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|**_Allocation of water and sanitati_**|**_on programmes i_**|**_n region of birth_**|(2.9283)**<br>**_x birth cohort_**<sup>**_(a)_**</sup>|(2.7950)**|
|---|---|---|---|---|
|Cohort 1: 1-6yrs in 1974||||-0.5387<br>(0.7051)|
|Cohort 2: 7-12yrs in 1974||||-0.4789<br>(0.8665)|
|Cohort 3: 13-18yrs in 1974||||-1.5619<br>(0.8907)|
|Cohort 4: 19-23 yrs in 1974||||-2.3435<br>(1.0646)*|
|Constant|3.7477|3.7898|3.2793|3.2523|
||(0.3506)**|(0.3554)**|(0.3736)**|(0.3734)**|
|Observations|5,875|5,820|5,788|5,439|
|R-squared<br>|0.14|0.14|0.2|0.19|
|Ftest: all interactions=0<sup>(b)</sup>|5.81|5.79|5.75|4.73|
|Prob>F|0.0000|0.0000|0.0000|0.0000|

Notes: (a) The control group was women aged 24 in 1974 (b) F-test to test the hypothesis that interaction coefficients between cohort of birth and programme intensity in the region of birth are jointly zero Robust standard errors in parentheses

* significant at 5%; ** significant at 1%

36
deciles are derived from available household survey data. There are three income sources: wages, profits from the home goods sector, and profits from the exports sector. Consumption expenditure is divided among imports and domestic goods. _Heterogeneity among households is thus reflected by differences in the sources of incomes and in the patterns of consumption_ . This helps capture the differential impacts among households of shocks and policies.

Consider, for instance, a cut in public expenditure that falls mainly on nontradable goods. If there is sufficient structural flexibility, the induced fall in the price of nontradables will lead to a real depreciation in the exchange rate. The production of tradable goods will increase, while that of nontradables will decline. The welfare impact at the household level will depend on whether a household is a net seller or a net buyer of tradable goods. Net sellers would gain, while net buyers would lose. Once these differential impacts are computed, standard techniques can be used to derive the desired poverty and inequality measures. However, it is important to keep in mind that, _even when abundant and relevant household-level data are available, the ability of a modular framework such as 123PRSP to account for heterogeneity is constrained by the degree of disaggregation at the meso-level_ . By unifying the labor market and dividing the economy into only two sectors, the 123PRSP model achieves a great deal of transparency at a significant cost in terms of heterogeneity.<sup>18</sup> 40
driven by the divergence in the impact of maize among the poorest quintiles in both rural and urban areas. In both cases households in the 1st quintile have smaller negative effects of maize price increase on average, but consistently have a higher negative effect owing to cassava price increase relative to the two quintiles above them. This is likely due to cassava being a cheaper source of calories. Based on the average daily quantity consumed and the value of consumption from the IOF-2014/15, we estimate the calories per MZN from cassava (322.0), maize (199.4) and rice (173.7). As a result, cassava is likely to form a more dominant part of the diet for the poorest households. At the same time, cassava witnessed a relatively lower price rise compared to maize. Panel C shows that the poorest quintile has a larger portion of their food budget devoted to cassava and a relatively smaller share towards maize in comparison to the 2nd and 3rd quintiles.

We also use the spatial imputation-based values to estimate the effect of the price change on shortrun poverty. We use a reference poverty line of 25.85 Metical per capita consumption expenditure.<sup>12</sup> For each household, we estimate the implicit effect on consumption expenditure based on the net welfare effects calculated above and measure the movement of individual households across the poverty threshold based on the updated expenditure. Table 11 shows that there is a net increase in the poverty headcount ratio (HCR) of about 5.9 percentage points from 46.7% to 52.6%. Much of the increase is in rural areas where the HCR rose by nearly 8.5 percentage points. At about 1.34 percentage points, the effect in urban areas is much smaller. The worst affected households are those in the poor, rural areas of Manica, Niassa and Sofala.

# **4.5 Discussion and sensitivity checks**

The changes in poverty presented above are best seen as short-run estimates. Given the relatively short time span within which the price increase occurred it is plausible that the households have limited potential for adjusting their consumption. The short window also limits changes in the households’ production decisions in response to the agricultural market price changes. These assumptions ignore any second order response (the higher order terms in Equation (2)). Much of the previous work on the welfare impacts of food price rises in the past has made similar assumptions as well. These assumptions have been found to be reasonably robust across studies

> 12 This is based on the World Bank poverty line estimates presented in “Strong but Not Broadly Shared Growth – Mozambique Poverty Assessment”.

23
nents of government expenditure (on infrastructure, education, and health) on the production process and the accumulation of physical and human capital by the private sector. It also captures credit market imperfections and linkages between the financial system and the real side of the economy, by relating firms' borrowing needs (for both working capital and physical capital formation) to bank lending. Because of its integrated treatment of the real and financial sides, the model allows policy analysts to study not only the impact of structural reforms (such as changes in tariffs or the composition of public expenditure) on relative prices and output, but also the effect of short-term stabilization policies (such as a cut in domestic credit or a rise in policy interest rates) as well as other financial shocks (such as an outflow of private capital or a rise in world interest rates). Our detailed treatment of the labor market is particularly important to assess the poverty reduction effects of adjustment programs, because the poor often generate their main source of income from wages, and the latter depend on available employment opportunities. By distinguishing between rural and urban sectors (and accounting for migration d'ynamics driven by relative wages), the model allows us to study separately the evolution of poverty in urban and rural areas and its relation with output and employment fluctuations across sectors.

