rafmacalaba/datause-encoder-data huggingface dataset:
model, informal employment is predicted to be countercyclical. This happens as shocks affect both sectors homogeneously so that the costs of formality become more binding when negative shocks occur, and conversely in the face of positive shocks. Moreover, the model shows that higher levels of informal employment reduce the counter-cyclical response of informality.

Our theoretical model draws from Rausch (1991), who looks at the relationship between the size of the informal sector and the minimum wage. In his model, people choose to become workers, formal, or informal entrepreneurs depending on their managerial ability; similarly, in our model workers choose to be active in the formal or informal sector depending on their skills. Less directly, our model also draws from previous theoretical papers that have studied the relationship between informality and growth (Loayza, 1996), informality and rent-seeking (Sarte, 2000), inequality and the size of the informal sector (Dessy and Pallage, 2003), and the relationship between informality and the structure of taxation (de Paula and Sheinkman, 2006).

The paper then tests the relevance of the model using cross-country and time-series data. It does so in the context of an error-correction framework: in the long run, informal employment moves in conjunction with the development characteristics that influence and are influenced by informality; in the short run, informal employment moves both in response to production shocks, and in order to close the gap that separates it from its trend, equilibrium level. We also examine how the response of informal employment to production shocks varies with the level of informality itself and its determinants. The empirical findings are broadly consistent with the model’s results.

The data consist of annual observations for a collection of developing and developed countries over diverse time periods. The observations are given at the national level, so that the variation in the data (and, thus, its informational value) resides on their cross-country and time-series dimensions. The key piece of information for the paper’s empirical analysis (and its main bottleneck) is data on informal employment. For this 4
of debt for households in the rest of Sri Lanka. What is of greater concern is that the loans from pawnshops and moneylenders are a much larger share of annual household consumption of households in the Northern and Eastern provinces than in the rest of the country. Loans from these informal sources often have higher interest rates than formal sources of finance such as banks or finance companies and could place a serious burden on household finances. Total household debt in the Northern and Eastern provinces is 56% and 47% of the annual household consumption, respectively, much higher than 36% in provinces other than these and Western provinces.<sup>5</sup>

**Table 6: Debt is a larger share of household consumption in the Northern and Eastern provinces and tends to be informal**

||**Northern**<br>**Province**|**Eastern**<br>**Province**|**Western**<br>**Province**|**Other**<br>**Provinces**|
|---|---|---|---|---|
|_Household has debt from the following so_|_urce (%)_||||
|Banks|28|24|26|31|
|Finance companies|5|8|10|8|
|Money lenders|10|16|8|6|
|Pawning of assets|36|41|44|39|
|Retail shops|17|24|9|17|
|Any kind of debt|63|69|69|67|
|Indebted households only:|||||
|_Share of total household debt from each s_<br>|_ource (%)_<br>||||
|Banks|30|23|24|30|
|Finance companies|4|5|9|8|
|Money lenders|8|11|5|4|
|Pawning of assets|43|44|43|39|
|Retail shops|8|10|4|9|
|Other (credit card, employer, instalment)|7|7|15|10|
|All household debt|100|100|100|100|
|_Debt as a share of annual household cons_|_umption (%)_||||
|Banks|16|11|14|12|
|Finance companies|4|6|7|5|
|Money lenders|6|5|2|1|
|Pawning of assets|28|22|16|13|
|Retail shops|0|1|0|0|
|Other (credit card, employer, instalment)|2|2|4|5|
|All household debt|56|47|43|36|
|Source: HIES 2012/13|||||

Employers in the Northern and Eastern provinces also cite access to finance as the major constraint they face. Figure 17 presents a comparison of the fraction of firms that cite different factors as the biggest constraint in the Northern and Eastern provinces as well as in all of Sri Lanka. The most common constraint in the Northern and Eastern provinces is access to finance, which is cited by 32% and 47% of all firms in the Northern and Eastern Provinces. For all of Sri Lanka, the practices of competitors in the informal sector is cited as the most common constraint that firms face. Other common concern of firms in the Northern and Eastern provinces are related to government policies such as labor regulations, business licensing and

> 5 We find a very weak correlation between employment status and indebtedness, although indebted individuals are slightly more likely to be employed (50%) than those that are not indebted (45%).

