rafmacalaba/datause-encoder-data huggingface dataset:
our primary focus is on the migration decision, we make use of individual level information on labor allocation decisions of current and past household members from 1995 onward. Individual‐level data collected in the supplementary survey included retrospective information on the current residence, migration and employment history of all individuals who had been household members at any time since 1995. By including information on former residents, primarily adult children of the household head who left after the 1995 base year, the supplemental survey allows us to complement household‐level information from the annual surveys and makes it possible to examine individual migration decisions over time. At the village level, the supplemental survey provides detail on family lineage groups, significant land reallocations since 1991, land use rights, village elections, and turnover of village leaders. The key respondents at the village level include current and former village leaders, village party secretaries and village accountants, and so while the survey is retrospective, election timing is asked of individual who were present in the village and for whom elections were salient. The supplementary data are matched with the RCRE regular household panel survey data from these four provinces, and allow us to characterize future reallocation risk based on the gross share of land subject to reallocation in the following year.<sup>11</sup> From the RCRE household panel, we obtain information on annual household consumption and land holding. Further, the RCRE village panel includes many of the descriptive statics on crime and conflict that inform our understanding of how village cohesiveness may vary with the size of the largest lineage group.

In the supplemental survey, a group of respondents at the village level identified years in which significant land reallocations were carried out at either the level of the village or within several village

> and supplemental village surveys conducted in eight provinces (2004 Village Governance Survey and Land Use Survey).

> 11 While the annual surveys allow one to distinguish differences in household managed land across years, documentation accompanying the survey makes clear that these differences may reflect either land transferred informally to family members or changes due to administrative reallocations. Further, year‐to‐year changes reflect net changes to land, not the gross amount of land that was subject to reallocation.

_Land Tenure Insecurity and Migration, Page 13_
PROVISION OF WATER TO THE POOR IN AFRICA hypothesized that efficient coverage and functioning of formal networks is associated with lower informal prices (figures 3.6 and 3.7). The burden of service disruption is unequally shared between the connected and the unconnected households, because connected households enjoy the low price charged by the utility, whereas the unconnected have to face the high prices associated with the rest of suppliers. For this reason, if one wants to assess how distorted the market is for the unconnected versus the connected, the WAIO can also be calculated just for the unconnected population.<sup>25</sup>

**Figure 3.6    Urban WAIO (HH consumption 4m3/month)**

<!-- Start of picture text --> 25<br>20<br>15<br>WAIO = 1<br>10<br>5<br>0<br>Total Unconnected<br>Figure 3.6 Urban WAIO (HH consumption 10m3/month)<br>25<br>20<br>15<br>WAIO = 1<br>10<br>5<br>0<br>Total Unconnected<br>Burkina FasoSouth Africa NamibiaMozambique Ethiopia NigerTanzania Zambia GhanaRwandaSenegal Benin Lesotho Malawi KenyaMadagascarCote d'Ivoire<br>Burkina FasoSouth Africa NamibiaMozambique Ethiopia NigerTanzania Zambia GhanaRwandaSenegal Benin Lesotho Malawi KenyaMadagascarCote d'Ivoire<br><!-- End of picture text -->

**Figure 3.6    Urban WAIO (HH consumption 10m3/month)**

Source: AICD WSS database 2007.

25 See Appendix 5-2 34
US government. In contrast, within a developing country context, we document the effects of informational treatments that emphasize past government success and support from nonstate actors on a wide range of beliefs and attitudes about the state, including perceptions of state capacity and trust in elected and non-elected state officials. Importantly, while applied in the context of the Covid-19 pandemic, our informational treatments represent more general communication strategies deployed by governments in a variety of scenarios.

