rafmacalaba/datause-encoder-data huggingface dataset:
private sector dynamics, as we are able to assess whether the regime change that accompanied the crisis differentially impacted firms connected with the Suharto regime.<sup>1</sup> The main findings can be summarized as follows: the data do not unequivocally support the cleansing paradigm. The crisis led to a spike in exit, a slowdown in entry, and excessive employment reallocation. Productive firms on average experienced lower employment losses, but the correlation between productivity and employment growth did not strengthen. Rather than raising the productivity threshold for survival, the crisis was more indiscriminate in terms of the productivity of firms driven out of business. Firms more vulnerable to changing credit market conditions were much more likely to exit during the crisis, but the attenuation of the link between productivity and survival did not increase with vulnerability to changing loan conditions. The attenuation was also not an artifact of reduced entry rates or driven by political transition. The results are robust to excluding firms with connection to the Suharto regime. Moreover, if anything, among the politically connected firms, productivity became a more important determinant of firm survival. By contrast, the attenuation was particularly pronounced in provinces with comparatively high minimum wages, suggesting that labor regulations obstructed efficiency enhancing reallocation. The effects are not all negative, however. The protective power of productivity against exit was restored post-crisis, and the crisis appears to have weeded out the weakest potential entrants; while entry rates were lower during the crisis, those firms that entered were on average much more productive, which helped mitigate the loss in aggregate productivity.

The remainder of this paper is organized as follows. The next section reviews related literature and describes the context. Section 3 summarizes our hypotheses and explains our approach. Section 4 describes the data, while section 5 presents descriptive statistics on job flows and decompositions of aggregate productivity growth. Section 6, which constitutes the core of the paper, presents firm survival and employment growth models. A final section concludes.

> 1 We use the data on political connections collected by Mobaraq and Purbasari, 2008.

4
# **Figure 2: Frequency and cost of government intervention over time**

## **A: Number of government interventions by year B: Cost of government intervention (% of total tax revenue)**

<!-- Start of picture text --> 8 50<br>45<br>40<br>6<br>35<br>30<br>4 25<br>20<br>15<br>2<br>10<br>5<br>0 0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Equity NPE acquisitions Debt Equity NPE acquisitions Debt<br>Number of government interventions<br>Government expenditures (% of Government revenues)<br><!-- End of picture text --> Source: Own elaboration using data from the National Bank of Kazakhstan, National Wealth Fund Samruk-Kazyna, the Kazakhstan Sustainability Fund, public news reports, and the World Bank. Note: In Panel B, we report the cumulative government expenditures on bailouts in a given year.

Quasi-fiscal entities played a crucial role in the structuring of bank bailouts. The most recent purchases by the Problem Loan Fund (PLF) of bank non-performing loans much above their market price and often at 100 percent of their nominal value—while their market price can be often lower than 10-20 cents on the dollar<sup>h</sup> —have been the costliest bailout options for the general government without any claim on the future profits of banks received later in the form of dividends (Figure 4). The recoveries from the NPE purchases have been minuscule and the prospects for further recoveries deteriorating with time. The procedural steps in a bailout using the PLF purchases of bank NPEs are described in table A1.B in the annex.

The use of debt bailouts through NBK’s Kazakhstan Sustainability Fund has been the second most recent type of bailout used by the government. Injecting public money against senior debt issuance by the banks is a more disciplining bailout for bankers but still a very soft one— especially, if the debt is underpriced. For example, the bailout of bank #16 in 2020 was structured based on a debt with a coupon of 0.1 percent—with little return for the taxpayer money and a subsidized loan to bank #16. A similar bailout approach has been applied through Samruk Kazyna and PLF deposits which were placed in banks at well below the market rate and, for example, in the case of bank #38 ultimately at as low as a 0.1 percent interest rate.

