rafmacalaba/data-use-mentions-extended huggingface dataset:
percent of people getting agricultural payments receive them into an account, in most cases a mobile money account.

In the developing economies of ECA, only about 10 percent of adults received payments for agricultural sales the year before the 2014 and 2017 Findex surveys. In 2014 only 5 percent of the agricultural product sellers reported having received payments via an account, and none reported receiving payment through a mobile phone. In 2017 these figures rose to 20 percent and 8 percent, respectively.

## _Domestic Remittance Payments_

Domestic remittances play an important role in many developing economies. In the developing economies of ECA, the share of adults sending or receiving domestic remittances increased from 22 percent in 2014 to 29 percent in 2017. Forty‐eight percent of these senders or recipients used a financial institution account in 2017, up from 33 percent in 2014. The share of senders or recipients using mobile phones increased from 4 percent in 2014 to 24 percent in 2017, and the share using cash or making transfers in person declined from 51 percent to 26 percent.

A few countries stood out in this trend. In Russia, for example, the share of domestic remittance senders or recipients using mobile phones soared from 8 percent in 2014 to 38 percent in 2017. The share using financial institution accounts increased by 10 percentage points, to 48 percent, over the same period. The proportion using cash fell from 36 percent to 14 percent.

In Turkey the increase in the use of accounts and mobile phones for remittances has been remarkable, rising from 35 percent in 2014 to 56 percent in 2017. Over the same period, the share of remittance senders or recipients using mobile phones skyrocketed, from 2 percent to 24 percent, and the share sending or receiving cash in person dropped from 60 percent to 23 percent.

# Use of Digitalized Accounts Is Also Rising

Account ownership is key to financial inclusion. But secure and convenient ways to use accounts are essential if users are to reap the full benefits of account ownership. The Global Findex database also sheds light
set of export-oriented firms benefiting from several preferential policies. We estimate that this policy could have cost up to 6% of GDP in foregone tax revenues.

Despite observing a substantial decline in the number of firms in that sector, driven by decreased entry, we do not find meaningful economic effects in terms of employment or revenue. That is true in aggregate terms, but also when we restrict our analysis to a balanced panel of firms that are active throughout the analysis period. As these incumbent firms account for the bulk of economic activity in Tunisia, the null effect of the reform on this sample helps reconcile the negative impact on entry with the zero impact on economic activity.

Our setting provides an important opportunity to evaluate how one specific component of the preferential policies in export-oriented regimes – namely, reduced CIT rates – affects economic activity. In that sense, our findings should not be interpreted as speaking to the overall impacts of the _existence_ of export-oriented regimes. Instead, we evaluate how increase CIT rates on firms in these regimes affect entry and economic activity decisions, holding constant a range of other existing benefits. In the specific case of _offshore_ firms in Tunisia, our results suggest that the previous CIT exemption did not boost employment or revenue.The results are in line with survey evidence among global investors that show that incentives are second order factors in deciding the location of investments relative to leading country characteristics, such as political and economic stability, a transparent regulatory framework, availability of skills, infrastructure quality and a large local market (World Bank, 2017; UNIDO, 2011).

32
Table 2: MATCHING IN TRANSFER NETWORKS AT BASELINE

||Ultra poor<br>(1)|Other poor<br>(2)|Middle class<br>(3)|Upper class<br>(4)|
|---|---|---|---|---|
|_Panel A:_|_Percentag_|_e of households_|_receiving trans_|_fers from ..._|
|Ultra poor|17.60|9.70|4.41|1.26|
|Other poor|32.58|39.51|17.49|6.00|
|Middle class|65.57|66.08|68.78|23.52|
|Upper class|21.33|23.50|21.51|26.14|
|_Panel B:_|_Percent_|_age of househo_|_lds giving trans_|_fers to ..._|
|Ultra poor|14.05|8.83|7.25|11.51|
|Other poor|21.60|31.43|21.76|28.89|
|Middle class|28.30|36.09|60.25|51.74|
|Upper class|3.12|5.26|11.23|28.76|
|Observations|6,732|7,340|6,742|2,215|