The remainder of the paper is organized as follows. Section II presents an overview of the main issues that our analysis focuses on and highlights areas in which our framework differs from some of the past and more recent research in the field. Section III describes in detail the structure of the prototype IMMPA model for low-income, highly-indebted countries, considering in turn production and employment, demand and external trade, prices, income and expenditure, the financial system, the public sector, the poverty and income distribution indicators that the model generates, and the link between consumption and income predictions and household expenditure surveys. Section IV discusses issues associated with the calibration and solution of the model and the extent to which our parameters and initial values can be deemed "representative" of a poor, highly-indebted economy. Section V presents the simulation results of various shocks (both exogenous and policy-induced) and discusses their real, financial, income distribution and poverty effects. We analyze, in turn, the short- and long-run impact of a temporary terms-of-trade shock, a permanent cut in domestic credit to the government, and a poverty-reduction program consisting of partial external debt forgiveness coupled with a reallocation of savings on debt service payments to three alternative forms of government expenditure: lump-sum 5
number of countries, which is the case of our data. However, the Blundell and Bond (1998) types of estimators assume that the instruments (lagged dependent variable) are uncorrelated with the individual effects (country/year). As in Gladdis and Klasen (2013), we find this assumption too restrictive and finally opt to use primarily model (4) for the panel equations. This is also the preferred choice in the recent literature.

The shape of the FLP-GDP per capita relation may be different across group of countries and across individual countries in the MENA region. This is expected and explained by the fact that different group of countries or individual countries may find themselves transiting on different parts of the U curve during the period considered. Hence, we could expect ̂ ̂ for U-shape transitions, ̂ ̂ for positive transitions and ̂ ̂ for negative transitions. It is also possible, of course, to find inverted U-shape transitions with ̂ ̂ .

We will also consider time-series models for individual MENA countries. For this purpose we will use standard OLS models, test for stationarity and correct by instrumenting with lagged dependent variables (OLS-IV). We will also use a reduced version of the Granger model to test how a number of key variables underlying the U-shape hypothesis affect FPRs. The model is as follows: where _X_ includes fertility rates, the share of agriculture in total value added, and value added in manufacturing and services. The number of lags of the dependent variable will depend on the stationarity test. The test used is the Breusch-Godfrey test for higher-order serial correlation.

# **Data**

The data on FPR are collected from the International Labor Organization Economically Active Population, Estimates and Projections (ILO-EAPEP) database. This is the same data used by the quasi totality of cross-country studies reviewed in the previous section. We use the latest edition available at the time of writing (6<sup>th</sup> edition, 2011). The rest of the data including GDP per capita, fertility rates, female education and the share of agricultural value added are taken from the World Bank open data repository. Annex 1 provides a full description of the variables used, including the corresponding codes in the World Bank open repository. We have a total of 172 countries and for all countries we have 23 annual observations for the period 1990-2012 for both FLP and GDP per capita, with the exceptions of 20 countries where the minimum amount of observations is less than 23.<sup>2</sup> We use therefore an unbalanced panel in the cross-country models. The full list of countries and years is provided in Annex 2.

# **Results**

We divide the analysis along parametric and nonparametric estimates as this distinction will be important to understand some of the misconceptions about the U-shape hypothesis. The nonparametric analysis is

> 2 The full list of countries and years is in annex. The country classification used is the international two digits ISO2 classification. Note that ILO-FLP data are missing for most countries before 1990, which explains the choice of the period 1990-2012.

6
sources of national law and compiled into indicators by the WBL team. We complement the ES and WBL with other data sources such as World Development Indicators (WDI), World Bank; Economic Freedom of the World, Fraser Institute, Maoz and Henderson (2013), and Inter Parliamentary Union (IPU).

The sample we use includes all countries and all firms surveyed by ES for which data on our main variables are available. Some countries in our sample have multiple rounds of ES. The ES are not conducted every year in a country and their frequency varies across regions and countries.<sup>5</sup> Our main regression results are based on a sample of 80,272 manufacturing and services firms in 111 countries. The number of ES rounds (country-year pair) in the sample is 191 spanning 2008-2019. In the Appendix, table A1 provides the list of countries in the sample along with the survey year, table A2 provides a formal definition of all the variables used in the regressions, and table A3 provides the summary statistics of the variables.

# 3.2 _Estimation methodology_

Our baseline results are obtained from estimating the following equation: Yijt = α+ β1Maternity leavejτ + YFEt + IFEj + Firm controlsijt + Country controlsjt−1 where subscript _i_ denotes the firm, _j_ denotes the country where the firm operates, _t_ denotes the year, and τ represents average values over t-1 to t-5. _Y_ is the share of females in total workers at the firm; _Maternity leave_ is (log of 1 plus) number of days of paid maternity leave mandated by

> 5 Typically, ES is repeated in a country every 5-6 years.