The equity injections performed by Samruk Kazyna as a form of public bailouts could be considered a fairer and more equitable bailout option—because they reduced/diluted the ownership stakes of bankers that brought the bank into distress and required them to pay back dividends on

> h Fair value to purchase price ranged from the case of non-bank entity at 10%, to bank #31 transaction at 10%, to bank #31 transaction 2 at 20%, and to bank #16 at 20%: PLF’s audited consolidated financial statements: as of 2018, pp. 40-45, <u>https://www.fpl.kz/media/file/2018_con_fin.pdf,</u> as of 2019, pp. 39-41 <u>https://www.fpl.kz/media/file/2019_con_fin.pdf,</u> as of 2020, pp. 39-42 <u>https://www.fpl.kz/media/file/Консолидированная_FS_2020_RUS.pdf</u>

8
**<u>Table 2. Household Characteristics in 2008, overall and by Tenure Type</u>**

|Variable|Total|Pure owners|Pure<br>bargadars|Owner-cum-<br>tenants|Pattadars|Landless|
|---|---|---|---|---|---|---|
|**Household characteristics**|||||||
|Household size|4.75|5.08|4.99|5.57|4.75|4.41|
|Members <14 years old|1.50|1.43|1.55|1.52|1.42|1.56|
|Members 14 to 60 years old|2.99|3.32|3.21|3.63|3.03|2.66|
|Members>60 years old|0.26|0.32|0.24|0.42|0.30|0.19|
|<br>Head's age|44.57|47.60|46.46|49.51|46.94|41.15|
|Head's educ (years)|3.11|4.48|1.82|3.65|1.45|2.27|
|Share of heads illiterate|0.54|0.42|0.70|0.47|0.74|0.62|
|Area of own land (ac.)|0.57|1.37|0.00|1.10|0.00|0.00|
|Area of Barga land|0.13|0.00|1.41|1.07|0.66|0.00|
|Area of Patta land|0.06|0.08|0.00|0.18|0.41|0.00|
|Barga under 25/75 sharing rule|-|-|0.48|0.44|-|-|
|Barga under 50/50 sharing rule|-|-|0.52|0.56|-|-|
|**Head’s occupation and income sources**|||||||
|Wage work main occupation|0.31|0.14|0.33|0.14|0.47|0.44|
|<br>Farming main occ.|0.30|0.58|0.48|0.70|0.30|0.03|
|Off-farm wage work main occ.|0.27|0.18|0.14|0.11|0.17|0.38|
|Off-farm self employment main occ.|0.12|0.09|0.05|0.05|0.06|0.15|
|Income per capita (Rs.)|5504.66|5913.92|4351.95|4665.49|4192.01|5469.39|
|.. share from wage & salaried work|0.65|0.43|0.58|0.38|0.72|0.77|
|.. share from crop prod|0.15|0.38|0.32|0.47|0.15|0.01|
|..share from self-employment|0.13|0.12|0.07|0.09|0.07|0.15|
|.. share from livestock prod|0.02|0.03|0.01|0.03|0.02|0.01|
|.. share from other sources|0.05|0.05|0.03|0.03|0.04|0.06|
|**Productivity**|||||||
|<br>Annual gross revenue (Rs./ac)|20408.59|21628.00|13216.82|18167.36|17611.82|-|
|Annual net revenue (Rs./ac)|8652.84|9112.45|5554.71|8178.62|7606.50|-|
|**Agricultural market participation**|||||||
|Ever rented out land|0.01|0.03|0.00|0.01|0.01|0.00|
|Would like to rent in land|0.64|0.64|0.78|0.70|0.78|0.62|
|Would like to rent out land|0.03|0.04|0.04|0.03|0.04|0.01|
|No. of observation|95926|36053|2740|3094|6457|46609|

_Source:_ 2008/9 West Bengal listing survey.

17
Dandekar, V. M. 1977. Crop Insurance for Developing Countries. Teaching and Research Forum Paper no. 10. New York: Agricultural Development Council.

Dercon, S., and L. Christiaensen (2007). Consumption Risk, Technology Adoption and Poverty Traps: Evidence from Ethiopia. World Bank Policy Research Working Paper 4257. Dercon, Stefan, and Ingo Outes. 2009. Income Dynamics in Rural India: Testing for Poverty Traps and Multiple Equilibria. Working paper, Oxford University.

Doherty, Neil, and Harris Schlesinger. 1990. Rational Insurance Purchasing: Consideration of Contract Nonperformance. _Quarterly Journal of Economics_ 105 (1): 243–253.