**Source:** Author’s analysis based on original survey data.

**Notes:** The sample includes observations from the baseline survey. In column 1, the sample is restricted to ultra-poor households; in column 2, to other poor households; in column 3, to middle class households; and in column 4 to upper class households. Panel A gives the percentage of households receiving transfers from the relevant group; Panel B gives the percentage of households giving
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 3. Benin’s road indicators**

|Indicator|Unit|**Low-**<br>**income**<br>**countries**|**Benin**|**Middle-**<br>**income**<br>**countries**|
|---|---|---|---|---|
|Classified road network density|km/1000 km2 of land area|88|75|278|
|Total road network density<sup>a</sup>|km/1000 km2 of land area|132|142|318|
|GIS rural accessibility index|% of rural pop within 2 km from all-season road|25|28|31|
|Main road network condition<sup>b</sup>|% in good or fair condition|72|69|86|
|Rural road network condition<sup>c</sup>|% in good or fair condition|53|66|65|
|Classified paved road traffic|Average annual daily traffic|1,131|2,041|2,451|
|Classified unpaved road traffic|Average annual daily traffic|57|63|107|
|Primary network overengineering|% of primary network paved with 300 AADT or less|30|10|18|
|Primary network underengineering|% of primary network unpaved with 300 AADT or more|13|34|20|
|Perceived transport quality<sup>d</sup>|% firms identifying transport as major business constraint|28|42|18|

_Source_ : AICD road sector database on 40 Sub-Saharan African countries accessed June 2010.

a. Total network includes the classified and estimates of unclassified and urban networks.

b. Main network for most countries is defined as result of adding the primary and secondary networks.
with the Objective Economic Decile being the decile of consumption or the decile of asset ownership. The sample consists of the repeated cross-section of the three rounds of the Life in Transition Survey. Limits to this method have been discussed by Ravallion (2012). Given the ordinal nature of the dependent variable, we follow Schroder (2017) by comparing both ordered logit and OLS results of the estimates. The variables that included in the regression are:

- Individual characteristics: gender, age, education, rural versus urban, family characteristics (number of members, marriage, share of females, share of old persons), the work profile (having worked in the last 12 months or not).

- Individual beliefs and perceptions: type of religion, the main reason why there are people in need, the most important factor to succeed in life, views on the market economy, perceptions about past intergenerational mobility and perceptions that the next generation will do better.

- Objective ranking: per-capita consumption per country or assets.

- Country and time dummies: _δc_ for country c and _δt_ for time t.

The results of table 4 show that, as expected, there is a correlation between the objective economic decile and the subjective ranking in the economic ladder - but most of the non-income factors included in the regression also have a significant correlation with the subjective ranking. In this sense, individuals’ perception about their economic welfare is associated to a combination of individual and social circumstances beyond their objective economic situation. The next paragraphs provide a more detailed description of these results.

Table 4: Perceived decile and non-income factors

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||O. Logit<br>Dep. var|O. Logit<br>iable: perc|OLS<br>eived econo|OLS<br>mic decile|
|Objective decile (
fairly uncontroversial: there was a dramatic reduction in the money supply of about 33 percent between 1929 and 1933,15 and this had a major impact in the transmission of a financial shock into a full blown economic depression. Where scholars’ views vary is in explaining the monetary contraction. As noted in Hsieh and Romer (2006), there are two general views on what actually happened: (1) the monetary contraction was the result of the international monetary system’s adherence to the gold standard; and (2) the United States could have enacted expansionary monetary policy despite the gold standard, so the contraction was a monumental policy blunder.<sup>16</sup> Under the gold standard, expansionary monetary policy via low interest rates had the potential to spur a speculative attack on a currency and to force an adjustment in gold parity—or even a full blown abandonment of gold convertibility. Hsieh and Romer (2006) use data on forward exchange rates and cross-country interest differentials—as well as the “narrative record” — to see if there was in fact an 12 There is a general view in the literature that the gold standard, with its implied fixed exchange rates, worked relatively well in terms of macroeconomic stability during the pre-World War I period, but this changed during the Inter-War period (discussed below). Chernyshoff et al (2009) provide empirical evidence on this view, with a driving force being the change in the degree of nominal price rigidities between the two periods.