More generally, our results speak to a growing literature on the role of citizens’ trust in state institutions in shaping state capacity.<sup>4</sup> Aside from focusing on a high stakes context offering an opportunity to elicit direct measures of citizens’ compliance, we also expand the types of informational treatments used to manipulate attitudes towards the state. The fact that we estimate null treatment effects does not render our results less important, especially since our study design was adequately powered to detect effects on self-reported behavior and post-treatment survey questions indicate that a large share of the respondents in all treatment arms retained the content of the informational treatments. Our findings thus highlight limitations to efforts to shape trust in state institutions through informational campaigns.

# **2 Experimental Design**

## **2.1 Sample**

We conducted our survey experiment with 5,771 residents of Lahore and Faisalabad over five weeks in May and June 2020.<sup>5</sup> The number of new coronavirus cases in Punjab peaked during this time, rising from approximately 1,500 new cases each day at the start of the experiment to 4,000 at the end.<sup>6</sup> We recruited individuals from a pool of 15,000 phone numbers, collected in an ongoing study on property tax and public goods provision (Khan et al., 2020). The phone numbers were randomly ordered and assigned to control or treatment groups. Surveyors called each phone number in the assigned order. If the respondent answered and consented, the surveyor initiated the survey. 47% of the calls were not answered and 21% of

> 4In economics, this literature builds on the literature studying the role of civic capital in influencing economic development (Guiso et al. (2016, 2011, 2008, 2004), Knack and Kneefer (1997), Putnam et al. (1994)). The importance of citizens’ cooperation for the development of state capacity has been highlighted theoretically in models of states that derive authority from citizens who have the capability to rein them back (Acemoglu and Robinson, 2019). Relatedly, Dell et al. (2018) argue that, historically, the greater state capacity of the North Vietnamese state relative to that of the South Vietnamese state is related to the greater cooperation of citizens with the state which in turn could depend on citizens’ trust in the state.

> 5 Our study protocols were reviewed and approved by the Institutional Review Board of the Lahore University of Management Sciences (Protocol Number: LUMS-IRB/004202020SN).

> 6The number of new cases started to decline after our experiment.

6
8 The first conclusion qualifies the standard tax-subsidy equivalence result usually demonstrated in the literature for emission based subsidies rather than for technology based subsidies. At very low levels of effective tax rates, taxes and subsidies are actually complements rather than substitutes. It is only at higher tax rate levels that they become substitutes from the firm's perspective.9 From a practical point of view, it is a crucial result as in many of the countries considering subsidies to polluters for investment in abatement, there is no pollution tax. The failure to combine the introduction of subsidies, whether from SEPA or FEPA, with the adoption of pollution taxes, at any government level, is unlikely to prove successful in terms of pollution reduction unless the firm already had the intention of investing in abatement. In fact, to be conservative, the model suggests that it is probably wiser to start a reform in pollution management in Brazil with the introduction of the tax to establish the credibility of reform intentions. Once the tax is being enforced, it makes sense to consider subsidies to cut the effective tax rate.

# ii. The SEPA's problem

The state government wants to maximize the sum of sales and pollution tax revenue net of the costs of: (i) subsidies to firms, (ii) monitoring the firms and (iii) fines due to the federal government if the FEPA finds out that the SEPA has not monitored the firm. The monitoring cost is assumed to be fixed. The Federal government may be willing to share the costs of monitoring the firm by providing a subsidy for resources spent on monitoring by the states. It represents essentially the cost of sending people to the polluting factory and the costs of the laboratory analysis of the samples collected. The tax and subsidy rates are imposed on the SEPA. Or equivalently, they have to be set an identical rate for all sectors for administrative or political reasons and can hence not bet selected as a 9 The case for tax-cum-subsidy solution for pollution control has already been argued for in the literature. See for instance, Yohe and MacAvoy (1987) and for a survey of the literature on taxcum-subsidies Kohn(1990)
WPS3795 level of defaults (Miller 2003). Commercial credit bureau data have also been shown to have power in predicting firm failure beyond financial ratios and other descriptive information about the firm (Kallberg and Udell 2003).

