage of Nigerians above the estimated threshold is roughly 20 percent. This 20 percent coincides with Standard Bank estimates when summing lower middle class and middle class (Standard Bank, 2014). Second, this middle class definition uses a very low threshold compared to other countries in the world (see Ferreira et al., 2013); if one uses the threshold used for Latin America for example, the share of Nigerians qualifying for middle class status will be more than halved.

Finally, there is high heterogeneity among zones. As shown in Figure 2, in the Southern regions the percentage of middle class people ranges from 27 to 31 percent, while in the Northern regions it ranges from roughly 9 to 18 percent. Most of the middle class households, thus, are concentrated in the South, particularly the South West. In each of the Southern regions the middle class makes up more than 26 percent of the region’s population. Meanwhile in the North East and North West less than 10 percent of each region’s population is considered middle class. In the North Central close 18 percent of the region’s population is considered middle class.

> 102010 prices, equivalent to $2.94

17