13 Given data limitations, DeLong (1999) uses US data on railroad construction to show the magnitude of the depression. The crisis included an international finance dimension due to the collapse of Barings bank and its exposure to Argentine and Brazilian sovereign debt. DeLong (1999) and Fishlow (1989), among others, have drawn comparisons between this crisis and the debt crises in emerging markets of the 1980s and 1990s. Mitchener and Weidenmier (2008) provide a more detailed analysis of the 1890s financial crisis’ impact
cases, not compatible with the nine-to-six workdays. In fact, most of the school systems in developing countries work on a half-day basis.<sup>2</sup> Female labor participation has experienced significant growth in Chile. The rate of female participation increased from 35 percent in 1990 to 49 percent in 2011 (in women from 15 to 65 years old). However, the situation of women in the Chilean labor market is still poor, particularly for women with lower income levels. Women who belong to the poorest 20 percent of the country’s population present a participation rate of 32 percent , while those from 20 percent of the wealthiest households achieve rates of 67 percent.<sup>3</sup> Female labor participation in Chile has been relatively late and is one of the lowest in Latin America in spite of the comparatively high levels of education that Chilean women achieve.<sup>4</sup> There are important reasons to study determinants of female labor supply in a developing country. First, the access of women into the labor market should have positive effects for the most vulnerable families as it represents a new source of income for these households and thus could lead to a reduction in poverty (Ganuza et al. 2001). Second, despite the fact that greater opportunities have emerged for women, there are still significant gaps between the participation of men and women in all areas and discriminatory cultural patterns persist (Antecol 2003; Araújo and Scalon 2005; Contreras and Plaza 2010).

> 2. However, there are various initiatives that promote the implementation of the full school day. In Latin America, Chile, Argentina, Uruguay, and Venezuela has promoted different policies to expand the school day. 3. Chilean Socio-Economic Characterization Survey (CASEN) 1990–2011.

> 4. According to ECLAC statistics on gender, education, and training for women (2007), economically active women (15 and over) in urban areas in Chile have an average of 11.6
_Table A2: Impact of two deprivations statuses (extreme & moderate) on poverty rates, preferred scenario_

|||**National poverty headco**<br>|**unts**<br>|
|---|---|---|---|
||**Others-**<br>**dimensions poor**<br>**(%)**|**Omitted**<br>**poor**<br>**(%)**|**Consistently**<br>**poor**<br>**(%)**|
|Extreme deprivation considered|34.9|15.7|19.1|
|All non-monetary deprivations are||||
|moderate|25.0|11.8|13.2|

_Source:_ Authors’ estimates based on data from NLSS 2019 _Note_ : The table presents other-dimensions poverty rates in Nigeria i) under the baseline scenario, ie, when the two-thresholds cutoffs (extreme and moderate deprivation) are applied for each non-monetary dimension and ii) under a modification of the baseline deprivation mapping such that only a one-threshold cutoff is applied for each non-monetary deprivation (that is, only the moderate deprivation status is considered: any extreme non-monetary deprivation is converted into a moderate deprivation). The overall other-dimensions poverty is decomposed into omitted poor and consistently poor to account for individuals who are both monetary and other-dimensions poor.