There is considerable variation across countries in terms of the existence of credit bureaus, whether they are publicly- or privately-owned, and the coverage of available information (Miller 2003). Empirical evidence suggests a statistically important link between the existence of third-party information exchanges and credit availability (Pagano and Jappelli 1993, Padilla and Pagano 1997, Love and Mylenko 2003). Specifically, countries with stronger formal information sharing exhibit greater bank lending relative to GNP and country-level credit risk is negatively correlated with measures of formal information sharing (Jappelli and Pagano 2002).

Credit bureau information, where available, is used in conjunction with all of the lending technologies, but it is a driving component of one of the lending technologies, small business credit scoring. It is necessary to have a large database on SME loan performance and the variables used to predict that performance in order to estimate a credible credit scoring model. In the U.S., most banks use external vendors to create the scores, and these vendors rely on a strong information environment in which credit bureaus share consumer and business information and financial institutions share their loan performance data to estimate the model. Some large institutions are also able to generate proprietary models based on their own loan performance experience, but still need information from credit bureaus for the exogenous variables in their models. The strong information environment requirements may help explain why small business credit scoring appears to be used on a widespread basis only in a limited number of developed nations (Miller and Rojas 2004).<sup>13</sup> The evidence also indicates that access to historical credit information such as business credit bureaus is positively related to the presence of factoring (Klapper 2005). Under certain circumstances factoring can also work well even in weak domestic information environments if the receivables are from obligors located in strong information environments. For example, the receivables of an Estonian firm whose customers are located in Germany may qualify for factoring because the factor can efficiently assess the creditworthiness of

> 13 There are some efforts to expand the use of this lending technology in developing nations.  The World Bank and Fair, Isaac have recently formed a joint venture to determine the feasibility of developing a pooled data small business credit scoring model using data from Brazil, Columbia, and Mexico (Miller and Rojas 2004).

15
MINIMUM WAGES IN CAMBODIA 24 <!-- Start of picture text --> HH with garment worker HH without garment worker<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantiles of wage distribution<br>Point estimates 95% CI<br>.2<br>.15<br>.1<br>.05<br>0<br>-.05<br>Quantile treatment effects<br>-.1<br>-.15<br><!-- End of picture text --> Figure 8. Quantile treatment effect of minimum wage hikes on hourly wage rate _Source_ : Author’s calculations from Cambodia SES 2007-2015.

_Note_ : The figure plots the estimate of _α_ 2 + _β_ 2 (left panel), and _α_ 2 (right panel) from from quantile treatment effect estimation of Equation (6) for total wage earnings at the household level. Quantiles are indicated by the horizontal axis. Sample restricted to those households with some wage income. Sample further restricted to households surveyed within 8 quarters of any of the minimum wage hikes. In addition to controls mentioned in the notes for Table 1, the estimates also control for garment sector specific time trend for each location, and garment sector specific global recession shock.

slightly increases by 0.14 (4 percent) and the hours worked by all its members fall by 3.6 percent (columns (3) and (4), Table 3). For household without a garment sector worker, there are no changes in either the number of jobs, or on hours worked by its members.

On the other hand, minimum wage hike does not significantly change total household wage earnings. In the 2+ quarters after the hike, total household wage earnings are not significantly different from their pre-hike levels for households with or without garment sector workers (column (5), Table 3). Households without a garment-worker do not observe any change in total wages, whereas households with a garment-worker observe a statistically insignificant decline of 2 percent. This is again, consistent with individual level results which show that the gains in earnings from the garment sector workers are negated by the loss of wage earnings from non-garment sector workers in the same households. This suggests that though minimum wage hikes changes wages and earnings at the individual levels, the overall gains at the household level is insignificant due to labor supply and earnings response of other household members.

These effects are not just the impact on the average, but also throughout the distribution of household wage earnings. Quantile treatment effect estimates suggest that for households
children as a comparison group (Online Appendix Table B4), measuring the effect on labor force participation (Online Appendix tables B5, and B6 with age restriction), or including five- and seven-years-old children in the treatment group (Online Appendix Table B7).