Duflo, Esther, Michael Kremer, and Jonathan Robinson. 2008. How High Are Rates of Return to Fertilizer? Evidence from Field Experiments in Kenya. _American Economic Review Papers and Proceedings_ 98 (2): 482–488.

Duflo, Esther, and Chris Udry. 2004. Intrahousehold Resource Allocation in Côte d’Ivoire: Social Norms, Separate Accounts and Consumption Choices. NBER Working Paper no. 10498.

Feder, Gershon, Richard Just, and David Zilberman. 1985. Adoption of Agricultural Innovations in Developing Countries: A Survey. _Economic Development and Cultural Change_ 33 (2), pp. 255–298.

Fein, J., and P. Stephens. 1987. _Monsoons_ . New York: Wiley.

Gadgil, Sulochana, and Siddhartha Gadgil. 2006. The Indian Monsoon, GDP and Agriculture.

_Economic and Political Weekly_ November 2006: 4887–4895.

Gadgil, S., P. R. Seshagiri Rao, and K. Narahari Rao. 2002. Use of Climate Information for Farm-Level Decision Making: Rainfed Groundnut in Southern India. _Agricultural Systems_ 74: 431–457.
- 18 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

- **H4** : Regular urban structures (associated to low values of _Circuity_avg_ ) are usually associated with more efficient and shorter trips, which reduce agglomeration costs and may lead to higher levels of productivity.

- **H5** : An increase of either _Intersection_density_ or _Street_density_ implies higher levels of street coverage that facilitates the mobility of people and products increasing the connectivity levels, and therefore, leading to higher productivity levels of the city.

Finally, as shown in Fallah et al (2011), the consideration of the distributional aspects of the population within the urban extent provides information on the land use pattern of the city. The authors proposed a measure of urban sprawl that allows differentiating between cities with an even distribution of its population, from cities with highly concentrated population. For completeness, we present in equation (8) the measure proposed by Fallah et al (2011), and adopted in this work: Dividing the urban extent in small areas, L% is the share of the urban population living in a small area with density below the median density calculated for the entire set of analyzed urban extents (in our case the 940 LAC cities). Since our population data are in raster format, we considered each 250 m pixel of the GHS layer as a small area. This measure ranges from 0 to 1, with 1 indicating a greater level of sprawl. Fallah et al (2011) find that higher levels of sprawl are associated with lower levels of productivity.

- **H6** :  The distribution of population density within a city affects its economic performance. According to Fallah et al (2011), an uneven distribution of population density, associated with high levels of _Sprawl_ , can be linked to deteriorating socioeconomic outcomes, inefficient provision of public goods, and lower productivity levels.

As a summary, Table 2 presents the proposed variables, their description, and data source for calculation, and Table 3 presents examples of urban areas with high, medium and low values of these seven variables that describe urban form. Based on our hypotheses, our benchmarkhighly efficient city would have the following values for our variables:
<!-- Start of picture text --> < 50 Failure [0 80 - 95 Average<br>End-of-season WRSI for maize {i 50 - 60 Poor [ll 95 - 99 Good<br>{_] 60 - 80 Mediocre [ll 99 - 100 Very good<br>_q Mar 2009 - SepSOUTH2009 SUDAN -q Oct 2009 - FebSOUTH SUDAN2010<br>y . \ ~<br>LS aa be ae<br>"he F . \<br>a<br>DEMOCRATIC we DEMOCRATIC f<br>OF CONGO ~a OF CONGO<br>| = Ps | xh<br>| 1 ‘ 5 Pa .<br>y,© = ryi | KENYA } i 7 a ee KENYA<br>SEs : a8 te /<br>i .<br>.<br>RWANDA \ r - “ RWANDA ’ r 5<br>| } a } : ~t. # TANZANIA<br><!-- End of picture text -->
Road information is presented in Table 2. For 1976 and 1986, the road information we have captures road quality, divided into four categories, plus a small ’quality unknown’ category. At that time, most of the roads included in this list are unpaved. According to these data, the proportion of cells with at least one road was 3% in 1976 and 3.4% in 1986. The 2008 data use a more stringent road definition, according to which 1.8% of cells had at least one road. The 2014 data includes two new categories: highways, which at the time only include on short stretch of motorway/freeway between Accra and a nearby town; and local roads. The meaning of the other three categories also seems to have changed between the two years, precluding a direct comparison. In the analysis we rely heavily on the 1976 data because it captures the predetermined road infrastructure at the beginning of our study period. Averages for road availability in the 8 _n_ 1 and 16 _n_ 2 neighboring cells are not shown to save space but, by construction, they are identical to the cell averages reported here.