21
and the census. The second assumption is more specific, and states that the conditional distribution of household consumption given �� is the same for both the survey and the census. This latter assumption is in fact indispensable for the inference from the estimates for the larger areas (as representative in the survey) to the smaller areas (as representative in the survey).

_Illustrative Example 4_ Figure 2—produced by the World Bank Poverty Team in Vietnam—provides a map of poverty estimates by imputing from the VHLSS in 2008 into the population census in 2009.<sup>31</sup> The differing poverty rates at the province level are depicted by the color intensity, where a darker color indicating a higher poverty rate. Figure 2 shows that provinces with the highest concentration of poverty are in the Northern region, the North West region, and the Central Highland. On the other hand, provinces in the coastal regions including the Red River Delta and the Mekong Delta have the lowest poverty rates.

# **V. Missing Panel Consumption Data**

We now turn the last category of missing panel data (Table 1, Category C). New methods have recently been proposed to deal with situations with missing panel household consumption by constructing synthetic panels from cross sections that can help provide insights into the dynamics of poverty (Dang _et al._ , 2014; Dang and Lanjouw, 2013) and other related welfare outcomes such as vulnerability and shared prosperity (Dang and Lanjouw, 2016 and 2017). These synthetic panels have also been increasingly employed in a variety of contexts.<sup>32</sup>

> 31 See Demombynes (2015) for an introduction on this map.

> 32 Recent applications and further validations include Ferreira _et al._ (2013), Cruces _et al._ (2015) and Vakis _et al._ (2015) for Latin American countries, Martinez _et al._ (2013) for the Philippines, Garbero (2014) for Vietnam, Bourguignon and Moreno (2015) and Foster and Rothbaum (2015) for Mexico, Cancho _et al._ (2015) for countries in Europe and Central Asia, Dang and Ianchovichina (2016) for countries in the Middle East and North Africa region, Dang and

29
- 19 -

Another unexpected result is that the coefficient of local agriculture production value is negative and significant. It means that firms are more likely to be located where agricultural production is lower. This is intuitively acceptable from the urban-rural division point of view. Firms are located in urban areas. However, it also means that firms, partly including agrobusinesses, are not well linked to agriculture production areas. This may raise certain concern and remind policy makers of the importance to strengthen infrastructure investments to connect farmers and firms.

**Table 4.** **<u>Spatial instrumental variable estimation results</u>**

||(1)||(2)||(3)||
|---|---|---|---|---|---|---|
|ln_RDDN_|0.959<br>(0.289)|***|||||
|ln_TCITY_|||-0.377<br>(0.175)|**|||
|ln_TPORT_|||||-2.444|*|
||||||(1.279)||
|ln_NUM_|0.113||0.137|*|0.138|*|
||(0.086)||(0.081)||(0.079)||
|ln_POP_|0.665|***|0.482|**|0.626|**|
||(0.228)||(0.230)||(0.283)||
|ln_WAGE_|0.300|*|0.050||0.203||
||(0.165)||(0.186)||(0.174)||
|ln_EDU1_|2.338|*|2.701|**|2.768|**|
||(1.297)||(1.156)||(1.187)||
|ln_EDU2_|-3.044||1.273||5.134||
||(4.574)||(5.368)||(5.560)||
|ln_RENT_|0.128||0.220|*|0.197||
||(0.124)||(0.125)||(0.124)||
|ln_AGV_|-0.062||-0.141|***|-0.178|***|
||(0.047)||(0.047)||(0.057)||
|ln_TAX_|0.157||0.414||-0.169||
||(0.264)||(0.309)||(0.382)||
|ln_POWER_|1.814||1.789||1.513||
||(1.882)||(1.633)||(1.572)||
|constant|-23.998|***|-22.520|***|-9.798||
||(4.296)||(4.242)||(6.803)||
|Obs|118||118||118||
|λ|0.440|***|0.341|***|0.339|***|
||(0.116)||(0.095)||(0.079)||
|ρ|0.498||0.057||-1.106||
||(1.011)||(1.120)||(1.110)||
Khatlon (54 percent), and lowest (41 percent) in Regions of Republican Subordination (RSS). Awareness levels among women in Tajikistan also tended to be somewhat lower than among men.