# **5.2 Heterogeneous and Subgroup Treatment Effects**

In Table 3 we test if the reform differs across groups of women to shed light on some of the mechanisms that could drive the increase in maternal employment of mothers with six-yearold children.

Table 3: The 2012 reform effect on employment rates by groups

||Rural|Child|Elderly|Partner in HH|Employed in HH|Self-employed|
|---|---|---|---|---|---|---|
|Mom(six-year-old) _×_ Post2012 _×_ Group|0.0420|0.0425|-0.0165|0.0388|0.0426|0.0366|
||(0.0138)|(0.0128)|(0.0195)|(0.00842)|(0.0127)|(0.0169)|
|Mean (Dep. Var)|.612|.612|.612|.612|.612|.612|
|St. Dv. (Dep. Var.)|.487|.487|.487|.487|.487|.487|
|Demographic controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|
|State fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|
|Region by year unemployment rate|Yes|Yes|Yes|Yes|Yes|Yes|
|N|26114|26114|26114|26114|23836|24601|

Standard errors in parentheses Source: EU-LSF (2010-2019). Standard errors are clustered. The sample consists of mothers of children between 3 and 4 or 6 years old.

Given the rural-urban divide on many development indicators in Romania, we test if mothers of six-year-old children living in rural areas benefit differently from the program than the ones living in urban areas. Figure 4 demonstrates that there is a clear urban-rural gap in the case of mothers of six-year-old and three- to four-year-old children in Romania. In addition, Figure 5 shows that the number of creches in rural areas is significantly lower than the number of creches in urban areas. As nearly half of the population in Romania lives in rural areas, these results indicate that mothers of children of pre-school age face significantly more constraints in rural than urban areas. The coefficient in Column 1 is positive and significant. These results indicate that exposed women in rural areas benefited more on average, which might be related to the fact that they had less access to public childcare previous to the reform.

13
or, equivalently, as a factor-augmented VAR with coefficient restrictions For the general case **A** (L) = **0** and **B** = **0** , estimation proceeds in two steps (see Stock and Watson, 2005; Song, 2013). We first estimate **A** (L), **B** , and **F** t doing OLS iteratively on (17). Specifically, we start with an initial guess for **F** t and update it with the first r principal components of �∆ **y** t − **A**<sup>**ˆ**</sup> (L)∆ **y** t−1 − **B**<sup>**ˆ**</sup> **F**<sup>**ˆ**</sup> t�. We use the information criteria of Bai and Ng (2002) and Choi (2013) to determine the number of static factors r (with r = q(s + 1) ). To perform inference on **A** (L) and **B** , we use the results of Bai and Ng (2006, 2013).

At the second step, we estimate **D** (L) in (18) by OLS using the estimated **F** t from the previous step. We use standard information criteria to determine the order of **D** (L) (generally given by max(1, h − s); see Bai and Ng, 2007).

Using these estimates, the quasi-innovations to the present value of income growth can be constructed in a straightforward manner using income growth forecasts computed recursively with (19).

# **3.3 Data**

The empirical sample is dictated by data availability. We work with the 50 largest countries in the world in terms of their respective GDP (in U.S. dollars in the year 2005) for which complete annual data on consumption and income, as well as measures of de jure and de facto financial openness (as described below) could be assembled. Table B.1 in the appendix lists the countries included in the analysis. Taken together, they account for almost 90 percent of world GDP in the year 2005.

Data on real GDP growth, aggregate consumption growth, and total population are taken from the Penn World Tables 7.1 (PWT 7.1). Along the time dimension, our sample runs from 1970 to 2010. We use the 1970-71 averages to construct the initial shares **W0** and population **H** .<sup>18</sup> The regression sample therefore runs from 1972 to 2010, and comprises 1,950 country-year observations. As explained in Section 2, for the calculation of the quasi-innovation to the discounted present value of income growth, we set ρ = 0.97.