In Table 3 we summarize our main time-invariant variables. Longitude and latitude are presented in decimal degrees. They are used to measure the distance between cells in the subsequent analysis. A tiny fraction of cells are on the border with a neighboring country or on the seashore, but 2.7% are partially covered by a water body such as Lake Volta. The predominant soil quality is either poor or moderate, a situation that typifies much of Sub-Saharan Africa and reflects its tropical nature: heavy rainfall combined with high evapotranspiration means considerable leaching of nutrients from the soil. Only 7.4% of soils are categorized as good. There is a small fraction of cells with hydroponic soils, which means they are located in river estuaries or flood plains. Some of these soils can be made fertile, but often at some cost in terms of infrastructure and equipment. Some 5% of cells have land unsuited for traditional agriculture either because they are waterlogged or too steep. Cultivating these soils requires investment in drainage or terracing. In terms of humidity, most of Ghana’s land is classified as sub-humid, which some areas being either drier or more humid. Rainfall typically follows a North-South gradient in western Africa, with more rains along the coast than in the North.<sup>12</sup> There are exceptions, however: coastal areas of Ghana tend to receive less rainfall than those located more inland. The humidity of the soil also depends on its texture. In Ghana, soils in the valley of the Volta river are classified as humid as they tend to

> 12An ordered probit regression of the climate variable on latitude returns a strongly negative coefficient with a pseudo- _R_<sup>2</sup> = 0 _._ 28.

13
Another important issue of comparability is the mix of income and consumption data in PovcalNet. Table 2 reveals that approximately 75 percent of the countries in the PovcalNet database have data on per capita consumption, while the remaining countries – mostly in Latin America and the Caribbean - have income per capita. Income and consumption measures are neither conceptually nor empirically comparable measures of welfare. Conceptually, income is usually described as defining the opportunity set, while consumption defines realized outcomes. While incomes are generally thought to be more volatile, consumption is expected to somewhat smooth out these fluctuations (assuming concave utility functions). These are all reasons why it is expected that consumption and income will differ, and treating these as comparable measures of wellbeing is a potential concern.

Empirically, these differences manifest themselves in two main ways. The first is that, by definition, zero income is a feasible value, while zero consumption is not a feasible value (someone with zero consumption cannot exist). Indeed this difference is observable in the data – there is essentially no mass point in any country with zero consumption,<sup>18</sup> but many countries that use income data have a significant mass of zero incomes in the data, all of which are treated as being poor. Latin America, in particular, predominantly uses income to measure poverty, and in many countries there are at least a few percent of the observations that are zero.

The difference between consumption and income can also be due to issues of data quality. It is typically presumed that data quality differs between income and consumption, often based on the size and formality of the economy. In very poor countries, with little formal employment and mostly agricultural activities, it can be very difficult for survey respondents to provide reasonable answers to income questions. In contrast, in richer countries where work week patterns are uniform, income might be relatively easy to collect while collecting data on consumption may well suffer bias from respondent fatigue. The “best” concept with which to measure household wellbeing may therefore depend on context – on the nature of the income and consumption flows in each society.

Mexico, for which income is used to measure poverty in PovcalNet, provides a useful example of the difference. PovcalNet includes both income and consumption measures for Mexico for the same years. Figure 2 compares the cumulative density functions for both income

> 18 Zero consumption values may exist in household survey data, but they are often times treated as nonresponders, non-compliant responders and are frequently deleted from the file.

12
al. 2020). The Nigerian data landscape presents two key advantages for survey-to-survey imputation techniques. First, while the GHS and NLSS differ in their data collection schedules, a wide range of nonmonetary variables were collected in exactly the same way in both surveys. Second, it is possible to check the comparability of the two surveys using data from the 2018/19 wave of the GHS, the timing of which partly overlaps with the data collection for the 2018/19 NLSS. The results of this analysis produce a poverty rate of about 44 percent for 2010/11, very close to the backcasted estimates of 42.8 (2010) and 41.8 (2011) percent. The imputed estimates for 2012/13 and 2015/16 suggest that poverty declined slightly until the 2016 recession, after which poverty stagnated or even increased: this is also entirely in line with the backcasted results. Changing the variables included in the consumption model does not substantially change this story, confirming the robustness of the results.