**Figure 3: Awareness of the Different Consequences of Climate Change**

**(a) Tajikistan vs. other countries:**

<!-- Start of picture text --> 100<br>9 10 9<br>90<br>80<br>70 33 37 34<br>60<br>50<br>40<br>48 48<br>30 49<br>20<br>10<br>9 9<br>0 5<br>EU27 countries Kazakhstan Tajikistan<br>Very well informed Fairly well informed Not very well informed Not at all informed<br>Percent of respondents<br><!-- End of picture text -->

- **(b) Across Region: Those that are very well informed / fairly well informed**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40 81<br>30 60<br>54<br>20 41<br>10<br>0<br>Dushanbe Soghd Khatlon RSS<br>Dushanbe Soghd Khatlon RSS<br>Percent of respondents<br><!-- End of picture text -->

**Source:** Authors’ calculations based on P-LITS 2 data. Only 2 sample points (20 interviews) were conducted in GBAO: these have been grouped together with Dushanbe because of the similarity in pattern of response.

17. Respondents in the survey were also asked if they felt they were well-informed about (a) the different causes of climate change, and (b) ways in which we can slow down climate change. Awareness of the different causes of climate change is somewhat lower in Tajikistan compared to Kazakhstan as well as EU27 countries: about 35 percent of respondents in Tajikistan felt they were very well-informed/fairly well-informed about the main causes of climate change, as compared to around 56-57 percent in Kazakhstan and EU27 countries (Figure 4). Similarly, public awareness of ways in which climate change could be slowed down was also considerably lower in both Tajikistan and Kazakhstan (24-27 percent of respondents very well/fairly well-informed about ways in which climate change can be slowed down) as compared to EU27 countries (53 percent).

9
# **VI. Net Food Importers**

The vulnerability of net food importing countries to food price increases was acknowledged during the Uruguay Round Negotiations leading to a ministerial decision that special measures should be taken to minimize the negative effects of global reforms on food importing developing countries and Least Developed Countries (WTO, 1994). An FAO report on Trade Liberalization and Food Security points out that the majority of low-income countries, especially Least Developed Countries, are net food importers (FAO, 2002). This point has been highlighted by Panagariya (2006), who argues that global reforms that increase food prices would hurt these poor countries, especially the low-income countries in Sub-Saharan Africa (SSA), as well as the poor rural households in the poorest countries.

Evidence for these conclusions relies primarily on the analysis undertaken by Valdes and McCalla (1999) and McCalla (2001). These authors showed that the majority of developing countries are net food importers. But they also show that developing countries are heterogeneous in terms of their food and agricultural trade status.

Ng and Aksoy (2008a) have argued that this worry, while highly important, has been somewhat exaggerated. They analyze the food and agriculture trade balances of different groups of countries for the period after 1980. To minimize yearly fluctuations, they use two-year averages for the years 1980/01, 1990/01, 2000/01 and 2004/05, last year of comprehensive trade data, and report primarily on the trade balances in 2004/5. They define food as meats and dairy, grains, and fruits and vegetables.<sup>19</sup> Their results show that many low-income countries that have larger food deficits are either oil exporters or countries in conflict. The remaining low-income countries, as a group, have a trade surplus in food. If trade balances are measured using broad agricultural commodities, rather than just the narrowly defined food, then low-income countries, and the low-income countries in SSA, have a large agricultural trade surplus. Food deficits of most low-income countries are not that significant as a percentage of their imports.<sup>20</sup> On

> 19 There are problems even with this classification. In excluding processed foods, we have excluded items such as refined sugars, peanut oil, cocoa oil and paste, etc. Depending on in which form the products are exported makes a big difference in estimating net trade balance. For example, Senegal exports peanut oil which is in the category of processed foods and are not included in the food trade balance.  For exact definitions of food and agriculture, see Annex table 1.

20 Their results also show that only 6 low-income countries have food deficits (food defined narrowly) that are more than 10 percent of their imports. Of these 6, 1 is a large oil exporter, 2 are in conflict, and only 3 – Benin, Guinea-Bissau, and Senegal – are in this category. These three countries export other agricultural products. Benin exports cotton, Guinea-Bissau has a large trade surplus in nuts, and Senegal exports peanut oil. Of these three, only Senegal has an agricultural trade deficit.