39
# **CONCLUSION**

Even as international trade in services has grown in importance, the systematic analysis of services trade policy has been inhibited by the dearth of internationally comparable data over time. The Services Trade Policy Database, developed jointly by the World Bank and the WTO with substantial inputs from the OECD, contributes to filling this gap. It provides comparable information on services trade policies for more than 68 economies on five key service sectors and, for 55 of those, at two points in time (2008-11 and 2016). While the rich policy information is the backbone of the Database, the qualitative information has been quantified to obtain Services Trade Restrictions Indices (STRI), building on the methodology previously used by the World Bank. A key methodological improvement is the aggregation of different applied policy measures on the basis of a Constant Elasticity of Substitution (CES) functional form, which offers a parsimonious, flexible and transparent way of calibrating the marginal contribution to restrictiveness of an additional trade policy measure.

Building on the policy data and the new STRI, the technical details of which are discussed in-depth in Borchert et al. (2019), the main contribution of this paper is to provide a response to two questions. First, what is the current pattern of services trade policy? Second, how has services trade policy evolved after the 2008-09 financial crisis?

Today, most economies covered by our analysis are fairly open to services trade (i.e. STRI value below 50), and none is highly restrictive at the overall economy level. Higher income economies are still more open on average than lower income economies in most sectors. Interestingly, some of the most restrictive policies today are enforced in some of the fastest-growing economies of Asia, the Middle East and Northern Africa.

Looking across sectors, distribution and telecommunications services appear as the most open, on account of the openness of wholesale distribution (compared to retail) and the continued regulatory reform of the telecommunication sector over the last 25 years. At the other extreme
in moderate poverty. This decoupling between the trends suggests that the poorest of the poor was not able to escape extreme poverty while those among the poor that were closer to the $5 line moved upward after 2011.

The overall findings are consistent with the poverty trends measured in consumption terms (Azevedo and Atamanov, 2014), although it is clear that an income based poverty measure, as one would expect, is able to capture the effect of the global financial crisis 2007-2009 much better than a consumption based poverty number. This results from the ability of households to make decisions that can smooth consumption, while wages and other sources of income cannot be similarly smoothed. As a result, during the financial crisis, Turkey experienced a larger increase in income poverty in comparison to consumption based poverty. An additional factor behind the difference of the two poverty measures might be the increasing level of household debt in Turkey, which almost tripled in real terms between 2006 and 2013. If households spend more than they earn, there could be a faster decline in consumption based poverty or even a decoupling between the trends of income and consumption measures (Figure 14).

At the NUTS2 level, 21 of 26 regions experienced moderate poverty reduction between 2006 and 2013 (Figure 1). Moderate poverty reduction was heterogeneous in Turkey both in terms of geography and the initial rate of poverty in the region.<sup>10</sup> Three of the five NUTS2 regions (TRA1, TRA2, and TRB1) that had increasing poverty rates between 2006 and 2013 were already among the poorest regions of Turkey in 2006. On the other hand, the three poorest regions (TRC1, TRC2, TRC3) in 2006 were successful in declining poverty over the next seven years, which suggests that the poverty reduction story in Turkey is too complex to pin down to one specific factor such as initial rate of poverty. Moreover, the two other regions that experienced an increase in poverty, TR10 and TR42, had significantly lower poverty rates than the national
within-siblings variation is very long and covers many fields in economics (see e.g. Krashinsky, 2008) measures family effects on voting preferences). However, when applied to siblings rather than twins, one should recognize that the idiosyncratic component which cannot be removed may be relevant. Griliches (1979) and Neumark (1999) caution that neglecting the individual component might induce a distortion in within-siblings estimates even larger than in pooled OLS estimates. To limit this problem we have included among the regressors the order of births of the siblings, which in combination with age should capture differential allocations of resources within the family as well as differential expositions to the family background. In Section 6 we specifically address this concern more in detail, by approximating the ideal case of twins by restricting the sample to the siblings whose age difference is at most two or three years.