For our analysis of the covariates of risk sharing, we measure countries’ international financial integration using data on financial openness drawn from the Chinn-Ito dataset, updated to 2012, as well as international asset and liability positions from the Lane and 18Estimation results change very little if we use instead the averages over 1970-74 or the year 1970 only.

15
# **1 Introduction**

Tax incentives are a ubiquitous component of policies aiming to attract investment and boost exports. OECD (2022) estimates that in 2022, 87% of the 52 developing economies surveyed had at least one type of Corporate Income Tax (CIT) exemption, while 69% and 65% had at least one type of reduced CIT rate or tax allowance, respectively. These incentives are economically significant. According to data collected by Redonda et al. (2024), tax relief schemes to businesses amounted to 1.4% of global GDP and 7.8% of global tax revenues in 2021.

In spite of the large footprint of these incentives, there is limited evidence on their causal impact on economic outcomes. A key reason is that changes in taxation are often bundled within broader policy packages that provide a wide range of benefits for affected firms. Special Economic Zones are a case in point. These regimes often include preferential customs rules, flexibility in labor regulations, and a range of tax benefits such as exemption from import duties, VAT on local purchases and, often, exemption on CIT. As the precise mix of benefits being offered varies among countries and over time, any assessment of one specific intervention will necessarily bundle all of these benefits together.

In this paper, we make progress in addressing the paucity of causal evidence by leveraging the phasing out of a large CIT exemption for export-oriented firms in Tunisia, during a period in which all other benefits remained unchanged. Tunisia is a lower-middle income country that has extensively relied on special regimes, the most important of which is the so-called _offshore regime_ . This entails radically different institutions and rules for firms that export the majority of their sales. Until 2013, these firms were subject to a zero CIT rate on their profits, whereas _onshore_ firms (i.e. all other firms outside the regime) faced a 30 percent CIT rate. This was the largest tax exemption scheme in Tunisia in the past decades.<sup>1</sup> Our identification leverages a large, arguably unanticipated policy shock. After almost a decade-long process to reduce the tax disparities between the two regimes, the CIT

> 1 According to our (upper-bound) estimates, it cost up to 6.8% of GDP in foregone tax revenues.

2
together, these results are consistent with the idea that even limited intertemporal storage may act as a buffer on local market producer price fluctuations for less perishable commodities.

A concern with the producer price data used in these estimations is the number of missing observations. Because demand for staple grains and cowpea is relatively constant throughout the year, consumer price data are readily available for each market and each month (Aker 2010). In contrast, farmers in Niger do not have sufficient stocks to sell throughout the year. As a result, producer price data are not available for some markets during certain periods of the year, especially during the season immediately prior to the annual harvest.

To check the robustness of our results to potential selection bias generated by missing data, we re-estimate equation (15) in two different ways. We first use a two-stage Heckman procedure, estimating a selection equation on market-pair data and adding the resulting inverse Mills’ ratio as a separate regressor to equation (15) (Heckman 1979). These results are shown in the supplemental appendix (Tables S1 and S2, available at <u>http://wber.oxfordjournals.org/). The results for cowpea are similar in</u> magnitude and statistical significance to those reported in Table 5. We also re-estimate equation (15) using a balanced panel of market pairs that have a full set of price data for all time periods in the sample (Table S2, columns 5-8). The point estimates are slightly smaller, but the coefficient estimates remain significant at the 1 percent level.

It is important to note that our results for millet _producer_ price dispersion differ from the results of Aker (2010), who found that mobile phone coverage reduces _consumer_ price dispersion for millet by 10 percent (Aker 2010). Both findings are consistent if millet is stored primarily in production areas. When there are insufficient stocks in consumer markets, unanticipated demand shocks are a source of price fluctuation. The effect of shocks on consumer prices can only be smoothed if traders can quickly determine where to send more supplies. Using the theoretical model in reverse, by facilitating the efficient allocation of millet from producer to consumer markets, mobile phones can explain the reduction in price dispersion across consumer markets.

16