22
data for a few Latin American countries which require frms to account separately for domestic and imported inputs that inventories of imported inputs are much higher. Second, a poorly functioning market can lead to shortages of certain goods; firms expecting these shortages would stock up on inventories in anticipation. In the Soviet Union, firms were known to maintain a high ratio of raw materials inventories to finished goods inventories for this reason.<sup>9</sup> Third, higher uncertainty of demand should lead firms to keep higher inventories according to the stockout model. Finally, the interest rates at which firms can borrow working capital determine the holding cost of the inventories. The higher the interest rates, the costlier are inventory levels; thus one would expect lower levels in equilibrium. Since developing countries have higher interest rates than developed countries, it is then, on that account surprising that their inventories are higher.

There are a number of additional factors that ideally should be included in the study but cannot due to a lack of cross-country data. If developing countries were more likely to use FIFO accounting while developed countries used LIFO accounting, their inventory stocks would appear to be higher, especially in cases of high inflation. Although we do not have evidence by country on this issue, our research on this topic indicates that LIFO, although allowed in the United States for tax purposes, is rare in both developing and other developed countries.<sup>10</sup> Other relevant factors are the degree of vertical integration, the concentration of upstream suppliers, production to stock vs. production to order, and the type of production technology. 1 ' We do s Since any additional orders would incur some fixed ordering costs, the firm may actually prefer a higher safety stock than indicated.

> 9 Chikan [1991] shows that socialist countries held a larger ratio of raw materials inventories to finished goods inventories.

> **10** See, for example, Nobes and Parker [1995], p.<sup>1</sup> 62.

11 For example, due to the fixed costs of rampup, it is more costly to run out of inventory in a continuous or batch process than in a discrete process.

10
in subsequent F2F interviews indicates that discussing sensitive topics in private initially helps respondents open up to enumerators later, or that respondents wish to maintain consistency in their responses. Overall, our results suggest that introducing respondents to sensitive questions privately, even if on a limited scale, is both feasible given high comprehension, and effective, as it helps respondents ease into difficult topics.

We implemented this experiment during in-person surveys from February to March 2023, involving over 6,000 currently married women from rural-poor households in the Layyah district of Punjab, Pakistan. All surveys were conducted by female enumerators.<sup>2</sup> Respondents belong to poor households which were within a +/- 5 bandwidth of a multi-dimensional poverty score cutoff used to determine eligibility for Pakistan’s national unconditional cash transfers program. All respondents are under the age of 60 years and the average age is 48 years. The vast majority of these women (93%) are illiterate. We relied on questions from the standard IPV module of the Demographic and Health Surveys (DHS), and asked 7 questions on experience of physical violence, 4 on sexual violence and 4 related to injuries due to violence.

The literature so far has focused on comparing the measurement of IPV using ACASI with F2F interviews by randomizing respondents to _either_ answer questions using ACASI or F2F surveys (Park _et al._ , 2021; Cullen, 2023; Peterman _et al._ , 2024). Our experiment exploits within subject randomization instead. Park _et al._ (2021) also report that ACASI based questioning was poorly comprehended by respondents. We successfully addressed this concern by making the ACASI interface more accessible to our respondents (i.e., illiterate, rural-poor women). Moreover, existing studies have generally found significantly higher rates of ACASI-based IPV reporting in Malawi, Rwanda and Senegal but the results are not always consistently higher across all types of IPV, and no significant differences were found for any type of IPV in Liberia (Park _et al._ , 2021). Our study explores a context outside Africa, where the majority of the evidence has been concentrated thus far. We also contribute to a broader literature on the use of innovative survey methods beyond

> 2Enumerators were trained to adhere to ethical protocols related to IPV, including sharing of referral services for IPV-related matters with the respondents. To protect their privacy in case perpetrators of violence within the family got a hold of these resources, we embedded information about IPV helplines in a broader manual on other health related resources.

3
additional non-reported robustness checks, I show that all results remain qualitatively unchanged when I omit households with non-matching land documents.