We have extracted from the EHHUES2002, which counts about 27,000 observations of people aged 16-64 (working age according to Egyptian regulations), the subsample of all heads’ sons and daughters residing in each household from which we have removed the only sons. The resulting sample, labeled “sons”, is then composed of at least two siblings per household. Furthermore, we have extracted from the full sample the sample composed of household heads and of their own brothers, exploiting the detailed information on household members’ relationships. In Egypt, as in many middle-income countries, it is rather common to find extended families, where several generations live together. We label the resulting sample “head/bros”. The union of “sons” and “head/bros” produces the sample “siblings” that we have used throughout the paper. In Table 1 we have reported some key summary statistics referred to “siblings”, to its component subsets, to the full sample of the aged 16-64 and to an additional sample composed of the “only sons”. The latter is the sample of heads’ sons who live with the household heads and have no
# **1. Introduction**

Much of the literature in public finance focuses on the role of tax reforms in affecting firm behavior. Policy makers in many countries use tax incentives to encourage firm investment, and China is no different. In China, the value-added tax is the major source of fiscal revenue for the Chinese government, generating much more revenues than any other types of tax. In 2002, the revenue from value-added tax was 814.4 billion RMB, accounting for around 48% of the state total tax revenue in that year. In 2009, the Chinese Ministry of Finance estimated that VAT revenue accounted for approximately 31 percent of China’s overall revenue.<sup>1</sup> Beginning in 2004, the Chinese government implemented a value-added tax reform in three northeast provinces which removes fixed asset investment from the value-added tax base. The reform has since been extended to the whole country, beginning in 2009. The objective of the 2004 reform was to encourage firms to raise investment on fixed assets for production (excluding structures) and to upgrade their machinery and equipment. The goals of the 2009 reform were similar, but in addition the government expressed the need to provide additional assistance to domestic enterprises to help them weather the adverse effects of the crisis, as well as to encourage fixed asset investments to promote an industrial policy now focused on more technologically advanced sectors.<sup>2</sup> In this paper, we use a firm-level panel dataset ranging from 1998 to 2007 to identify the effect of the 2004 value-added tax reduction in selected provinces on firm behavior,

> 1 See www.dorsey.com/china_vat_reform

> 2 According to the People’s Daily Online, December 9, 2008, quoting Zheng Jianxin, deputy director general of the taxation department of China’s Ministry of Finance, “The VAT reform would encourage investment and technological upgrading at Chinese companies, boost domestic demand, improve companies’ competitive strength and play a positive role in helping companies tackle the financial
<!-- Start of picture text --> Figure 2c. Nicaragua & Costa Rica ‐ Inflation<br>100<br>80<br>60<br>40<br>20<br>0<br>1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983<br>‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5<br>Nicagarua Costa Rica Onset of conflict<br>Annual inflation (%)<br><!-- End of picture text --> Source: Authors’ calculations based on data from Penn World Tables 9.0, International Financial Statistics Database (IMF), and Historical Public Debt Database (IMF)/Abbas et al. (2011).

# **_Example 2. Algeria and Tunisia in the early 1990s_**

The Algerian civil conflict started after controversial parliamentary elections held in 1991. The first year the conflict reached more than 1,000 battle‐related deaths (Definition 3) was 1994.<sup>7</sup> The government subsequently implemented a series of policies aiming to raise public‐sector investment. However, prior to the onset of conflict, a decline of oil prices in the 1980s that lingered well into the 1990s limited the ability of the public sector to finance investment with oil revenues, and thus public debt rose alongside investment (Shabafrouz, 2010).

At that time, Algeria’s non‐conflict neighbor, Tunisia, was in the process of liberalizing its economy. The 1995 association agreement with the European Union and the import‐tariff reforms of early 1990s signaled that Tunisia’s economic management was diverging from Algeria’s. In contrast with Tunisia, public debt in Algeria jumped, reaching its peak of almost 98% of the nation’s GDP in 1992, the same year the conflict saw an upsurge in its levels of violence (Figure 3a). However, it is unclear, given the pre‐ existing oil