The finding that poverty decreased slightly in the first part of the 2010s, but subsequently stagnated is in stark contrast with the 17-percentage point drop in poverty suggested by simply comparing the estimates from the 2009/10 HNLSS and 2018/19 NLSS. The evidence presented in this paper should therefore encourage caution when using incomparable survey-based measures of household consumption for poverty measurement purposes. In the case of Nigeria, drawing a trend between the 2009/10 HNLSS and 2018/19 NLSS would impact not only the policy discussion on poverty reduction at the national level, but also any analysis of poverty trends in West Africa, Sub-Saharan Africa as a whole, and – as poverty becomes more concentrated in Sub-Saharan Africa and in conflict-affected situations – potentially the entire world (World Bank, 2020).

The paper is organized as follows. Section 2 provides general information on the country context and on Nigeria’s data landscape. Section 3 explains the data used in the analysis. Section 4 outlines the two methodologies used to estimate poverty trends for Nigeria. Section 5 presents the main results. Section 6 describes a series of robustness checks. Section 7 discusses the implications of these findings and possible caveats of this analysis. Section 8 concludes.

# 2. Context

Nigeria is Africa’s most populous country, with over 200 million people, and Africa’s largest economy, with a nominal gross domestic product (GDP) of around US$450 billion (in 2019). At the same time, with around 4 in 10 Nigerans living poverty, Nigeria has the largest population of extreme poor people – those living on less than US$1.90 per person per day – in Sub-Saharan Africa and the second largest population of extreme poor people in the world. Therefore, Nigeria’s importance for regional and global poverty reduction efforts cannot be overstated.

4
still be an issue. Oversampling is not common in LMICs but is undertaken in some surveys – for example measuring wealth in Chile and Uruguay (Gandelman and Lluberas, 2023).

## _Unit non-response_

We noted that a solution to sparseness is to oversample high-income individuals. But if there is a lack of high-income individuals because of unit non-response of high-income individuals then oversampling can also play a role in ameliorating possible unit non-response bias.

Unit non-response can also be reduced if survey protocols require enumerators to visit a certain number of times on different days before recording a non-contact (Ravallion, 2022). Another potential solution to unit non-response that has been discussed in the literature is to provide incentives to participate in the surveys. Ravallion (2022) points out that is a concern, especially for measuring the top tail of the income distribution, because the poor may be more likely to be persuaded than the rich, which could make non-response bias worse (Ravallion, 2022). The impact of incentives on response rates was investigated by Stecklov et al. (2018), who randomized a small monetary incentive in an Indian household survey, finding that whilst it did improve response rates it also resulted in those households receiving the incentive reporting lower consumption. The authors hypothesized that this was due to respondents exaggerating their poverty in the hopes of future payments from the surveyors.

## _Item non-response_

Lepkowski (2005) notes that one way of reducing item non-response to income or wealth questions asking for a monetary value a priori is to ask “unfolding bracket” questions if a respondent is unwilling or unable to give a monetary value, in which respondents are asked whether their income lies above or below a certain value. Depending on the answer, further questions about the range in which income lies are asked. This method was used in the South African National Income Dynamics Study. A simpler version of this is one question where individuals are asked which bracket their incomes fall into. This at least provides some indication of the level of income. It is used by Statistics South Africa in the Quarterly Labour Force Surveys and General Household Surveys. In 2020 quarter 1 around 20% of employed individuals give bracket responses, around 50% give amounts and 30% refuse or don’t know.

# Ex post Solutions for the Challenges Encountered When Using Household Survey Data

Despite the best efforts of survey organizations to lessen item and unit non-response and sparsity, the income or wealth data from household surveys may still be missing part of the top of the distribution. In this case ex-post methods are required to solve missing top incomes. Hlasny and Verme (2022) characterized ex-post solutions to under-capturing of the top tail of the income distribution in 13