Figure 2 (a) illustrates the first-stage discontinuity in implemented debt relief for all households in the survey sample frame and Figure 2 (b) confirms that the same discontinuity holds in the sample of surveyed households. As an additional robustness test, Figure 3 presents evidence from placebo discontinuity regressions. The figure shows the absolute value of t-statistics for estimates of _τRD_ obtained at a range of placebo discontinuities _<u>xi ∈</u>_ [ _−._ 25 _,._ 25], and provides evidence that the discontinuity induced by the program indeed occurs at _<u>x</u>_ = 0.

Table 4 reports numerical estimates of the discontinuity in implemented debt relief. On average, households marginally below the two hectare eligibility threshold received Rs 37,156 (US$ 840) _more_ debt relief than households just above the cutoff. Placebo regressions reported in the subsequent panels show that the discontinuity occurs in the amount of debt relief that was granted, but not in the amount of borrowers’ overdue total balance, outstanding principal or outstanding interest in the vicinity of the eligibility threshold _<u>x</u>_ <u>. The summary figures also show that at Rs 44,037 (US$ 995), the discontinuity is</u> more pronounced for commercial than cooperative bank accounts (Rs 34,339 or US$ 776), which is not surprising, given that credit cooperatives generally cater to lower-income borrowers than commercial banks. Overall, the difference in relief at the discontinuity is statistically significant and economically substantial: the estimated difference in debt relief between treatment and control households in the survey sample (Rs 34,339) corresponds to approximately 77% of India’s 2010 annual per capita income of Rs 44,345 (US$ 1,002).

# **5 Data Description**

The analysis draws on data from two main sources. The first set of data consists of bank lists on all debt relief accounts in the sample districts. As a transparency measure, all banks and credit cooperatives were required to disclose details about all accounts qualifying for full or partial debt relief. This information was posted on public notice boards of local bank branches throughout the country, and several banks also published detailed beneficiary information on their websites. The second set of data comes from a detailed household survey of debt relief beneficiaries, conducted in late 2009, approximately one 14
_Sources: Beaudry and Sowa (1994): (1) Ewusi (1984); (2) and (3) Authors' calculations from the GLSS._ Evidence from the pattern of migration flows cannot really provide firm conclusions about the difference in the incidence of poverty. The absence of significant movement of labor from the rural to the urban economy need not necessarily imply that the poverty ratio is no worse in the latter. Migrants may respond to the expectation of employment _a la_ Harris-Todaro, or to the expectation of earnings growth. Alternatively, there might be serious impediments to the movement of labor due to economic or social barriers. On the other hand the existence of significant urban-to-rural migration would suggest that there is prima facie evidence of higher incomes, and hence lower incidence of poverty in the urban sector.

We have seen above that rural-urban migration was an important part of the economic landscape of Ghana in the years before its decline. Beaudrey and Sowa (1994) have put together data on net migration (i.e. inflow minus outflow) for different regions and periods. They are reproduced in Table 6.9.

Column (1) of the table indicates that prior to 1970 46.5 percent of net migration was directed towards Accra. The next two columns show that the pattern of migration was completely reversed in the 1980s. Instead of being a region of net inflow, Accra has become the major source of migrants. Between 1982 and 1987, net migration out of Accra accounted for almost 60 percent of net outward migration in Ghana. The main destination migrants has become the Western region, which is the region of expanding cocoa production. For some purposes information on the volume and pattern of gross migration might be useful. We produced tabulations for such migration as it took place over the last three years prior to the surveys of 1988-89 and 1991-92. These data are reproduced in Table 6.10 below.

# **Table 6.10: Migration between Urban and Rural areas in last three years, 1988-89 and 1990-91**

||_<br>1988-89|1990-91|
|---|---|---|
|Didnot migrate|78%|90.2%|
|Migrated from Rural areas|5.8%|3.9%|
|To Accra|0.4|0.2|
|To Other Urban areas|0.9|0.6|
|To Rural Areas|_4.5_|3.1|
|Migrated from Other Urban areas|14.5%|4.8%|
|To Accra|1.8|0.7|
|To Other Urban areas|3.6|1.6|
|To Rural Areas|9.2|2.5|
|Migrated from Accra|1.7%|1.1%|
|To Accra|_0.5_|_0.5_|
|To Other Urban areas|1.0|0.6|

48