19
Correlations between the qualitative and quantitative indicators could simply reflect differences in the firms’ use or reliance on a condition, e.g. firms producing ice cream will need electricity, and thus will be more likely to report outages and report unreliable electricity as more constraining than a tailor shop that uses electricity intermittently. Thus, these regressions look at variations within sectors, controlling for firm size, export status, ownership, age and location ( _Xkit_ ). The aim is test among similar firms within the same sector, whether and how size and performance affect the qualitative rankings. Thus the results are not based on differences between ice cream makers and tailors, but between large and small (or growing and contracting) ice cream makers.

Table 2 reports the effect of a one standard deviation change in the objective measure on the probability that the issue is ranked as an above-average constraint for that firm. Each row represents a separate regression, with the full set of controls indicated at the bottom of the table. As the units for the various objective measures vary, the use of the one standard deviation facilitates comparisons.

The effects can be large; reducing the size of bribes by 1 standard deviation, lowers the probability a firm sees the constraint as above its average level by 23 percentage points. Improving the number of days without power would reduce electricity as an above average constraint by 19 percentage points. Lowering the time to get goods through customs is associated with a 13 percentage point reduction in customs being seen as an above average constraint.

Table 3 uses measures of the investment climate drawn from non-ES sources such as the World Development Indicators, Kaufmann-Kraay Governance Indicators and Doing Business. As the external indicators are only available at the country level, the firm 13
between the fraction of smaller loans a lender provides and its self-reported fraction of poor borrowers served.

# **5. Regulation**

In a third study, again using an updated MIX dataset, we examine the effect of regulatory supervision on the profitability of microfinance institutions (Cull et al. 2009b). In particular, we investigate how regulated institutions manage the financial and administrative burdens of complying with regulation, looking at profits, business orientation, outreach, and the share of employees who work in the field. We also look for evidence that regulation provides benefits by improving loan quality.

We conduct econometric analyses of the dataset described above, and of a subset, the 154 institutions that both reported detailed financial information and were subject to regulatory supervision (2009b). We estimate the impact of prudential regulation on profitability and financial self-sufficiency, using for the key regressors three dummy variables that summarize whether an institution faces prudential supervision and the intensity of that supervision. These dummy variables measure whether (1) an MFI faces a regular reporting requirement to a regulatory authority; (2) the MFI faces onsite supervision; and (3) onsite supervision occurs at regular intervals. We control for the same variables as in the 2007 study on contracts and added a measure of staff concentration and _Premium_ , the difference between the interest rates an institution charges its borrowers and the “market” rate for capital. To account for country characteristics, we also include the growth rate of real GDP, the rate of inflation, and an index of institutional development developed by Kaufman, Kraay, and Mastruzzi (2007).

We find that onsite supervision of microfinance institutions varies, even within the same country and among profit-oriented institutions. Whether an institution faces onsite supervision depends on its ownership structure, funding sources, activities, and organizational charter. In terms of trade-offs, we find that microfinance institutions subjected to more rigorous and regular supervision are as profitable as others, despite facing higher costs of supervision. This finding may in part reflect the fact that being regulated often permits institutions to collect deposits and thus gain a cheaper and/or more stable source of capital. For example, Ledgerwood and White (2006, pg. 174) draw on four to six years of data for nine microfinance institutions to report that 14
We investigate in this paper the poverty and vulnerability dynamics in India between 2004/05 and 20011/12 using three rounds of the NSSs. In the absence of actual panel data, we construct synthetic panels using statistical methods that were recently developed by Dang et al. (2014a) and Dang and Lanjouw (2013). We present analysis using vulnerability lines that correspond to a vulnerability index of 20 percent and that are also close to twice the national poverty line.

Estimation results point to faster poverty reduction and more upward mobility in the period 2009-11 than in the period 2004-09. In particular, while the vulnerable category makes up more than half of the population in 2004-09, it accounts for less than half of the population in 2009-11, and the middle class grows two and a half times as large in the second period. While 23 percent and 17 percent of the population, respectively, experience upward and downward mobility in the first period, the corresponding figures in the second period are larger at respectively 30 percent and 19 percent. This pattern of stronger upward mobility is qualitatively similar when considered over the longer period 2004-11.

Factors including more educational achievement, urban residence, wage work, and belonging to socio-ethnic groups other than the scheduled or backward groups are positively associated with higher-than-average chances of upward mobility and lower-than-average changes of downward mobility.

Our paper also presents a two-step analysis procedure where careful checks should be done in the first step to ensure data comparability across survey rounds before synthetic panels can be constructed in the second step. This procedure may be relevant to quite a few other contexts since situations where data are not comparable across survey rounds—leading to, for example, the recent debate on poverty decline in 2011/12 in India—appear to occur more frequently than one might think. We discuss a statistical method (Dang et al., 2014b) that can be employed for this checking 26